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Sample records for affect soil moisture

  1. Volatilization of EPTC as affected by soil moisture

    NASA Astrophysics Data System (ADS)

    Fu, Liqun

    Volatilization is an important process that controls the dissipation of pesticides after field application. Soil moisture plays an important role in controlling the volatilization of pesticides. However, the extent of this role is unclear. This study was conducted to determine how soil moisture affects the sorption capacity and vapor loss of EPTC (S-ethyl dipropyl carbamothioate) from two soils, Weswood clay loam (fine- silty, mixed, thermic fluventic ustochrepts) and Padina loamy sand (loamy, siliceous, thermic grossarenic paleustalfs). Soil samples with different moisture contents were exposed to saturated EPTC vapor for 1, 2, 5, or 12 days and sorbed concentrations were measured. Sorption capacity of Weswood after 12 days exposure was about 12 times higher with air-dry soil than at the wilting point (-1500 kPa). For Padina, after 12 days exposure, the sorption capacity was about 18 times higher at air- dry than at -1500 kPa. The maximum sorption extrapolated from the partitioning coefficients determined with an equilibrium batch system and Henry's law were similar to the sorption capacities when moisture content was close to the wilting point for both soils. Desorption of EPTC vapor from soils with different moistures was determined by a purge and trap method. EPTC vapor losses strongly depended on the soil moisture and/or the humidity of the air. If the air was dry, volatilization of EPTC was much larger when the soil was wet. If humidity of the air was high, the effect of soil moisture on volatilization was not as great. No significant correlation at a confidence level of 95% was found between water and EPTC vapor losses for either soil when water saturated air was used as a purge gas. When purged with dry air, losses of water and EPTC vapor were strongly correlated at a confidence level of 99%. This study indicates that decreasing soil moisture significantly increases EPTC sorption and decreases volatilization. Simulation of volatilization with a one

  2. Observational Evidence that Soil Moisture Variations Affect Precipitation

    NASA Technical Reports Server (NTRS)

    Koster, Randal D.; Suarez, Max J.; Higgins, R. Wayne; VandenDool, Huug M.

    2002-01-01

    Land-atmosphere feedback, by which precipitation-induced soil moisture anomalies affect subsequent precipitation, may be an important element of Earth's climate system, but its very existence has never been demonstrated conclusively at regional to continental scales. Evidence for the feedback is sought in a 50-year observational precipitation dataset covering the United States. The precipitation variance and autocorrelation fields are characterized by features that agree (in structure, though not in magnitude) with those produced by an atmospheric general circulation model (AGCM). Because the model-generated features are known to result from land-atmosphere feedback alone, the observed features are highly suggestive of the existence of feedback in nature.

  3. Sensitivity of Polygonum aviculare Seeds to Light as Affected by Soil Moisture Conditions

    PubMed Central

    Batlla, Diego; Nicoletta, Marcelo; Benech-Arnold, Roberto

    2007-01-01

    Background and Aims It has been hypothesized that soil moisture conditions could affect the dormancy status of buried weed seeds, and, consequently, their sensitivity to light stimuli. In this study, an investigation is made of the effect of different soil moisture conditions during cold-induced dormancy loss on changes in the sensitivity of Polygonum aviculare seeds to light. Methods Seeds buried in pots were stored under different constant and fluctuating soil moisture environments at dormancy-releasing temperatures. Seeds were exhumed at regular intervals during storage and were exposed to different light treatments. Changes in the germination response of seeds to light treatments during storage under the different moisture environments were compared in order to determine the effect of soil moisture on the sensitivity to light of P. aviculare seeds. Key Results Seed acquisition of low-fluence responses during dormancy release was not affected by either soil moisture fluctuations or different constant soil moisture contents. On the contrary, different soil moisture environments affected seed acquisition of very low fluence responses and the capacity of seeds to germinate in the dark. Conclusions The results indicate that under field conditions, the sensitivity to light of buried weed seeds could be affected by the soil moisture environment experienced during the dormancy release season, and this could affect their emergence pattern. PMID:17430979

  4. Soil Tillage Management Affects Maize Grain Yield by Regulating Spatial Distribution Coordination of Roots, Soil Moisture and Nitrogen Status

    PubMed Central

    Wang, Xinbing; Zhou, Baoyuan; Sun, Xuefang; Yue, Yang; Ma, Wei; Zhao, Ming

    2015-01-01

    The spatial distribution of the root system through the soil profile has an impact on moisture and nutrient uptake by plants, affecting growth and productivity. The spatial distribution of the roots, soil moisture, and fertility are affected by tillage practices. The combination of high soil density and the presence of a soil plow pan typically impede the growth of maize (Zea mays L.).We investigated the spatial distribution coordination of the root system, soil moisture, and N status in response to different soil tillage treatments (NT: no-tillage, RT: rotary-tillage, SS: subsoiling) and the subsequent impact on maize yield, and identify yield-increasing mechanisms and optimal soil tillage management practices. Field experiments were conducted on the Huang-Huai-Hai plain in China during 2011 and 2012. The SS and RT treatments significantly reduced soil bulk density in the top 0–20 cm layer of the soil profile, while SS significantly decreased soil bulk density in the 20–30 cm layer. Soil moisture in the 20–50 cm profile layer was significantly higher for the SS treatment compared to the RT and NT treatment. In the 0-20 cm topsoil layer, the NT treatment had higher soil moisture than the SS and RT treatments. Root length density of the SS treatment was significantly greater than density of the RT and NT treatments, as soil depth increased. Soil moisture was reduced in the soil profile where root concentration was high. SS had greater soil moisture depletion and a more concentration root system than RT and NT in deep soil. Our results suggest that the SS treatment improved the spatial distribution of root density, soil moisture and N states, thereby promoting the absorption of soil moisture and reducing N leaching via the root system in the 20–50 cm layer of the profile. Within the context of the SS treatment, a root architecture densely distributed deep into the soil profile, played a pivotal role in plants’ ability to access nutrients and water. An

  5. Soil Tillage Management Affects Maize Grain Yield by Regulating Spatial Distribution Coordination of Roots, Soil Moisture and Nitrogen Status.

    PubMed

    Wang, Xinbing; Zhou, Baoyuan; Sun, Xuefang; Yue, Yang; Ma, Wei; Zhao, Ming

    2015-01-01

    The spatial distribution of the root system through the soil profile has an impact on moisture and nutrient uptake by plants, affecting growth and productivity. The spatial distribution of the roots, soil moisture, and fertility are affected by tillage practices. The combination of high soil density and the presence of a soil plow pan typically impede the growth of maize (Zea mays L.).We investigated the spatial distribution coordination of the root system, soil moisture, and N status in response to different soil tillage treatments (NT: no-tillage, RT: rotary-tillage, SS: subsoiling) and the subsequent impact on maize yield, and identify yield-increasing mechanisms and optimal soil tillage management practices. Field experiments were conducted on the Huang-Huai-Hai plain in China during 2011 and 2012. The SS and RT treatments significantly reduced soil bulk density in the top 0-20 cm layer of the soil profile, while SS significantly decreased soil bulk density in the 20-30 cm layer. Soil moisture in the 20-50 cm profile layer was significantly higher for the SS treatment compared to the RT and NT treatment. In the 0-20 cm topsoil layer, the NT treatment had higher soil moisture than the SS and RT treatments. Root length density of the SS treatment was significantly greater than density of the RT and NT treatments, as soil depth increased. Soil moisture was reduced in the soil profile where root concentration was high. SS had greater soil moisture depletion and a more concentration root system than RT and NT in deep soil. Our results suggest that the SS treatment improved the spatial distribution of root density, soil moisture and N states, thereby promoting the absorption of soil moisture and reducing N leaching via the root system in the 20-50 cm layer of the profile. Within the context of the SS treatment, a root architecture densely distributed deep into the soil profile, played a pivotal role in plants' ability to access nutrients and water. An optimal

  6. Emission and distribution of fumigants as affected by soil moistures in three different textured soils.

    PubMed

    Qin, Ruijun; Gao, Suduan; Ajwa, Husein

    2013-01-01

    Water application is a low-cost strategy to control emissions of soil fumigant to meet the requirements of the stringent environmental regulations and it is applicable for a wide range of commodity groups. Although it is known that an increase in soil moisture reduces emissions, the range of soil moisture for minimizing emissions without risking pest control, is not well defined for various types of soils. With two column studies, we determined the effect of different soil moisture levels on emission and distribution of 1,3-dichloropropene and chloropicrin in three different textured soils. Results on sandy loam and loam soils showed that by increasing soil moisture from 30% to 100% of field capacity (FC), peak fluxes were lowered by 77-88% and their occurrences were delayed 5-15 h, and cumulative emissions were reduced 24-49%. For the sandy soil, neither peak fluxes nor the cumulative emissions were significantly different when soil moisture increased from 30% to 100% FC. Compared to the drier soils, the wetter soils retained consistently higher fumigant concentrations in the gas-phase, suggesting efficacy may not be impacted in these soils. The air-filled porosity positively and linearly correlated with the cumulative emission loss across all soil types indicating that it may serve as a good indicator for estimating emissions. These laboratory findings can be further tested under field conditions to conclude what irrigation regime should be used for increasing soil water content before fumigant application that can achieve maximum emission reduction and uniform fumigant distribution with high exposure index values. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Harvest residue and competing vegetation affect soil moisture, soil temperature, N availability, and Douglas-fir seedling growth.

    Treesearch

    Scott D. Roberts; Constance A. Harrington; Thomas A. Terry

    2005-01-01

    Decisions made during stand regeneration that affect subsequent levels of competing vegetation and residual biomass can have important short-term consequences for early stand growth, and may affect long-term site productivity. Competing vegetation clearly affects the availability of site resources such as soil moisture and nutrients. Harvest residues can also impact...

  8. Soil residue analysis and degradation of saflufenacil as affected by moisture content and soil characteristics.

    PubMed

    Camargo, Edinalvo R; Senseman, Scott A; Haney, Richard L; Guice, John B; McCauley, Garry N

    2013-12-01

    Saflufenacil dissipation in soils under different moisture conditions is not available in the scientific literature. The objective of this study was to evaluate saflufenacil degradation and persistence in soils from rice regions under field capacity (non-flooded) and saturated (flooded) conditions. The accelerated solvent extraction (ASE) residue analytical method developed to conduct the study resulted in recovery greater than 80% for the combinations of soils and moisture conditions. Saflufenacil degradation was faster at field capacity for all soils, except for Morey soil. Herbicide half-life was 28.6, 15.0 and 23.1 days under field capacity treatments and 58.8, 36.9 and 79.7 under saturated conditions for Nada, Crowley and Gilbert soils respectively. A half-life no longer than 80 days was observed for the combination of soils and moisture treatments. An ASE method was developed and used to extract saflufenacil from soil samples. Half-life averaged among soils was 59 and 33 days for saturated and field capacity respectively. Saflufenacil persistence in the environment was 2-3 times longer under flooded conditions for most of the soils studied. © 2013 Society of Chemical Industry.

  9. Soil moisture

    Treesearch

    L. L. Boersma; D. Kirkham; D. Norum; R. Ziemer; J. C. Guitjens; J. Davidson; J. N. Luthin

    1971-01-01

    Infiltration continues to occupy the attention of soil physicists and engineers. A theoretical and experimental analysis of the effect of surface sealing on infiltration by Edwards and Larson [1969] showed that raindrops reduced the infiltration rate by as much as 50% for a two-hour period of infiltration. The effect of raindrops on the surface infiltration rate of...

  10. Summer regional climate over East Asia affected by soil moisture modification at the Sichuan basin

    NASA Astrophysics Data System (ADS)

    Sugimoto, S.; Takahashi, H. G.; Sekiyama, H.

    2016-12-01

    An artificial land-use change is a factor to modify the regional-scale climate. For example, double cropping over the eastern coastal area of China controls the East Asian monsoon circulation, not only the local-scale temperature over the cropland area (Jeong et al. 2014). The Sichuan Basin is also major cropland in China (Liu et al. 2005) and a key region to form convective systems with a zone of active cloud convection (Ueno et al. 2011; Sugimoto and Ueno 2012). Therefore, we investigated an effect of the soil moisture over the Sichuan Basin on the regional-scale climate modification over the surrounding regions. Two kinds of numerical simulation were conducted using Weather Research and Forecasting (WRF; Skamarock et al. 2008) model between April 1 and September 30 for 8 years (during 2003-2010). One is an experiment that soil moisture is calculated in the land surface scheme (Control run; it assumes natural soil moisture variation affecting rainfall), and other is an experiment including a phase with soil moisture increase (between May 6 and June 10 for each year) which is estimated by AMSR-E dataset but cannot calculate in the Control run (Sensitivity run). At the Sichuan Basin in the Control run, monthly mean of atmospheric surface temperature at 2m is higher than the observation during the whole analysis period. A frequency distribution in daily mean temperature also shifts to warm. Meanwhile, the Sensitivity run simulates a decrease of daily atmospheric surface temperature at the Sichuan basin relative to that in the Control run especially during May, June, and July, and its frequency distribution was consistent with the observation. The increase of soil moisture over the Sichuan Basin (i.e., in the Sensitivity run) tended to enhance a cloud convective activity over the eastern-central China, which decreased a downward shortwave radiation and daily maximum of surface temperature. Meanwhile, the active cloud convection induced a northward moisture flow which

  11. Soil moisture variability across climate zones

    NASA Astrophysics Data System (ADS)

    Lawrence, Justin E.; Hornberger, George M.

    2007-10-01

    Variability in soil moisture is controlled by temporal variability in atmospheric conditions and spatial variability in land-surface conditions. Observations of soil moisture have revealed a variety of patterns - generally, in semiarid regions variance increased as mean soil moisture content increased, in humid regions variance decreased as mean soil moisture content increased, and in temperate regions variance peaked at intermediate moisture contents. We collected soil moisture data at Big Meadows, an upland wetland in Shenandoah National Park, Virginia, and we observed peak variance at intermediate moisture contents, consistent with other sites in temperate climate zones. A modified soil moisture dynamics model was used to examine how soil moisture patterns evolve through time, how topography, soil, and vegetation affect these patterns, and how the trends observed across climate zones can be explained. The trends observed relate to theoretical bounds on soil moisture distributions, i.e., the wilting point and the porosity.

  12. Spatial patterns of soil moisture as affected by shrubs, in different climatic conditions.

    PubMed

    Pariente, Sarah

    2002-02-01

    Abstract. At three study sites, representing Mediterranean, semi-arid and mildly-arid climatic conditions, the effect of shrubs on the spatial patterns of soil moisture was studied. At each site soil moisture was measured, on hillslopes, at the vicinity of 8 shrubs. For each shrub the measurements have been taken at 3 microenvironments, i.e. under the shrub (US), at the margins of shrub (MS) and between shrubs (BS). At the microenvironments US and MS the measurements were taken at 3 directions: upslope, downslope and sideslope of the shrubs. At all sampling points soil samples were taken from 3 depths: 0-2, 2-5 and 5-10 cm. In addition, rock fragments cover percentage near the shrubs was determined. A soil moisture pattern was found, around each shrub, which is composed of a radial gradient and a downslope gradient. The radial gradient is expressed by soil moisture decreasing from the US microenvironment, in all directions, through the MS towards the BS microenvironment. The US microenvironment has a 'spatial advantage' of higher soil moisture content due to (1) relatively higher infiltration rate, (2) capture overland flow from the BS area upslope that shrub and (3) low evaporation rate because of the shading effect. The downslope gradient is expressed by decreasing soil moisture from the upslope direction of each shrub (MS and US microenvironments) towards the downslope direction of that shrub (MS and US microenvironments, respectively). This gradient is controlled by the relatively high content of rock fragments near the shrubs at their upslope direction. Such rock fragments spatial distribution is attributed to (1) the detachment and transport of rock fragments by sheep and goats trampling and (2) the effect of shrub on the continuity of overland flow and sediment transport. The effect of rock fragments is similar to that of shrubs regarding increasing infiltration and decreasing evaporation rate. The relatively high soil moisture at the upslope direction of

  13. Soil Moisture Workshop

    NASA Technical Reports Server (NTRS)

    Heilman, J. L. (Editor); Moore, D. G. (Editor); Schmugge, T. J. (Editor); Friedman, D. B. (Editor)

    1978-01-01

    The Soil Moisture Workshop was held at the United States Department of Agriculture National Agricultural Library in Beltsville, Maryland on January 17-19, 1978. The objectives of the Workshop were to evaluate the state of the art of remote sensing of soil moisture; examine the needs of potential users; and make recommendations concerning the future of soil moisture research and development. To accomplish these objectives, small working groups were organized in advance of the Workshop to prepare position papers. These papers served as the basis for this report.

  14. Factors affecting temporal and spatial soil moisture variation in and adjacent to group selection openings

    Treesearch

    W.H. McNab

    1991-01-01

    Soil moisture content was intensively sampled in three, 1-acre blocks containing an opening and surrounding mature upland hardwoods. Openings covering 0.19-0.26 ac were created by group-selection cutting, and they were occupied by 1-year-old trees and shrubs.

  15. Soil Moisture Sensing

    USDA-ARS?s Scientific Manuscript database

    Soil moisture monitoring can be useful as an irrigation management tool for both landscapes and agriculture, sometimes replacing an evapotranspiration (ET) based approach or as a useful check on ET based approaches since the latter tend to drift off target over time. All moisture sensors, also known...

  16. Soil Moisture and Fungi Affect Seed Survival in California Grassland Annual Plants

    PubMed Central

    Mordecai, Erin A.

    2012-01-01

    Survival of seeds in the seed bank is important for the population dynamics of many plant species, yet the environmental factors that control seed survival at a landscape level remain poorly understood. These factors may include soil moisture, vegetation cover, soil type, and soil pathogens. Because many soil fungi respond to moisture and host species, fungi may mediate environmental drivers of seed survival. Here, I measure patterns of seed survival in California annual grassland plants across 15 species in three experiments. First, I surveyed seed survival for eight species at 18 grasslands and coastal sage scrub sites ranging across coastal and inland Santa Barbara County, California. Species differed in seed survival, and soil moisture and geographic location had the strongest influence on survival. Grasslands had higher survival than coastal sage scrub sites for some species. Second, I used a fungicide addition and exotic grass thatch removal experiment in the field to tease apart the relative impact of fungi, thatch, and their interaction in an invaded grassland. Seed survival was lower in the winter (wet season) than in the summer (dry season), but fungicide improved winter survival. Seed survival varied between species but did not depend on thatch. Third, I manipulated water and fungicide in the laboratory to directly examine the relationship between water, fungi, and survival. Seed survival declined from dry to single watered to continuously watered treatments. Fungicide slightly improved seed survival when seeds were watered once but not continually. Together, these experiments demonstrate an important role of soil moisture, potentially mediated by fungal pathogens, in driving seed survival. PMID:22720037

  17. Emission and distribution of fumigants as affected by soil moistures in three different textured soils

    USDA-ARS?s Scientific Manuscript database

    Stringent environmental regulations are being developed to control the emission of soil fumigants to reduce air pollution. Water application is a low-cost strategy for fumigant emission control and applicable for a wide range of commodity groups, especially those with low profit margins. Although it...

  18. The soil carbon/nitrogen ratio and moisture affect microbial community structures in alkaline permafrost-affected soils with different vegetation types on the Tibetan plateau.

    PubMed

    Zhang, Xinfang; Xu, Shijian; Li, Changming; Zhao, Lin; Feng, Huyuan; Yue, Guangyang; Ren, Zhengwei; Cheng, Guogdong

    2014-01-01

    In the Tibetan permafrost region, vegetation types and soil properties have been affected by permafrost degradation, but little is known about the corresponding patterns of their soil microbial communities. Thus, we analyzed the effects of vegetation types and their covariant soil properties on bacterial and fungal community structure and membership and bacterial community-level physiological patterns. Pyrosequencing and Biolog EcoPlates were used to analyze 19 permafrost-affected soil samples from four principal vegetation types: swamp meadow (SM), meadow (M), steppe (S) and desert steppe (DS). Proteobacteria, Acidobacteria, Bacteroidetes and Actinobacteria dominated bacterial communities and the main fungal phyla were Ascomycota, Basidiomycota and Mucoromycotina. The ratios of Proteobacteria/Acidobacteria decreased in the order: SM>M>S>DS, whereas the Ascomycota/Basidiomycota ratios increased. The distributions of carbon and nitrogen cycling bacterial genera detected were related to soil properties. The bacterial communities in SM/M soils degraded amines/amino acids very rapidly, while polymers were degraded rapidly by S/DS communities. UniFrac analysis of bacterial communities detected differences among vegetation types. The fungal UniFrac community patterns of SM differed from the others. Redundancy analysis showed that the carbon/nitrogen ratio had the main effect on bacteria community structures and their diversity in alkaline soil, whereas soil moisture was mainly responsible for structuring fungal communities. Thus, microbial communities and their functioning are probably affected by soil environmental change in response to permafrost degradation. Copyright © 2014 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  19. Soil Moisture or Groundwater?

    NASA Astrophysics Data System (ADS)

    Swenson, S. C.; Lawrence, D. M.

    2017-12-01

    Partitioning the vertically integrated water storage variations estimated from GRACE satellite data into the components of which it is comprised requires independent information. Land surface models, which simulate the transfer and storage of moisture and energy at the land surface, are often used to estimate water storage variability of snow, surface water, and soil moisture. To obtain an estimate of changes in groundwater, the estimates of these storage components are removed from GRACE data. Biases in the modeled water storage components are therefore present in the residual groundwater estimate. In this study, we examine how soil moisture variability, estimated using the Community Land Model (CLM), depends on the vertical structure of the model. We then explore the implications of this uncertainty in the context of estimating groundwater variations using GRACE data.

  20. Soil Moisture Project Evaluation Workshop

    NASA Technical Reports Server (NTRS)

    Gilbert, R. H. (Editor)

    1980-01-01

    Approaches planned or being developed for measuring and modeling soil moisture parameters are discussed. Topics cover analysis of spatial variability of soil moisture as a function of terrain; the value of soil moisture information in developing stream flow data; energy/scene interactions; applications of satellite data; verifying soil water budget models; soil water profile/soil temperature profile models; soil moisture sensitivity analysis; combinations of the thermal model and microwave; determing planetary roughness and field roughness; how crust or a soil layer effects microwave return; truck radar; and truck/aircraft radar comparison.

  1. Southern U.S. Soil Moisture Map

    NASA Image and Video Library

    2015-05-19

    Southern U.S. NASA's SMAP soil moisture retrievals from April 27, 2015, when severe storms were affecting Texas. Top: radiometer data alone. Bottom: combined radar and radiometer data with a resolution of 5.6 miles (9 kilometers). The combined product reveals more detailed surface soil moisture features. http://photojournal.jpl.nasa.gov/catalog/PIA19338

  2. Changes in soil moisture affect carbon and water fluxes from trees and soils differently in a young semi-arid ponderosa pine stand

    NASA Astrophysics Data System (ADS)

    Ruehr, N. K.; Martin, J.; Pettijohn, J. C.; Law, B. E.

    2010-12-01

    A potential decline in the global trend in land evapotranspiration due to soil moisture limitation may alter the C balance of forest ecosystems, especially in water-limited Mediterranean and semi-arid climate zones. Despite the wide distribution of ponderosa pine forests in semi-arid climate zones of the USA, detailed studies on how these ecosystems may respond to changes in soil water availability are rather rare. To provide better insights on this relevant topic, we conducted a soil moisture manipulation experiment and investigated the response of tree and soil carbon and water fluxes in a young ponderosa pine stand in Oregon (Ameriflux site US-Me6) during summer 2010. Irrigation started with the onset of the dry season at the end of June, maintaining volumetric soil moisture content constantly above 20%. In contrast, in the control treatment soil moisture dried down with regional drought and was below 10% and 15% in 10 cm and 30 cm depth by the end of August. Results show that irrigation increased soil CO2 efflux by 40% at the end of July and reached a maximum of 60% in mid August, with about one-third to two-thirds originating from root-rhizosphere respiration (soil CO2 efflux under tree - soil CO2 efflux in the open). Photosynthesis (Amax), stomatal conductance (gs) and transpiration (T) rates were not affected by irrigation in early summer. However, Amax, gs and T rates in both treatments suddenly decreased, most likely caused by increased VPD and decreased soil water availability (predawn needle water potentials) at the end of July. Irrigation dampened that decrease and caused Amax, gs and T to remain on average about 25% higher, following largely the course of VPD during August. In summary, our preliminary results indicate that higher soil water content affected in particular soil activity and root-rhizosphere respiration rates. Photosynthesis and transpiration appeared to depend to a lesser extent and later in the season on irrigation water, yet both

  3. Evaluation of soil moisture barrier.

    DOT National Transportation Integrated Search

    2000-06-01

    This report is an extension report and examines one of the measures being tried to stabilize the development : of pavement damage on expansive soils, which is the use of horizontal moisture barriers. The moisture barrier : will not stop horizontal fl...

  4. Soil moisture: Some fundamentals. [agriculture - soil mechanics

    NASA Technical Reports Server (NTRS)

    Milstead, B. W.

    1975-01-01

    A brief tutorial on soil moisture, as it applies to agriculture, is presented. Information was taken from books and papers considered freshman college level material, and is an attempt to briefly present the basic concept of soil moisture and a minimal understanding of how water interacts with soil.

  5. Zeolite Soil Application Method Affects Inorganic Nitrogen, Moisture, and Corn Growth

    USDA-ARS?s Scientific Manuscript database

    Adoption of new management techniques which improve soil water storage and soil nitrogen plant availability yet limit nitrogen leaching may help improve environmental quality. A benchtop study was conducted to determine the influence of a single urea fertilizer rate (224 kilograms of Nitrogen per ...

  6. CO2, Temperature, and Soil Moisture Interactions Affect NDVI and Reproductive Phenology in Old-Field Plant Communities

    NASA Astrophysics Data System (ADS)

    Engel, C.; Weltzin, J.; Norby, R.

    2004-12-01

    Plant community composition and ecosystem function may be altered by global atmospheric and climate change, including increased atmospheric [CO2], temperature, and varying precipitation regimes. We are conducting an experiment at Oak Ridge National Laboratory (ORNL) utilizing open-top chambers to administer experimental treatments of elevated CO2 (+300 ppm), warming (+ 3 degrees Celsius), and varying soil moisture availability to experimental plant communities constructed of seven common old-field species, including C3 and C4 grasses, forbs, and legumes. During 2004 we monitored plant community phenology (NDVI) and plant reproductive phenology. Early in the year, NDVI was greater in wet treatment plots, and was unaffected by main effects of temperature or CO2. This result suggests that early in the season warming is insufficient to affect early canopy development. Differences in soil moisture sustained throughout the winter and into early spring may constitute an important control on early canopy greenup. Elevated CO2 alleviated detrimental effects of warming on NDVI, but only early in the season. As ambient temperatures increased, elevated temperatures negatively impacted NDVI only in the dry plots. Wetter conditions ameliorate the effects of warming on canopy greenness during the warmer seasons of the year. Warming increased rates of bolting, number of inflorescences, and time to reproductive maturity for Andropogon virginicus (a C4 bunchgrass). Solidago Canadensis (a C3 late-season forb) also produced flowers earlier in elevated temperatures. Conversely, none of the C3 grasses and forbs that bolt or flower in late spring or early summer responded to temperature or CO2. Results indicate that warming and drought may impact plant community phenology, and plant species reproductive phenology. Clearly community phenology is driven by complex interactions among temperature, water, and CO2 that change throughout the season. Our data stresses the importance of

  7. Diameter Growth of Loblolly Pine Trees as Affected by Soil-Moisture Availibility

    Treesearch

    John R. Bassett

    1964-01-01

    In a 30-year-old even-aged stand of loblolly pine on a site 90 loessial soil in southeast Arkansas during foul growing seasons, most trees on plots thinned to 125 square feet of basal area per acre increased in basal area continuously when, under the crown canopy, available water in the surface foot remained above 65 percent. Measurable diameter growth ceased when...

  8. Soil Moisture Retrieval from Aquarius

    USDA-ARS?s Scientific Manuscript database

    Aquarius observations over land offer an unprecedented opportunity to provide a value-added product, land surface soil moisture, which will contribute to a better understanding of the Earth’s climate and water cycle. Additionally, Aquarius will provide the first spaceborne data that can be used to a...

  9. The soil moisture velocity equation

    NASA Astrophysics Data System (ADS)

    Ogden, Fred L.; Allen, Myron B.; Lai, Wencong; Zhu, Jianting; Seo, Mookwon; Douglas, Craig C.; Talbot, Cary A.

    2017-06-01

    Numerical solution of the one-dimensional Richards' equation is the recommended method for coupling groundwater to the atmosphere through the vadose zone in hyperresolution Earth system models, but requires fine spatial discretization, is computationally expensive, and may not converge due to mathematical degeneracy or when sharp wetting fronts occur. We transformed the one-dimensional Richards' equation into a new equation that describes the velocity of moisture content values in an unsaturated soil under the actions of capillarity and gravity. We call this new equation the Soil Moisture Velocity Equation (SMVE). The SMVE consists of two terms: an advection-like term that accounts for gravity and the integrated capillary drive of the wetting front, and a diffusion-like term that describes the flux due to the shape of the wetting front capillarity profile divided by the vertical gradient of the capillary pressure head. The SMVE advection-like term can be converted to a relatively easy to solve ordinary differential equation (ODE) using the method of lines and solved using a finite moisture-content discretization. Comparing against analytical solutions of Richards' equation shows that the SMVE advection-like term is >99% accurate for calculating infiltration fluxes neglecting the diffusion-like term. The ODE solution of the SMVE advection-like term is accurate, computationally efficient and reliable for calculating one-dimensional vadose zone fluxes in Earth system and large-scale coupled models of land-atmosphere interaction. It is also well suited for use in inverse problems such as when repeat remote sensing observations are used to infer soil hydraulic properties or soil moisture.Plain Language SummarySince its original publication in 1922, the so-called Richards' equation has been the only rigorous way to couple groundwater to the land surface through the unsaturated zone that lies between the water table and land</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/35309','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/35309"><span><span class="hlt">Soil-moisture</span> constants and their variation</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Walter M. Broadfoot; Hubert D. Burke</p> <p>1958-01-01</p> <p>"Constants" like field capacity, liquid limit, <span class="hlt">moisture</span> equivalent, and wilting point are used by most students and workers in <span class="hlt">soil</span> <span class="hlt">moisture</span>. These constants may be equilibrium points or other values that describe <span class="hlt">soil</span> <span class="hlt">moisture</span>. Their values under specific <span class="hlt">soil</span> and cover conditions have been discussed at length in the literature, but few general analyses and...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/31692','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/31692"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> depletion patterns around scattered trees</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Robert R. Ziemer</p> <p>1968-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> was measured around an isolated mature sugar pine tree (Pinus lambertiana Dougl.) in the mixed conifer forest type of the north central Sierra Nevada, California, from November 1965 to October 1966. From a sequence of measurements, horizontal and vertical <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles were developed. Estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> depletion from the 61-foot radius plot...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=251815','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=251815"><span>The Temperature in Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>In the near future two dedicated <span class="hlt">soil</span> <span class="hlt">moisture</span> satellites will be launched, the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite and the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite that are expected to contribute to our understanding of the global hydrological cycle. It is well known that microwa...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/13714','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/13714"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> patterns in a northern coniferous forest</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Thomas F. McLintock</p> <p>1959-01-01</p> <p>The trend of <span class="hlt">soil</span> <span class="hlt">moisture</span> during the growing season, the alternate wetting from rainfall and drying during clear weather, determines the amount of <span class="hlt">moisture</span> available for tree growth and also fixes, in part, the environment for root growth. In much of the northern coniferous region both <span class="hlt">moisture</span> content and root environment are in turn <span class="hlt">affected</span> by the hummock-and-...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170003709&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170003709&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsoil"><span>Evaluation of Assimilated SMOS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data for US Cropland <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Yang, Zhengwei; Sherstha, Ranjay; Crow, Wade; Bolten, John; Mladenova, Iva; Yu, Genong; Di, Liping</p> <p>2016-01-01</p> <p>Remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data can provide timely, objective and quantitative crop <span class="hlt">soil</span> <span class="hlt">moisture</span> information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity (SMOS)Mission L-band passive microwave data for operational US cropland <span class="hlt">soil</span> surface <span class="hlt">moisture</span> monitoring. The assimilated SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data are first categorized to match with the United States Department of Agriculture (USDA)National Agricultural Statistics Service (NASS) survey based weekly <span class="hlt">soil</span> <span class="hlt">moisture</span> observation data, which are ordinal. The categorized assimilated SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data are compared with NASSs survey-based weekly <span class="hlt">soil</span> <span class="hlt">moisture</span> data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1430531','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1430531"><span>Long-term nitrogen addition <span class="hlt">affects</span> the phylogenetic turnover of <span class="hlt">soil</span> microbial community responding to <span class="hlt">moisture</span> pulse</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Liu, Chi; Yao, Minjie; Stegen, James C.</p> <p>2017-12-13</p> <p>How press disturbance (long-term) influences the phylogenetic turnover of <span class="hlt">soil</span> microbial communities responding to pulse disturbances (short-term) is not fully known. Understanding the complex connections between the history of environmental conditions, assembly processes and microbial community dynamics is necessary to predict microbial response to perturbation. Here, we started by investigating phylogenetic spatial turnover (based on DNA) of <span class="hlt">soil</span> prokaryotic communities after long-term nitrogen (N) deposition and temporal turnover (based on RNA) of communities responding to pulse by conducting short-term rewetting experiments. The results showed that moderate N addition increased ecological stochasticity and phylogenetic diversity. In contrast, high N addition slightlymore » increased homogeneous selection and decreased phylogenetic diversity. Examining the system with higher phylogenetic resolution revealed a moderate contribution of variable selection across the whole N gradient. The <span class="hlt">moisture</span> pulse experiment showed that high N <span class="hlt">soils</span> had higher rates of phylogenetic turnover across short phylogenetic distances and significant changes in community compositions through time. Long-term N input history influenced spatial turnover of microbial communities, but the dominant community assembly mechanisms differed across different N deposition gradients. We further revealed an interaction between press and pulse disturbances whereby deterministic processes were particularly important following pulse disturbances in high N <span class="hlt">soils</span>.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4800F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4800F"><span>Influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> on <span class="hlt">soil</span> respiration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fer, Miroslav; Kodesova, Radka; Nikodem, Antonin; Klement, Ales; Jelenova, Klara</p> <p>2015-04-01</p> <p>The aim of this work was to describe an impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> on <span class="hlt">soil</span> respiration. Study was performed on <span class="hlt">soil</span> samples from morphologically diverse study site in loess region of Southern Moravia, Czech Republic. The original <span class="hlt">soil</span> type is Haplic Chernozem, which was due to erosion changed into Regosol (steep parts) and Colluvial <span class="hlt">soil</span> (base slope and the tributary valley). <span class="hlt">Soil</span> samples were collected from topsoils at 5 points of the selected elevation transect and also from the parent material (loess). Grab <span class="hlt">soil</span> samples, undisturbed <span class="hlt">soil</span> samples (small - 100 cm3, and large - 713 cm3) and undisturbed <span class="hlt">soil</span> blocks were taken. Basic <span class="hlt">soil</span> properties were determined on grab <span class="hlt">soil</span> samples. Small undisturbed <span class="hlt">soil</span> samples were used to determine the <span class="hlt">soil</span> water retention curves and the hydraulic conductivity functions using the multiple outflow tests in Tempe cells and a numerical inversion with HYDRUS 1-D. During experiments performed in greenhouse dry large undisturbed <span class="hlt">soil</span> samples were wetted from below using a kaolin tank and cumulative water inflow due to capillary rise was measured. Simultaneously net CO2 exchange rate and net H2O exchange rate were measured using LCi-SD portable photosynthesis system with <span class="hlt">Soil</span> Respiration Chamber. Numerical inversion of the measured cumulative capillary rise data using the HYDRUS-1D program was applied to modify selected <span class="hlt">soil</span> hydraulic parameters for particular conditions and to simulate actual <span class="hlt">soil</span> water distribution within each <span class="hlt">soil</span> column in selected times. Undisturbed <span class="hlt">soil</span> blocks were used to prepare thin <span class="hlt">soil</span> sections to study <span class="hlt">soil</span>-pore structure. Results for all <span class="hlt">soil</span> samples showed that at the beginning of <span class="hlt">soil</span> samples wetting the CO2 emission increased because of improving condition for microbes' activity. The maximum values were reached for <span class="hlt">soil</span> column average <span class="hlt">soil</span> water content between 0.10 and 0.15 cm3/cm3. Next CO2 emission decreased since the pore system starts filling by water (i.e. aggravated conditions for microbes</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H41B1165F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H41B1165F"><span>The North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ford, T.; Quiring, S.</p> <p>2012-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important variable in the climate system, yet in situ observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> are not prevalent in most regions of the world. The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite recently launched by the European Space Agency and NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP) mission underscore the need for better in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data for validation and accuracy assessment. The North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database is a harmonized and quality-controlled <span class="hlt">soil</span> <span class="hlt">moisture</span> dataset that is being developed to support investigations of land-atmosphere interactions, validating the accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations in global land surface models, and describing how <span class="hlt">soil</span> <span class="hlt">moisture</span> influences climate on seasonal to interannual timescales. Currently the database is comprised of well over 1,300 <span class="hlt">soil</span> <span class="hlt">moisture</span> observation stations from more than 20 networks in the United States. The data is subjected to rigorous quality control procedures. Upon completion, the database will consist of homogenized and standardized <span class="hlt">soil</span> <span class="hlt">moisture</span> data products that will be published on a dedicated website and made available to the scientific community to support research efforts such as EaSM, SMAP and SMOS.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26259464','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26259464"><span>[<span class="hlt">Soil</span> <span class="hlt">moisture</span> dynamics of apple orchard in Loess Plateau dryland].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhao, Gang; Fan, Ting-lu; Li, Shang-zhong; Zhang, Jian-jun; Wang, Yong; Dang, Yi; Wang, Lei</p> <p>2015-04-01</p> <p>The <span class="hlt">soil</span> <span class="hlt">moisture</span> of 0-500 cm <span class="hlt">soil</span> layer in a dryland orchard at its full fruit period was measured from 2009 to 2013 to explore the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Results indicated that <span class="hlt">soil</span> water consumption mainly occurred in the <span class="hlt">soil</span> layer of 0-300 cm in normal rainfall year and below the 300 cm <span class="hlt">soil</span> layer when the annual rainfall was less than 400 mm. The <span class="hlt">soil</span> <span class="hlt">moisture</span> in the 200-300 cm <span class="hlt">soil</span> layer fluctuated most and was <span class="hlt">affected</span> by rainfall and apple consumption. Seasonal drought usually happened between April and late June, while the accumulation of <span class="hlt">soil</span> <span class="hlt">moisture</span> mainly occurred in the rainy season from July to mid-October to alleviate the drought effectively in next spring.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8307Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8307Z"><span>Carbon use efficiency (CUE) and biomass turnover of <span class="hlt">soil</span> microbial communities as <span class="hlt">affected</span> by bedrock, land management and <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zheng, Qing; Hu, Yuntao; Richter, Andreas; Wanek, Wolfgang</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> microbial carbon use efficiency (CUE), defined as the proportion of organic C taken up that is allocated to microbial growth, represents an important synthetic representation of microbial community C metabolism that describes the flux partitioning between microbial respiration and growth. Therefore, studying microbial CUE is critical for the understanding of <span class="hlt">soil</span> C cycling. Microbial CUE is thought to vary with environmental conditions (e.g. temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span>). Microbial CUE is thought to decrease with increasing temperature and declining <span class="hlt">soil</span> <span class="hlt">moisture</span>, as the latter may trigger stress responses (e.g. the synthesis of stress metabolites), which may consequently lower microbial community CUE. However, these effects on microbial CUE have not been adequately measured so far due to methodological restrictions. The most widely used methods for microbial CUE estimation are based on tracing 13C-labeled substrates into microbial biomass and respiratory CO2, approaches that are known to overestimate microbial CUE of native organic matter in <span class="hlt">soil</span>. Recently, a novel substrate-independent approach based on the measurement of (i) respiration rates and (ii) the incorporation rates of 18O from labelled water into newly formed microbial DNA has been developed in our laboratory for measuring microbial CUE. This approach overcomes the shortcomings of previously used methods and has already been shown to yield realistic estimations of <span class="hlt">soil</span> microbial CUE. This approach can also be applied to concurrently measure microbial biomass turnover rates, which also influence the sequestration of <span class="hlt">soil</span> organic C. Microbial turnover rates are also thought to be impacted by environmental factors, but rarely have been directly measured so far. Here, we aimed at determining the short-term effects of environmental factors (<span class="hlt">soil</span> temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span>) on microbial CUE and microbial biomass turnover rates based on the novel 18O approach. <span class="hlt">Soils</span> from three land-use types (arable</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1615537S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1615537S"><span>SMALT - <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from Altimetry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smith, Richard; Salloway, Mark; Berry, Philippa; Hahn, Sebastian; Wagner, Wolfgang; Egido, Alejandro; Dinardo, Salvatore; Lucas, Bruno Manuel; Benveniste, Jerome</p> <p>2014-05-01</p> <p><span class="hlt">Soil</span> surface <span class="hlt">moisture</span> is a key scientific parameter; however, it is extremely difficult to measure remotely, particularly in arid and semi-arid terrain. This paper outlines the development of a novel methodology to generate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates in these regions from multi-mission satellite radar altimetry. Key to this approach is the development of detailed DRy Earth ModelS (DREAMS), which encapsulate the detailed and intricate surface brightness variations over the Earth's land surface, resulting from changes in surface roughness and composition. DREAMS have been created over a number of arid and semi-arid deserts worldwide to produce historical SMALT timeseries over <span class="hlt">soil</span> <span class="hlt">moisture</span> variation. These products are available in two formats - a high resolution track product which utilises the altimeter's high frequency content alongtrack and a multi-looked 6" gridded product at facilitate easy comparison/integeration with other remote sensing techniques. An overview of the SMALT processing scheme, covering the progression of the data from altimeter sigma0 through to final <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate, is included along with example SMALT products. Validation has been performed over a number of deserts by comparing SMALT products with other remote sensing techniques, results of the comparison between SMALT and Metop Warp 5.5 are presented here. Comparisons with other remote sensing techniques have been limited in scope due to differences in the operational aspects of the instruments, the restricted geographical coverage of the DREAMS and the low repeat temporal sampling rate of the altimeter. The potential to expand the SMALT technique into less arid areas has been investigated. Small-scale comparison with in-situ and GNSS-R data obtained by the LEiMON experimental campaign over Tuscany, where historical trends exist within both SMALT and SMC probe datasets. A qualitative analysis of unexpected backscatter characteristics in dedicated dry environments is performed with</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_2 --> <div id="page_3" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="41"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8129E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8129E"><span>Impacts of <span class="hlt">soil</span> <span class="hlt">moisture</span> content on visual <span class="hlt">soil</span> evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Emmet-Booth, Jeremy; Forristal, Dermot; Fenton, Owen; Bondi, Giulia; Creamer, Rachel; Holden, Nick</p> <p>2017-04-01</p> <p>Visual <span class="hlt">Soil</span> Examination and Evaluation (VSE) techniques offer tools for <span class="hlt">soil</span> quality assessment. They involve the visual and tactile assessment of <span class="hlt">soil</span> properties such as aggregate size and shape, porosity, redox morphology, <span class="hlt">soil</span> colour and smell. An increasing body of research has demonstrated the reliability and utility of VSE techniques. However a number of limitations have been identified, including the potential impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> variation during sampling. As part of a national survey of grassland <span class="hlt">soil</span> quality in Ireland, an evaluation of the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> on two widely used VSE techniques was conducted. The techniques were Visual Evaluation of <span class="hlt">Soil</span> Structure (VESS) (Guimarães et al., 2011) and Visual <span class="hlt">Soil</span> Assessment (VSA) (Shepherd, 2009). Both generate summarising numeric scores that indicate <span class="hlt">soil</span> structural quality, though employ different scoring mechanisms. The former requires the assessment of properties concurrently and the latter separately. Both methods were deployed on 20 sites across Ireland representing a range of <span class="hlt">soils</span>. Additional samples were taken for <span class="hlt">soil</span> volumetric water (θ) determination at 5-10 and 10-20 cm depth. No significant correlation was observed between θ 5-10 cm and either VSE technique. However, VESS scores were significantly related to θ 10-20 cm (rs = 0.40, sig = 0.02) while VSA scores were not (rs = -0.33, sig = 0.06). VESS and VSA scores can be grouped into quality classifications (good, moderate and poor). No significant mean difference was observed between θ 5-10 cm or θ 10-20 cm according to quality classification by either method. It was concluded that VESS scores may be <span class="hlt">affected</span> by <span class="hlt">soil</span> <span class="hlt">moisture</span> variation while VSA appear unaffected. The different scoring mechanisms, where the separate assessment and scoring of individual properties employed by VSA, may limit <span class="hlt">soil</span> <span class="hlt">moisture</span> effects. However, <span class="hlt">moisture</span> content appears not to <span class="hlt">affect</span> overall structural quality classification by either method. References</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H51R..06F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H51R..06F"><span>SMAP Radiometer <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Downscaling in CONUS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fang, B.; Lakshmi, V.; Bindlish, R.; Jackson, T. J.</p> <p>2017-12-01</p> <p>Remote sensing technology has been providing <span class="hlt">soil</span> <span class="hlt">moisture</span> observations for the study of the global hydrological cycle for land-air interactions, ecology and agriculture. Passive microwave sensors that have provided operational products include AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System), AMSR2 (Advanced Microwave Scanning Radiometer 2), SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity), as and SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active/Passive). The SMAP radiometer provides <span class="hlt">soil</span> <span class="hlt">moisture</span> with a grid resolution of 9 km. However, higher spatial resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> is still required for various applications in weather, agriculture and watershed studies. This study focuses on providing a higher resolution product by downscaling the SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> over CONUS (Contiguous United States). This algorithm is based on the long term thermal inertia relationship between daily temperature variation and average <span class="hlt">soil</span> <span class="hlt">moisture</span> modulated by vegetation. This relationship is modeled using the variables from the NLDAS (North America Land Data Assimilation System) and LTDR (Land Long Term Data Record) from 1981-2016 and is applied to calculate 1 km <span class="hlt">soil</span> <span class="hlt">moisture</span> from MODIS land data products and then used to downscale SMAP Level-3 9 km radiometer <span class="hlt">soil</span> <span class="hlt">moisture</span> to 1 km over CONUS. The downscaled results are evaluated by comparison with in situ observations from ISMN (International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network), SMAPVEX (SMAP Validation Experiment), MESONET (Mesoscale Network), <span class="hlt">Soil</span> Climate Analysis Network (SCAN) and other established networks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010027896','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010027896"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Memory in Climate Models</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal D.; Suarez, Max J.; Zukor, Dorothy J. (Technical Monitor)</p> <p>2000-01-01</p> <p>Water balance considerations at the <span class="hlt">soil</span> surface lead to an equation that relates the autocorrelation of <span class="hlt">soil</span> <span class="hlt">moisture</span> in climate models to (1) seasonality in the statistics of the atmospheric forcing, (2) the variation of evaporation with <span class="hlt">soil</span> <span class="hlt">moisture</span>, (3) the variation of runoff with <span class="hlt">soil</span> <span class="hlt">moisture</span>, and (4) persistence in the atmospheric forcing, as perhaps induced by land atmosphere feedback. Geographical variations in the relative strengths of these factors, which can be established through analysis of model diagnostics and which can be validated to a certain extent against observations, lead to geographical variations in simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> memory and thus, in effect, to geographical variations in seasonal precipitation predictability associated with <span class="hlt">soil</span> <span class="hlt">moisture</span>. The use of the equation to characterize controls on <span class="hlt">soil</span> <span class="hlt">moisture</span> memory is demonstrated with data from the modeling system of the NASA Seasonal-to-Interannual Prediction Project.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/15184676','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15184676"><span>Preferential states in <span class="hlt">soil</span> <span class="hlt">moisture</span> and climate dynamics.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>D'Odorico, Paolo; Porporato, Amilcare</p> <p>2004-06-15</p> <p>Summer precipitation in continental midlatitude regions is significantly contributed by local recycling, i.e., by <span class="hlt">moisture</span> returning to the atmosphere through evapotranspiration from the same region. On the other hand, reduced <span class="hlt">soil</span> <span class="hlt">moisture</span> availability may limit evapotranspiration rates with effects on the planetary boundary layer dynamics through the partitioning between sensible and latent heat fluxes. Thus, a dependence may exist between precipitation and antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. Here we provide theoretical and experimental evidence in support of the hypothesis that in continental regions summer <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies <span class="hlt">affect</span> the probability of occurrence of subsequent precipitation. Owing to these feedbacks, two preferential states may arise in summer <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics, which thus tend to remain locked either in a "dry" or a "wet" state, whereas intermediate conditions have low probability of occurrence. In this manner, such land-atmosphere interactions would explain the possible persistence of summer droughts sustained by positive feedbacks in response to initial (spring) surface <span class="hlt">moisture</span> anomalies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011BGD.....8.2811N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011BGD.....8.2811N"><span>Seasonality in a boreal forest ecosystem <span class="hlt">affects</span> the use of <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> as predictors of <span class="hlt">soil</span> CO2 efflux</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Niinistö, S. M.; Kellomäki, S.; Silvola, J.</p> <p>2011-03-01</p> <p>Our objectives were to identify factors related to temporal variation of <span class="hlt">soil</span> CO2 efflux in a boreal pine forest and to evaluate simple predictive models of temporal variation of <span class="hlt">soil</span> CO2 efflux. <span class="hlt">Soil</span> CO2 efflux was measured with a portable chamber in a Finnish Scots pine forest for three years, with a fourth year for model evaluation. Plot averages for <span class="hlt">soil</span> CO2 efflux ranged from 0.04 to 0.90 g CO2 m-2 h-1 during the snow-free period, i.e. May-October, and from 0.04 to 0.13 g CO2 m-2 h-1 in winter. <span class="hlt">Soil</span> temperature was a good predictor of <span class="hlt">soil</span> CO2 efflux. A quadratic model of ln-transformed efflux explained 76-82% of the variation over the snow-free period. The results revealed strong seasonality: at a given <span class="hlt">soil</span> temperature, <span class="hlt">soil</span> CO2 efflux was higher later in the snow-free period than in spring and early summer. Regression coefficients for temperature (approximations of a Q10 value) of month-specific models decreased with increasing average <span class="hlt">soil</span> temperatures. Efflux in July, the month of peak photosynthesis, showed no clear response to temperature or <span class="hlt">moisture</span>. Inclusion of a seasonality index, degree days, improved the accuracy of temperature response models to predict efflux for the fourth year of measurements, which was not used in building of regression models. Underestimation during peak efflux (mid-July to late-August) remained uncorrected. The strong influence of the flux of photosynthates belowground and the importance of root respiration could explain the relative temperature insensitivity observed in July and together with seasonality of growth of root and root-associated mycorrhizal fungi could explain partial failure of models to predict magnitude of efflux in the peak season from mid-July to August. The effect of <span class="hlt">moisture</span> early in the season was confounded by simultaneous advancement of the growing season and increase in temperature. In a dry year, however, the effect of drought was evident as <span class="hlt">soil</span> CO2 efflux was some 30% smaller in September than in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19840014939','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19840014939"><span>A microwave systems approach to measuring root zone <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Newton, R. W.; Paris, J. F.; Clark, B. V.</p> <p>1983-01-01</p> <p>Computer microwave satellite simulation models were developed and the program was used to test the ability of a coarse resolution passive microwave sensor to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> over large areas, and to evaluate the effect of heterogeneous ground covers with the resolution cell on the accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate. The use of realistic scenes containing only 10% to 15% bare <span class="hlt">soil</span> and significant vegetation made it possible to observe a 60% K decrease in brightness temperature from a 5% <span class="hlt">soil</span> <span class="hlt">moisture</span> to a 35% <span class="hlt">soil</span> <span class="hlt">moisture</span> at a 21 cm microwave wavelength, providing a 1.5 K to 2 K per percent <span class="hlt">soil</span> <span class="hlt">moisture</span> sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span>. It was shown that resolution does not <span class="hlt">affect</span> the basic ability to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> with a microwave radiometer system. Experimental microwave and ground field data were acquired for developing and testing a root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> prediction algorithm. The experimental measurements demonstrated that the depth of penetration at a 21 cm microwave wavelength is not greater than 5 cm.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=313267','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=313267"><span><span class="hlt">Soil-moisture</span> sensors and irrigation management</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>This agricultural irrigation seminar will cover the major classes of <span class="hlt">soil-moisture</span> sensors; their advantages and disadvantages; installing and reading <span class="hlt">soil-moisture</span> sensors; and using their data for irrigation management. The <span class="hlt">soil</span> water sensor classes include the resistance sensors (gypsum blocks, g...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913136S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913136S"><span>Assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> into an Earth System Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stacke, Tobias; Hagemann, Stefan</p> <p>2017-04-01</p> <p>Several modelling studies reported potential impacts of <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies on regional climate. In particular for short prediction periods, perturbations of the <span class="hlt">soil</span> <span class="hlt">moisture</span> state may result in significant alteration of surface temperature in the following season. However, it is not clear yet whether or not <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies <span class="hlt">affect</span> climate also on larger temporal and spatial scales. In an earlier study, we showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies can persist for several seasons in the deeper <span class="hlt">soil</span> layers of a land surface model. Additionally, those anomalies can influence root zone <span class="hlt">moisture</span>, in particular during explicitly dry or wet periods. Thus, one prerequisite for predictability, namely the existence of long term memory, is evident for simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> and might be exploited to improve climate predictions. The second prerequisite is the sensitivity of the climate system to <span class="hlt">soil</span> <span class="hlt">moisture</span>. In order to investigate this sensitivity for decadal simulations, we implemented a <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation scheme into the Max-Planck Institute for Meteorology's Earth System Model (MPI-ESM). The assimilation scheme is based on a simple nudging algorithm and updates the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> state once per day. In our experiments, the MPI-ESM is used which includes model components for the interactive simulation of atmosphere, land and ocean. Artificial assimilation data is created from a control simulation to nudge the MPI-ESM towards predominantly dry and wet states. First analyses are focused on the impact of the assimilation on land surface variables and reveal distinct differences in the long-term mean values between wet and dry state simulations. Precipitation, evapotranspiration and runoff are larger in the wet state compared to the dry state, resulting in an increased <span class="hlt">moisture</span> transport from the land to atmosphere and ocean. Consequently, surface temperatures are lower in the wet state simulations by more than one Kelvin. In terms of spatial pattern</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20180000660','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20180000660"><span>Improving Water Level and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Over Peatlands in a Global Land Modeling System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bechtold, M.; De Lannoy, G. J. M.; Roose, D.; Reichle, R. H.; Koster, R. D.; Mahanama, S. P.</p> <p>2017-01-01</p> <p>New model structure for peatlands results in improved skill metrics (without any parameter calibration) Simulated surface <span class="hlt">soil</span> <span class="hlt">moisture</span> strongly <span class="hlt">affected</span> by new model, but reliable <span class="hlt">soil</span> <span class="hlt">moisture</span> data lacking for validation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H31I..02S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H31I..02S"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Memory in Karst and Non-Karst Landscapes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sobocinski-Norton, H. E.; Dirmeyer, P.</p> <p>2016-12-01</p> <p>Underlying geology plays an important role in <span class="hlt">soil</span> column hydrology that is largely overlooked within the land surface model (LSM) parameterizations used in weather and climate models. LSMs typically treat the <span class="hlt">soil</span> column as a set of horizontally homogeneous layers through which liquid water diffuses. These models parameterize the flow of water out of the bottom of the active <span class="hlt">soil</span> column as "baseflow" that is typically a function of mean surface slope and the <span class="hlt">soil</span> <span class="hlt">moisture</span> in the lowest model layer. However, roughly 25% of the United States is underlain by karst systems that are characterized by heavily fractured bedrock or unconsolidated materials. These heavily fractured systems allow for more rapid drainage, increasing "baseflow" and reducing the amount of <span class="hlt">soil</span> <span class="hlt">moisture</span> available for surface fluxes. This increased drainage can also <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> memory, which is key to determining the strength of land-atmosphere coupling. We examine lagged autocorrelations of in-situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data from climatologically similar stations over different substrates, to determine the extent to which karst <span class="hlt">affects</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> memory. These results are compared to simulations with the NCEP Noah LSM with both default parameters and setting all <span class="hlt">soil</span> types to sand to enhance drainage in a crude approximation of karst macropores. Given the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span> in surface fluxes and in turn land-atmospheric coupling, we will demonstrate the importance of representing shallow geology as realistically as possible, and develop better parameterizations of these processes for LSMs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=266165','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=266165"><span>Validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> ocean salinity (SMOS) satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The surface <span class="hlt">soil</span> <span class="hlt">moisture</span> state controls the partitioning of precipitation into infiltration and runoff. High-resolution observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> will lead to improved flood forecasts, especially for intermediate to large watersheds where most flood damage occurs. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is also key in d...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=264667','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=264667"><span>The international <span class="hlt">soil</span> <span class="hlt">moisture</span> network: A data hosting facility for global in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>In situ measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> are invaluable for calibrating and validating land surface models and satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. In addition, long-term time series of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements themselves can reveal trends in the water cycle related to climate or land co...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730000503','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730000503"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> by extraction and gas chromatography</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Merek, E. L.; Carle, G. C.</p> <p>1973-01-01</p> <p>To determine <span class="hlt">moisture</span> content of <span class="hlt">soils</span> rapidly and conveniently extract <span class="hlt">moisture</span> with methanol and determine water content of methanol extract by gas chromatography. <span class="hlt">Moisture</span> content of sample is calculated from weight of water and methanol in aliquot and weight of methanol added to sample.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760008444','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760008444"><span>Electrical methods of determining <span class="hlt">soil</span> <span class="hlt">moisture</span> content</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Silva, L. F.; Schultz, F. V.; Zalusky, J. T.</p> <p>1975-01-01</p> <p>The electrical permittivity of <span class="hlt">soils</span> is a useful indicator of <span class="hlt">soil</span> <span class="hlt">moisture</span> content. Two methods of determining the permittivity profile in <span class="hlt">soils</span> are examined. A method due to Becher is found to be inapplicable to this situation. A method of Slichter, however, appears to be feasible. The results of Slichter's method are extended to the proposal of an instrument design that could measure available <span class="hlt">soil</span> <span class="hlt">moisture</span> profile (percent available <span class="hlt">soil</span> <span class="hlt">moisture</span> as a function of depth) from a surface measurement to an expected resolution of 10 to 20 cm.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19800014268','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19800014268"><span>Survey of methods for <span class="hlt">soil</span> <span class="hlt">moisture</span> determination</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schmugge, T. J.; Jackson, T. J.; Mckim, H. L.</p> <p>1979-01-01</p> <p>Existing and proposed methods for <span class="hlt">soil</span> <span class="hlt">moisture</span> determination are discussed. These include: (1) in situ investigations including gravimetric, nuclear, and electromagnetic techniques; (2) remote sensing approaches that use the reflected solar, thermal infrared, and microwave portions of the electromagnetic spectrum; and (3) <span class="hlt">soil</span> physics models that track the behavior of water in the <span class="hlt">soil</span> in response to meteorological inputs (precipitation) and demands (evapotranspiration). The capacities of these approaches to satisfy various user needs for <span class="hlt">soil</span> <span class="hlt">moisture</span> information vary from application to application, but a conceptual scheme for merging these approaches into integrated systems to provide <span class="hlt">soil</span> <span class="hlt">moisture</span> information is proposed that has the potential for meeting various application requirements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017HESS...21.6049M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017HESS...21.6049M"><span>Multiscale <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates using static and roving cosmic-ray <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McJannet, David; Hawdon, Aaron; Baker, Brett; Renzullo, Luigi; Searle, Ross</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring platform, known as the <q>rover</q>, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to <span class="hlt">soil</span> <span class="hlt">moisture</span> and discuss the factors controlling <span class="hlt">soil</span> <span class="hlt">moisture</span> variability. We use independent gravimetric and modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates collected across both space and time to validate rover <span class="hlt">soil</span> <span class="hlt">moisture</span> products. Measurements revealed that temporal patterns in <span class="hlt">soil</span> <span class="hlt">moisture</span> were preserved through time and regression modelling approaches were utilised to produce time series of property-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates at 1 km resolution while broad-scale surveys produced <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates at 9 km resolution. We conclude that the multiscale <span class="hlt">soil</span> <span class="hlt">moisture</span> products produced in this study are well suited to future analysis of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals and finer-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/8717','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/8717"><span>Logging effects on <span class="hlt">soil</span> <span class="hlt">moisture</span> losses</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Robert R. Ziemer</p> <p>1978-01-01</p> <p>Abstract - The depletion of <span class="hlt">soil</span> <span class="hlt">moisture</span> within the surface 15 feet by an isolated mature sugar pine and an adjacent uncut forest in the California Sierra Nevada was measured by the neutron method every 2 weeks for 5 consecutive summers. <span class="hlt">Soil</span> <span class="hlt">moisture</span> recharge was measured periodically during the intervening winters. Groundwater fluctuations within the surface 50...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19780006666','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19780006666"><span>Passive microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kondratyev, K. Y.; Melentyev, V. V.; Rabinovich, Y. I.; Shulgina, E. M.</p> <p>1977-01-01</p> <p>The theory and calculations of microwave emission from the medium with the depth-dependent physical properties are discussed; the possibility of determining the vertical profiles of temperature and humidity is considered. Laboratory and aircraft measurements of the <span class="hlt">soil</span> <span class="hlt">moisture</span> are described; the technique for determining the productive-<span class="hlt">moisture</span> content in <span class="hlt">soil</span>, and the results of aircraft measurements are given.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=323266','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=323266"><span>SMAP and SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The SMOS and SMAP satellite missions each produce global <span class="hlt">soil</span> <span class="hlt">moisture</span> products using L-band radiometry. Both missions begin with the same fundamental equations in developing their <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm but implement it differently due to design differences of the instruments. SMOS with ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950027382','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950027382"><span>Measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> with imaging radars</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dubois, Pascale C.; Vanzyl, Jakob; Engman, Ted</p> <p>1995-01-01</p> <p>An empirical model was developed to infer <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness from radar data. The accuracy of the inversion technique is assessed by comparing <span class="hlt">soil</span> <span class="hlt">moisture</span> obtained with the inversion technique to in situ measurements. The effect of vegetation on the inversion is studied and a method to eliminate the areas where vegetation impairs the algorithm is described.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20050181940&hterms=Moisture&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D10%26Ntt%3DMoisture','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20050181940&hterms=Moisture&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D10%26Ntt%3DMoisture"><span>Converting <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observations to Effective Values for Improved Validation of Remotely Sensed <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Laymon, Charles A.; Crosson, William L.; Limaye, Ashutosh; Manu, Andrew; Archer, Frank</p> <p>2005-01-01</p> <p>We compare <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieved with an inverse algorithm with observations of mean <span class="hlt">moisture</span> in the 0-6 cm <span class="hlt">soil</span> layer. A significant discrepancy is noted between the retrieved and observed <span class="hlt">moisture</span>. Using emitting depth functions as weighting functions to convert the observed mean <span class="hlt">moisture</span> to observed effective <span class="hlt">moisture</span> removes nearly one-half of the discrepancy noted. This result has important implications in remote sensing validation studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/1015737-inverse-method-estimating-spatial-variability-soil-particle-size-distribution-from-observed-soil-moisture','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1015737-inverse-method-estimating-spatial-variability-soil-particle-size-distribution-from-observed-soil-moisture"><span>Inverse Method for Estimating the Spatial Variability of <span class="hlt">Soil</span> Particle Size Distribution from Observed <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Pan, Feifei; Peters-lidard, Christa D.; King, Anthony Wayne</p> <p>2010-11-01</p> <p><span class="hlt">Soil</span> particle size distribution (PSD) (i.e., clay, silt, sand, and rock contents) information is one of critical factors for understanding water cycle since it <span class="hlt">affects</span> almost all of water cycle processes, e.g., drainage, runoff, <span class="hlt">soil</span> <span class="hlt">moisture</span>, evaporation, and evapotranspiration. With information about <span class="hlt">soil</span> PSD, we can estimate almost all <span class="hlt">soil</span> hydraulic properties (e.g., saturated <span class="hlt">soil</span> <span class="hlt">moisture</span>, field capacity, wilting point, residual <span class="hlt">soil</span> <span class="hlt">moisture</span>, saturated hydraulic conductivity, pore-size distribution index, and bubbling capillary pressure) based on published empirical relationships. Therefore, a regional or global <span class="hlt">soil</span> PSD database is essential for studying water cycle regionally or globally. At the present stage, threemore » <span class="hlt">soil</span> geographic databases are commonly used, i.e., the <span class="hlt">Soil</span> Survey Geographic database, the State <span class="hlt">Soil</span> Geographic database, and the National <span class="hlt">Soil</span> Geographic database. Those <span class="hlt">soil</span> data are map unit based and associated with great uncertainty. Ground <span class="hlt">soil</span> surveys are a way to reduce this uncertainty. However, ground surveys are time consuming and labor intensive. In this study, an inverse method for estimating mean and standard deviation of <span class="hlt">soil</span> PSD from observed <span class="hlt">soil</span> <span class="hlt">moisture</span> is proposed and applied to Throughfall Displacement Experiment sites in Walker Branch Watershed in eastern Tennessee. This method is based on the relationship between spatial mean and standard deviation of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The results indicate that the suggested method is feasible and has potential for retrieving <span class="hlt">soil</span> PSD information globally from remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011BGeo....8.3169N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011BGeo....8.3169N"><span>Seasonality in a boreal forest ecosystem <span class="hlt">affects</span> the use of <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> as predictors of <span class="hlt">soil</span> CO2 efflux</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Niinistö, S. M.; Kellomäki, S.; Silvola, J.</p> <p>2011-11-01</p> <p>Our objectives were to identify factors related to temporal variation of <span class="hlt">soil</span> CO2 efflux in a boreal pine forest and to evaluate simple predictive models of temporal variation of <span class="hlt">soil</span> CO2 efflux. <span class="hlt">Soil</span> CO2 efflux was measured with a portable chamber in a Finnish Scots pine forest for three years, with a fourth year for model evaluation. Plot averages for <span class="hlt">soil</span> CO2 efflux ranged from 0.04 to 0.90 g CO2 m-2 h-1 during the snow-free period, i.e. May-October, and from 0.04 to 0.13 g CO2 m-2 h-1 in winter. <span class="hlt">Soil</span> temperature was a good predictor of <span class="hlt">soil</span> CO2 efflux. A quadratic model of ln-transformed efflux explained 76-82 % of the variation over the snow-free period. The results revealed an effect of season: at a given temperature of the organic layer, <span class="hlt">soil</span> CO2 efflux was higher later in the snow-free period (in August and September) than in spring and early summer (in May and June). Regression coefficients for temperature (approximations of a Q10 value) of month-specific models decreased with increasing average <span class="hlt">soil</span> temperatures. Efflux in July, the month of peak photosynthesis, showed no clear response to temperature or <span class="hlt">moisture</span>. Inclusion of a seasonality index, degree days, improved the accuracy of temperature response models to predict efflux for the fourth year of measurements, which was not used in building of regression models. During peak efflux from mid-July to late-August, efflux was underestimated with the models that included degree days as well as with the models that did not. The strong influence of the flux of photosynthates belowground and the importance of root respiration could explain the relative temperature insensitivity observed in July and together with seasonality of growth of root and root-associated mycorrhizal fungi could explain partial failure of models to predict magnitude of efflux in the peak season from mid-July to August. The effect of <span class="hlt">moisture</span> early in the season was confounded by simultaneous advancement of the growing season and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-PIA17798.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-PIA17798.html"><span>SMAP Radiometer Captures Views of Global <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-05-06</p> <p>These maps of global <span class="hlt">soil</span> <span class="hlt">moisture</span> were created using data from the radiometer instrument on NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive SMAP observatory. Evident are regions of increased <span class="hlt">soil</span> <span class="hlt">moisture</span> and flooding during April, 2015.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70159491','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70159491"><span>Remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> using airborne hyperspectral data</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Finn, Michael P.; Lewis, Mark (David); Bosch, David D.; Giraldo, Mario; Yamamoto, Kristina H.; Sullivan, Dana G.; Kincaid, Russell; Luna, Ronaldo; Allam, Gopala Krishna; Kvien, Craig; Williams, Michael S.</p> <p>2011-01-01</p> <p>Landscape assessment of <span class="hlt">soil</span> <span class="hlt">moisture</span> is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes <span class="hlt">affected</span> by global climate changes in temperature and precipitation. Traditional efforts to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span>, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify <span class="hlt">soil</span> <span class="hlt">moisture</span> for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the <span class="hlt">soil</span> <span class="hlt">moisture</span> probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> to the same degree.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70034255','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70034255"><span>Remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> using airborne hyperspectral data</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Finn, M.; Lewis, M.; Bosch, D.; Giraldo, Mario; Yamamoto, K.; Sullivan, D.; Kincaid, R.; Luna, R.; Allam, G.; Kvien, Craig; Williams, M.</p> <p>2011-01-01</p> <p>Landscape assessment of <span class="hlt">soil</span> <span class="hlt">moisture</span> is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes <span class="hlt">affected</span> by global climate changes in temperature and precipitation. Traditional efforts to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span>, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify <span class="hlt">soil</span> <span class="hlt">moisture</span> for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> values. A significant statistical correlation (R2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the <span class="hlt">soil</span> <span class="hlt">moisture</span> probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> to the same degree.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B41I2077M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B41I2077M"><span>Effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the temperature sensitivity of Northern <span class="hlt">soils</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Minions, C.; Natali, S.; Ludwig, S.; Risk, D.; Macintyre, C. M.</p> <p>2017-12-01</p> <p>Arctic and boreal ecosystems are vast reservoirs of carbon and are particularly sensitive to climate warming. Changes in the temperature and precipitation regimes of these regions could significantly alter <span class="hlt">soil</span> respiration rates, impacting atmospheric concentrations and <span class="hlt">affecting</span> climate change feedbacks. Many incubation studies have shown that both temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> are important environmental drivers of <span class="hlt">soil</span> respiration; this relationship, however, has rarely been demonstrated with in situ data. Here we present the results of a study at six field sites in Alaska from 2016 to 2017. Low-power automated <span class="hlt">soil</span> gas systems were used to measure <span class="hlt">soil</span> surface CO2 flux from three forced diffusion chambers and <span class="hlt">soil</span> profile concentrations from three <span class="hlt">soil</span> depth chambers at hourly intervals at each site. HOBO Onset dataloggers were used to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature profiles. Temperature sensitivity (Q10) was determined at each site using inversion analysis applied over different time periods. With highly resolved data sets, we were able to observe the changes in <span class="hlt">soil</span> respiration in response to changes in temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span>. Through regression analysis we confirmed that temperature is the primary driver in <span class="hlt">soil</span> respiration, but <span class="hlt">soil</span> <span class="hlt">moisture</span> becomes dominant beyond a certain threshold, suppressing CO2 flux in <span class="hlt">soils</span> with high <span class="hlt">moisture</span> content. This field study supports the conclusions made from previous <span class="hlt">soil</span> incubation studies and provides valuable insights into the impact of both temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> changes on <span class="hlt">soil</span> respiration.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.8870K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.8870K"><span>Nitric oxide (NO) emissions from N-saturated subtropical forest <span class="hlt">soils</span> are strongly <span class="hlt">affected</span> by spatial and temporal variability in <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kang, Ronghua; Dörsch, Peter; Mulder, Jan</p> <p>2016-04-01</p> <p>Subtropical forests in Southwest China have chronically high nitrogen (N) deposition. This results in high emission rates of N gasses, including N2O, NO and N2. In contrast to N2O, NO emission in subtropical China has received little attention, partly because its quantification is challenging. Here we present NO fluxes in a Masson pine-dominated headwater catchment with acrisols on mesic, well-drained hill slopes at TieShanPing (Chongqing, SW China). Measurements were conducted from July to September in 2015, using a dynamic chamber technique and a portable and highly sensitive chemiluminesence NOx analyzer (LMA-3M, Drummond Technology Inc, Canada). Mean NO fluxes as high as 120 μg N m-2 h-1 (± 56 μg N m-2 h-1) were observed at the foot of the hill slope. Mid-slope positions had intermediate NO emission rates (47 ± 17 μg N m-2 h-1), whereas the top of the hill slope showed the lowest NO fluxes (3 ± 3 μg N m-2 h-1). The magnitude of NO emission seemed to be controlled mainly by site-specific <span class="hlt">soil</span> <span class="hlt">moisture</span>, which was on average lower at the foot of the hill slope and in mid-slope positions than at the top of the hill slope. Rainfall episodes caused a pronounced decline in NO emission fluxes in all hill slope positions, whereas the subsequent gradual drying of the <span class="hlt">soil</span> resulted in an increase. NO fluxes were negatively correlated with <span class="hlt">soil</span> <span class="hlt">moisture</span> (r2 = 0.36, p ˂ 0.05). The NO fluxes increased in the early morning, and decreased in the late afternoon, with peak emissions occurring between 2 and 3 pm. The diurnal variation of NO fluxes on mid-slope positions was positively correlated with <span class="hlt">soil</span> temperature (r2 = 0.9, p ˂ 0.05). Our intensive measurements indicate that NO-N emissions in N-saturated subtropical forests are significant and strongly controlled by local hydrological conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1614928R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1614928R"><span>Response of grassland ecosystems to prolonged <span class="hlt">soil</span> <span class="hlt">moisture</span> deficit</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ross, Morgan A.; Ponce-Campos, Guillermo E.; Barnes, Mallory L.; Hottenstein, John D.; Moran, M. Susan</p> <p>2014-05-01</p> <p> species assemblage. The magnitude of change was related to the precipitation regime, where grasslands in hyper-arid and humid regimes were least likely to be <span class="hlt">affected</span> by prolonged <span class="hlt">soil</span> <span class="hlt">moisture</span> deficit, and semiarid and mesic grasslands were most likely to be impacted, depending on the duration of the deficit. These results were applied to a large grassland region in Australia with <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates from the European Space Agency (ESA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity (SMOS) sensor to demonstrate the continental-scale potential of this application with satellite measurements. These results are even more relevant for application with the higher-resolution NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) products to be available in 2015.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000004216&hterms=passive+solar+design&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dpassive%2Bsolar%2Bdesign','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000004216&hterms=passive+solar+design&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dpassive%2Bsolar%2Bdesign"><span>Passive Microwave Remote Sensing of <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Njoku, Eni G.; Entekhabi, Dara</p> <p>1996-01-01</p> <p>Microwave remote sensing provides a unique capability for direct observation of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Remote measurements from space afford the possibility of obtaining frequent, global sampling of <span class="hlt">soil</span> <span class="hlt">moisture</span> over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates are limited to regions that have either bare <span class="hlt">soil</span> or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover <span class="hlt">soil</span> <span class="hlt">moisture</span> is the dominant effect on the received signal. The spatial resolutions of passive Microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface <span class="hlt">soil</span> characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> information from remote observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70187027','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70187027"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> sensors for continuous monitoring</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Amer, Saud A.; Keefer, T. O.; Weltz, M.A.; Goodrich, David C.; Bach, Leslie</p> <p>1995-01-01</p> <p>Certain physical and chemical properties of <span class="hlt">soil</span> vary with <span class="hlt">soil</span> water content. The relationship between these properties and water content is complex and involves both the pore structure and constituents of the <span class="hlt">soil</span> solution. One of the most economical techniques to quantify <span class="hlt">soil</span> water content involves the measurement of electrical resistance of a dielectric medium that is in equilibrium with the <span class="hlt">soil</span> water content. The objective of this research was to test the reliability and accuracy of fiberglass <span class="hlt">soil-moisture</span> electrical resistance sensors (ERS) as compared to gravimetric sampling and Time Domain Reflectometry (TDR). The response of the ERS was compared to gravimetric measurements at eight locations on the USDA-ABS Walnut Gulch Experimental Watershed. The comparisons with TDR sensors were made at three additional locations on the same watershed. The high <span class="hlt">soil</span> rock content (>45 percent) at seven locations resulted in consistent overestimation of <span class="hlt">soil</span> water content by the ERS method. Where rock content was less than 10 percent, estimation of <span class="hlt">soil</span> water was within 5 percent of the gravimetric <span class="hlt">soil</span> water content. New methodology to calibrate the ERS sensors for rocky <span class="hlt">soils</span> will need to be developed before <span class="hlt">soil</span> water content values can be determined with these sensors. (KEY TERMS: <span class="hlt">soil</span> <span class="hlt">moisture</span>; <span class="hlt">soil</span> water; infiltration; instrumentation; <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors.)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29604221','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29604221"><span>Quantifying <span class="hlt">soil</span> <span class="hlt">moisture</span> impacts on light use efficiency across biomes.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stocker, Benjamin D; Zscheischler, Jakob; Keenan, Trevor F; Prentice, I Colin; Peñuelas, Josep; Seneviratne, Sonia I</p> <p>2018-03-31</p> <p>Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for <span class="hlt">soil</span> <span class="hlt">moisture</span>. However, <span class="hlt">soil</span> <span class="hlt">moisture</span> limitation is known to strongly <span class="hlt">affect</span> plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to <span class="hlt">soil</span> <span class="hlt">moisture</span> (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets and remotely sensed greenness. This reveals substantial impacts of <span class="hlt">soil</span> <span class="hlt">moisture</span> alone that reduce GPP by up to 40% at sites located in sub-humid, semi-arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying <span class="hlt">soil</span>, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly-based drought indices. Counter to common assumptions, fLUE reductions are largest in drought-deciduous vegetation, including grasslands. Our results highlight the necessity to account for <span class="hlt">soil</span> <span class="hlt">moisture</span> limitation in terrestrial primary productivity data products, especially for drought-related assessments. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=288987','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=288987"><span>The <span class="hlt">soil</span> <span class="hlt">moisture</span> active passive experiments (SMAPEx): Towards <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval from the SMAP mission</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>NASA’s <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, scheduled for launch in 2014, will carry the first combined L-band radar and radiometer system with the objective of mapping near surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state globally at near-daily time step (2-3 days). SMAP will provide three <span class="hlt">soil</span> ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AdWR..109..343C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AdWR..109..343C"><span>Space-time modeling of <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Zijuan; Mohanty, Binayak P.; Rodriguez-Iturbe, Ignacio</p> <p>2017-11-01</p> <p>A physically derived space-time mathematical representation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> field is carried out via the <span class="hlt">soil</span> <span class="hlt">moisture</span> balance equation driven by stochastic rainfall forcing. The model incorporates spatial diffusion and in its original version, it is shown to be unable to reproduce the relative fast decay in the spatial correlation functions observed in empirical data. This decay resulting from variations in local topography as well as in local <span class="hlt">soil</span> and vegetation conditions is well reproduced via a jitter process acting multiplicatively over the space-time <span class="hlt">soil</span> <span class="hlt">moisture</span> field. The jitter is a multiplicative noise acting on the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics with the objective to deflate its correlation structure at small spatial scales which are not embedded in the probabilistic structure of the rainfall process that drives the dynamics. These scales of order of several meters to several hundred meters are of great importance in ecohydrologic dynamics. Properties of space-time correlation functions and spectral densities of the model with jitter are explored analytically, and the influence of the jitter parameters, reflecting variabilities of <span class="hlt">soil</span> <span class="hlt">moisture</span> at different spatial and temporal scales, is investigated. A case study fitting the derived model to a <span class="hlt">soil</span> <span class="hlt">moisture</span> dataset is presented in detail.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060041690&hterms=mapping+soil&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dmapping%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060041690&hterms=mapping+soil&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dmapping%2Bsoil"><span>Radar Mapping of Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ulaby, F. T.; Dubois, P. C.; van Zyl, J.</p> <p>1997-01-01</p> <p>Intended as an overview aimed at potential users of remotely sensed spatial distributions and temporal variations of <span class="hlt">soil</span> <span class="hlt">moisture</span>, this paper begins with an introductory section on the fundamentals of radar imaging and associated attributes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110016746','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110016746"><span>Radar for Measuring <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Under Vegetation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moghaddam, Mahta; Moller, Delwyn; Rodriguez, Ernesto; Rahmat-Samii, Yahya</p> <p>2004-01-01</p> <p>A two-frequency, polarimetric, spaceborne synthetic-aperture radar (SAR) system has been proposed for measuring the <span class="hlt">moisture</span> content of <span class="hlt">soil</span> as a function of depth, even in the presence of overlying vegetation. These measurements are needed because data on <span class="hlt">soil</span> <span class="hlt">moisture</span> under vegetation canopies are not available now and are necessary for completing mathematical models of global energy and water balance with major implications for global variations in weather and climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007HydJ...15..121W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007HydJ...15..121W"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> from operational meteorological satellites</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wagner, Wolfgang; Naeimi, Vahid; Scipal, Klaus; de Jeu, Richard; Martínez-Fernández, José</p> <p>2007-02-01</p> <p>In recent years, unforeseen advances in monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span> from operational satellite platforms have been made, mainly due to improved geophysical retrieval methods. In this study, four recently published <span class="hlt">soil-moisture</span> datasets are compared with in-situ observations from the REMEDHUS monitoring network located in the semi-arid part of the Duero basin in Spain. The remotely sensed <span class="hlt">soil-moisture</span> products are retrieved from (1) the Advanced Microwave Scanning Radiometer (AMSR-E), which is a passive microwave sensor on-board NASA’s Aqua satellite, (2) European Remote Sensing satellite (ERS) scatterometer, which is an active microwave sensor on-board the two ERS satellites and (3) visible and thermal images from the METEOSAT satellite. Statistical analysis indicates that three satellite datasets contribute effectively to the monitoring of trends in surface <span class="hlt">soil-moisture</span> conditions, but not to the estimation of absolute <span class="hlt">soil-moisture</span> values. These sensors, or rather their successors, will be flown on operational meteorological satellites in the near future. With further improvements in processing techniques, operational meteorological satellites will increasingly deliver high-quality <span class="hlt">soil-moisture</span> data. This may be of particular interest for hydrogeological studies that investigate long-term processes such as groundwater recharge.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890018809','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890018809"><span>Microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shiue, J. C.; Wang, J. R.</p> <p>1988-01-01</p> <p>Knowledge of <span class="hlt">soil</span> <span class="hlt">moisture</span> is important to many disciplines, such as agriculture, hydrology, and meteorology. <span class="hlt">Soil</span> <span class="hlt">moisture</span> distribution of vast regions can be measured efficiently only with remote sensing techniques from airborne or satellite platforms. At low microwave frequencies, water has a much larger dielectric constant than dry <span class="hlt">soil</span>. This difference manifests itself in surface emissivity (or reflectivity) change between dry and wet <span class="hlt">soils</span>, and can be measured by a microwave radiometer or radar. The Microwave Sensors and Data Communications Branch is developing microwave remote sensing techniques using both radar and radiometry, but primarily with microwave radiometry. The efforts in these areas range from developing algorithms for data interpretation to conducting feasibility studies for space systems, with a primary goal of developing a microwave radiometer for <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement from satellites, such as EOS or the Space Station. These efforts are listed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/54065','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/54065"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and temperature conditions <span class="hlt">affect</span> survival and sporulation capacity of Rhododendron leaf disks infested with Phytophthora ramorum</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Ebba K. Peterson; Niklaus J. Grünwald; Jennifer L. ParkeSoil</p> <p>2017-01-01</p> <p>Soilborne inoculum (infested leaf debris which has become incorporated into the <span class="hlt">soil</span>) may be an important contributor to the persistence of the sudden oak death pathogen Phytophthora ramorum in recurrently positive nurseries. To initiate new epidemics, soilborne inoculum must not only be able to survive over time, but also be capable of...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/26152','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/26152"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> availability as a factor <span class="hlt">affecting</span> valley oak (Quercus lobata Neé) seedling establishment and survival in a riparian habitat, Cosumnes River Preserve, Sacramento County, California</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Virginia C. Meyer</p> <p>2002-01-01</p> <p>The lack of valley oak (Quercus lobata Neé) regeneration throughout much of its historical range appears to be related to both habitat destruction and <span class="hlt">soil</span> <span class="hlt">moisture</span> availability. The water relations, growth and survival of greenhouse potted seedlings, field-planted and natural seedlings were monitored through the growing season, 1989. The age...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24434132','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24434132"><span>Effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on chlorine deposition.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hearn, John; Eichler, Jeffery; Hare, Christopher; Henley, Michael</p> <p>2014-02-28</p> <p>The effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on chlorine (Cl(2)) deposition was examined in laboratory chamber experiments at high Cl(2) exposures by measuring the concentration of chloride (Cl(-)) in <span class="hlt">soil</span> columns. <span class="hlt">Soil</span> mixtures with varying amounts of clay, sand, and organic matter and with <span class="hlt">moisture</span> contents up to 20% (w/w) were exposed to ≈3×10(4)ppm Cl(2) vapor. For low water content <span class="hlt">soils</span>, additional water increased the reaction rate as evidenced by higher Cl(-) concentration at higher <span class="hlt">soil</span> <span class="hlt">moisture</span> content. Results also showed that the presence of water restricted transport of Cl(2) into the <span class="hlt">soil</span> columns and caused lower overall deposition of Cl(2) in the top 0.48-cm layer of <span class="hlt">soil</span> when water filled ≈60% or more of the void space in the column. Numerical solutions to partial differential equations of Fick's law of diffusion and a simple rate law for Cl(2) reaction corroborated conclusions derived from the data. For the <span class="hlt">soil</span> mixtures and conditions of these experiments, <span class="hlt">moisture</span> content that filled 30-50% of the available void space yielded the maximum amount of Cl(2) deposition in the top 0.48cm of <span class="hlt">soil</span>. Published by Elsevier B.V.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A33C3189M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A33C3189M"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Characterization for Biogenic Emissions Modeling in Texas</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McGaughey, G.; Sun, Y.; Kimura, Y.; Huang, L.; Fu, R.; McDonald-Buller, E.</p> <p>2014-12-01</p> <p>The role of isoprene and other biogenic volatile organic compounds (BVOCs) in the formation of tropospheric ozone has been recognized as critical for air quality planning in Texas. In the southwestern United States, drought has become a recurring phenomenon and, in addition to other extreme weather events, can impose profound and complex effects on human populations and the environment. Understanding these effects on vegetation and biogenic emissions is important as Texas concurrently faces requirements to achieve and maintain attainment with the National Ambient Air Quality Standard (NAAQS) for ozone in several large metropolitan areas. This research evaluated the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> through the use of simulated and observational datasets on emissions estimates of isoprene. <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurements (e.g., Climate Reference Network, <span class="hlt">Soil</span> Climate Analysis Network) at limited locations in eastern Texas during 2006-2011 showed spatial and temporal variability associated with environmental drivers such as meteorology and physical <span class="hlt">soil</span> characteristics; low volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> values (< 0.05 m3/m3) were observed during 2011, a year characterized by all-time record drought over the majority of Texas. Comparisons of <span class="hlt">soil</span> <span class="hlt">moisture</span> observations in the upper one meter to predictions from the North American Land Data Assimilation System (NLDAS) indicated a tendency towards a dry bias for NLDAS especially at depths greater than 10 cm. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) was used to explore the sensitivity of biogenic emissions estimates to alternative <span class="hlt">soil</span> <span class="hlt">moisture</span> representations for year 2011. A range of <span class="hlt">soil</span> <span class="hlt">moisture</span> inputs over eastern Texas informed by the observed to simulated comparisons demonstrated that the impact on predicted isoprene emissions was <span class="hlt">affected</span> by both the <span class="hlt">soil</span> <span class="hlt">moisture</span> and specific wilting point datasets employed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.H13A1346L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.H13A1346L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variability and Critical <span class="hlt">Moisture</span> Levels at Big Meadows, Shenandoah National Park, Virginia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lawrence, J. E.; Hornberger, G. M.</p> <p>2006-12-01</p> <p>Variability in <span class="hlt">soil</span> <span class="hlt">moisture</span> is controlled by temporal variability in atmospheric conditions and spatial variability in land surface conditions. Patterns relating the mean <span class="hlt">moisture</span> content in a field to the variance have been studied using assumed random variations in the land surface variables. Although purely random calculations may describe the relationship between mean and variance, observed spatial patterns are <span class="hlt">affected</span> strongly by non-random patterns in <span class="hlt">soil</span> properties and in topography. We used a <span class="hlt">soil</span> <span class="hlt">moisture</span> balance model at 75 points at Big Meadows, an upland wetland in Shenandoah National Park, Virginia and captured the land surface variability using <span class="hlt">soil</span> conductivity measured with an infiltrometer along with slope and aspect derived from a DEM. We calibrated the model using volumetric <span class="hlt">moisture</span> content measurements made with a TDR probe. We used a series of numerical experiments to examine the effects of rainfall variability on the spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span>. A study of critical water stress periods is of particular importance at Big Meadows because this area is extensively used during summer months. To explore how normal climate variability <span class="hlt">affects</span> the amount of time <span class="hlt">soil</span> <span class="hlt">moisture</span> is below the critical <span class="hlt">moisture</span> content, θc, we ran the model for 3 drought years and 3 wet years. We used stochastically derived time series to reproduce local precipitation and temperature patterns. To explore potential impacts of climate change: we (1) held the total annual precipitation constant, increased the magnitude and decreased the intensity of storms, and we (2) increased the mean annual temperature by 5 degrees C. On average, vegetation is stressed 76.3% of the year in wet years, 80.5% of the year in drought years, and 78.1% of the year in climate change years.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ISPAr42W4..133K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ISPAr42W4..133K"><span>Estimating <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Using Polsar Data: a Machine Learning Approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khedri, E.; Hasanlou, M.; Tabatabaeenejad, A.</p> <p>2017-09-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important parameter that <span class="hlt">affects</span> several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for <span class="hlt">moisture</span> calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating <span class="hlt">soil</span> <span class="hlt">moisture</span> in vast territories and high-cost nature as well as spatial and local variability of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating <span class="hlt">soil</span> <span class="hlt">moisture</span>. These images provide a wide field of view and high spatial resolution. For estimating <span class="hlt">soil</span> <span class="hlt">moisture</span>, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. We compare the obtained data with in-situ data. Output results show that the SBS-SVR method results in higher modeling accuracy compared to SFS-SVR model. Statistical parameters obtained from this method show an R2 of 97% and an RMSE of lower than 0.00041 (m3/m3) for P, L, and C channels, which has provided better accuracy compared to other feature selection algorithms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5260S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5260S"><span>Comparing <span class="hlt">soil</span> <span class="hlt">moisture</span> memory in satellite observations and models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stacke, Tobias; Hagemann, Stefan; Loew, Alexander</p> <p>2013-04-01</p> <p> latter in the deepest layer. From this we conclude that the seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span> variations dominate the memory close to the surface but these are dampened in lower layers where the memory is mainly <span class="hlt">affected</span> by longer term variations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H43H1625P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H43H1625P"><span>Comparing and Combining Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products from AMSR2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Parinussa, R.; Kim, S.; Liu, Y.; Johnson, F.; Sharma, A.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important variable in hydrological systems as its part of the water cycle in the atmosphere, the land surface and subsurface. Microwave remote sensing is a viable tool to monitor global <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions at regular time intervals. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is a sensor onboard the Global Change Observation Mission 1 - Water that was launched in May 2012. Multiple <span class="hlt">soil</span> <span class="hlt">moisture</span> products from AMSR2 observations exist; these were compared and combined with special emphasis to the global scale. The first product is retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm, the other uses the Land Parameter Retrieval Model (LPRM). These two products were compared against each other and evaluated against COSMOS data over the United States, Australia, Europe and Africa. The temporal correlations highlight differences in the representation of the seasonal cycle of <span class="hlt">soil</span> <span class="hlt">moisture</span>. It is hypothesized that four factors, physical surface temperatures, surface roughness, vegetation and ground <span class="hlt">soil</span> wetness conditions, <span class="hlt">affect</span> the quality of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. The complementary between the products led to the opportunity to combine them into a superior one that benefits from the strengths of both algorithms.These <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms share the same background in the radiative transfer model, but each algorithm applies different approaches to reflect various external conditions. As a result, the performance of the products is complementary in many locations in terms of bias, RMSE and, most importantly temporal correlation coefficients. Here, we present a methodology that combines the two AMSR2 based <span class="hlt">soil</span> <span class="hlt">moisture</span> products into a single product, which improves the overall performance by leveraging the strengths of the individual products. The new product is combined by applying an optimal weighting factor, calculated based on variance and correlation coefficients against a reference dataset. The complementary</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=275880','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=275880"><span>Long term analysis of PALS <span class="hlt">soil</span> <span class="hlt">moisture</span> campaign measurements for global <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithm development</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>An important component of satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithm development and validation is the comparison of coincident remote sensing and in situ observations that are typically provided by intensive field campaigns. The planned NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission has unique requi...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20140002131&hterms=Moisture&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3DMoisture','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20140002131&hterms=Moisture&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3DMoisture"><span>Using Polarimetric SAR Data to Infer <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from Surfaces with Varying Subsurface <span class="hlt">Moisture</span> Profiles</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Khankhoje, Uday K.; van Zyl, Jakob; Kim, Yunjin; Cwik, Thomas</p> <p>2012-01-01</p> <p>A time-series approach is used to estimate the <span class="hlt">moisture</span> content-based on polarimetric SAR data. It is found that under the assumption of constant <span class="hlt">soil</span> <span class="hlt">moisture</span>, empirically observed relationships between radar backscatter and <span class="hlt">moisture</span> are only half as sensitive to <span class="hlt">moisture</span> as compared to actual radar data. A numerical finite element method is used to calculate the radar backscatter for rough <span class="hlt">soils</span> with arbitrarily varying <span class="hlt">soil</span> <span class="hlt">moisture</span> as a function of depth. Several instance of drying and wetting <span class="hlt">moisture</span> profiles are considered and the radar backscatter is calculated in each case. Radar backscatter is found to crucially depend on the <span class="hlt">soil</span> <span class="hlt">moisture</span> variation in the top half wavelength of <span class="hlt">soil</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/28625','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/28625"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and vegetation patterns in northern California forests</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>James R. Griffin</p> <p>1967-01-01</p> <p>Twenty-nine <span class="hlt">soil</span>-vegetation plots were studied in a broad transect across the southern Cascade Range. Variations in <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns during the growing season and in <span class="hlt">soil</span> <span class="hlt">moisture</span> tension values are discussed. Plot <span class="hlt">soil</span> <span class="hlt">moisture</span> values for 40- and 80-cm. depths in August and September are integrated into a <span class="hlt">soil</span> drought index. Vegetation patterns are described in...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H12D..05S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H12D..05S"><span>Hydrological connectivity drives microbial responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schimel, J.</p> <p>2013-12-01</p> <p>Biogeochemical models generally fit microbial responses to <span class="hlt">moisture</span> with smooth functions--as <span class="hlt">soils</span> dry, processes slow. Microbial physiology, in contrast, has focused on how cells synthesize organic solutes to remain hydrated. Increasingly, however, we recognize that drying <span class="hlt">affects</span> <span class="hlt">soil</span> processes through resource constraints that develop when hydrological connection breaks down and organisms and resources become isolated in disconnected water pockets. Thus, microbial activity is regulated by abrupt breaks in connectivity and resources become unavailable to synthesize organic osmolytes; i.e. both biogeochemical models and pure-culture physiology perspectives are flawed. Hydrological connectivity fails before microbes become substantially stressed and before extracellular enzymes become inactive. Thus, resources can accumulate in dry <span class="hlt">soils</span>, even as microbial activity shuts down because of resource limitation. The differential <span class="hlt">moisture</span> responses of enzymes, organisms, and transport explains why microbial biomass and extractable C pools increase through the dry summer in California annual grasslands, why the size of the respiration pulse on rewetting increases with the length of drought, and even why <span class="hlt">soils</span> from a wide range of biomes show the same relative response to <span class="hlt">soil</span> <span class="hlt">moisture</span>. I will discuss the evidence that supports the hydrological connectivity hypothesis for <span class="hlt">soil</span> microbial <span class="hlt">moisture</span> responses, how it <span class="hlt">affects</span> a range of ecosystem processes, and how we can use it to develop simple, yet mechanistically rich, models of <span class="hlt">soil</span> dynamics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AdWR...84...14D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdWR...84...14D"><span>Root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation from assimilation of downscaled <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dumedah, Gift; Walker, Jeffrey P.; Merlin, Olivier</p> <p>2015-10-01</p> <p>The crucial role of root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> is widely recognized in land-atmosphere interaction, with direct practical use in hydrology, agriculture and meteorology. But it is difficult to estimate the root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> accurately because of its space-time variability and its nonlinear relationship with surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Typically, direct satellite observations at the surface are extended to estimate the root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> through data assimilation. But the results suffer from low spatial resolution of the satellite observation. While advances have been made recently to downscale the satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> from <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission using methods such as the Disaggregation based on Physical And Theoretical scale Change (DisPATCh), the assimilation of such data into high spatial resolution land surface models has not been examined to estimate the root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span>. Consequently, this study assimilates the 1-km DisPATCh surface <span class="hlt">soil</span> <span class="hlt">moisture</span> into the Joint UK Land Environment Simulator (JULES) to better estimate the root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span>. The assimilation is demonstrated using the advanced Evolutionary Data Assimilation (EDA) procedure for the Yanco area in south eastern Australia. When evaluated using in-situ OzNet <span class="hlt">soil</span> <span class="hlt">moisture</span>, the open loop was found to be 95% as accurate as the updated output, with the updated estimate improving the DisPATCh data by 14%, all based on the root mean square error (RMSE). Evaluation of the root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> with in-situ OzNet data found the updated output to improve the open loop estimate by 34% for the 0-30 cm <span class="hlt">soil</span> depth, 59% for the 30-60 cm <span class="hlt">soil</span> depth, and 63% for the 60-90 cm <span class="hlt">soil</span> depth, based on RMSE. The increased performance of the updated output over the open loop estimate is associated with (i) consistent estimation accuracy across the three <span class="hlt">soil</span> depths for the updated output, and (ii) the deterioration of the open loop output for deeper <span class="hlt">soil</span> depths. Thus, the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JHyd..556..349R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JHyd..556..349R"><span>Temporal transferability of <span class="hlt">soil</span> <span class="hlt">moisture</span> calibration equations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rowlandson, Tracy L.; Berg, Aaron A.; Bullock, Paul R.; Hanis-Gervais, Krista; Ojo, E. RoTimi; Cosh, Michael H.; Powers, Jarrett; McNairn, Heather</p> <p>2018-01-01</p> <p>Several large-scale field campaigns have been conducted over the last 20 years that require accurate measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. These measurements are manually conducted using <span class="hlt">soil</span> <span class="hlt">moisture</span> probes which require calibration. The calibration process involves the collection of hundreds of <span class="hlt">soil</span> <span class="hlt">moisture</span> cores, which is extremely labor intensive. In 2012, a field campaign was conducted in southern Manitoba in which 55 fields were sampled and calibration equations were derived for each field. The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Experiment 2016 (SMAPVEX16) was conducted in this same region, and 21 of the same fields were resampled. This study examines the temporal transferability of calibration equations between these two field campaigns. It was found that the larger range in <span class="hlt">soil</span> <span class="hlt">moisture</span> over which samples were collected in 2012 (average range 0.11-0.41 m3 m-3) generally resulted in lower errors when used in 2016 (average range 0.24-0.44 m3 m-3) than the equations derived in 2016 when used with data collected in 2012. Combining the data collected in 2012 and 2016 did not improve the errors, overall. These results suggest that the transfer of calibration equations from one year to the next is not recommended.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9971Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9971Z"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> in sessile oak forest gaps</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zagyvainé Kiss, Katalin Anita; Vastag, Viktor; Gribovszki, Zoltán; Kalicz, Péter</p> <p>2015-04-01</p> <p>By social demands are being promoted the aspects of the natural forest management. In forestry the concept of continuous forest has been an accepted principle also in Hungary since the last decades. The first step from even-aged stand to continuous forest can be the forest regeneration based on gap cutting, so small openings are formed in a forest due to forestry interventions. This new stand structure modifies the hydrological conditions for the regrowth. Without canopy and due to the decreasing amounts of forest litter the interception is less significant so higher amount of precipitation reaching the <span class="hlt">soil</span>. This research focuses on <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns caused by gaps. The spatio-temporal variability of <span class="hlt">soil</span> water content is measured in gaps and in surrounding sessile oak (Quercus petraea) forest stand. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was determined with manual <span class="hlt">soil</span> <span class="hlt">moisture</span> meter which use Time-Domain Reflectometry (TDR) technology. The three different sizes gaps (G1: 10m, G2: 20m, G3: 30m) was opened next to Sopron on the Dalos Hill in Hungary. First, it was determined that there is difference in <span class="hlt">soil</span> <span class="hlt">moisture</span> between forest stand and gaps. Second, it was defined that how the gap size influences the <span class="hlt">soil</span> <span class="hlt">moisture</span> content. To explore the short term variability of <span class="hlt">soil</span> <span class="hlt">moisture</span>, two 24-hour (in growing season) and a 48-hour (in dormant season) field campaign were also performed in case of the medium-sized G2 gap along two/four transects. Subdaily changes of <span class="hlt">soil</span> <span class="hlt">moisture</span> were performed. The measured <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern was compared with the radiation pattern. It was found that the non-illuminated areas were wetter and in the dormant season the subdaily changes cease. According to our measurements, in the gap there is more available water than under the forest stand due to the less evaporation and interception loss. Acknowledgements: The research was supported by TÁMOP-4.2.2.A-11/1/KONV-2012-0004 and AGRARKLIMA.2 VKSZ_12-1-2013-0034.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H54D..04D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H54D..04D"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and properties estimation by assimilating <span class="hlt">soil</span> temperatures using particle batch smoother: A new perspective for DTS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dong, J.; Steele-Dunne, S. C.; Ochsner, T. E.; Van De Giesen, N.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span>, hydraulic and thermal properties are critical for understanding the <span class="hlt">soil</span> surface energy balance and hydrological processes. Here, we will discuss the potential of using <span class="hlt">soil</span> temperature observations from Distributed Temperature Sensing (DTS) to investigate the spatial variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> properties. With DTS <span class="hlt">soil</span> temperature can be measured with high resolution (spatial <1m, and temporal < 1min) in cables up to kilometers in length. <span class="hlt">Soil</span> temperature evolution is primarily controlled by the <span class="hlt">soil</span> thermal properties, and the energy balance at the <span class="hlt">soil</span> surface. Hence, <span class="hlt">soil</span> <span class="hlt">moisture</span>, which <span class="hlt">affects</span> both <span class="hlt">soil</span> thermal properties and the energy that participates the evaporation process, is strongly correlated to the <span class="hlt">soil</span> temperatures. In addition, the dynamics of the <span class="hlt">soil</span> <span class="hlt">moisture</span> is determined by the <span class="hlt">soil</span> hydraulic properties.Here we will demonstrate that <span class="hlt">soil</span> <span class="hlt">moisture</span>, hydraulic and thermal properties can be estimated by assimilating observed <span class="hlt">soil</span> temperature at shallow depths using the Particle Batch Smoother (PBS). The PBS can be considered as an extension of the particle filter, which allows us to infer <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> properties using the dynamics of <span class="hlt">soil</span> temperature within a batch window. Both synthetic and real field data will be used to demonstrate the robustness of this approach. We will show that the proposed method is shown to be able to handle different sources of uncertainties, which may provide a new view of using DTS observations to estimate sub-meter resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> and properties for remote sensing product validation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H23B1577K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H23B1577K"><span>Derivation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Patterns from a simple <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Index</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Korres, W.; Schneider, K.; Reichenau, T. G.; Esch, S.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> and its spatio-temporal pattern is one of the main drivers in complex <span class="hlt">soil</span>-vegetation-atmosphere exchange processes. In order to observe long-term patterns of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, we analyzed a historical data set of ERS SAR (synthetic aperture radar) data using 85 ERS scenes from 1995-2003 for the Rur catchment (2364 km2) in Western Germany. The ERS satellites operated in C-band and single-channel VV polarization. To derive surface <span class="hlt">soil</span> <span class="hlt">moisture</span> from the microwave backscatter intensity, the influence of surface roughness and vegetation biomass on the backscatter must be taken into account. Thus, a simple <span class="hlt">soil</span> <span class="hlt">moisture</span> index was developed to retrieve semi-quantitative information about spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns with a simple yet robust approach. By using data from all available scenes for each month of the year, histograms of σ0-values for each agricultural land use class (cereals, sugar beet, pasture) were generated. Within each of these histograms, the influence of biomass and surface roughness on backscatter is assumed to be constant. Thus, changes in backscatter intensity are due to changes in surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Since the histograms are based on data from 8 years, we assume that each histogram contains pixels representing the wet and the dry <span class="hlt">soil</span> <span class="hlt">moisture</span> state. An index was spanned between high and low backscatter values, identifying wet and dry areas. By using <span class="hlt">soil</span> texture information of the given location, the qualitative index can be translated into volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span>. The resulting <span class="hlt">soil</span> <span class="hlt">moisture</span> maps were compared to precipitation data from nearby meteorological stations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980018613','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980018613"><span>Microstrip Ring Resonator for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sarabandi, Kamal; Li, Eric S.</p> <p>1993-01-01</p> <p>Accurate determination of spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution and monitoring its temporal variation have a significant impact on the outcomes of hydrologic, ecologic, and climatic models. Development of a successful remote sensing instrument for <span class="hlt">soil</span> <span class="hlt">moisture</span> relies on the accurate knowledge of the <span class="hlt">soil</span> dielectric constant (epsilon(sub <span class="hlt">soil</span>)) to its <span class="hlt">moisture</span> content. Two existing methods for measurement of dielectric constant of <span class="hlt">soil</span> at low and high frequencies are, respectively, the time domain reflectometry and the reflection coefficient measurement using an open-ended coaxial probe. The major shortcoming of these methods is the lack of accurate determination of the imaginary part of epsilon(sub <span class="hlt">soil</span>). In this paper a microstrip ring resonator is proposed for the accurate measurement of <span class="hlt">soil</span> dielectric constant. In this technique the microstrip ring resonator is placed in contact with <span class="hlt">soil</span> medium and the real and imaginary parts of epsilon(sub <span class="hlt">soil</span>) are determined from the changes in the resonant frequency and the quality factor of the resonator respectively. The solution of the electromagnetic problem is obtained using a hybrid approach based on the method of moments solution of the quasi-static formulation in conjunction with experimental data obtained from reference dielectric samples. Also a simple inversion algorithm for epsilon(sub <span class="hlt">soil</span>) = epsilon'(sub r) + j(epsilon"(sub r)) based on regression analysis is obtained. It is shown that the wide dynamic range of the measured quantities provides excellent accuracy in the dielectric constant measurement. A prototype microstrip ring resonator at L-band is designed and measurements of <span class="hlt">soil</span> with different <span class="hlt">moisture</span> contents are presented and compared with other approaches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19750010561','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19750010561"><span>Radar measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> content</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ulaby, F. T.</p> <p>1973-01-01</p> <p>The effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the radar backscattering coefficient was investigated by measuring the 4-8 GHz spectral response from two types of bare-<span class="hlt">soil</span> fields: slightly rough and very rough, in terms of the wavelength. An FM-CW radar system was used to measure the return at 10 frequency points across the 4-8 GHz band, at different look angles, and for all polarization combinations. The results indicate that the radar response to <span class="hlt">soil</span> <span class="hlt">moisture</span> content is highly dependent on the surface roughness, microwave frequency, and look angle. The response seems to be linear over the range 15%-30% <span class="hlt">moisture</span> content for all angles, frequencies, polarizations and surface conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.9193A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.9193A"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> estimation in cereal fields using multipolarized SAR data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alvarez-Mozos, J.; Izagirre, A.; Larrañaga, A.</p> <p>2012-04-01</p> <p>The retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> from remote sensing data is an extremely active research topic with applications on a wide range of disciplines. Microwave observations represent the most viable approach due to the influence of <span class="hlt">soils</span>' dielectric constant (and thus <span class="hlt">soil</span> <span class="hlt">moisture</span>) on both the emission and backscatter of waves in this region of the spectrum. Passive observations provide higher temporal resolutions, whereas active (SAR) observations have a higher spatial detail. Even if operational <span class="hlt">moisture</span> products, based on passive data, exist, retrieval algorithms using active observations still face several problems. Surface roughness and vegetation cover are probably the disturbing factors most <span class="hlt">affecting</span> the accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. In this communication the influence of vegetation cover is investigated and a retrieval technique based on multipolarized C band SAR observations is proposed. With this aim a dedicated field campaign was carried out in La Tejería watershed (north of Spain) from January to August 2010. Eight RADARSAT-2 Fine-Quadpol scenes were acquired in order to investigate the role of vegetation cover on the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span>, as well as the sensitivity of different polarimetric parameters to vegetation cover condition. Coinciding with image acquisitions <span class="hlt">soil</span> <span class="hlt">moisture</span>, plant density and crop height measurements were acquired in eight control fields (cultivated with barley and wheat crops). The sensitivity of backscatter coefficients (in HH, HV and VV polarizations) and backscatter ratios (p=HH/VV and q=HV/VV) to <span class="hlt">soil</span> <span class="hlt">moisture</span> and crop condition were evaluated and the semi-empirical Water Cloud Model was fitted to the observations. The results obtained showed that the contribution of the cereal vegetation cover was minimal in HH and HV polarizations, whereas the VV channel appeared to be significantly attenuated by the cereal cover, so its value decreased as the crops grew. As a result, the ratios p and q showed a very good</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011HESSD...8.1609D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011HESSD...8.1609D"><span>The International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network: a data hosting facility for global in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dorigo, W. A.; Wagner, W.; Hohensinn, R.; Hahn, S.; Paulik, C.; Drusch, M.; Mecklenburg, S.; van Oevelen, P.; Robock, A.; Jackson, T.</p> <p>2011-02-01</p> <p>In situ measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> are invaluable for calibrating and validating land surface models and satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. In addition, long-term time series of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring <span class="hlt">soil</span> <span class="hlt">moisture</span>, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network (ISMN; <a href="http://www.ipf.tuwien.ac.at/insitu" target="_blank">http://www.ipf.tuwien.ac.at/insitu</a>) was initiated to serve as a centralized data hosting facility where globally available in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets with the ISMN on a voluntary and no-cost basis. Incoming <span class="hlt">soil</span> <span class="hlt">moisture</span> data are automatically transformed into common volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> units and checked for outliers and implausible values. Apart from <span class="hlt">soil</span> water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and <span class="hlt">soil</span> temperature) are stored in the database. These will assist the user in correctly interpreting the <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status January 2011), the ISMN contains data of 16 networks and more than 500 stations located in the North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011HESS...15.1675D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011HESS...15.1675D"><span>The International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network: a data hosting facility for global in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dorigo, W. A.; Wagner, W.; Hohensinn, R.; Hahn, S.; Paulik, C.; Xaver, A.; Gruber, A.; Drusch, M.; Mecklenburg, S.; van Oevelen, P.; Robock, A.; Jackson, T.</p> <p>2011-05-01</p> <p>In situ measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> are invaluable for calibrating and validating land surface models and satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. In addition, long-term time series of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring <span class="hlt">soil</span> <span class="hlt">moisture</span>, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network (ISMN; <a href="http://www.ipf.tuwien.ac.at/insitu" target="_blank">http://www.ipf.tuwien.ac.at/insitu</a>) was initiated to serve as a centralized data hosting facility where globally available in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets with the ISMN on a voluntary and no-cost basis. Incoming <span class="hlt">soil</span> <span class="hlt">moisture</span> data are automatically transformed into common volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> units and checked for outliers and implausible values. Apart from <span class="hlt">soil</span> water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and <span class="hlt">soil</span> temperature) are stored in the database. These will assist the user in correctly interpreting the <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080008HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080008HQ.html"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Media Briefing</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-01-09</p> <p>Kent Kellogg, SMAP project manager at NASA’s Jet Propulsion Laboratory (JPL) in Pasadena, CA, speaks during a briefing about the upcoming launch of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, Thursday, Jan. 08, 2015, at NASA Headquarters in Washington DC. The mission is scheduled for a Jan. 29 launch from Vandenberg Air Force Base in California, and will provide the most accurate, highest-resolution global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> ever obtained from space. The data will be used to enhance scientists' understanding of the processes that link Earth's water, energy and carbon cycles. Photo Credit: (NASA/Aubrey Gemignani)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080007HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080007HQ.html"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Media Briefing</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-01-09</p> <p>Christine Bonniksen, SMAP program executive with the Science Mission Directorate’s Earth Science Division at NASA Headquarters speaks during a briefing about the upcoming launch of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, Thursday, Jan. 08, 2015, at NASA Headquarters in Washington DC. The mission is scheduled for a Jan. 29 launch from Vandenberg Air Force Base in California, and will provide the most accurate, highest-resolution global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> ever obtained from space. The data will be used to enhance scientists' understanding of the processes that link Earth's water, energy and carbon cycles. Photo Credit: (NASA/Aubrey Gemignani)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080005HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080005HQ.html"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Media Briefing</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-01-09</p> <p>Brad Doorn, SMAP applications lead, Science Mission Directorate’s Applied Sciences Program at NASA Headquarters speaks during a briefing about the upcoming launch of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, Thursday, Jan. 08, 2015, at NASA Headquarters in Washington DC. The mission is scheduled for a Jan. 29 launch from Vandenberg Air Force Base in California, and will provide the most accurate, highest-resolution global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> ever obtained from space. The data will be used to enhance scientists' understanding of the processes that link Earth's water, energy and carbon cycles. Photo Credit: (NASA/Aubrey Gemignani)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080004HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080004HQ.html"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Media Briefing</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-01-09</p> <p>Dara Entekhabi, SMAP science team lead, Massachusetts Institute of Technology, center, speaks during a briefing about the upcoming launch of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, Thursday, Jan. 08, 2015, at NASA Headquarters in Washington DC. The mission is scheduled for a Jan. 29 launch from Vandenberg Air Force Base in California, and will provide the most accurate, highest-resolution global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> ever obtained from space. The data will be used to enhance scientists' understanding of the processes that link Earth's water, energy and carbon cycles. Photo Credit: (NASA/Aubrey Gemignani)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080006HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080006HQ.html"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Media Briefing</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-01-09</p> <p>Dara Entekhabi, SMAP science team lead, Massachusetts Institute of Technology, speaks during a briefing about the upcoming launch of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, Thursday, Jan. 08, 2015, at NASA Headquarters in Washington DC. The mission is scheduled for a Jan. 29 launch from Vandenberg Air Force Base in California, and will provide the most accurate, highest-resolution global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> ever obtained from space. The data will be used to enhance scientists' understanding of the processes that link Earth's water, energy and carbon cycles. Photo Credit: (NASA/Aubrey Gemignani)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810012896','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810012896"><span>Multispectral determination of <span class="hlt">soil</span> <span class="hlt">moisture</span>. [Guymon, Oklahoma</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Estes, J. E.; Simonett, D. S. (Principal Investigator); Hajic, E. J.; Blanchard, B. J.</p> <p>1980-01-01</p> <p>The edited Guymon <span class="hlt">soil</span> <span class="hlt">moisture</span> data collected on August 2, 5, 14, 17, 1978 were grouped into four field cover types for statistical analysis. These are the bare, milo with rows parallel to field of view, milo with rows perpendicular to field of view and alfalfa cover groups. There are 37, 22, 24 and 14 observations respectively in each group for each sensor channel and each <span class="hlt">soil</span> <span class="hlt">moisture</span> layer. A subset of these data called the 'five cover set' (VEG5) limited the scatterometer data to the 15 deg look angle and was used to determine discriminant functions and combined group regressions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.7936B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.7936B"><span>Investigating local controls on <span class="hlt">soil</span> <span class="hlt">moisture</span> temporal stability using an inverse modeling approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogena, Heye; Qu, Wei; Huisman, Sander; Vereecken, Harry</p> <p>2013-04-01</p> <p>A better understanding of the temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its relation to local and nonlocal controls is a major challenge in modern hydrology. Both local controls, such as <span class="hlt">soil</span> and vegetation properties, and non-local controls, such as topography and climate variability, <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Wireless sensor networks are becoming more readily available, which opens up opportunities to investigate spatial and temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> with unprecedented resolution. In this study, we employed the wireless sensor network <span class="hlt">Soil</span>Net developed by the Forschungszentrum Jülich to investigate <span class="hlt">soil</span> <span class="hlt">moisture</span> variability of a grassland headwater catchment in Western Germany within the framework of the TERENO initiative. In particular, we investigated the effect of <span class="hlt">soil</span> hydraulic parameters on the temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span>. For this, the HYDRUS-1D code coupled with a global optimizer (DREAM) was used to inversely estimate Mualem-van Genuchten parameters from <span class="hlt">soil</span> <span class="hlt">moisture</span> observations at three depths under natural (transient) boundary conditions for 83 locations in the headwater catchment. On the basis of the optimized parameter sets, we then evaluated to which extent the variability in <span class="hlt">soil</span> hydraulic conductivity, pore size distribution, air entry suction and <span class="hlt">soil</span> depth between these 83 locations controlled the temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span>, which was independently determined from the observed <span class="hlt">soil</span> <span class="hlt">moisture</span> data. It was found that the saturated hydraulic conductivity (Ks) was the most significant attribute to explain temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span> as expressed by the mean relative difference (MRD).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28559315','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28559315"><span>Historical climate controls <span class="hlt">soil</span> respiration responses to current <span class="hlt">soil</span> <span class="hlt">moisture</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hawkes, Christine V; Waring, Bonnie G; Rocca, Jennifer D; Kivlin, Stephanie N</p> <p>2017-06-13</p> <p>Ecosystem carbon losses from <span class="hlt">soil</span> microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40-70 Pg carbon from <span class="hlt">soil</span> to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to <span class="hlt">soil</span> <span class="hlt">moisture</span>, which remain unresolved in ecosystem models. A common assumption of large-scale models is that <span class="hlt">soil</span> microorganisms respond to <span class="hlt">moisture</span> in the same way, regardless of location or climate. Here, we show that <span class="hlt">soil</span> respiration is constrained by historical climate. We find that historical rainfall controls both the <span class="hlt">moisture</span> dependence and sensitivity of respiration. <span class="hlt">Moisture</span> sensitivity, defined as the slope of respiration vs. <span class="hlt">moisture</span>, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter <span class="hlt">soils</span> compared with historically drier <span class="hlt">soils</span>. The respiration-<span class="hlt">moisture</span> relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5474806','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5474806"><span>Historical climate controls <span class="hlt">soil</span> respiration responses to current <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Waring, Bonnie G.; Rocca, Jennifer D.; Kivlin, Stephanie N.</p> <p>2017-01-01</p> <p>Ecosystem carbon losses from <span class="hlt">soil</span> microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40–70 Pg carbon from <span class="hlt">soil</span> to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to <span class="hlt">soil</span> <span class="hlt">moisture</span>, which remain unresolved in ecosystem models. A common assumption of large-scale models is that <span class="hlt">soil</span> microorganisms respond to <span class="hlt">moisture</span> in the same way, regardless of location or climate. Here, we show that <span class="hlt">soil</span> respiration is constrained by historical climate. We find that historical rainfall controls both the <span class="hlt">moisture</span> dependence and sensitivity of respiration. <span class="hlt">Moisture</span> sensitivity, defined as the slope of respiration vs. <span class="hlt">moisture</span>, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter <span class="hlt">soils</span> compared with historically drier <span class="hlt">soils</span>. The respiration–<span class="hlt">moisture</span> relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall. PMID:28559315</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H43E1492L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H43E1492L"><span>Variation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from Rainfall Effects in Hillslope</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, H.; Lee, J. Y.</p> <p>2016-12-01</p> <p>The purpose of this study is to verify the variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> with respect to rainfall patterns. Thus, we analyze the data that are measured in selected sites of sloped region. The observation was performed in 10 minute-interval, from June 14th 2016 to July 20th 2016. A monitoring station was designed and installed on the hillslope, which is located on the Haean-basin in Korea. We observed four different locations (YMSL1, YMSL2, YMSL3, and YMSL4) and in which each location was sampled as three depths (30, 60, and 100 cm). The profile of this monitored region shows concave shape. In other words, the slope is decreasing when its altitude is decreasing. The data from the sites were measured by <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Loggers (SMLs). In addition, Automatic Weather System was installed in the area adjacent to the site that has SMLs. The rainfall was 493.8 mm over 18 days during the observation period. In particular, the maximum rainfall showed 247 mm, from July 4 to 6 in 2016. Heavy rain occurred twice. We could determine how rainfalls at this site <span class="hlt">affect</span> to the change of <span class="hlt">soil</span> <span class="hlt">moisture</span> by comparing these two key factors in terms of geometrical slope of region and the depth of each sampled site. Delayed variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> on YMSL1, the sensor located at the 30 cm depth recorded a 0.3 h response delay to the rainfall event, and the 100 cm sensor recorded a 24 h delay. Delayed variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> on YMSL2, the sensor located at the 30, and 60 cm depth recorded a 0.8 h response delay to the rainfall event, and the 100 cm sensor recorded a 32 h delay. Delayed variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> on YMSL3, the sensor located at the 30 cm depth recorded a 0.8 h response delay to the rainfall event, the 60 cm sensor recorded a 88 h delay, and the 100 cm sensor recorded a 44 h delay. Delayed variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> on YMSL4, the sensor located at the 30, and 60 cm depth recorded a 4 h response delay to the rainfall event. From these results, it is concluded that the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=269815','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=269815"><span>Implementation of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation with watershed scale distributed hydrological model</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>This paper aims to investigate how surface <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation <span class="hlt">affects</span> each hydrologic process and how spatially varying inputs <span class="hlt">affect</span> the potential capability of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation at the watershed scale. The Ensemble Kalman Filter (EnKF) is coupled with a watershed scal...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27764203','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27764203"><span>The Impact of Rainfall on <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Dynamics in a Foggy Desert.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Bonan; Wang, Lixin; Kaseke, Kudzai F; Li, Lin; Seely, Mary K</p> <p>2016-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and <span class="hlt">affects</span> the development of weather patterns including rainfall. However, the lack of ground observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months' continuous daily records of rainfall and <span class="hlt">soil</span> <span class="hlt">moisture</span> (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in water-limited systems was used to study the relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> and rainfall dynamics. Model sensitivity in response to different <span class="hlt">soil</span> and vegetation parameters under diverse <span class="hlt">soil</span> textures was also investigated. Our field observations showed that surface <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics generally follow rainfall patterns at the two gravel plain sites, whereas <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that <span class="hlt">soil</span> hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling <span class="hlt">soil</span> <span class="hlt">moisture</span> output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., <span class="hlt">soil</span> hydraulic conductivity (Ks) and <span class="hlt">soil</span> porosity (n)). Field observations, stochastic modeling</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5072646','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5072646"><span>The Impact of Rainfall on <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Dynamics in a Foggy Desert</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.</p> <p>2016-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and <span class="hlt">affects</span> the development of weather patterns including rainfall. However, the lack of ground observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and <span class="hlt">soil</span> <span class="hlt">moisture</span> (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in water-limited systems was used to study the relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> and rainfall dynamics. Model sensitivity in response to different <span class="hlt">soil</span> and vegetation parameters under diverse <span class="hlt">soil</span> textures was also investigated. Our field observations showed that surface <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics generally follow rainfall patterns at the two gravel plain sites, whereas <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that <span class="hlt">soil</span> hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling <span class="hlt">soil</span> <span class="hlt">moisture</span> output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., <span class="hlt">soil</span> hydraulic conductivity (Ks) and <span class="hlt">soil</span> porosity (n)). Field observations, stochastic modeling</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-PIA19337.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-PIA19337.html"><span>High-Resolution Global <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Map</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-05-19</p> <p>High-resolution global <span class="hlt">soil</span> <span class="hlt">moisture</span> map from NASA SMAP combined radar and radiometer instruments, acquired between May 4 and May 11, 2015 during SMAP commissioning phase. The map has a resolution of 5.6 miles (9 kilometers). The data gap is due to turning the instruments on and off during testing. http://photojournal.jpl.nasa.gov/catalog/PIA19337</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=336620','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=336620"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Remote Sensing: Status and Outlook</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Satellite-based passive microwave sensors have been available for thirty years and provide the basis for <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring and mapping. The approach has reached a level of maturity that is now limited primarily by technology and funding. This is a result of extensive research and development ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=311911','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=311911"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and temperature algorithms and validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Passive microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170007425','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170007425"><span>AMSR2 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product Validation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bindlish, R.; Jackson, T.; Cosh, M.; Koike, T.; Fuiji, X.; de Jeu, R.; Chan, S.; Asanuma, J.; Berg, A.; Bosch, D.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20170007425'); toggleEditAbsImage('author_20170007425_show'); toggleEditAbsImage('author_20170007425_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20170007425_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20170007425_hide"></p> <p>2017-01-01</p> <p>The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness temperature observations that are used to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span>. Merging AMSR-E and AMSR2 will help build a consistent long-term dataset. Before tackling the integration of AMSR-E and AMSR2 it is necessary to conduct a thorough validation and assessment of the AMSR2 <span class="hlt">soil</span> <span class="hlt">moisture</span> products. This study focuses on validation of the AMSR2 <span class="hlt">soil</span> <span class="hlt">moisture</span> products by comparison with in situ reference data from a set of core validation sites. Three products that rely on different algorithms were evaluated; the JAXA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Algorithm (JAXA), the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). Results indicate that overall the SCA has the best performance based upon the metrics considered.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ESASP.722E.327S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ESASP.722E.327S"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> From Satellite Radar Altimetry (SMALT)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smith, R. G.; Salloway, M. K.; Berry, P. A. M.; Dowson, M.; Hahn, S.; Wagner, W.; Egibo, A.; Benveniste, J.</p> <p>2013-12-01</p> <p><span class="hlt">Soil</span> surface <span class="hlt">moisture</span> is a key scientific parameter; however, it is extremely difficult to measure remotely, particularly in arid and semi-arid terrain. This paper outlines the development of a novel methodology to generate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates in these regions from multi-mission satellite radar altimetry. Key to this approach is the development of detailed DRy Earth ModelS (DREAMS), which encapsulate the detailed and intricate surface brightness variations over the Earth's land surface, resulting from changes in surface roughness and composition. These DREAMS are complicated to build and require multiple stages of processing and manual intervention. However, this approach obviates the requirement for detailed ground truth to populate theoretical models, facilitating derivation of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates over arid regions, where detailed survey data are generally not available. DREAMS have been produced over a number of deserts worldwide and a selection are presented in this paper. An overview of the SMALT processing scheme, covering the progression of the data from altimeter sigma0 through to final <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate, is included along with example SMALT products. In order to validate these products comparisons with other remote sensing techniques and in-situ data have been performed over a number of desert regions. SMALT products are made freely available to the scientific community through the website http://tethys.eaprs.cse.dmu.ac.uk/SMALT</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=291074','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=291074"><span>SMAP validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite will be launched by the National Aeronautics and Space Administration in October 2014. SMAP will also incorporate a rigorous calibration and validation program that will support algorithm refinement and provide users with information on the accuracy ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..325a2019G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..325a2019G"><span>Characterization of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Level for Rice and Maize Crops using GSM Shield and Arduino Microcontroller</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gines, G. A.; Bea, J. G.; Palaoag, T. D.</p> <p>2018-03-01</p> <p><span class="hlt">Soil</span> serves a medium for plants growth. One factor that <span class="hlt">affects</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> is drought. Drought has been a major cause of agricultural disaster. Agricultural drought is said to occur when <span class="hlt">soil</span> <span class="hlt">moisture</span> is insufficient to meet crop water requirements, resulting in yield losses. In this research, it aimed to characterize <span class="hlt">soil</span> <span class="hlt">moisture</span> level for Rice and Maize Crops using Arduino and applying fuzzy logic. System architecture for <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor and water pump were the basis in developing the equipment. The data gathered was characterized by applying fuzzy logic. Based on the results, applying fuzzy logic in validating the characterization of <span class="hlt">soil</span> <span class="hlt">moisture</span> level for Rice and Maize crops is accurate as attested by the experts. This will help the farmers in monitoring the <span class="hlt">soil</span> <span class="hlt">moisture</span> level of the Rice and Maize crops.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5273K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5273K"><span>Effect of management and <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes on wetland <span class="hlt">soils</span> total carbon and nitrogen in Tanzania</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kamiri, Hellen; Kreye, Christine; Becker, Mathias</p> <p>2013-04-01</p> <p>Wetland <span class="hlt">soils</span> play an important role as storage compartments for water, carbon and nutrients. These <span class="hlt">soils</span> implies various conditions, depending on the water regimes that <span class="hlt">affect</span> several important microbial and physical-chemical processes which in turn influence the transformation of organic and inorganic components of nitrogen, carbon, <span class="hlt">soil</span> acidity and other nutrients. Particularly, <span class="hlt">soil</span> carbon and nitrogen play an important role in determining the productivity of a <span class="hlt">soil</span> whereas management practices could determine the rate and magnitude of nutrient turnover. A study was carried out in a floodplain wetland planted with rice in North-west Tanzania- East Africa to determine the effects of different management practices and <span class="hlt">soil</span> water regimes on paddy <span class="hlt">soil</span> organic carbon and nitrogen. Four management treatments were compared: (i) control (non weeded plots); (ii) weeded plots; (iii) N fertilized plots, and (iv) non-cropped (non weeded plots). Two <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes included <span class="hlt">soil</span> under field capacity (rainfed conditions) and continuous water logging compared side-by-side. <span class="hlt">Soil</span> were sampled at the start and end of the rice cropping seasons from the two fields differentiated by <span class="hlt">moisture</span> regimes during the wet season 2012. The <span class="hlt">soils</span> differed in the total organic carbon and nitrogen between the treatments. <span class="hlt">Soil</span> management including weeding and fertilization is seen to <span class="hlt">affect</span> <span class="hlt">soil</span> carbon and nitrogen regardless of the <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. Particularly, the padddy <span class="hlt">soils</span> were higher in the total organic carbon under continuous water logged field. These findings are preliminary and a more complete understanding of the relationships between management and <span class="hlt">soil</span> <span class="hlt">moisture</span> on the temporal changes of <span class="hlt">soil</span> properties is required before making informed decisions on future wetland <span class="hlt">soil</span> carbon and nitrogen dynamics. Keywords: Management, nitrogen, paddy <span class="hlt">soil</span>, total carbon, Tanzania,</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820017732','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820017732"><span>Plan of research for integrated <span class="hlt">soil</span> <span class="hlt">moisture</span> studies. Recommendations of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Working Group</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1980-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> information is a potentially powerful tool for applications in agriculture, water resources, and climate. At present, it is difficult for users of this information to clearly define their needs in terms of accuracy, resolution and frequency because of the current sparsity of data. A plan is described for defining and conducting an integrated and coordinated research effort to develop and refine remote sensing techniques which will determine spatial and temporal variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and to utilize <span class="hlt">soil</span> <span class="hlt">moisture</span> information in support of agricultural, water resources, and climate applications. The <span class="hlt">soil</span> <span class="hlt">moisture</span> requirements of these three different application areas were reviewed in relation to each other so that one plan covering the three areas could be formulated. Four subgroups were established to write and compile the plan, namely models, ground-based studies, aircraft experiments, and spacecraft missions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=276404','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=276404"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> mapping for aquarius</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Aquarius is the first satellite to provide both passive and active L-band observations of the Earth. In addition, the instruments on Satelite de Aplicaciones Cientificas-D (SAC-D) provide complementary information for analysis and retrieval algorithms. Our research focuses on the retrieval of <span class="hlt">soil</span> m...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940018659','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940018659"><span>Estimates of monthly mean <span class="hlt">soil</span> <span class="hlt">moisture</span> for 1979-1989</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schemm, J.; Schubert, S.; Terry, J.; Bloom, S.</p> <p>1992-01-01</p> <p>This technical report presents estimated monthly mean global <span class="hlt">soil</span> <span class="hlt">moisture</span> distributions for 1979-1989. The <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets were prepared as part of the boundary conditions for an atmospheric general circulation model (GEOS-1). Also included are the 11-year averages of monthly mean <span class="hlt">soil</span> <span class="hlt">moisture</span>, surface air temperature, monthly total precipitation, evapotranspiration, and potential evapotranspiration. The standard deviation of the monthly mean <span class="hlt">soil</span> <span class="hlt">moisture</span> is provided as a measure of year-to-year variability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.1723F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.1723F"><span>The effect of horizontal <span class="hlt">soil</span> <span class="hlt">moisture</span> heterogeneity on the cosmic-ray neutron probe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Franz, Trenton; Zreda, Marek; Ferre, Ty; Rosolem, Rafael</p> <p>2013-04-01</p> <p>Given the horizontal measurement area of a cosmic-ray neutron probe at ~35 ha, the probe has the potential to fill a critical measurement gap for validating and calibrating hyper-resolution land surface models. Because the relationship between neutron counts and average <span class="hlt">soil</span> <span class="hlt">moisture</span> is nonlinear, previous work has shown that averaging over vertical profiles during wetting and drying states may potentially result in non-uniqueness. Here we investigate the effect of horizontal heterogeneity on the relationship between neutron counts and average <span class="hlt">soil</span> <span class="hlt">moisture</span>. Observations from a distributed sensor network at a study site in southern Arizona indicate that the horizontal component of the total standard deviation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> field is nearly constant in time. In addition, electromagnetic induction surveys at the site suggest that the <span class="hlt">soil</span> <span class="hlt">moisture</span> has a Gaussian or bimodal distribution following a rain event. Using neutron particle transport simulations we demonstrate that 1-dimensional binary distributions of <span class="hlt">soil</span> <span class="hlt">moisture</span> may result in different mean neutron counts and standard deviation of neutron counts for a randomly placed detector in a <span class="hlt">soil</span> <span class="hlt">moisture</span> field. However, simulations of 1 and 2-dimensional Gaussian <span class="hlt">soil</span> <span class="hlt">moisture</span> fields indicate consistent mean and standard deviations of a randomly placed detector with short correlation length scales. Based on <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from this study site and numerical simulations of the detector response we conclude that horizontal heterogeneity does not greatly <span class="hlt">affect</span> the relationship between mean neutron counts and average <span class="hlt">soil</span> <span class="hlt">moisture</span> for horizontal <span class="hlt">soil</span> <span class="hlt">moisture</span> fields with near Gaussian distributions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19860018232','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19860018232"><span>Preliminary assessment of <span class="hlt">soil</span> <span class="hlt">moisture</span> over vegetation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carlson, T. N.</p> <p>1986-01-01</p> <p>Modeling of surface energy fluxes was combined with in-situ measurement of surface parameters, specifically the surface sensible heat flux and the substrate <span class="hlt">soil</span> <span class="hlt">moisture</span>. A vegetation component was incorporated in the atmospheric/substrate model and subsequently showed that fluxes over vegetation can be very much different than those over bare <span class="hlt">soil</span> for a given surface-air temperature difference. The temperature signatures measured by a satellite or airborne radiometer should be interpreted in conjunction with surface measurements of modeled parameters. Paradoxically, analyses of the large-scale distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> availability shows that there is a very high correlation between antecedent precipitation and inferred surface <span class="hlt">moisture</span> availability, even when no specific vegetation parameterization is used in the boundary layer model. Preparatory work was begun in streamlining the present boundary layer model, developing better algorithms for relating surface temperatures to substrate <span class="hlt">moisture</span>, preparing for participation in the French HAPEX experiment, and analyzing aircraft microwave and radiometric surface temperature data for the 1983 French Beauce experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810020962','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810020962"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> inferences from thermal infrared measurements of vegetation temperatures</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jackson, R. D. (Principal Investigator)</p> <p>1981-01-01</p> <p>Thermal infrared measurements of wheat (Triticum durum) canopy temperatures were used in a crop water stress index to infer root zone <span class="hlt">soil</span> <span class="hlt">moisture</span>. Results indicated that one time plant temperature measurement cannot produce precise estimates of root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> due to complicating plant factors. Plant temperature measurements do yield useful qualitative information concerning <span class="hlt">soil</span> <span class="hlt">moisture</span> and plant condition.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=331905','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=331905"><span>Downscaled <span class="hlt">soil</span> <span class="hlt">moisture</span> from SMAP evaluated using high density observations</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Recently, a <span class="hlt">soil</span> <span class="hlt">moisture</span> downscaling algorithm based on a regression relationship between daily temperature changes and daily average <span class="hlt">soil</span> <span class="hlt">moisture</span> was developed to produce an enhanced spatial resolution on <span class="hlt">soil</span> <span class="hlt">moisture</span> product for the Advanced Microwave Scanning Radiometer–EOS (AMSR-E) satellite ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H41D1482M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H41D1482M"><span>Measuring <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Skeletal <span class="hlt">Soils</span> Using a COSMOS Rover</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medina, C.; Neely, H.; Desilets, D.; Mohanty, B.; Moore, G. W.</p> <p>2017-12-01</p> <p>The presence of coarse fragments directly influences the volumetric water content of the <span class="hlt">soil</span>. Current surface <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors often do not account for the presence of coarse fragments, and little research has been done to calibrate these sensors under such conditions. The cosmic-ray <span class="hlt">soil</span> <span class="hlt">moisture</span> observation system (COSMOS) rover is a passive, non-invasive surface <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor with a footprint greater than 100 m. Despite its potential, the COSMOS rover has yet to be validated in skeletal <span class="hlt">soils</span>. The goal of this study was to validate measurements of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> as taken by a COSMOS rover on a Texas skeletal <span class="hlt">soil</span>. Data was collected for two <span class="hlt">soils</span>, a Marfla clay loam and Chinati-Boracho-Berrend association, in West Texas. Three levels of data were collected: 1) COSMOS surveys at three different <span class="hlt">soil</span> <span class="hlt">moistures</span>, 2) electrical conductivity surveys within those COSMOS surveys, and 3) ground-truth measurements. Surveys with the COSMOS rover covered an 8000-h area and were taken both after large rain events (>2") and a long dry period. Within the COSMOS surveys, the EM38-MK2 was used to estimate the spatial distribution of coarse fragments in the <span class="hlt">soil</span> around two COSMOS points. Ground truth measurements included coarse fragment mass and volume, bulk density, and water content at 3 locations within each EM38 survey. Ground-truth measurements were weighted using EM38 data, and COSMOS measurements were validated by their distance from the samples. There was a decrease in water content as the percent volume of coarse fragment increased. COSMOS estimations responded to both changes in coarse fragment percent volume and the ground-truth volumetric water content. Further research will focus on creating digital <span class="hlt">soil</span> maps using landform data and water content estimations from the COSMOS rover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=344556','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=344556"><span>Inference of <span class="hlt">soil</span> hydrologic parameters from electronic <span class="hlt">soil</span> <span class="hlt">moisture</span> records</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important control on hydrologic function, as it governs vertical fluxes from and to the atmosphere, groundwater recharge, and lateral fluxes through the <span class="hlt">soil</span>. Historically, the traditional model parameters of saturation, field capacity, and permanent wilting point have been deter...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H52B..06H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H52B..06H"><span>Tree Species Specific <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Patterns and Dynamics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heidbuechel, I.; Dreibrodt, J.; Guntner, A.; Blume, T.</p> <p>2014-12-01</p> <p>Land use has a major influence on the hydrologic processes that take place in <span class="hlt">soils</span>. <span class="hlt">Soil</span> compaction on pastures for example leads to infiltration patterns that differ considerably from the ones observable in forests. It is not clear, however, how different forest stands influence <span class="hlt">soil</span> infiltration and <span class="hlt">soil</span> <span class="hlt">moisture</span> distributions. Factors that that vary amongst different stands and potentially <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> processes in forests are, amongst others, canopy density, throughfall patterns, the intensity and frequency of stem flow, litter type, root distributions and rooting depth. To investigate how different tree species influence the way <span class="hlt">soils</span> partition, store and conduct incoming precipitation we selected 15 locations under different tree stands within the TERENO observatory in north-east Germany. The forest stands under investigation were mature oak, young pine, mature pine, young beech and mature beech. At each location we installed 30 FDR <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors grouped into five depth profiles (monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span> from 10 cm to 200 cm) and 5 additional near surface sensors. The profile locations within each forest stand covered most of the anticipated variability by ranging from minimum to maximum distance to the trees including locations under more and less dense canopy. Supplementary to the FDR sensors, throughfall measurements, tensiometers and groundwater data were available to observe dynamics of tree water availability, water fluxes within the <span class="hlt">soils</span> and percolation towards the groundwater. To identify patterns in space and time we referred to the statistical methods of wavelet analysis and temporal stability analysis. Finally, we tried to link the results from these analyses to specific hydrologic processes at the different locations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080038045','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080038045"><span>Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval Under Trees</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>O'Neill, P.; Lang, R.; Kurum, M.; Joseph, A.; Jackson, T.; Cosh, M.</p> <p>2008-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is recognized as an important component of the water, energy, and carbon cycles at the interface between the Earth's surface and atmosphere. Current baseline <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithms for microwave space missions have been developed and validated only over grasslands, agricultural crops, and generally light to moderate vegetation. Tree areas have commonly been excluded from operational <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval plans due to the large expected impact of trees on masking the microwave response to the underlying <span class="hlt">soil</span> <span class="hlt">moisture</span>. Our understanding of the microwave properties of trees of various sizes and their effect on <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithms at L band is presently limited, although research efforts are ongoing in Europe, the United States, and elsewhere to remedy this situation. As part of this research, a coordinated sequence of field measurements involving the ComRAD (for Combined Radar/Radiometer) active/passive microwave truck instrument system has been undertaken. Jointly developed and operated by NASA Goddard Space Flight Center and George Washington University, ComRAD consists of dual-polarized 1.4 GHz total-power radiometers (LH, LV) and a quad-polarized 1.25 GHz L band radar sharing a single parabolic dish antenna with a novel broadband stacked patch dual-polarized feed, a quad-polarized 4.75 GHz C band radar, and a single channel 10 GHz XHH radar. The instruments are deployed on a mobile truck with an 19-m hydraulic boom and share common control software; real-time calibrated signals, and the capability for automated data collection for unattended operation. Most microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithms developed for use at L band frequencies are based on the tau-omega model, a simplified zero-order radiative transfer approach where scattering is largely ignored and vegetation canopies are generally treated as a bulk attenuating layer. In this approach, vegetation effects are parameterized by tau and omega, the microwave</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JGRD..11715115O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JGRD..11715115O"><span>Analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory from observations in Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Orth, R.; Seneviratne, S. I.</p> <p>2012-08-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is known to show distinctive persistence characteristics compared to other quantities in the climate system. As <span class="hlt">soil</span> <span class="hlt">moisture</span> is governing land-atmosphere feedbacks to a large extent, its persistence can provide potential to improve seasonal climate predictions. So far, many modeling studies have investigated the nature of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory, with consistent, but model-dependent results. This study investigates <span class="hlt">soil</span> <span class="hlt">moisture</span> memory in long-term observational records based on data from five stations across Europe. We investigate spatial and seasonal variations in <span class="hlt">soil</span> <span class="hlt">moisture</span> memory and identify their main climatic drivers. Also, we test an existing framework and introduce an extension thereof to approximate <span class="hlt">soil</span> <span class="hlt">moisture</span> memory and evaluate the contributions of its driving processes. At the analyzed five sites, we identify the variability of initial <span class="hlt">soil</span> <span class="hlt">moisture</span> divided by that of the accumulated forcing over the considered time frame as a main driver of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory that reflects the impact of the precipitation regime and of <span class="hlt">soil</span> and vegetation characteristics. Another important driver is found to be the correlation of initial <span class="hlt">soil</span> <span class="hlt">moisture</span> with subsequent forcing that captures forcing memory as it propagates to the <span class="hlt">soil</span> and also land-atmosphere interactions. Thereby, the role of precipitation is found to be dominant for the forcing. In contrast to results from previous modeling studies, the runoff and evapotranspiration sensitivities to <span class="hlt">soil</span> <span class="hlt">moisture</span> are found to have only a minor influence on <span class="hlt">soil</span> <span class="hlt">moisture</span> persistence at the analyzed sites. For the central European sites, the seasonal cycles of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory display a maximum in late summer and a minimum in spring. An opposite seasonal cycle is found at the analyzed site in Italy. High <span class="hlt">soil</span> <span class="hlt">moisture</span> memory is shown to last up to 40 days in some seasons at most sites. Extremely dry or wet states of the <span class="hlt">soil</span> tend to increase <span class="hlt">soil</span> <span class="hlt">moisture</span> memory, suggesting enhanced prediction</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H43F1712G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H43F1712G"><span>Enhanced simulations of CH4 and CO2 production in permafrost-<span class="hlt">affected</span> <span class="hlt">soils</span> address <span class="hlt">soil</span> <span class="hlt">moisture</span> controls on anaerobic decomposition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Graham, D. E.; Zheng, J.; Moon, J. W.; Painter, S. L.; Thornton, P. E.; Gu, B.; Wullschleger, S. D.</p> <p>2017-12-01</p> <p>Rapid warming of Arctic ecosystems exposes <span class="hlt">soil</span> organic carbon (SOC) to accelerated microbial decomposition, leading to increased emissions of carbon dioxide (CO2) and methane (CH4) that have a positive feedback on global warming. The magnitude, timing, and form of carbon release will depend not only on changes in temperature, but also on biogeochemical and hydrological properties of <span class="hlt">soils</span>. In this synthesis study, we assessed the decomposability of thawed organic carbon from active layer <span class="hlt">soils</span> and permafrost from the Barrow Environmental Observatory across different microtopographic positions under anoxic conditions. The main objectives of this study were to (i) examine environmental conditions and <span class="hlt">soil</span> properties that control anaerobic carbon decomposition and carbon release (as both CO2 and CH4); (ii) develop a common set of parameters to simulate anaerobic CO2 and CH4 production; and (iii) evaluate uncertainties generated from representations of pH and temperature effects in the current model framework. A newly developed anaerobic carbon decomposition framework simulated incubation experiment results across a range of <span class="hlt">soil</span> water contents. Anaerobic CO2 and CH4 production have different temperature and pH sensitivities, which are not well represented in current biogeochemical models. Distinct dynamics of CH4 production at -2° C suggest methanogen biomass and growth rate limit activity in these near-frozen <span class="hlt">soils</span>, compared to warmer temperatures. Anaerobic CO2 production is well constrained by the model using data-informed labile carbon pool and fermentation rate initialization to accurately simulate its temperature sensitivity. On the other hand, CH4 production is controlled by water content, methanogenesis biomass, and the presence of alternative electron acceptors, producing a high temperature sensitivity with large uncertainties for methanogenesis. This set of environmental constraints to methanogenesis is likely to undergo drastic changes due to permafrost</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=266872','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=266872"><span>SMOS validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and ocen salinity (SMOS) <span class="hlt">soil</span> <span class="hlt">moisture</span> over watershed networks in the U.S.</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods. The most recent source of <span class="hlt">soil</span> <span class="hlt">moisture</span> is the European Space Agency <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission. A thorough validation must b...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=332939','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=332939"><span>A comparative study of the SMAP passive <span class="hlt">soil</span> <span class="hlt">moisture</span> product with existing satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product (L2...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H42B..02K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H42B..02K"><span>Land-atmosphere coupling and <span class="hlt">soil</span> <span class="hlt">moisture</span> memory contribute to long-term agricultural drought</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kumar, S.; Newman, M.; Lawrence, D. M.; Livneh, B.; Lombardozzi, D. L.</p> <p>2017-12-01</p> <p>We assessed the contribution of land-atmosphere coupling and <span class="hlt">soil</span> <span class="hlt">moisture</span> memory on long-term agricultural droughts in the US. We performed an ensemble of climate model simulations to study <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics under two atmospheric forcing scenarios: active and muted land-atmosphere coupling. Land-atmosphere coupling contributes to a 12% increase and 36% decrease in the decorrelation time scale of <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies in the US Great Plains and the Southwest, respectively. These differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> memory <span class="hlt">affect</span> the length and severity of modeled drought. Consequently, long-term droughts are 10% longer and 3% more severe in the Great Plains, and 15% shorter and 21% less severe in the Southwest. An analysis of Coupled Model Intercomparsion Project phase 5 data shows four fold uncertainty in <span class="hlt">soil</span> <span class="hlt">moisture</span> memory across models that strongly <span class="hlt">affects</span> simulated long-term droughts and is potentially attributable to the differences in <span class="hlt">soil</span> water storage capacity across models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080003HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080003HQ.html"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Media Briefing</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-01-09</p> <p>Christine Bonniksen, SMAP program executive with the Science Mission Directorate’s Earth Science Division, NASA Headquarters, left, Kent Kellogg, SMAP project manager, NASA Jet Propulsion Laboratory (JPL), second from left, Dara Entekhabi, SMAP science team lead, Massachusetts Institute of Technology, second from right, and Brad Doorn, SMAP applications lead, Science Mission Directorate’s Applied Sciences Program, NASA Headquarters, right, are seen during a briefing about the upcoming launch of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, Thursday, Jan. 08, 2015, at NASA Headquarters in Washington DC. The mission is scheduled for a Jan. 29 launch from Vandenberg Air Force Base in California, and will provide the most accurate, highest-resolution global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> ever obtained from space. The data will be used to enhance scientists' understanding of the processes that link Earth's water, energy and carbon cycles. Photo Credit: (NASA/Aubrey Gemignani)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170010181','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170010181"><span>Uncertainty Assessment of Space-Borne Passive <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Quets, Jan; De Lannoy, Gabrielle; Reichle, Rolf; Cosh, Michael; van der Schalie, Robin; Wigneron, Jean-Pierre</p> <p>2017-01-01</p> <p>The uncertainty associated with passive <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval is hard to quantify, and known to be underlain by various, diverse, and complex causes. Factors <span class="hlt">affecting</span> space-borne retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation include: (i) the optimization or inversion method applied to the radiative transfer model (RTM), such as e.g. the Single Channel Algorithm (SCA), or the Land Parameter Retrieval Model (LPRM), (ii) the selection of the observed brightness temperatures (Tbs), e.g. polarization and incidence angle, (iii) the definition of the cost function and the impact of prior information in it, and (iv) the RTM parameterization (e.g. parameterizations officially used by the SMOS L2 and SMAP L2 retrieval products, ECMWF-based SMOS assimilation product, SMAP L4 assimilation product, and perturbations from those configurations). This study aims at disentangling the relative importance of the above-mentioned sources of uncertainty, by carrying out <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval experiments, using SMOS Tb observations in different settings, of which some are mentioned above. The ensemble uncertainties are evaluated at 11 reference CalVal sites, over a time period of more than 5 years. These experimental retrievals were inter-compared, and further confronted with in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements and operational SMOS L2 retrievals, using commonly used skill metrics to quantify the temporal uncertainty in the retrievals.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19930036630&hterms=Moisture&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D10%26Ntt%3DMoisture','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930036630&hterms=Moisture&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D10%26Ntt%3DMoisture"><span>An overview of the measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and modeling of <span class="hlt">moisture</span> flux in FIFE</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, J. R.</p> <p>1992-01-01</p> <p>Measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and calculations of <span class="hlt">moisture</span> transfer in the <span class="hlt">soil</span> medium and at the air-<span class="hlt">soil</span> interface were performed over a 15-km by 15-km test site during FIFE in 1987 and 1989. The measurements included intensive <span class="hlt">soil</span> <span class="hlt">moisture</span> sampling at the ground level and surveys at aircraft altitudes by several passive and active microwave sensors as well as a gamma radiation device.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1614609O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1614609O"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Orr, Barron; Moran, M. Susan; Escobar, Vanessa; Brown, Molly E.</p> <p>2014-05-01</p> <p>The launch of the NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission in 2014 will provide global <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze-thaw measurements at moderate resolution (9 km) with latency as short as 24 hours. The resolution, latency and global coverage of SMAP products will enable new applications in the fields of weather, climate, drought, flood, agricultural production, human health and national security. To prepare for launch, the SMAP mission has engaged more than 25 Early Adopters. Early Adopters are users who have a need for SMAP-like <span class="hlt">soil</span> <span class="hlt">moisture</span> or freeze-thaw data, and who agreed to apply their own resources to demonstrate the utility of SMAP data for their particular system or model. In turn, the SMAP mission agreed to provide Early Adopters with simulated SMAP data products and pre-launch calibration and validation data from SMAP field campaigns, modeling, and synergistic studies. The applied research underway by Early Adopters has provided fundamental knowledge of how SMAP data products can be scaled and integrated into users' policy, business and management activities to improve decision-making efforts. This presentation will cover SMAP applications including weather and climate forecasting, vehicle mobility estimation, quantification of greenhouse gas emissions, management of urban potable water supply, and prediction of crop yield. The presentation will end with a discussion of potential international applications with focus on the ESA/CEOS TIGER Initiative entitled "looking for water in Africa", the United Nations (UN) Convention to Combat Desertification (UNCCD) which carries a specific mandate focused on Africa, the UN Framework Convention on Climate Change (UNFCCC) which lists <span class="hlt">soil</span> <span class="hlt">moisture</span> as an Essential Climate Variable (ECV), and the UN Food and Agriculture Organization (FAO) which reported a food and nutrition crisis in the Sahel.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H21I1596S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H21I1596S"><span>New Physical Algorithms for Downscaling SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sadeghi, M.; Ghafari, E.; Babaeian, E.; Davary, K.; Farid, A.; Jones, S. B.; Tuller, M.</p> <p>2017-12-01</p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission provides new means for estimation of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at the global scale. However, for many hydrological and agricultural applications the spatial SMAP resolution is too low. To address this scale issue we fused SMAP data with MODIS observations to generate <span class="hlt">soil</span> <span class="hlt">moisture</span> maps at 1-km spatial resolution. In course of this study we have improved several existing empirical algorithms and introduced a new physical approach for downscaling SMAP data. The universal triangle/trapezoid model was applied to relate <span class="hlt">soil</span> <span class="hlt">moisture</span> to optical/thermal observations such as NDVI, land surface temperature and surface reflectance. These algorithms were evaluated with in situ data measured at 5-cm depth. Our results demonstrate that downscaling SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> data based on physical indicators of <span class="hlt">soil</span> <span class="hlt">moisture</span> derived from the MODIS satellite leads to higher accuracy than that achievable with empirical downscaling algorithms. Keywords: <span class="hlt">Soil</span> <span class="hlt">moisture</span>, microwave data, downscaling, MODIS, triangle/trapezoid model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050238481','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050238481"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data Products and Their Incorporation in DREAM</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blonski, Slawomir; Holland, Donald; Henderson, Vaneshette</p> <p>2005-01-01</p> <p>NASA provides <span class="hlt">soil</span> <span class="hlt">moisture</span> data products that include observations from the Advanced Microwave Scanning Radiometer on the Earth Observing System Aqua satellite, field measurements from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment campaigns, and model predictions from the Land Information System and the Goddard Earth Observing System Data Assimilation System. Incorporation of the NASA <span class="hlt">soil</span> <span class="hlt">moisture</span> products in the Dust Regional Atmospheric Model is possible through use of the satellite observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> to set initial conditions for the dust simulations. An additional comparison of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> observations with mesoscale atmospheric dynamics modeling is recommended. Such a comparison would validate the use of NASA <span class="hlt">soil</span> <span class="hlt">moisture</span> data in applications and support acceptance of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation in weather and climate modeling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21608243','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21608243"><span>[<span class="hlt">Soil</span> <span class="hlt">moisture</span> content and fine root biomass of rubber tree (Hevea brasiliensis) plantations at different ages].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lin, Xi-Hao; Chen, Qiu-Bo; Hua, Yuan-Gang; Yang, Li-Fu; Wang, Zhen-Hui</p> <p>2011-02-01</p> <p>By using <span class="hlt">soil</span> core sampling method, this paper studied the <span class="hlt">soil</span> <span class="hlt">moisture</span> regime of rubber plantations and the fine root biomass of Hevea brasiliensis in immature period (5 a), early yielding period (9 a), and peak yielding period (16 a). With the increasing age of rubber trees, the <span class="hlt">soil</span> <span class="hlt">moisture</span> content of rubber plantations increased but the fine root biomass decreased. The <span class="hlt">soil</span> <span class="hlt">moisture</span> content at the depth of 0-60 cm in test rubber plantations increased with <span class="hlt">soil</span> depth, and presented a double-peak pattern over the period of one year. The fine root biomass of rubber trees at different ages had the maximum value in the top 10 cm <span class="hlt">soil</span> layers and decreased with <span class="hlt">soil</span> depth, its seasonal variation also showed a double-peak pattern, but the peak values appeared at different time. <span class="hlt">Soil</span> <span class="hlt">moisture</span> content and <span class="hlt">soil</span> depth were the main factors <span class="hlt">affecting</span> the fine root biomass of H. brasiliensis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120013591','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120013591"><span>Assimilation of Passive and Active Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Draper, C. S.; Reichle, R. H.; DeLannoy, G. J. M.; Liu, Q.</p> <p>2012-01-01</p> <p>Root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> is an important control over the partition of land surface energy and <span class="hlt">moisture</span>, and the assimilation of remotely sensed near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> has been shown to improve model profile <span class="hlt">soil</span> <span class="hlt">moisture</span> [1]. To date, efforts to assimilate remotely sensed near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at large scales have focused on <span class="hlt">soil</span> <span class="hlt">moisture</span> derived from the passive microwave Advanced Microwave Scanning Radiometer (AMSR-E) and the active Advanced Scatterometer (ASCAT; together with its predecessor on the European Remote Sensing satellites (ERS. The assimilation of passive and active microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> observations has not yet been directly compared, and so this study compares the impact of assimilating ASCAT and AMSR-E <span class="hlt">soil</span> <span class="hlt">moisture</span> data, both separately and together. Since the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval skill from active and passive microwave data is thought to differ according to surface characteristics [2], the impact of each assimilation on the model <span class="hlt">soil</span> <span class="hlt">moisture</span> skill is assessed according to land cover type, by comparison to in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014cosp...40E3147S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014cosp...40E3147S"><span>SMALT - <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from Altimetry project</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smith, Richard; Benveniste, Jérôme; Dinardo, Salvatore; Lucas, Bruno Manuel; Berry, Philippa; Wagner, Wolfgang; Hahn, Sebastian; Egido, Alejandro</p> <p></p> <p><span class="hlt">Soil</span> surface <span class="hlt">moisture</span> is a key scientific parameter; however, it is extremely difficult to measure remotely, particularly in arid and semi-arid terrain. This paper outlines the development of a novel methodology to generate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates in these regions from multi-mission satellite radar altimetry. Key to this approach is the development of detailed DRy Earth ModelS (DREAMS), which encapsulate the detailed and intricate surface brightness variations over the Earth’s land surface, resulting from changes in surface roughness and composition. DREAMS have been created over a number of arid and semi-arid deserts worldwide to produce historical SMALT timeseries over <span class="hlt">soil</span> <span class="hlt">moisture</span> variation. These products are available in two formats - a high resolution track product which utilises the altimeter’s high frequency content alongtrack and a multi-looked 6” gridded product at facilitate easy comparison/integeration with other remote sensing techniques. An overview of the SMALT processing scheme, covering the progression of the data from altimeter sigma0 through to final <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate, is included along with example SMALT products. Validation has been performed over a number of deserts by comparing SMALT products with other remote sensing techniques, results of the comparison between SMALT and Metop Warp 5.5 are presented here. Comparisons with other remote sensing techniques have been limited in scope due to differences in the operational aspects of the instruments, the restricted geographical coverage of the DREAMS and the low repeat temporal sampling rate of the altimeter. The potential to expand the SMALT technique into less arid areas has been investigated. Small-scale comparison with in-situ and GNSS-R data obtained by the LEiMON experimental campaign over Tuscany, where historical trends exist within both SMALT and SMC probe datasets. A qualitative analysis of unexpected backscatter characteristics in dedicated dry environments is performed</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/27083','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/27083"><span>Interpretation of in situ tests as <span class="hlt">affected</span> by <span class="hlt">soil</span> suction.</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2013-07-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> conditions are subject to change depending on the season in which they are tested. In : unsaturated <span class="hlt">soils</span> the <span class="hlt">moisture</span> at which a <span class="hlt">soil</span> is tested can directly <span class="hlt">affect</span> strength and stiffness of the : material. In situ testing is commonly u...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913930C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913930C"><span>Using satellite image data to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chuang, Chi-Hung; Yu, Hwa-Lung</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is considered as an important parameter in various study fields, such as hydrology, phenology, and agriculture. In hydrology, <span class="hlt">soil</span> <span class="hlt">moisture</span> is an significant parameter to decide how much rainfall that will infiltrate into permeable layer and become groundwater resource. Although <span class="hlt">soil</span> <span class="hlt">moisture</span> is a critical role in many environmental studies, so far the measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> is using ground instrument such as electromagnetic <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor. Use of ground instrumentation can directly obtain the information, but the instrument needs maintenance and consume manpower to operation. If we need wide range region information, ground instrumentation probably is not suitable. To measure wide region <span class="hlt">soil</span> <span class="hlt">moisture</span> information, we need other method to achieve this purpose. Satellite remote sensing techniques can obtain satellite image on Earth, this can be a way to solve the spatial restriction on instrument measurement. In this study, we used MODIS data to retrieve daily <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern estimation, i.e., crop water stress index (cwsi), over the year of 2015. The estimations are compared with the observations at the <span class="hlt">soil</span> <span class="hlt">moisture</span> stations from Taiwan Bureau of <span class="hlt">soil</span> and water conservation. Results show that the satellite remote sensing data can be helpful to the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. Further analysis can be required to obtain the optimal parameters for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation in Taiwan.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..552..578W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..552..578W"><span>Spatial patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> from two regional monitoring networks in the United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Tiejun; Liu, Qin; Franz, Trenton E.; Li, Ruopu; Lang, Yunchao; Fiebrich, Christopher A.</p> <p>2017-09-01</p> <p>Understanding <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial variability (SMSV) at regional scales is of great value for various purposes; however, relevant studies are still limited and have yielded inconsistent findings about the primary controls on regional SMSV. To further address this issue, long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> data were retrieved from two large scale monitoring networks located in the continental United States, including the Michigan Automated Weather Network and the Oklahoma Mesonet. To evaluate different controls on SMSV, supporting datasets, which contained data on climate, <span class="hlt">soil</span>, topography, and vegetation, were also compiled from various sources. Based on temporal stability analysis, the results showed that the mean relative difference (MRD) of <span class="hlt">soil</span> <span class="hlt">moisture</span> was more correlated with <span class="hlt">soil</span> texture (e.g., negative correlations between MRD and sand fraction, and positive ones between MRD and silt and clay fractions) than with meteorological forcings in both regions, which differed from the traditional notion that meteorological forcings were the dominant controls on regional SMSV. Moreover, the results revealed that contrary to the previous conjecture, the use of <span class="hlt">soil</span> <span class="hlt">moisture</span> temporal anomaly did not reduce the impacts of static properties (e.g., <span class="hlt">soil</span> properties) on <span class="hlt">soil</span> <span class="hlt">moisture</span> temporal dynamics. Instead, it was found that the magnitude of <span class="hlt">soil</span> <span class="hlt">moisture</span> temporal anomaly was mainly negatively correlated with sand fraction and positively with silt and clay fractions in both regions. Finally, the relationship between the spatial average and standard deviation of <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as <span class="hlt">soil</span> <span class="hlt">moisture</span> temporal anomaly was investigated using the data from both networks. The field data showed that the relationship for both <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> <span class="hlt">moisture</span> temporal anomaly was more <span class="hlt">affected</span> by <span class="hlt">soil</span> texture than by climatic conditions (e.g., precipitation). The results of this study provided strong field evidence that local factors (e.g., <span class="hlt">soil</span> properties) might outweigh regional</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19740041948&hterms=mineral+extraction&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dmineral%2Bextraction','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19740041948&hterms=mineral+extraction&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dmineral%2Bextraction"><span>The determination of <span class="hlt">soil</span> <span class="hlt">moisture</span> by extraction and gas chromatography</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Merek, E. L.; Carle, G. C.</p> <p>1974-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content was determined by extracting <span class="hlt">soil</span> with methanol and subsequently analyzing the extract for water by gas chromatography. With air-dried mineral <span class="hlt">soils</span>, this method gave slightly higher <span class="hlt">moisture</span> content values than those obtained by the oven-dry method. <span class="hlt">Moisture</span> content was determined quantitatively in <span class="hlt">soils</span> to which various amounts of water had been added. The complete procedure, including extraction and analysis, requires less than one hour and gives results that closely compare to the oven-dry method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000032277&hterms=ultrasonic+sensor&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dultrasonic%2Bsensor','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000032277&hterms=ultrasonic+sensor&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dultrasonic%2Bsensor"><span>Ultrasonic Velocity Variations with <span class="hlt">Soil</span> Composition for <span class="hlt">Moisture</span> Measurement</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Metzl, R.; Choi, J.; Aggarwal, M. D.; Manu, A.</p> <p>1998-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content may be measured by many methods, but the presently available techniques all have drawbacks when used in ground truth measurements for remote sensing. Ultrasonic velocity varies with <span class="hlt">soil</span> <span class="hlt">moisture</span> content, and may be used as the basis of a new measurement technique. In order to characterize a sensor capable of field use, <span class="hlt">soil</span> particle size distribution data are compared to ultrasonic velocity in a variety of <span class="hlt">soils</span> over a wide <span class="hlt">moisture</span> range.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=281536','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=281536"><span>Factors <span class="hlt">affecting</span> <span class="hlt">soil</span> cohesion</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p><span class="hlt">Soil</span> erodibility is a measure of a soil’s resistance against erosive forces and is <span class="hlt">affected</span> by both intrinsic (or inherent) <span class="hlt">soil</span> property and the extrinsic condition at the time erodibility measurement is made. Since <span class="hlt">soil</span> erodibility is usually calculated from results obtained from erosion experimen...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H43N..01M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H43N..01M"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> memory at sub-monthly time scales</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mccoll, K. A.; Entekhabi, D.</p> <p>2017-12-01</p> <p>For <span class="hlt">soil</span> <span class="hlt">moisture</span>-climate feedbacks to occur, the <span class="hlt">soil</span> <span class="hlt">moisture</span> storage must have `memory' of past atmospheric anomalies. Quantifying <span class="hlt">soil</span> <span class="hlt">moisture</span> memory is, therefore, essential for mapping and characterizing land-atmosphere interactions globally. Most previous studies estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> memory using metrics based on the autocorrelation function of the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series (e.g., the e-folding autocorrelation time scale). This approach was first justified by Delworth and Manabe (1988) on the assumption that monthly <span class="hlt">soil</span> <span class="hlt">moisture</span> time series can be modelled as red noise. While this is a reasonable model for monthly <span class="hlt">soil</span> <span class="hlt">moisture</span> averages, at sub-monthly scales, the model is insufficient due to the highly non-Gaussian behavior of the precipitation forcing. Recent studies have shown that significant <span class="hlt">soil</span> <span class="hlt">moisture</span>-climate feedbacks appear to occur at sub-monthly time scales. Therefore, alternative metrics are required for defining and estimating <span class="hlt">soil</span> <span class="hlt">moisture</span> memory at these shorter time scales. In this study, we introduce metrics, based on the positive and negative increments of the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series, that can be used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> memory at sub-monthly time scales. The positive increments metric corresponds to a rapid drainage time scale. The negative increments metric represents a slower drying time scale that is most relevant to the study of land-atmosphere interactions. We show that autocorrelation-based metrics mix the two time scales, confounding physical interpretation. The new metrics are used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> memory at sub-monthly scales from in-situ and satellite observations of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Reference: Delworth, Thomas L., and Syukuro Manabe. "The Influence of Potential Evaporation on the Variabilities of Simulated <span class="hlt">Soil</span> Wetness and Climate." Journal of Climate 1, no. 5 (May 1, 1988): 523-47. doi:10.1175/1520-0442(1988)001<0523:TIOPEO>2.0.CO;2.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008257','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008257"><span>Contributions of Precipitation and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observations to the Skill of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimates in a Land Data Assimilation System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.</p> <p>2011-01-01</p> <p>The contributions of precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> observations to the skill of <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates <span class="hlt">soil</span> <span class="hlt">moisture</span> skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). <span class="hlt">Soil</span> <span class="hlt">moisture</span> skill is measured against in situ observations in the continental United States at 44 single-profile sites within the <span class="hlt">Soil</span> Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure <span class="hlt">soil</span> <span class="hlt">moisture</span> at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface <span class="hlt">moisture</span> skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals increases surface <span class="hlt">soil</span> <span class="hlt">moisture</span> skill levels by IDDeltaR=0.06-0.08, and root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface <span class="hlt">soil</span> <span class="hlt">moisture</span> skill levels by DeltaR=0.13, and root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals contribute similar and largely independent amounts of information.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..519.2747R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..519.2747R"><span>Continental satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation improves root-zone <span class="hlt">moisture</span> analysis for water resources assessment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Renzullo, L. J.; van Dijk, A. I. J. M.; Perraud, J.-M.; Collins, D.; Henderson, B.; Jin, H.; Smith, A. B.; McJannet, D. L.</p> <p>2014-11-01</p> <p>A framework was developed for the continental assimilation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) into an operational water balance modelling system. The ensemble Kalman filter (EnKF) was implemented to assimilate AMSR-E and ASCAT-derived SM products into the landscape model of the Australian Water Resources Assessment system (AWRA-L) and generate ensembles of daily top-layer and shallow root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> analyses for the continent at 0.05° resolution. We evaluated the AWRA-L SM estimates with and without assimilation against in situ <span class="hlt">moisture</span> measurements in southeast Australia (OzNet), as well as against a new network of cosmic-ray <span class="hlt">moisture</span> probes (CosmOz) spread across the country. Results show that AWRA-L root-zone <span class="hlt">moisture</span> estimates are improved though the assimilation of satellite SM: model estimates of 0-30 cm <span class="hlt">moisture</span> content improved for more than 90% of OzNet sites, with an increase in average correlation from 0.68 (before assimilation) to 0.73 (after assimilation); while estimates 0-90 cm <span class="hlt">moisture</span> improved for 60% of sites with increased average correlation from 0.56 to 0.65. The assimilation of AMSR-E and ASCAT appeared to yield similar performance gains for the top-layer, however ASCAT data assimilation improved root-zone estimation for more sites. Poor performance of one data set was compensated by the other through joint assimilation. The most significant improvements in AWRA-L root-zone <span class="hlt">moisture</span> estimation (with increases in correlation as high as 90%) occurred for sites where both the assimilation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> improved top-layer SM accuracy and the open-loop deep-layer storage estimates were reasonably good. CosmOz SM measurements exhibited highest correlation with AWRA-L estimates for modelled root-zones layer thicknesses ranging from 20 cm to 1 m. Slight improvements through satellite data assimilation were observed for only 2 of 7 CosmOz sites, but the comparison was <span class="hlt">affected</span> by a short data overlap period. The location of some of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JHyd..536..327M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..536..327M"><span>Examining diel patterns of <span class="hlt">soil</span> and xylem <span class="hlt">moisture</span> using electrical resistivity imaging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mares, Rachel; Barnard, Holly R.; Mao, Deqiang; Revil, André; Singha, Kamini</p> <p>2016-05-01</p> <p>The feedbacks among forest transpiration, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and subsurface flowpaths are poorly understood. We investigate how <span class="hlt">soil</span> <span class="hlt">moisture</span> is <span class="hlt">affected</span> by daily transpiration using time-lapse electrical resistivity imaging (ERI) on a highly instrumented ponderosa pine and the surrounding <span class="hlt">soil</span> throughout the growing season. By comparing sap flow measurements to the ERI data, we find that periods of high sap flow within the diel cycle are aligned with decreases in ground electrical conductivity and <span class="hlt">soil</span> <span class="hlt">moisture</span> due to drying of the <span class="hlt">soil</span> during <span class="hlt">moisture</span> uptake. As sap flow decreases during the night, the ground conductivity increases as the <span class="hlt">soil</span> <span class="hlt">moisture</span> is replenished. The mean and variance of the ground conductivity decreases into the summer dry season, indicating drier <span class="hlt">soil</span> and smaller diel fluctuations in <span class="hlt">soil</span> <span class="hlt">moisture</span> as the summer progresses. Sap flow did not significantly decrease through the summer suggesting use of a water source deeper than 60 cm to maintain transpiration during times of shallow <span class="hlt">soil</span> <span class="hlt">moisture</span> depletion. ERI captured spatiotemporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> on daily and seasonal timescales. ERI data on the tree showed a diel cycle of conductivity, interpreted as changes in water content due to transpiration, but changes in sap flow throughout the season could not be interpreted from ERI inversions alone due to daily temperature changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813805M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813805M"><span>Capacitive <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensor for Plant Watering</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maier, Thomas; Kamm, Lukas</p> <p>2016-04-01</p> <p>How can you realize a water saving and demand-driven plant watering device? To achieve this you need a sensor, which precisely detects the <span class="hlt">soil</span> <span class="hlt">moisture</span>. Designing such a sensor is the topic of this poster. We approached this subject with comparing several physical properties of water, e.g. the conductivity, permittivity, heat capacity and the <span class="hlt">soil</span> water potential, which are suitable to detect the <span class="hlt">soil</span> <span class="hlt">moisture</span> via an electronic device. For our project we have developed a sensor device, which measures the <span class="hlt">soil</span> <span class="hlt">moisture</span> and provides the measured values for a plant watering system via a wireless bluetooth 4.0 network. Different sensor setups have been analyzed and the final sensor is the result of many iterative steps of improvement. In the end we tested the precision of our sensor and compared the results with theoretical values. The sensor is currently being used in the Botanical Garden of the Friedrich-Alexander-University in a long-term test. This will show how good the usability in the real field is. On the basis of these findings a marketable sensor will soon be available. Furthermore a more specific type of this sensor has been designed for the EU:CROPIS Space Project, where tomato plants will grow at different gravitational forces. Due to a very small (15mm x 85mm x 1.5mm) and light (5 gramm) realisation, our sensor has been selected for the space program. Now the scientists can monitor the water content of the substrate of the tomato plants in outer space and water the plants on demand.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7286D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7286D"><span>The Integration of SMOS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in a Consistent <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Climate Record</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>de Jeu, Richard; Kerr, Yann; Wigneron, Jean Pierre; Rodriguez-Fernandez, Nemesio; Al-Yaari, Amen; van der Schalie, Robin; Dolman, Han; Drusch, Matthias; Mecklenburg, Susanne</p> <p>2015-04-01</p> <p>Recently, a study funded by the European Space Agency (ESA) was set up to provide guidelines for the development of a global <span class="hlt">soil</span> <span class="hlt">moisture</span> climate record with a special emphasis on the integration of SMOS. Three different data fusion approaches were designed and implemented on 10 year passive microwave data (2003-2013) from two different satellite sensors; the ESA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity Mission (SMOS) and the NASA/JAXA Advanced Scanning Microwave Radiometer (AMSR-E). The AMSR-E data covered the period from January 2003 until Oct 2011 and SMOS data covered the period from June 2010 until the end of 2013. The fusion approaches included a neural network approach (Rodriguez-Fernandez et al., this conference session HS6.4), a regression approach (Wigneron et al., 2004), and an approach based on the baseline algorithm of ESAs current Climate Change Initiative <span class="hlt">soil</span> <span class="hlt">moisture</span> program, the Land Parameter Retrieval Model (Van der Schalie et al., this conference session HS6.4). With this presentation we will show the first results from this study including a description of the different approaches and the validation activities using both globally covered modeled datasets and ground observations from the international <span class="hlt">soil</span> <span class="hlt">moisture</span> network. The statistical validation analyses will give us information on the temporal and spatial performance of the three different approaches. Based on these results we will then discuss the next steps towards a seamless integration of SMOS in a consistent <span class="hlt">soil</span> <span class="hlt">moisture</span> climate record. References Wigneron J.-P., J.-C. Calvet, P. de Rosnay, Y. Kerr, P. Waldteufel, K. Saleh, M. J. Escorihuela, A. Kruszewski, '<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals from Bi-Angular L-band Passive Microwave Observations', IEEE Trans. Geosc. Remote Sens. Let., vol 1, no. 4, 277-281, 2004.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.9543P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.9543P"><span>Evaluation of <span class="hlt">soil</span> and vegetation response to drought using SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> satellite observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Piles, Maria; Sánchez, Nilda; Vall-llossera, Mercè; Ballabrera, Joaquim; Martínez, Justino; Martínez-Fernández, José; Camps, Adriano; Font, Jordi</p> <p>2014-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays an important role in determining the likelihood of droughts and floods that may <span class="hlt">affect</span> an area. Knowledge of <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution as a function of time and space is highly relevant for hydrological, ecological and agricultural applications, especially in water-limited or drought-prone regions. However, measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> is challenging because of its high variability; point-scale in-situ measurements are scarce being remote sensing the only practical means to obtain regional- and global-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates. The ESA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) is the first satellite mission ever designed to measuring the Earth's surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at near daily time scales with levels of accuracy previously not attained. Since its launch in November 2009, significant efforts have been dedicated to validate and fine-tune the retrieval algorithms so that SMOS-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates meet the standards required for a wide variety of applications. In this line, the SMOS Barcelona Expert Center (BEC) is distributing daily, monthly, and annual temporal averages of 0.25-deg global <span class="hlt">soil</span> <span class="hlt">moisture</span> maps, which have proved useful for assessing drought and water-stress conditions. In addition, a downscaling algorithm has been developed to combine SMOS and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data into fine-scale (< 1km) <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates, which permits extending the applicability of the data to regional and local studies. Fine-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> maps are currently limited to the Iberian Peninsula but the algorithm is dynamic and can be transported to any region. <span class="hlt">Soil</span> <span class="hlt">moisture</span> maps are generated in a near real-time fashion at BEC facilities and are used by Barcelona's fire prevention services to detect extremely dry <span class="hlt">soil</span> and vegetation conditions posing a risk of fire. Recently, they have been used to explain drought-induced tree mortality episodes and forest decline in the Catalonia region. These</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-PIA19236.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-PIA19236.html"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Mapper Takes First SMAPshots</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-03-09</p> <p>Fresh off the recent successful deployment of its 20-foot (6-meter) reflector antenna and associated boom arm, NASA's new <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) observatory has successfully completed a two-day test of its science instruments. On Feb. 27 and 28, SMAP's radar and radiometer instruments were successfully operated for the first time with SMAP's antenna in a non-spinning mode. The test was a key step in preparation for the planned spin-up of SMAP's antenna to approximately 15 revolutions per minute in late March. The spin-up will be performed in a two-step process after additional tests and maneuvers adjust the observatory to its final science orbit over the next couple of weeks. Based on the data received, mission controllers at NASA's Jet Propulsion Laboratory, Pasadena, California; and NASA's Goddard Space Flight Center, Greenbelt, Maryland; concluded that the radar and radiometer performed as expected. SMAP launched Jan. 31 on a minimum three-year mission to map global <span class="hlt">soil</span> <span class="hlt">moisture</span> and detect whether <span class="hlt">soils</span> are frozen or thawed. The mission will help scientists understand the links in Earth's water, energy and carbon cycles, help reduce uncertainties in predicting weather and climate, and enhance our ability to monitor and predict natural hazards such as floods and droughts The first test image illustrates the significance of SMAP's spinning instrument design. For this initial test with SMAP's antenna not yet spinning, the observatory's measurement swath width -- the strips observed on Earth in the image -- was limited to 25 miles (40 kilometers). When fully spun up and operating, SMAP's antenna will measure a 620-mile-wide (1,000-kilometer) swath of the ground as it flies above Earth at an altitude of 426 miles (685 kilometers). This will allow SMAP to map the entire globe with high-resolution radar data every two to three days, filling in all of the land surface detail that is not available in this first image. The radar data illustrated in the upper</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/13771','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/13771"><span>Determining <span class="hlt">soil</span> volumetric <span class="hlt">moisture</span> content using time domain reflectometry</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>1998-02-01</p> <p>Time domain reflectometry (TDR) is a technique used to measure indirectly the in situ volumetric <span class="hlt">moisture</span> content of <span class="hlt">soil</span>. Current research provides a variety of prediction equations that estimate the volumetric <span class="hlt">moisture</span> content using the dielectric ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870052970&hterms=drying+leaves&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Ddrying%2Bleaves','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870052970&hterms=drying+leaves&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Ddrying%2Bleaves"><span>Concerning the relationship between evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wetzel, Peter J.; Chang, Jy-Tai</p> <p>1987-01-01</p> <p>The relationship between the evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> during the drying, supply-limited phase is studied. A second scaling parameter, based on the evapotranspirational supply and demand concept of Federer (1982), is defined; the parameter, referred to as the threshold evapotranspiration, occurs in vegetation-covered surfaces just before leaf stomata close and when surface tension restricts <span class="hlt">moisture</span> release from bare <span class="hlt">soil</span> pores. A simple model for evapotranspiration is proposed. The effects of natural <span class="hlt">soil</span> heterogeneities on evapotranspiration computed from the model are investigated. It is observed that the natural variability in <span class="hlt">soil</span> <span class="hlt">moisture</span>, caused by the heterogeneities, alters the relationship between regional evapotranspiration and the area average <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=308108','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=308108"><span>Different rates of <span class="hlt">soil</span> drying after rainfall are observed by the SMOS satellite and the South Fork In Situ <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> <span class="hlt">affects</span> the spatial variation of land–atmosphere interactions through its in'uence on the balance of latent and sensible heat 'ux. Wetter <span class="hlt">soils</span> are more prone to 'ooding because a smaller fraction of rainfall can in'ltrate into the <span class="hlt">soil</span>. The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Oceanic Salinity (SMOS) sa...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.7027K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.7027K"><span>The prototype SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> Algorithm</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kerr, Y.; Waldteufel, P.; Richaume, P.; Cabot, F.; Wigneron, J. P.; Ferrazzoli, P.; Mahmoodi, A.; Delwart, S.</p> <p>2009-04-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission is ESA's (European Space Agency ) second Earth Explorer Opportunity mission, to be launched in September 2007. It is a joint programme between ESA CNES (Centre National d'Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnologico Industrial). SMOS carries a single payload, an L-band 2D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere and hence the instrument probes the Earth surface emissivity. Surface emissivity can then be related to the <span class="hlt">moisture</span> content in the first few centimeters of <span class="hlt">soil</span>, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. In order to prepare the data use and dissemination, the ground segment will produce level 1 and 2 data. Level 1 will consists mainly of angular brightness temperatures while level 2 will consist of geophysical products. In this context, a group of institutes prepared the <span class="hlt">soil</span> <span class="hlt">moisture</span> and ocean salinity Algorithm Theoretical Basis documents (ATBD) to be used to produce the operational algorithm. The consortium of institutes preparing the <span class="hlt">Soil</span> <span class="hlt">moisture</span> algorithm is led by CESBIO (Centre d'Etudes Spatiales de la BIOsphère) and Service d'Aéronomie and consists of the institutes represented by the authors. The principle of the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm is based on an iterative approach which aims at minimizing a cost function given by the sum of the squared weighted differences between measured and modelled brightness temperature (TB) data, for a variety of incidence angles. This is achieved by finding the best suited set of the parameters which drive the direct TB model, e.g. <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) and vegetation characteristics. Despite the simplicity of this principle, the main reason for the complexity of the algorithm is that SMOS "pixels" can correspond to rather large, inhomogeneous surface areas whose contribution to the radiometric</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010000376','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010000376"><span>Ultrasound Algorithm Derivation for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Content Estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Belisle, W.R.; Metzl, R.; Choi, J.; Aggarwal, M. D.; Coleman, T.</p> <p>1997-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content can be estimated by evaluating the velocity at which sound waves travel through a known volume of solid material. This research involved the development of three <span class="hlt">soil</span> algorithms relating the <span class="hlt">moisture</span> content to the velocity at which sound waves moved through dry and moist media. Pressure and shear wave propagation equations were used in conjunction with <span class="hlt">soil</span> property descriptions to derive algorithms appropriate for describing the effects of <span class="hlt">moisture</span> content variation on the velocity of sound waves in <span class="hlt">soils</span> with and without complete <span class="hlt">soil</span> pore water volumes, An elementary algorithm was used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> contents ranging from 0.08 g/g to 0.5 g/g from sound wave velocities ranging from 526 m/s to 664 m/s. Secondary algorithms were also used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> content from sound wave velocities through <span class="hlt">soils</span> with pores that were filled predominantly with air or water.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSM.H43A..07A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSM.H43A..07A"><span>Evaluating Electromagnetic Induction Techniques for Predicting <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Loamy <span class="hlt">Soils</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aufman, M. S.; Holt, R. M.; Hickey, C. J.</p> <p>2006-05-01</p> <p>We are conducting a study to evaluate usefulness of electromagnetic induction (EM) methods for predicting <span class="hlt">soil</span> <span class="hlt">moisture</span> in loamy <span class="hlt">soils</span> present at the University of Mississippi (UM) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observatory (SMO). The 5 acre SMO is located in a former agricultural field at the UM Field Station, a 740 acre tract of land located 11 miles from the UM campus in Oxford, Mississippi. Neutron probe access tubes are installed at 118 locations in the SMO. <span class="hlt">Soil</span> <span class="hlt">moisture</span> at each of the access tubes was surveyed bi-weekly using a CPN 503DR Hydroprobe at depths of 30, 60, and 90cm below ground surface. EM responses were also surveyed at each of the access tube locations using a Geonics EM38 in both a vertical and horizontal dipole position, which correspond to deep (~1m) and shallow (~0.5) measurements, respectively. Variograms of both <span class="hlt">moisture</span> content and apparent electrical conductivity (EC) show higher sill values (greater overall variance) at low overall <span class="hlt">moisture</span> contents reflecting increased contrast between wet and drier regions during drier conditions. <span class="hlt">Moisture</span> content and EC variograms at the 60 and 90 cm depth are similar indicating that near surface processes are attenuated with depth. We found a strong negative correlation between <span class="hlt">moisture</span> content and the high temperature on the day of each survey, regardless of precipitation history, suggesting that daily ET is strongly <span class="hlt">affecting</span> our <span class="hlt">moisture</span> content results. A linear regression models, including EC values, precipitation history, and the daily high temperature, account for up to 58% of the variability in <span class="hlt">moisture</span> content. Additional predictive accuracy incorporating EM results may be attained using a non-linear model, e.g., a neural-network model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H51I1511P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H51I1511P"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring at Watershed Scale in Eastern India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Panda, R. K.</p> <p>2015-12-01</p> <p>Understanding the spatio-temporal variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> on time scales that range from minute to decades on the watershed scale is important for the hydrological, meteorological and agricultural communities. Lack of reliable, longterm <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets in developing countries like India, is a bottleneck for <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis and prediction. Recognizing the need of continuous, automated in-situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations, three in-situ <span class="hlt">soil</span> <span class="hlt">moisture</span> test-beds have been established in an agricultural watershed of the Eastern India. Test-beds have been specifically designed to capture the root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamic at different crop fields under both surplus and water deficit conditions in low, medium and up-lands of the study region. Both volumetric and tensiometric method based sensors, Campbell Scientific <span class="hlt">soil</span> water content reflectometer (CS650) and matric potential sensor (CS229) are installed at depths of 5, 15, 30, 60 and 100 cm below the surface. GPRS communication modems were installed at each station for remote communication from the data loggers (Campbell Scientific, CR1000) for automatic data collection. To achieve a better understanding of the spatial variation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> on watershed scale, the strategic ground-based surface measurements were made in diverse landscape using portable impedance probe. The primary aim of spatial and temporal scale <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement is to validate current remote sensing products of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP). In order to improve validation procedure, the <span class="hlt">soil</span> texture and <span class="hlt">soil</span> hydraulic parameters are also estimated across the spatial scales to develop dynamic relationship between these parameters. Herein, the strategies for the site selection, calibration of the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors, ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring, hydraulic properties estimation at spatial scale and the quality assurance techniques applied to the observations are provided.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000032232&hterms=shaffer&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dshaffer','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000032232&hterms=shaffer&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dshaffer"><span>Effect of land-use practice on <span class="hlt">soil</span> <span class="hlt">moisture</span> variability for <span class="hlt">soils</span> covered with dense forest vegetation of Puerto Rico</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tsegaye, T.; Coleman, T.; Senwo, Z.; Shaffer, D.; Zou, X.</p> <p>1998-01-01</p> <p>Little is known about the landuse management effect on <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> pH distribution on a landscape covered with dense tropical forest vegetation. This study was conducted at three locations where the history of the landuse management is different. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was measured using a 6-cm three-rod Time Domain Reflectometery (TDR) probe. Disturbed <span class="hlt">soil</span> samples were taken from the top 5-cm at the up, mid, and foothill landscape position from the same spots where <span class="hlt">soil</span> <span class="hlt">moisture</span> was measured. The results showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> varies with landscape position and depth at all three locations. <span class="hlt">Soil</span> pH and <span class="hlt">moisture</span> variability were found to be <span class="hlt">affected</span> by the change in landuse management and landscape position. <span class="hlt">Soil</span> <span class="hlt">moisture</span> distribution usually expected to be relatively higher in the foothill (P3) area of these forests than the uphill (P1) position. However, our results indicated that in the Luquillo and Guanica site the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> was significantly higher for P1 than P3 position. These suggest that the surface and subsurface drainage in these two sites may have been poor due to the nature of <span class="hlt">soil</span> formation and type.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810020956','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810020956"><span>Evaluation of gravimetric ground truth <span class="hlt">soil</span> <span class="hlt">moisture</span> data collected for the agricultural <span class="hlt">soil</span> <span class="hlt">moisture</span> experiment, 1978 Colby, Kansas, aircraft mission</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Arya, L. M.; Phinney, D. E. (Principal Investigator)</p> <p>1980-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> data acquired to support the development of algorithms for estimating surface <span class="hlt">soil</span> <span class="hlt">moisture</span> from remotely sensed backscattering of microwaves from ground surfaces are presented. Aspects of field uniformity and variability of gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements are discussed. <span class="hlt">Moisture</span> distribution patterns are illustrated by frequency distributions and contour plots. Standard deviations and coefficients of variation relative to degree of wetness and agronomic features of the fields are examined. Influence of sampling depth on observed <span class="hlt">moisture</span> content an variability are indicated. For the various sets of measurements, <span class="hlt">soil</span> <span class="hlt">moisture</span> values that appear as outliers are flagged. The distribution and legal descriptions of the test fields are included along with examinations of <span class="hlt">soil</span> types, agronomic features, and sampling plan. Bulk density data for experimental fields are appended, should analyses involving volumetric <span class="hlt">moisture</span> content be of interest to the users of data in this report.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010000505','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010000505"><span>Use of Ultrasonic Technology for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurement</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Choi, J.; Metzl, R.; Aggarwal, M. D.; Belisle, W.; Coleman, T.</p> <p>1997-01-01</p> <p>In an effort to improve existing <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement techniques or find new techniques using physics principles, a new technique is presented in this paper using ultrasonic techniques. It has been found that ultrasonic velocity changes as the <span class="hlt">moisture</span> content changes. Preliminary values of velocities are 676.1 m/s in dry <span class="hlt">soil</span> and 356.8 m/s in 100% moist <span class="hlt">soils</span>. Intermediate values can be calibrated to give exact values for the <span class="hlt">moisture</span> content in an unknown sample.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19740026660','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19740026660"><span>Dual frequency microwave radiometer measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> for bare and vegetated rough surfaces</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lee, S. L.</p> <p>1974-01-01</p> <p>Controlled ground-based passive microwave radiometric measurements on <span class="hlt">soil</span> <span class="hlt">moisture</span> were conducted to determine the effects of terrain surface roughness and vegetation on microwave emission. Theoretical predictions were compared with the experimental results and with some recent airborne radiometric measurements. The relationship of <span class="hlt">soil</span> <span class="hlt">moisture</span> to the permittivity for the <span class="hlt">soil</span> was obtained in the laboratory. A dual frequency radiometer, 1.41356 GHz and 10.69 GHz, took measurements at angles between 0 and 50 degrees from an altitude of about fifty feet. Distinct surface roughnesses were studied. With the roughness undisturbed, oats were later planted and vegetated and bare field measurements were compared. The 1.4 GHz radiometer was less <span class="hlt">affected</span> than the 10.6 GHz radiometer, which under vegetated conditions was incapable of detecting <span class="hlt">soil</span> <span class="hlt">moisture</span>. The bare surface theoretical model was inadequate, although the vegetation model appeared to be valid. <span class="hlt">Moisture</span> parameters to correlate apparent temperature with <span class="hlt">soil</span> <span class="hlt">moisture</span> were compared.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/15473633','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15473633"><span>Persistence, distribution, and emission of Telone C35 injected into a Florida sandy <span class="hlt">soil</span> as <span class="hlt">affected</span> by <span class="hlt">moisture</span>, organic matter, and plastic film cover.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Thomas, J E; Ou, L T; Allen, L H; McCormack, L A; Vu, J C; Dickson, D W</p> <p>2004-05-01</p> <p>With the phase-out of methyl bromide scheduled for 2005, alternative fumigants are being sought. This study of Telone C35, a mixture of (Z)- and (E)-1,3-dichloropropene (1,3-D) with chloropicirin (CP), focuses on its emissions, distribution, and persistence in Florida sandy <span class="hlt">soil</span> in microplots with different <span class="hlt">soil</span>-water and organic matter carbon (C) content with and without two different plastic film mulches. The addition of CP did not <span class="hlt">affect</span> the physical behavior of the isomers of 1,3-D. Slower subsurface dispersion and longer residence time of the mixed fumigant occurred at higher water content. An increase in the percent organic carbon in the <span class="hlt">soil</span> led to a more rapid decrease for chloropicirin than for 1,3-dichloropene isomers. The use of a virtually impermeable film (VIF) for <span class="hlt">soil</span> cover provided a more even distribution and longer persistence under all the conditions studied in comparison to polyethylene (PE) film cover or no cover. The conditions of near field capacity water content, low organic matter, and a virtually impermeable film cover yielded optimum conditions for the distribution, emission control, and persistence of Telone C35 in a Florida sandy <span class="hlt">soil</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20100031193&hterms=Moisture&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3DMoisture','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20100031193&hterms=Moisture&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3DMoisture"><span>Australian <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Field Experiments in Support of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Satellite Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kim, Edward; Walker, Jeff; Rudiger, Christopher; Panciera, Rocco</p> <p>2010-01-01</p> <p>Large-scale field campaigns provide the critical fink between our understanding retrieval algorithms developed at the point scale, and algorithms suitable for satellite applications at vastly larger pixel scales. Retrievals of land parameters must deal with the substantial sub-pixel heterogeneity that is present in most regions. This is particularly the case for <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing, because of the long microwave wavelengths (L-band) that are optimal. Yet, airborne L-band imagers have generally been large, heavy, and required heavy-lift aircraft resources that are expensive and difficult to schedule. Indeed, US <span class="hlt">soil</span> <span class="hlt">moisture</span> campaigns, have been constrained by these factors, and European campaigns have used non-imagers due to instrument and aircraft size constraints. Despite these factors, these campaigns established that large-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing was possible, laying the groundwork for satellite missions. Starting in 2005, a series of airborne field campaigns have been conducted in Australia: to improve our understanding of <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing at large scales over heterogeneous areas. These field data have been used to test and refine retrieval algorithms for <span class="hlt">soil</span> <span class="hlt">moisture</span> satellite missions, and most recently with the launch of the European Space Agency's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity (SMOS) mission, to provide validation measurements over a multi-pixel area. The campaigns to date have included a preparatory campaign in 2005, two National Airborne Field Experiments (NAFE), (2005 and 2006), two campaigns to the Simpson Desert (2008 and 2009), and one Australian Airborne Cal/val Experiment for SMOS (AACES), just concluded in the austral spring of 2010. The primary airborne sensor for each campaign has been the Polarimetric L-band Microwave Radiometer (PLMR), a 6-beam pushbroom imager that is small enough to be compatible with light aircraft, greatly facilitating the execution of the series of campaigns, and a key to their success. An</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/31968','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/31968"><span><span class="hlt">Moisture</span>-strength-constructability guidelines for subgrade foundation <span class="hlt">soils</span> found in Indiana.</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2016-09-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important indicator of constructability in the field. Construction activities become difficult when the <span class="hlt">soil</span> <span class="hlt">moisture</span> content is excessive, especially in fine-grained <span class="hlt">soils</span>. Change orders caused by excessive <span class="hlt">soil</span> <span class="hlt">moisture</span> during c...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..552..620M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..552..620M"><span>Drought monitoring with <span class="hlt">soil</span> <span class="hlt">moisture</span> active passive (SMAP) measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara</p> <p>2017-09-01</p> <p>Recent launch of space-borne systems to estimate surface <span class="hlt">soil</span> <span class="hlt">moisture</span> may expand the capability to map <span class="hlt">soil</span> <span class="hlt">moisture</span> deficit and drought with global coverage. In this study, we use <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) <span class="hlt">soil</span> <span class="hlt">moisture</span> geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the <span class="hlt">Soil</span> Water Deficit Index (SWDI) based on SMAP and <span class="hlt">soil</span> properties (field capacity and available water content) information. The <span class="hlt">soil</span> properties are computed using pedo-transfer function with <span class="hlt">soil</span> characteristics derived from Harmonized World <span class="hlt">Soil</span> Database. The SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> product needs to be rescaled to be compatible with the <span class="hlt">soil</span> parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> well and shows the expected lag between accumulations of precipitation and delayed increased in surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. However, the SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> itself does not reveal the drought information. Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval satellite technology. The formulation of SWDI does not depend on longer data and it will overcome the limited (short) length of SMAP data for agricultural drought studies. The SMAP_SWDI is further compared with in situ Atmospheric Water Deficit (AWD) Index. The comparison shows close agreement between SMAP_SWDI and AWD in drought monitoring over Contiguous United States (CONUS), especially in terms of drought characteristics. The SMAP_SWDI was used to construct drought maps for CONUS and compared with well-known drought indices, such as, AWD, Palmer Z-Index, sc-PDSI and SPEI. Overall the SMAP_SWDI is an effective agricultural drought indicator and it provides continuity and introduces new spatial mapping capability for drought monitoring. As an</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC33C..02Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC33C..02Q"><span>Building the North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> (NASM) Database</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Quiring, S. M.</p> <p>2011-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important variable in the climate system. To date, relatively little work has been done to assemble and homogenize in situ measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and to utilize these measurements for investigating land-atmosphere interactions. This research addresses the critical need to develop high quality <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets from disparate sources and to use these data to improve our understanding of climatic variability on seasonal to interannual timescales. This project will assemble, quality control and harmonize the existing in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations in the United States (and eventually beyond) and develop a <span class="hlt">soil</span> <span class="hlt">moisture</span> database for investigating the nature of land-atmosphere interactions, validating the accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations in global land surface models, and describing how <span class="hlt">soil</span> <span class="hlt">moisture</span> influences climate on seasonal to interannual timescales. These data will be published on a dedicated website and made available to the scientific community to support research efforts such as Decadal and Regional Climate Prediction Using Earth System Models (EaSM), the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite recently launched by the European Space Agency and NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP) mission (planned launch in 2015).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1413308O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1413308O"><span>Propagation of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory into the climate system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Orth, R.; Seneviratne, S. I.</p> <p>2012-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is known for its integrative behaviour and resulting memory characteristics. Associated anomalies can persist for weeks or even months into the future, making initial <span class="hlt">soil</span> <span class="hlt">moisture</span> an important potential component in weather forecasting. This is particularly crucial given the role of <span class="hlt">soil</span> <span class="hlt">moisture</span> for land-atmosphere interactions and its impacts on the water and energy balances on continents. We present here an analysis of the characteristics of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory and of its propagation into runoff and evapotranspiration in Europe, based on available measurements from several sites across the continent and expanding a previous analysis focused on <span class="hlt">soil</span> <span class="hlt">moisture</span> [1]. We identify the main drivers of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory at the analysed sites, as well as their role for the propagation of <span class="hlt">soil</span> <span class="hlt">moisture</span> persistence into runoff and evapotranspiration memory characteristics. We focus on temporal and spatial variations in these relationships and identify seasonal and latitudinal differences in the persistence of <span class="hlt">soil</span> <span class="hlt">moisture</span>, evapotranspiration and runoff. Finally, we assess the role of these persistence characteristics for the development of agricultural and hydrological droughts. [1] Orth and Seneviratne: Analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory from observations in Europe; submitted to J. Geophysical Research.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012HESSD...912103O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012HESSD...912103O"><span>Propagation of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory to runoff and evapotranspiration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Orth, R.; Seneviratne, S. I.</p> <p>2012-10-01</p> <p>As a key variable of the land-climate system <span class="hlt">soil</span> <span class="hlt">moisture</span> is a main driver of runoff and evapotranspiration under certain conditions. <span class="hlt">Soil</span> <span class="hlt">moisture</span> furthermore exhibits outstanding memory (persistence) characteristics. Also for runoff many studies report distinct low frequency variations that represent a memory. Using data from over 100 near-natural catchments located across Europe we investigate in this study the connection between <span class="hlt">soil</span> <span class="hlt">moisture</span> memory and the respective memory of runoff and evapotranspiration on different time scales. For this purpose we use a simple water balance model in which dependencies of runoff (normalized by precipitation) and evapotranspiration (normalized by radiation) on <span class="hlt">soil</span> <span class="hlt">moisture</span> are fitted using runoff observations. The model therefore allows to compute memory of <span class="hlt">soil</span> <span class="hlt">moisture</span>, runoff and evapotranspiration on catchment scale. We find considerable memory in <span class="hlt">soil</span> <span class="hlt">moisture</span> and runoff in many parts of the continent, and evapotranspiration also displays some memory on a monthly time scale in some catchments. We show that the memory of runoff and evapotranspiration jointly depend on <span class="hlt">soil</span> <span class="hlt">moisture</span> memory and on the strength of the coupling of runoff and evapotranspiration to <span class="hlt">soil</span> <span class="hlt">moisture</span>. Furthermore we find that the coupling strengths of runoff and evapotranspiration to <span class="hlt">soil</span> <span class="hlt">moisture</span> depend on the shape of the fitted dependencies and on the variance of the meteorological forcing. To better interpret the magnitude of the respective memories across Europe we finally provide a new perspective on hydrological memory by relating it to the mean duration required to recover from anomalies exceeding a certain threshold.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H31C1516D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H31C1516D"><span>Stochastic Analysis and Probabilistic Downscaling of <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deshon, J. P.; Niemann, J. D.; Green, T. R.; Jones, A. S.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable for rainfall-runoff response estimation, ecological and biogeochemical flux estimation, and biodiversity characterization, each of which is useful for watershed condition assessment. These applications require not only accurate, fine-resolution <span class="hlt">soil-moisture</span> estimates but also confidence limits on those estimates and <span class="hlt">soil-moisture</span> patterns that exhibit realistic statistical properties (e.g., variance and spatial correlation structure). The Equilibrium <span class="hlt">Moisture</span> from Topography, Vegetation, and <span class="hlt">Soil</span> (EMT+VS) model downscales coarse-resolution (9-40 km) <span class="hlt">soil</span> <span class="hlt">moisture</span> from satellite remote sensing or land-surface models to produce fine-resolution (10-30 m) estimates. The model was designed to produce accurate deterministic <span class="hlt">soil-moisture</span> estimates at multiple points, but the resulting patterns do not reproduce the variance or spatial correlation of observed <span class="hlt">soil-moisture</span> patterns. The primary objective of this research is to generalize the EMT+VS model to produce a probability density function (pdf) for <span class="hlt">soil</span> <span class="hlt">moisture</span> at each fine-resolution location and time. Each pdf has a mean that is equal to the deterministic <span class="hlt">soil-moisture</span> estimate, and the pdf can be used to quantify the uncertainty in the <span class="hlt">soil-moisture</span> estimates and to simulate <span class="hlt">soil-moisture</span> patterns. Different versions of the generalized model are hypothesized based on how uncertainty enters the model, whether the uncertainty is additive or multiplicative, and which distributions describe the uncertainty. These versions are then tested by application to four catchments with detailed <span class="hlt">soil-moisture</span> observations (Tarrawarra, Satellite Station, Cache la Poudre, and Nerrigundah). The performance of the generalized models is evaluated by comparing the statistical properties of the simulated <span class="hlt">soil-moisture</span> patterns to those of the observations and the deterministic EMT+VS model. The versions of the generalized EMT+VS model with normally distributed stochastic components produce <span class="hlt">soil-moisture</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H21I1602X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H21I1602X"><span>Downscaling SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Using Geoinformation Data and Geostatistics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, Y.; Wang, L.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is important for agricultural and hydrological studies. However, ground truth <span class="hlt">soil</span> <span class="hlt">moisture</span> data for wide area is difficult to achieve. Microwave remote sensing such as <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) can offer a solution for wide coverage. However, existing global <span class="hlt">soil</span> <span class="hlt">moisture</span> products only provide observations at coarse spatial resolutions, which often limit their applications in regional agricultural and hydrological studies. This paper therefore aims to generate fine scale <span class="hlt">soil</span> <span class="hlt">moisture</span> information and extend <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial availability. A statistical downscaling scheme is presented that incorporates multiple fine scale geoinformation data into the downscaling of coarse scale SMAP data in the absence of ground measurement data. Geoinformation data related to <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns including digital elevation model (DEM), land surface temperature (LST), land use and normalized difference vegetation index (NDVI) at a fine scale are used as auxiliary environmental variables for downscaling SMAP data. Generalized additive model (GAM) and regression tree are first conducted to derive statistical relationships between SMAP data and auxiliary geoinformation data at an original coarse scale, and residuals are then downscaled to a finer scale via area-to-point kriging (ATPK) by accounting for the spatial correlation information of the input residuals. The results from standard validation scores as well as the triple collocation (TC) method against <span class="hlt">soil</span> <span class="hlt">moisture</span> in-situ measurements show that the downscaling method can significantly improve the spatial details of SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> while maintain the accuracy.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H31F1258M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H31F1258M"><span>AMSR2 <span class="hlt">soil</span> <span class="hlt">moisture</span> validation in South Korea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moon, H.; Choi, M.</p> <p>2013-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is one of the most crucial factors to consider in the understanding and prediction of hydrometeorological interactions between land surface and atmosphere. As <span class="hlt">soil</span> <span class="hlt">moisture</span> observation over large regions at short timescales is required for substantial usage in the field of water resources management or prediction of natural hazards, many spaceborne sensors were developed to be contributed to a better estimation of spatio-temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Validation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> product is essential but demanding due to the different spatial scales of in situ and satellite observations. In this study, volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> product of Advanced Microwave Scanning Radiometer 2 (AMSR2), boarded on Global Change Observation Mission (GCOM)-W1 launched by Japan Aerospace Exploration Agency (JAXA) in May 2012, is validated in South Korea with Rural Development Administration (RDA) in situ measurements during the growing season of 2013. AMSR2 is currently encountering its validation phase as it began to provide level 2 (L2) and level 3 (L3) <span class="hlt">soil</span> <span class="hlt">moisture</span> product in May 2013. The root-mean-square error (rmse) of the AMSR2 <span class="hlt">soil</span> <span class="hlt">moisture</span> comparing to the in situ data is applied for the validation. Then, the analysis on spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> is conducted with regards to the vegetational characteristics of the study area. Further suggestions on modifying <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm will be proposed based on the analysis result.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=115365&keyword=bags&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=115365&keyword=bags&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>EVALUATION OF RADON EMANATION FROM <span class="hlt">SOIL</span> WITH VARYING <span class="hlt">MOISTURE</span> CONTENT IN A <span class="hlt">SOIL</span> CHAMBER</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The paper describes measurements to quantitatively identify the extent to which <span class="hlt">moisture</span> <span class="hlt">affects</span> radon emanation and diffusive transport components of a sandy <span class="hlt">soil</span> radon concentration gradient obtained in the EPA test chamber. The chamber (2X2X4 m long) was constructed to study t...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H41D1464Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H41D1464Y"><span>Remote Sensing <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Analysis by Unmanned Aerial Vehicles Digital Imaging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yeh, C. Y.; Lin, H. R.; Chen, Y. L.; Huang, S. Y.; Wen, J. C.</p> <p>2017-12-01</p> <p>In recent years, remote sensing analysis has been able to apply to the research of climate change, environment monitoring, geology, hydro-meteorological, and so on. However, the traditional methods for analyzing wide ranges of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> of spatial distribution surveys may require plenty resources besides the high cost. In the past, remote sensing analysis performed <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates through shortwave, thermal infrared ray, or infrared satellite, which requires lots of resources, labor, and money. Therefore, the digital image color was used to establish the multiple linear regression model. Finally, we can find out the relationship between surface <span class="hlt">soil</span> color and <span class="hlt">soil</span> <span class="hlt">moisture</span>. In this study, we use the Unmanned Aerial Vehicle (UAV) to take an aerial photo of the fallow farmland. Simultaneously, we take the surface <span class="hlt">soil</span> sample from 0-5 cm of the surface. The <span class="hlt">soil</span> will be baking by 110° C and 24 hr. And the software ImageJ 1.48 is applied for the analysis of the digital images and the hue analysis into Red, Green, and Blue (R, G, B) hue values. The correlation analysis is the result from the data obtained from the image hue and the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at each sampling point. After image and <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis, we use the R, G, B and <span class="hlt">soil</span> <span class="hlt">moisture</span> to establish the multiple regression to estimate the spatial distributions of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. In the result, we compare the real <span class="hlt">soil</span> <span class="hlt">moisture</span> and the estimated <span class="hlt">soil</span> <span class="hlt">moisture</span>. The coefficient of determination (R2) can achieve 0.5-0.7. The uncertainties in the field test, such as the sun illumination, the sun exposure angle, even the shadow, will <span class="hlt">affect</span> the result; therefore, R2 can achieve 0.5-0.7 reflects good effect for the in-suit test by using the digital image to estimate the <span class="hlt">soil</span> <span class="hlt">moisture</span>. Based on the outcomes of the research, using digital images from UAV to estimate the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> is acceptable. However, further investigations need to be collected more than ten days (four</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000117691','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000117691"><span>BOREAS HYD-1 Volumetric <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cuenca, Richard H.; Kelly, Shaun F.; Stangel, David E.; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Smith, David E. (Technical Monitor)</p> <p>2000-01-01</p> <p>The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-1 team made measurements of volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> at the Southern Study Area (SSA) and Northern Study Area (NSA) tower flux sites in 1994 and at selected tower flux sites in 1995-97. Different methods were used to collect these measurements, including neutron probe and manual and automated Time Domain Reflectometry (TDR). In 1994, the measurements were made every other day at the NSA-OJP (Old Jack Pine), NSA-YJP (Young Jack Pine), NSA-OBS (Old Black Spruce), NSA-Fen, SSA-OJP, SSA-YJP, SSA-Fen, SSA-YA (Young Aspen), and SSA-OBS sites. In 1995-97, when automated equipment was deployed at NSA-OJP, NSA-YJP, NSA-OBS, SSA-OBS, and SSA-OA (Old Aspen), the measurements were made as often as every hour. The data are stored in tabular ASCII files. The volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..44.4152W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..44.4152W"><span>Validation of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval in an Arctic tundra environment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wrona, Elizabeth; Rowlandson, Tracy L.; Nambiar, Manoj; Berg, Aaron A.; Colliander, Andreas; Marsh, Philip</p> <p>2017-05-01</p> <p>This study examines the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive <span class="hlt">soil</span> <span class="hlt">moisture</span> product on the Equal Area Scalable Earth-2 (EASE-2) 36 km Global cylindrical and North Polar azimuthal grids relative to two in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring networks that were installed in 2015 and 2016. Results indicate that there is no relationship between the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Level-2 passive <span class="hlt">soil</span> <span class="hlt">moisture</span> product and the upscaled in situ measurements. Additionally, there is very low correlation between modeled brightness temperature using the Community Microwave Emission Model and the Level-1 C SMAP brightness temperature interpolated to the EASE-2 Global grid; however, there is a much stronger relationship to the brightness temperature measurements interpolated to the North Polar grid, suggesting that the <span class="hlt">soil</span> <span class="hlt">moisture</span> product could be improved with interpolation on the North Polar grid.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H51H1607F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H51H1607F"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Mapping from ASAR Imagery of the Mulargia basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fois, L.; Montaldo, N.</p> <p>2016-12-01</p> <p>The state of the <span class="hlt">soil</span> <span class="hlt">moisture</span> is a key variable controlling surface water and energy balances. High resolution data of the ASAR (advanced synthetic aperture radar) sensor aboard European Space Agency's Envisat satellite offers the opportunity for monitoring surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at high resolution (up to 30 m), which is suitable for distributed mapping within the small scales of typical Mediterranean basins. These basins are characterized by strong topography and high spatial variability of physiographic properties, and only high spatial resolution satellite images allow to estimate adequately <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial variability. ASAR-based <span class="hlt">soil</span> <span class="hlt">moisture</span> mapping of the Mulargia basin (area of about 65 sq.km) are collected for 2003-2006 years. In Mediterranean basins, such as the Mulargia basin, characterized by water-limited conditions, even though there is no universal relationship between vegetation and <span class="hlt">soil</span> patterns in water-limited conditions some relationship between <span class="hlt">soil</span> water storage capacity and vegetation type and density can be found: for instance, typically an increase of woody vegetation dimension and canopy density when moving from uplands of a hillslope (with thin coarse textured <span class="hlt">soils</span>) to alluvial fans (with deep <span class="hlt">soils</span> of finer texture). We investigated the relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial variability, <span class="hlt">soil</span> depth and vegetation distribution, which impact strongly <span class="hlt">soil</span>, vegetation and atmosphere interactions. For the case study ASAR products at single and double polarization are tested and validated. For validating radar <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates, spatially distributed <span class="hlt">soil</span> <span class="hlt">moisture</span> ground-truth data have also been collected over the whole basin through the TDR technique and the gravimetric method, in days with available radar images. Results shows: 1) the high resolution ASAR imagery accuracy for producing maps of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns at the catchment scale and their reliability for different seasons (wet vs dry), and 2) a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B51G1898N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B51G1898N"><span>Hotspots for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval Failure across Continental USA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Neelam, M.; Mohanty, B.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is the key link between climate fluctuations and vegetation dynamics in space and time. In this work, an ecohydrological framework is proposed to understand <span class="hlt">soil</span> <span class="hlt">moisture</span> mechanisms underlying the climate-<span class="hlt">soil</span>-vegetation interactions. This framework is proposed for Level I Ecoregion (15) and Level II Ecoregion (50) across continental USA. A spatio-temporal variability in climate-<span class="hlt">soil</span>-vegetation interactions across all ecoregions, and its influence on Radiative Transfer Model (RTM) to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> is highlighted. These interactions are analyzed across all ecoregions, and presented through a probability distribution function (PDF). We observe <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics to play a key role in variability of climate-<span class="hlt">soil</span>-vegetation interactions at all spatio-temporal scales. Our analysis shows that, <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics enhance both mean and variance of <span class="hlt">soil</span>-vegetation coupling, and these regions are deemed as "hotspots". As these hotspots also fail to achieve the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval accuracy, and this can be attributed towards inefficacy in radiative transfer model. These hotspots are categorized accordingly to yield ecoregion specific scaling function to RTM for retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span>. A relationship is established between these hotspot regions and its correspondence with evapotranspiration. Thus, this work presents quantifiable measures of climate-<span class="hlt">soil</span>-vegetation interactions, discussing hydrologic mechanisms underlying basic ecologic patterns.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100032937','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100032937"><span>Evaluation and Application of Remotely Sensed <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bolten, J.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.; Rodell, Matt</p> <p>2010-01-01</p> <p>Whereas in-situ measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> are very accurate, achieving accurate regional <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates derived solely from point measurements is difficult because of the dependence upon the density of the gauge network and the proper upkeep of these instruments, which can be costly. Microwave remote sensing is the only technology capable of providing timely direct measurements of regional <span class="hlt">soil</span> <span class="hlt">moisture</span> in areas that are lacking in-situ networks. <span class="hlt">Soil</span> <span class="hlt">moisture</span> remote sensing technology is well established has been successfully applied in many fashions to Earth Science applications. Since the microwave emission from the <span class="hlt">soil</span> surface has such a high dependency upon the <span class="hlt">moisture</span> content within the <span class="hlt">soil</span>, we can take advantage of this relationship and combined with physically-based models of the land surface, derive accurate regional estimates of the <span class="hlt">soil</span> column water content from the microwave brightness temperature observed from satellite-based remote sensing instruments. However, there still remain many questions regarding the most efficient methodology for evaluating and applying satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates. As discussed below, we to use satellite-based estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics to improve the predictive capability of an optimized hydrologic model giving more accurate root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMEP41C0922Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMEP41C0922Z"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and its impact on the East Asian summer monsoon</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zuo, Z.; Zhang, R.</p> <p>2016-12-01</p> <p>Statistical characteristics of spring <span class="hlt">soil</span> <span class="hlt">moisture</span> in different reanalysis datasets of ERA-Interim, MERRA, JRA-25, CFSR and NCEP/NCAR-R1 are intercompared with each other and with the in situ observations over China. The intercomparision shows that the reanalyses can reproduce a gradual increase of <span class="hlt">soil</span> <span class="hlt">moisture</span> from northwestern China to northeastern and southeastern China in observations except NCEP/NCAR-R1. MERRA presents the best climatological <span class="hlt">soil</span> <span class="hlt">moisture</span> among them. Only ERA-Interim can well represent the interannual variations of observed <span class="hlt">soil</span> <span class="hlt">moisture</span>. The reasons causing differences between reanalyses of <span class="hlt">soil</span> <span class="hlt">moisture</span> are also investigated in terms of two main factors <span class="hlt">affecting</span> <span class="hlt">soil</span> <span class="hlt">moisture</span>, precipitation and evaporation. The ERA-Interim can well reproduce the observed precipitation and evaporation as well as their relations to <span class="hlt">soil</span> <span class="hlt">moisture</span>, resulting in a preferable ability to represent the spatial and temporal characteristics of observed <span class="hlt">soil</span> <span class="hlt">moisture</span>. Although the other four reanalysis datasets reproduce precipitation well, their poor ability to describe the evaporation causes large differences between their simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> and the observational data. Using both observed and reanalysis data and numerical model, the spring (April-May) <span class="hlt">soil</span> <span class="hlt">moisture</span> is found to have a significant impact on the summer (June-August) monsoon circulation over East Asia and precipitation in East China through changing surface thermal conditions. Abnormally wet <span class="hlt">soil</span> would increase surface evaporation, and hence decrease surface air temperature (Ta). The reduced Ta in late spring would narrow down the land-sea temperature difference, resulting in weakened East Asian monsoon in an abnormally strengthened western Pacific subtropical high that is also located further south than its normal position. This would then enhance precipitation in the Yangtze River Valley.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H12B..04M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H12B..04M"><span>Predicting root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> with satellite near-surface <span class="hlt">moisture</span> data in semiarid environments</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manfreda, S.; Baldwin, D. C.; Keller, K.; Smithwick, E. A. H.; Caylor, K. K.</p> <p>2015-12-01</p> <p>One of the most critical variables in semiarid environment is the <span class="hlt">soil</span> water content that represents a controlling factor for both ecological and hydrological processes. <span class="hlt">Soil</span> <span class="hlt">moisture</span> monitoring over large scales may be extremely useful, but it is limited by the fact that most of the available tools provides only surface measurements not representative of the effective amount of water stored in the subsurface. Therefore, a methodology able to infer root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> starting from surface measurements is highly desirable. Recently a new simplified formulation has been introduced to provide a formal description of the mathematical relationship between surface measurements and root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> (Manfreda et al., HESS 2014). This is a physically based approach derived from the <span class="hlt">soil</span> water balance equation, where different <span class="hlt">soil</span> water loss functions have been explored in order to take into account for the non-linear processes governing <span class="hlt">soil</span> water fluxes. The study highlighted that the <span class="hlt">soil</span> loss function is the key for such relationship that is therefore strongly influenced by <span class="hlt">soil</span> type and physiological plant types. The new formulation has been tested on <span class="hlt">soil</span> <span class="hlt">moisture</span> based on measurements taken from the African Monsoon Multidisciplinary Analysis (AMMA) and the <span class="hlt">Soil</span> Climate Analysis Network (SCAN) databases. The method sheds lights on the physical controls for <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics and on the possibility to use such a simplified method for the description of root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span>. Furthermore, the method has been also couple with an Enasamble Kalman Filter (EnKF) in order to optimize its performances for the large scale monitoring based the new satellite near-surface <span class="hlt">moisture</span> data (SMAP). The optimized SMAR-EnKF model does well in both wet and dry climates and across many different <span class="hlt">soil</span> types (51 SCAN locations) providing a strategy for real-time <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.6410P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.6410P"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> as an Estimator for Crop Yield in Germany</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peichl, Michael; Meyer, Volker; Samaniego, Luis; Thober, Stephan</p> <p>2015-04-01</p> <p>, phenological, geological, agronomic, and socio-economic variables are also considered to extend the model in order to reveal the proper causal relation. First results show that dry as well as wet extremes of SMI have a negative impact on crop yield for winter wheat. This indicates that <span class="hlt">soil</span> <span class="hlt">moisture</span> has at least a limiting <span class="hlt">affect</span> on crop production.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JHyd..475..111Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JHyd..475..111Y"><span>Response of deep <span class="hlt">soil</span> <span class="hlt">moisture</span> to land use and afforestation in the semi-arid Loess Plateau, China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Lei; Wei, Wei; Chen, Liding; Mo, Baoru</p> <p>2012-12-01</p> <p>Summary<span class="hlt">Soil</span> <span class="hlt">moisture</span> is an effective water source for plant growth in the semi-arid Loess Plateau of China. Characterizing the response of deep <span class="hlt">soil</span> <span class="hlt">moisture</span> to land use and afforestation is important for the sustainability of vegetation restoration in this region. In this paper, the dynamics of <span class="hlt">soil</span> <span class="hlt">moisture</span> were quantified to evaluate the effect of land use on <span class="hlt">soil</span> <span class="hlt">moisture</span> at a depth of 2 m. Specifically, the gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> content was measured in the <span class="hlt">soil</span> layer between 0 and 8 m for five land use types in the Longtan catchment of the western Loess Plateau. The land use types included traditional farmland, native grassland, and lands converted from traditional farmland (pasture grassland, shrubland and forestland). Results indicate that the deep <span class="hlt">soil</span> <span class="hlt">moisture</span> content decreased more than 35% after land use conversion, and a <span class="hlt">soil</span> <span class="hlt">moisture</span> deficit appeared in all types of land with introduced vegetation. The introduced vegetation decreased the <span class="hlt">soil</span> <span class="hlt">moisture</span> content to levels lower than the reference value representing no human impact in the entire 0-8 m <span class="hlt">soil</span> profile. No significant differences appeared between different land use types and introduced vegetation covers, especially in deeper <span class="hlt">soil</span> layers, regardless of which plant species were introduced. High planting density was found to be the main reason for the severe deficit of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Landscape management activities such as tillage activities, micro-topography reconstruction, and fallowed farmland <span class="hlt">affected</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> in both shallow and deep <span class="hlt">soil</span> layers. Tillage and micro-topography reconstruction can be used as effective countermeasures to reduce the <span class="hlt">soil</span> <span class="hlt">moisture</span> deficit due to their ability to increase <span class="hlt">soil</span> <span class="hlt">moisture</span> content. For sustainable vegetation restoration in a vulnerable semi-arid region, the plant density should be optimized with local <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions and appropriate landscape management practices.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.2539H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.2539H"><span>Wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor networks for environmental monitoring and irrigation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hübner, Christof; Cardell-Oliver, Rachel; Becker, Rolf; Spohrer, Klaus; Jotter, Kai; Wagenknecht, Tino</p> <p>2010-05-01</p> <p>Dependable spatial-temporal <span class="hlt">soil</span> parameter data is required for informed decision making in precision farming and hydrological applications. Wireless sensor networks are seen as a key technology to satisfy these demands. Hence, research and development focus is on reliable outdoor applications. This comprises sensor design improvement, more robust communication protocols, less power consumption as well as better deployment strategies and tools. Field trials were performed to investigate and iteratively improve wireless sensor networks in the above-mentioned areas. They accounted for different climate conditions, <span class="hlt">soil</span> types and salinity, irrigation practices, solar power availability and also for different radio spectrum use which <span class="hlt">affects</span> the reliability of the wireless links. E.g. 868 MHz and 2.4 GHz wireless nodes were compared in the field with regard to range. Furthermore a low-cost <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor was developed to allow for large-scale field experiments. It is based on the measurement of the high frequency dielectric properties of the <span class="hlt">soil</span>. Two agricultural sites were equipped with 80 sensors and 20 wireless nodes each. The <span class="hlt">soil</span> <span class="hlt">moisture</span> data is collected in regular intervals, aggregated in a base station and visualized through a web-based geographical information system. The complete system and results of field experiments are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.7800U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.7800U"><span>Spatial and temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the field with and without plants*</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Usowicz, B.; Marczewski, W.; Usowicz, J. B.</p> <p>2012-04-01</p> <p> <span class="hlt">moisture</span> runs in particular objects and of precipitation distribution shows clearly that rainfall has an effect on the <span class="hlt">soil</span> <span class="hlt">moisture</span>. The amount of precipitation water that increased the <span class="hlt">soil</span> <span class="hlt">moisture</span> depended on the strength of the rainfall, on the hydrological properties of the <span class="hlt">soil</span> (primarily the <span class="hlt">soil</span> density), the status of the plant cover, and surface runoff. Basing on the precipitation distribution and on the <span class="hlt">soil</span> <span class="hlt">moisture</span> runs, an attempt was made at finding a temporal and spatial relationship between those variables, employing for the purpose the geostatistical methods which permit time and space to be included in the analysis. The geostatistical parameters determined showed the temporal dependence of <span class="hlt">moisture</span> distribution in the <span class="hlt">soil</span> profile, with the autocorrelation radius increasing with increasing depth in the profile. The highest values of the radius were observed in the plots with plant cover below the arable horizon, and the lowest in the arable horizon on the barley and fallow plots. The fractal dimensions showed a clear decrease in values with increasing depth in the plots with plant cover, while in the bare plots they were relatively constant within the <span class="hlt">soil</span> profile under study. Therefore, they indicated that the temporal distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> within the <span class="hlt">soil</span> profile in the bare field was more random in character than in the plots with plants. The results obtained and the analyses indicate that the <span class="hlt">moisture</span> in the <span class="hlt">soil</span> profile, its variability and determination, are significantly <span class="hlt">affected</span> by the type and condition of plant canopy. The differentiation in <span class="hlt">moisture</span> content between the plots studied resulted from different precipitation interception and different intensity of water uptake by the roots. * The work was financially supported in part by the ESA Programme for European Cooperating States (PECS), No.98084 "SWEX-R, <span class="hlt">Soil</span> Water and Energy Exchange/Research", AO-3275.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=268995','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=268995"><span>Shrub encroachment alters sensitivity of <span class="hlt">soil</span> respiration to temperature and <span class="hlt">moisture</span> 2115</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Shrub encroachment into grasslands creates a mosaic of different <span class="hlt">soil</span> microsites ranging from open spaces to well-developed shrub canopies, and it is unclear how this <span class="hlt">affects</span> the spatial variability in <span class="hlt">soil</span> respiration characteristics, such as the sensitivity to <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span>. This i...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/42056','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/42056"><span>Simulating <span class="hlt">soil</span> <span class="hlt">moisture</span> change in a semiarid rangeland watershed with a process-based water-balance model</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Howard Evan Canfield; Vicente L. Lopes</p> <p>2000-01-01</p> <p>A process-based, simulation model for evaporation, <span class="hlt">soil</span> water and streamflow (BROOK903) was used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> change on a semiarid rangeland watershed in southeastern Arizona. A sensitivity analysis was performed to select parameters <span class="hlt">affecting</span> ET and <span class="hlt">soil</span> <span class="hlt">moisture</span> for calibration. Automatic parameter calibration was performed using a procedure based on a...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70185708','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70185708"><span>Divergent surface and total <span class="hlt">soil</span> <span class="hlt">moisture</span> projections under global warming</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Berg, Alexis; Sheffield, Justin; Milly, Paul C.D.</p> <p>2017-01-01</p> <p>Land aridity has been projected to increase with global warming. Such projections are mostly based on off-line aridity and drought metrics applied to climate model outputs but also are supported by climate-model projections of decreased surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Here we comprehensively analyze <span class="hlt">soil</span> <span class="hlt">moisture</span> projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer-by-layer <span class="hlt">soil</span> <span class="hlt">moisture</span>. We identify a robust vertical gradient of projected mean <span class="hlt">soil</span> <span class="hlt">moisture</span> changes, with more negative changes near the surface. Some regions of the northern middle to high latitudes exhibit negative annual surface changes but positive total changes. We interpret this behavior in the context of seasonal changes in the surface water budget. This vertical pattern implies that the extensive drying predicted by off-line drought metrics, while consistent with the projected decline in surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, will tend to overestimate (negatively) changes in total <span class="hlt">soil</span> water availability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JHyd..535..637Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..535..637Z"><span>Misrepresentation and amendment of <span class="hlt">soil</span> <span class="hlt">moisture</span> in conceptual hydrological modelling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhuo, Lu; Han, Dawei</p> <p>2016-04-01</p> <p>Although many conceptual models are very effective in simulating river runoff, their <span class="hlt">soil</span> <span class="hlt">moisture</span> schemes are generally not realistic in comparison with the reality (i.e., getting the right answers for the wrong reasons). This study reveals two significant misrepresentations in those models through a case study using the Xinanjiang model which is representative of many well-known conceptual hydrological models. The first is the setting of the upper limit of its <span class="hlt">soil</span> <span class="hlt">moisture</span> at the field capacity, due to the 'holding excess runoff' concept (i.e., runoff begins on repletion of its storage to the field capacity). The second is neglect of capillary rise of water movement. A new scheme is therefore proposed to overcome those two issues. The amended model is as effective as its original form in flow modelling, but represents more logically realistic <span class="hlt">soil</span> water processes. The purpose of the study is to enable the hydrological model to get the right answers for the right reasons. Therefore, the new model structure has a better capability in potentially assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> observations to enhance its real-time flood forecasting accuracy. The new scheme is evaluated in the Pontiac catchment of the USA through a comparison with satellite observed <span class="hlt">soil</span> <span class="hlt">moisture</span>. The correlation between the XAJ and the observed <span class="hlt">soil</span> <span class="hlt">moisture</span> is enhanced significantly from 0.64 to 0.70. In addition, a new <span class="hlt">soil</span> <span class="hlt">moisture</span> term called SMDS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit to Saturation) is proposed to complement the conventional SMD (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/22075','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/22075"><span>Typical <span class="hlt">moisture</span>-density curves : part II : lime treated <span class="hlt">soils</span>.</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>1966-05-01</p> <p>The objective of the study covered by this report was to determine whether the family of curves developed for untreated <span class="hlt">soils</span>, could be used for determining the optimum <span class="hlt">moisture</span> and maximum density of lime treated <span class="hlt">soils</span>. This investigation was initia...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19790008160&hterms=soil+erosion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsoil%2Berosion','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19790008160&hterms=soil+erosion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsoil%2Berosion"><span>Remote sensing as a tool in assessing <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carlson, C. W.</p> <p>1978-01-01</p> <p>The effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> as it relates to agriculture is briefly discussed. The use of remote sensing to predict scheduling of irrigation, runoff and <span class="hlt">soil</span> erosion which contributes to the prediction of crop yield is also discussed.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/10464','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/10464"><span><span class="hlt">Soil</span> <span class="hlt">moisture-soil</span> temperature interrelationships on a sandy-loam <span class="hlt">soil</span> exposed to full sunlight</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>David A. Marquis</p> <p>1967-01-01</p> <p>In a study of birch regeneration in New Hampshire, <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature were found to be intimately related. Not only does low <span class="hlt">moisture</span> lead to high temperature, but high temperature undoubtedly accelerates <span class="hlt">soil</span> drying, setting up a vicious cycle of heating and drying that may prevent seed germination or kill seedlings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.5721P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.5721P"><span>Modelling The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> - Rainfall Feedback: Multiple Regional Climate Equilibria</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pal, J. S.; Eltahir, E. A. B.; Giorgi, F.</p> <p></p> <p>In this study we investigate the <span class="hlt">soil</span> <span class="hlt">moisture</span> - rainfall feedback using a regional cli- mate model (RegCM). Numerical experiments are performed with perpetual day forc- ing for the 1993 summer flood conditions over North America. Each simulation is initialized under different <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. In one set of simulations, the <span class="hlt">soil</span> <span class="hlt">moisture</span> is fully interactive, while in the second set, the <span class="hlt">soil</span> <span class="hlt">moisture</span> is held fixed (one-way interaction). The experiment results show that after approximately 40 days of simulation, three dis- tinct statistical regional climate equilibria develop over the Midwest flood region: a <span class="hlt">soil</span> limiting mode; an atmosphere limiting mode; and an intermediate mode. The <span class="hlt">soil</span> limiting mode, characterized by a weakened and northward displacement of the flood peak, results from the simulations in which the <span class="hlt">soil</span> <span class="hlt">moisture</span> is initialized with dry conditions. In this mode, precipitation is negligible over the flood region. The atmo- sphere limiting mode occurs from the simulations in which the <span class="hlt">soil</span> <span class="hlt">moisture</span> is initial- ized with wet conditions. This mode is defined by a broad flood peak which occurs to the south of observed. Lastly, the intermediate mode occurs from the simulations initialized with realistic <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. This mode reproduces the flood con- ditions for nearly 50 days and then begins to dissipate into a lower precipitation mode. Under the fixed <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions, the three modes become intransitive. Over- all, the three modes are sustained from the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the local and remote climate conditions via feedbacks involving the energy and water budgets and the large-scale dynamics. The results presented here suggest that springtime <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions may con- tribute to the establishment of stable climate modes in the summer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810004909','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810004909"><span>Joint microwave and infrared studies for <span class="hlt">soil</span> <span class="hlt">moisture</span> determination</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Njoku, E. G.; Schieldge, J. P.; Kahle, A. B. (Principal Investigator)</p> <p>1980-01-01</p> <p>The feasibility of using a combined microwave-thermal infrared system to determine <span class="hlt">soil</span> <span class="hlt">moisture</span> content is addressed. Of particular concern are bare <span class="hlt">soils</span>. The theoretical basis for microwave emission from <span class="hlt">soils</span> and the transport of heat and <span class="hlt">moisture</span> in <span class="hlt">soils</span> is presented. Also, a description is given of the results of two field experiments held during vernal months in the San Joaquin Valley of California.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19288708','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19288708"><span>[Relationships of <span class="hlt">soil</span> <span class="hlt">moisture</span> content with precipitation and evaporation in rehabilitated forests in degraded limestone red-<span class="hlt">soil</span> region of Jiangxi Province].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Yuan-qiu; Wang, Hong-sheng; Guo, Sheng-mao; Fu, Min-ning</p> <p>2008-12-01</p> <p>By using time series analysis, the relationships of 0-40 cm <span class="hlt">soil</span> <span class="hlt">moisture</span> content with precipitation and evaporation in four main rehabilitated forests in degraded limestone red-<span class="hlt">soil</span> region of Xiushui County, Jiangxi Province were studied. The results showed that in the four rehabilitated forests, the previous month's <span class="hlt">soil</span> <span class="hlt">moisture</span> content had stronger effects on the current month's <span class="hlt">soil</span> <span class="hlt">moisture</span> content in 0-40 cm and 20-40 cm layers, but had lesser effects on that in 0-10 cm layer. The <span class="hlt">soil</span> <span class="hlt">moisture</span> content in 20-40 cm layer was mainly <span class="hlt">affected</span> by current month's precipitation, while that in 0-10 cm layer was mainly <span class="hlt">affected</span> by current month's evaporation. The correlation coefficient between current month's precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> content was the largest in pure Liquidambar formosana plantation, and the auto-interrelation coefficient of <span class="hlt">soil</span> <span class="hlt">moisture</span> content was larger in mixed forests than in pure L. formosana plantation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017FrEaS...5...25C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017FrEaS...5...25C"><span>Inference of <span class="hlt">Soil</span> Hydrologic Parameters from Electronic <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Records</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chandler, David G.; Seyfried, Mark S.; McNamara, James P.; Hwang, Kyotaek</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important control on hydrologic function, as it governs vertical fluxes from and to the atmosphere, groundwater recharge and lateral fluxes through the <span class="hlt">soil</span>. Historically, the traditional model parameters of saturation, field capacity and permanent wilting point have been determined by laboratory methods. This approach is challenged by issues of scale, boundary conditions and <span class="hlt">soil</span> disturbance. We develop and compare four methods to determine values of field saturation, field capacity, plant extraction limit and initiation of plant water stress from long term in-situ monitoring records of TDR-measured volumetric water content (Q). The monitoring sites represent a range of <span class="hlt">soil</span> textures, <span class="hlt">soil</span> depths, effective precipitation and plant cover types in a semi-arid climate. The Q records exhibit attractors (high frequency values) that correspond to field capacity and the plant extraction limit at both annual and longer time scales, but the field saturation values vary by year depending on seasonal wetness in the semi-arid setting. The analysis for five sites in two watersheds is supported by comparison to values determined by a common pedotransfer function and measured <span class="hlt">soil</span> characteristic curves. Frozen <span class="hlt">soil</span> is identified as a complicating factor for the analysis and users are cautioned to filter data by temperature, especially for near surface <span class="hlt">soils</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050192452','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050192452"><span>Estimating Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Simulated AVIRIS Spectra</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Whiting, Michael L.; Li, Lin; Ustin, Susan L.</p> <p>2004-01-01</p> <p><span class="hlt">Soil</span> albedo is influenced by many physical and chemical constituents, with <span class="hlt">moisture</span> being the most influential on the spectra general shape and albedo (Stoner and Baumgardner, 1981). Without <span class="hlt">moisture</span>, the intrinsic or matrix reflectance of dissimilar <span class="hlt">soils</span> varies widely due to differences in surface roughness, particle and aggregate sizes, mineral types, including salts, and organic matter contents. The influence of <span class="hlt">moisture</span> on <span class="hlt">soil</span> reflectance can be isolated by comparing similar <span class="hlt">soils</span> in a study of the effects that small differences in <span class="hlt">moisture</span> content have on reflectance. However, without prior knowledge of the <span class="hlt">soil</span> physical and chemical constituents within every pixel, it is nearly impossible to accurately attribute the reflectance variability in an image to <span class="hlt">moisture</span> or to differences in the physical and chemical constituents in the <span class="hlt">soil</span>. The effect of <span class="hlt">moisture</span> on the spectra must be eliminated to use hyperspectral imagery for determining minerals and organic matter abundances of bare agricultural <span class="hlt">soils</span>. Accurate <span class="hlt">soil</span> mineral and organic matter abundance maps from air- and space-borne imagery can improve GIS models for precision farming prescription, and managing irrigation and salinity. Better models of <span class="hlt">soil</span> <span class="hlt">moisture</span> and reflectance will also improve the selection of <span class="hlt">soil</span> endmembers for spectral mixture analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=234051','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=234051"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Validation Experiment 2008 (SMAPVEX08)</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Mission (SMAP) is currently addressing issues related to the development and selection of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithms. Several forums have identified a number of specific questions that require supporting field experiments. Addressing these issues as soon as p...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170002508','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170002508"><span>Evaluating ESA CCI <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in East Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.</p> <p>2016-01-01</p> <p>To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from missions like the European Space Agency (ESA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) and NASAs <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R greater than 0.5) with modeled, seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span>, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=336447','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=336447"><span>A review of the applications of ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wid...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=336401','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=336401"><span>Use of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors for irrigation scheduling</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Various types of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing devices have been developed and are commercially available for water management applications. Each type of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors has its advantages and shortcomings in terms of accuracy, reliability, and cost. Resistive and capacitive based sensors, and time-d...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29599664','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29599664"><span>Evaluating ESA CCI <span class="hlt">soil</span> <span class="hlt">moisture</span> in East Africa.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R; Wang, Shugong; Peters-Lidard, Christa D; Verdin, James P</p> <p>2016-06-01</p> <p>To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from missions like the European Space Agency (ESA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) and NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span>, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=271413','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=271413"><span>Long term observation and validation of windsat <span class="hlt">soil</span> <span class="hlt">moisture</span> data</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The surface <span class="hlt">soil</span> <span class="hlt">moisture</span> controls surface energy budget. It is a key environmental variable in the coupled atmospheric and hydrological processes that are related to drought, heat waves and monsoon formation. Satellite remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> provides information that can contribute to unde...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H12G..03M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H12G..03M"><span>The Value of SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observations For Agricultural Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mladenova, I. E.; Bolten, J. D.; Crow, W.; Reynolds, C. A.</p> <p>2017-12-01</p> <p>Knowledge of the amount of <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) in the root zone (RZ) is critical source of information for crop analysts and agricultural agencies as it controls crop development and crop condition changes and can largely impact end-of-season yield. Foreign Agricultural Services (FAS), a subdivision of U.S. Department of Agriculture (USDA) that is in charge with providing information on current and expected global crop supply and demand estimates, has been relying on RZSM estimates generated by the modified two-layer Palmer model, which has been enhanced to allow the assimilation of satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> data. Generally the accuracy of model-based <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates is dependent on the precision of the forcing data that drives the model and more specifically, the accuracy of the precipitation data. Data assimilation gives the opportunity to correct for such precipitation-related inaccuracies and enhance the quality of the model estimates. Here we demonstrate the value of ingesting passive-based <span class="hlt">soil</span> <span class="hlt">moisture</span> observations derived from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission. In terms of agriculture, general understanding is that the change in <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions precede the change in vegetation status, suggesting that <span class="hlt">soil</span> <span class="hlt">moisture</span> can be used as an early indicator of expected crop conditions. Therefore, we assess the accuracy of the SMAP enhanced Palmer model by examining the lag rank cross-correlation coefficient between the model generated <span class="hlt">soil</span> <span class="hlt">moisture</span> observations and the Normalized Difference Vegetation Index (NDVI).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5871431','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5871431"><span>Evaluating ESA CCI <span class="hlt">soil</span> <span class="hlt">moisture</span> in East Africa</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.</p> <p>2018-01-01</p> <p>To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from missions like the European Space Agency (ESA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) and NASA’s <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span>, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions. PMID:29599664</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/8694','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/8694"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and groundwater recharge under a mixed conifer forest</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Robert R. Ziemer</p> <p>1978-01-01</p> <p>The depletion of <span class="hlt">soil</span> <span class="hlt">moisture</span> within the surface 7 m by a mixed conifer forest in the Sierra Nevada was measured by the neutron method every 2 weeks during 5 consecutive summers. <span class="hlt">Soil</span> <span class="hlt">moisture</span> recharge was measured periodically during the intervening winters. Groundwater fluctuations within the surface 17 m were continuously recorded during the same period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=269106','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=269106"><span>Enhancing agricultural forecasting using SMOS surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>With the onset of data availability from the ESA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission (Kerr and Levine, 2008) and the expected 2015 launch of the NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP) mission (Entekhabi et al., 2010), the next five years should see a significant expansion in our ab...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..4410341T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..4410341T"><span>Irrigation Patterns Resemble ERA-Interim Reanalysis <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Additions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tuinenburg, O. A.; de Vries, J. P. R.</p> <p>2017-10-01</p> <p>Irrigation modulates the water cycle by making water available for plants, increasing transpiration and atmospheric humidity, while decreasing temperatures due to the energy that is needed for evaporation. Irrigation is usually not included in atmospheric reanalysis systems, but <span class="hlt">moisture</span> can be added to the <span class="hlt">soil</span> due to data assimilation. This paper compares these <span class="hlt">soil</span> <span class="hlt">moisture</span> additions to the irrigation patterns. In the ERA-interim atmospheric reanalysis, 2 m temperature observations are assimilated. A mismatch between modeled and observed temperatures is corrected by adding or removing <span class="hlt">moisture</span> from the <span class="hlt">soil</span>. These corrections show a clear pattern of mean <span class="hlt">soil</span> <span class="hlt">moisture</span> additions in many areas. To determine the cause of these increments, the spatial and temporal patterns of these <span class="hlt">soil</span> <span class="hlt">moisture</span> increments are compared to irrigation water demand and precipitation bias. In irrigated areas, the annual means and cycles of <span class="hlt">soil</span> <span class="hlt">moisture</span> increments correlate well with irrigation, and less with precipitation bias. Therefore, in irrigated areas, the <span class="hlt">soil</span> <span class="hlt">moisture</span> increments are more likely caused by irrigation than by the precipitation bias. In nonirrigated areas, a weak statistical relation between <span class="hlt">soil</span> <span class="hlt">moisture</span> increments and precipitation bias is present. Irrigation is currently not included in reanalysis systems. However, as irrigation indirectly influences the water balance in atmospheric reanalysis systems, we recommend to include this process in reanalysis models. Moreover, the influence of irrigation on the local and regional atmosphere should be taken into account when interpreting atmospheric data over strongly irrigated areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=337772','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=337772"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> remote sensing: State of the science</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Satellites (e.g., SMAP, SMOS) using passive microwave techniques, in particular at L band frequency, have shown good promise for global mapping of near-surface (0-5 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> at a spatial resolution of 25-40 km and temporal resolution of 2-3 days. C- and X-band <span class="hlt">soil</span> <span class="hlt">moisture</span> records date bac...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=330158','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=330158"><span>Challenges in Interpreting and Validating Satellite <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Information</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Global <span class="hlt">soil</span> <span class="hlt">moisture</span> products are now being generated routinely using microwave-based satellite observing systems. These include the NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission. In order to fully exploit these observations they must be integrated with both in situ measurements and model-based e...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=324624','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=324624"><span>Assessment of the SMAP level 2 passive <span class="hlt">soil</span> <span class="hlt">moisture</span> product</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite mission was launched on Jan 31, 2015. The observatory was developed to provide global mapping of high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze-thaw state every 2–3 days using an L-band (active) radar and an L-band (passive) radiometer. SMAP provides ...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=317570','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=317570"><span>Recent advances in (<span class="hlt">soil</span> <span class="hlt">moisture</span>) triple collocation analysis</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>To date, triple collocation (TC) analysis is one of the most important methods for the global scale evaluation of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets. In this study we review existing implementations of <span class="hlt">soil</span> <span class="hlt">moisture</span> TC analysis as well as investigations of the assumptions underlying the method....</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=253460','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=253460"><span>ALOS PALSAR and UAVSAR <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Field Campaigns</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>As part of our ongoing analysis of L-band radar mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> we are evaluating the role that ALOS PALSAR data can play in the development of radar retrieval algorithms for the future NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite. Differences in configurations must be assessed to det...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=344643','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=344643"><span>Spatially enhanced passive microwave derived <span class="hlt">soil</span> <span class="hlt">moisture</span>: capabilities and opportunities</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Low frequency passive microwave remote sensing is a proven technique for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval, but its coarse resolution restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution <span class="hlt">soil</span> <span class="hlt">moistur</span>...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=228415','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=228415"><span>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active/Passive Mission (SMAP)</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active/Passive (SMAP) mission will deliver global views of <span class="hlt">soil</span> <span class="hlt">moisture</span> content and its freeze/thaw state that are critical terrestrial water cycle state variables. Polarized measurements obtained with a shared antenna L-band radar and radiometer system will allow accurate estima...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017NatGe..10..100M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017NatGe..10..100M"><span>The global distribution and dynamics of surface <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McColl, Kaighin A.; Alemohammad, Seyed Hamed; Akbar, Ruzbeh; Konings, Alexandra G.; Yueh, Simon; Entekhabi, Dara</p> <p>2017-01-01</p> <p>Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> has a direct impact on food security, human health and ecosystem function. It also plays a key role in the climate system, and the development and persistence of extreme weather events such as droughts, floods and heatwaves. However, sparse and uneven observations have made it difficult to quantify the global distribution and dynamics of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Here we introduce a metric of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory and use a full year of global observations from NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive mission to show that surface <span class="hlt">soil</span> <span class="hlt">moisture</span>--a storage believed to make up less than 0.001% of the global freshwater budget by volume, and equivalent to an, on average, 8-mm thin layer of water covering all land surfaces--plays a significant role in the water cycle. Specifically, we find that surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retains a median 14% of precipitation falling on land after three days. Furthermore, the retained fraction of the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> storage after three days is highest over arid regions, and in regions where drainage to groundwater storage is lowest. We conclude that lower groundwater storage in these regions is due not only to lower precipitation, but also to the complex partitioning of the water cycle by the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> storage layer at the land surface.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/33766','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/33766"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> dynamics and smoldering combustion limits of pocosin <span class="hlt">soils</span> in North Carolina, USA</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>James Reardon; Gary Curcio; Roberta Bartlette</p> <p>2009-01-01</p> <p>Smoldering combustion of wetland organic <span class="hlt">soils</span> in the south-eastern USA is a serious management concern. Previous studies have reported smoldering was sensitive to a wide range of <span class="hlt">moisture</span> contents, but studies of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics and changing smoldering combustion potential in wetland communities are limited. Linking <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements with estimates of...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMIN43B0078S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMIN43B0078S"><span>Drive by <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurement: A Citizen Science Project</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Senanayake, I. P.; Willgoose, G. R.; Yeo, I. Y.; Hancock, G. R.</p> <p>2017-12-01</p> <p>Two of the common attributes of <span class="hlt">soil</span> <span class="hlt">moisture</span> are that at any given time it varies quite markedly from point to point, and that there is a significant deterministic pattern that underlies this spatial variation and which is typically 50% of the spatial variability. The spatial variation makes it difficult to determine the time varying catchment average <span class="hlt">soil</span> <span class="hlt">moisture</span> using field measurements because any individual measurement is unlikely to be equal to the average for the catchment. The traditional solution to this is to make many measurements (e.g. with <span class="hlt">soil</span> <span class="hlt">moisture</span> probes) spread over the catchment, which is very costly and manpower intensive, particularly if we need a time series of <span class="hlt">soil</span> <span class="hlt">moisture</span> variation across a catchment. An alternative approach, explored in this poster is to use the deterministic spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> to calibrate one site (e.g. a permanent <span class="hlt">soil</span> <span class="hlt">moisture</span> probe at a weather station) to the spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> over the study area. The challenge is then to determine the spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span>. This poster will present results from a proof of concept project, where data was collected by a number of undergraduate engineering students, to estimate the spatial pattern. The approach was to drive along a series of roads in a catchment and collect <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements at the roadside using field portable <span class="hlt">soil</span> <span class="hlt">moisture</span> probes. This drive was repeated a number of times over the semester, and the time variation and spatial persistence of the <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern were examined. Provided that the students could return to exactly the same location on each collection day there was a strong persistent pattern in the <span class="hlt">soil</span> <span class="hlt">moisture</span>, even while the average <span class="hlt">soil</span> <span class="hlt">moisture</span> varied temporally as a result of preceding rainfall. The poster will present results and analysis of the student data, and compare these results with several field sites where we have spatially distributed permanently installed <span class="hlt">soil</span> <span class="hlt">moisture</span> probes. The</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.jstor.org/stable/2426646','USGSPUBS'); return false;" href="http://www.jstor.org/stable/2426646"><span>Effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature on overwintering survival of Curculio larvae (Coleoptera : Curculionidae)</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Ricca, M.A.; Weckerly, F.W.; Semlitsch, R.D.</p> <p>1996-01-01</p> <p>Few studies to date have investigated factors, other than mast crop size, that influence the dynamics of Curculio populations.W e examined the effects of varying levels of <span class="hlt">soil</span> <span class="hlt">moisture</span> (0.35, 0.4 and 0.5 g water/g <span class="hlt">soil</span>) and temperature (8, 14 and 20 C) on over wintering survival of Curculio larvae collected from Quercus michauxii acorns. Survival of larvae, analyzed using log-linear analysis, was adversely <span class="hlt">affected</span> by <span class="hlt">soil</span> <span class="hlt">moisture</span> but not by <span class="hlt">soil</span> temperature. Larvae that overwinter in drier <span class="hlt">soil</span> may have higher probabilities of successfully metamorphosing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1610993G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1610993G"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> responses to vapour pressure deficit in polytunnel-grown tomato under <span class="hlt">soil</span> <span class="hlt">moisture</span> triggered irrigation control</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goodchild, Martin; Kühn, Karl; Jenkins, Dick</p> <p>2014-05-01</p> <p>The aim of this work has been to investigate <span class="hlt">soil</span>-to-atmosphere water transport in potted tomato plants by measuring and processing high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> data against the environmental driver of vapour pressure deficit (VPD). Whilst many researchers have successfully employed sap flow sensors to determine water uptake by roots and transport through the canopy, the installation of sap flow sensors is non-trivial. This work presents an alternative method that can be integrated with irrigation controllers and data loggers that employ <span class="hlt">soil</span> <span class="hlt">moisture</span> feedback which can allow water uptake to be evaluated against environmental drivers such as VPD between irrigation events. In order to investigate water uptake against VPD, <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements were taken with a resolution of 2 decimal places - and <span class="hlt">soil</span> <span class="hlt">moisture</span>, air temperature and relative humidity measurements were logged every 2 minutes. Data processing of the <span class="hlt">soil</span> <span class="hlt">moisture</span> was performed in an Excel spread sheet where changes in water transport were derived from the rate of change of <span class="hlt">soil</span> <span class="hlt">moisture</span> using the Slope function over 5 <span class="hlt">soil</span> <span class="hlt">moisture</span> readings. Results are presented from a small scale experiment using a GP2-based irrigation controller and data logger. <span class="hlt">Soil</span> <span class="hlt">moisture</span> feedback is provided from a single SM300 <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor in order to regulate the <span class="hlt">soil</span> <span class="hlt">moisture</span> level and to assess the water flow from potted tomato plants between irrigation events. <span class="hlt">Soil</span> <span class="hlt">moisture</span> levels were set to avoid drainage water losses. By determining the rate of change in <span class="hlt">soil</span> <span class="hlt">moisture</span> between irrigation events, over a 16 day period whilst the tomato plant was in flower, it has been possible to observe very good correlation between <span class="hlt">soil</span> water uptake and VPD - illustrating the link between plant physiology and environmental conditions. Further data is presented for a second potted tomato plant where the <span class="hlt">soil</span> <span class="hlt">moisture</span> level is switched between the level that avoids drainage losses and a significantly lower level. This data</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1989JCli....2.1362O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1989JCli....2.1362O"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> and the Persistence of North American Drought.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Oglesby, Robert J.; Erickson, David J., III</p> <p>1989-11-01</p> <p>We describe numerical sensitivity experiments exploring the effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on North American summertime climate using the NCAR CCMI, a 12-layer global atmospheric general circulation model. In particular. the hypothesis that reduced <span class="hlt">soil</span> <span class="hlt">moisture</span> may help induce and amplify warm, dry summers over midlatitude continental interiors is examined. Equilibrium climate statistics are computed for the perpetual July model response to imposed <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies over North America between 36° and 49°N. In addition, the persistence of imposed <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies is examined through use of the seasonal cycle mode of operation with use of various initial atmospheric states both equilibrated and nonequilibrated to the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> anomaly.The climate statistics generated by thew model simulations resemble in a general way those of the summer of 1988, when extensive heat and drought occurred over much of North America. A reduction in <span class="hlt">soil</span> <span class="hlt">moisture</span> in the model leads to an increase in surface temperature, lower surface pressure, increased ridging aloft, and a northward shift of the jet stream. Low-level <span class="hlt">moisture</span> advection from the Gulf of Mexico is important in determining where persistent <span class="hlt">soil</span> <span class="hlt">moisture</span> deficits can be maintained. In seasonal cycle simulations, it lock longer for an initially unequilibrated atmosphere to respond to the imposed <span class="hlt">soil</span> <span class="hlt">moisture</span> anomaly, via <span class="hlt">moisture</span> transport from the Gulf of Mexico, than when initially the atmosphere was in equilibrium with the imposed anomaly., i.e., the initial state was obtained from the appropriate perpetual July simulation. The results demonstrate the important role of <span class="hlt">soil</span> <span class="hlt">moisture</span> in prolonging and/or amplifying North American summertime drought.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014BGeo...11..259W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014BGeo...11..259W"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> modifies the response of <span class="hlt">soil</span> respiration to temperature in a desert shrub ecosystem</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, B.; Zha, T. S.; Jia, X.; Wu, B.; Zhang, Y. Q.; Qin, S. G.</p> <p>2014-01-01</p> <p>The current understanding of the responses of <span class="hlt">soil</span> respiration (Rs) to <span class="hlt">soil</span> temperature (Ts) and <span class="hlt">soil</span> <span class="hlt">moisture</span> is limited for desert ecosystems. <span class="hlt">Soil</span> CO2 efflux from a desert shrub ecosystem was measured continuously with automated chambers in Ningxia, northwest China, from June to October 2012. The diurnal responses of Rs to Ts were <span class="hlt">affected</span> by <span class="hlt">soil</span> <span class="hlt">moisture</span>. The diel variation in Rs was strongly related to Ts at 10 cm depth under moderate and high volumetric <span class="hlt">soil</span> water content (VWC), unlike under low VWC. Ts typically lagged Rs by 3-4 h. However, the lag time varied in relation to VWC, showing increased lag times under low VWC. Over the seasonal cycle, daily mean Rs was correlated positively with Ts, if VWC was higher than 0.08 m3 m-3. Under lower VWC, it became decoupled from Ts. The annual temperature sensitivity of Rs (Q10) was 1.5. The short-term sensitivity of Rs to Ts varied significantly over the seasonal cycle, and correlated negatively with Ts and positively with VWC. Our results highlight the biological causes of diel hysteresis between Rs and Ts, and that the response of Rs to <span class="hlt">soil</span> <span class="hlt">moisture</span> may result in negative feedback to climate warming in desert ecosystems. Thus, global carbon cycle models should account the interactive effects of Ts and VWC on Rs in desert ecosystems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=272272','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=272272"><span>Upscaling sparse ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> observations for the validation of satellite surface <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The contrast between the point-scale nature of current ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> instrumentation and the footprint resolution (typically >100 square kilometers) of satellites used to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> poses a significant challenge for the validation of data products from satellite missions suc...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMEP43E..05X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMEP43E..05X"><span>Making <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensors Better for Hydroclimatic Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, C.; Zhang, K.; Hasan, E.; Hong, Y.</p> <p>2016-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> (SM) is key to understanding the flows of water and heat energy between the surface and atmosphere that impact weather and climate. The recent advances in remote sensing sensors, remarkably passive microwave, have provided significant information on <span class="hlt">soil</span> water content and, if augmented with existing <span class="hlt">soil</span> and other geographic information, such as terrain elevation and slope, may provide accurate data on <span class="hlt">soil</span> water content. NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission is an orbiting observatory that measures the top 5 cm of SM everywhere on Earth's surface over a three-year period, every 2-3 days. ESA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission is partly dedicated to making global observations of SM. JAXA's Advanced Microwave Scanning Radiometer (AMSR)-2 measures weak microwave emissions from the surface and the atmosphere of the Earth and offers a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. Factors such as vegetation cover, <span class="hlt">soil</span> properties (density and texture), and surface roughness may <span class="hlt">affect</span> the accuracy of remotely-sensed SM. Therefore, it is critical to compare the remotely-sensed SM data with in situ observations for calibration. The Oklahoma Mesonet monitors a wealth of atmospheric and hydrologic variables including solar radiation, humidity, temperature, wind speed and direction, and SM to aid in operational weather forecasting and environmental research across the state. The objective of this study is to evaluate the potential utility of the SM data retrieved from remote sensing techniques (SMAP, SMOS, and AMSR-2) by comparing them to Oklahoma Mesonet data. The correlation between the remotely-sensed SM data and daily Mesonet SM observations from the top 5, 25, and 60 cm of <span class="hlt">soil</span> are determined for each site. This work is aimed at assessing the effectiveness of remotely-sensed data at observing hydro-climatological phenomena, calibrating the error in remote sensing observations, and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AdWR..113...23L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AdWR..113...23L"><span>The impact of fog on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in the Namib Desert</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Vogt, Roland; Li, Lin; K. Seely, Mary</p> <p>2018-03-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a crucial component supporting vegetation dynamics in drylands. Despite increasing attention on fog in dryland ecosystems, the statistical characterization of fog distribution and how fog <span class="hlt">affects</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics have not been seen in literature. To this end, daily fog records over two years (Dec 1, 2014-Nov 1, 2016) from three sites within the Namib Desert were used to characterize fog distribution. Two sites were located within the Gobabeb Research and Training Center vicinity, the gravel plains and the sand dunes. The third site was located at the gravel plains, Kleinberg. A subset of the fog data during rainless period was used to investigate the effect of fog on <span class="hlt">soil</span> <span class="hlt">moisture</span>. A stochastic modeling framework was used to simulate the effect of fog on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Our results showed that fog distribution can be characterized by a Poisson process with two parameters (arrival rate λ and average depth α (mm)). Fog and <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from eighty (Aug 19, 2015-Nov 6, 2015) rainless days indicated a moderate positive relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and fog in the Gobabeb gravel plains, a weaker relationship in the Gobabeb sand dunes while no relationship was observed at the Kleinberg site. The modeling results suggested that mean and major peaks of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics can be captured by the fog modeling. Our field observations demonstrated the effects of fog on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics during rainless periods at some locations, which has important implications on <span class="hlt">soil</span> biogeochemical processes. The statistical characterization and modeling of fog distribution are of great value to predict fog distribution and investigate the effects of potential changes in fog distribution on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/1415421-manipulative-experiments-demonstrate-how-long-term-soil-moisture-changes-alter-controls-plant-water-use','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1415421-manipulative-experiments-demonstrate-how-long-term-soil-moisture-changes-alter-controls-plant-water-use"><span>Manipulative experiments demonstrate how long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> changes alter controls of plant water use</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Grossiord, Charlotte; Sevanto, Sanna Annika; Limousin, Jean -Marc</p> <p>2017-12-14</p> <p>Tree transpiration depends on biotic and abiotic factors that might change in the future, including precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> status. Although short-term sap flux responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> and evaporative demand have been the subject of attention before, the relative sensitivity of sap flux to these two factors under long-term changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions has rarely been determined experimentally. We tested how long-term artificial change in <span class="hlt">soil</span> <span class="hlt">moisture</span> <span class="hlt">affects</span> the sensitivity of tree-level sap flux to daily atmospheric vapor pressure deficit ( VPD) and <span class="hlt">soil</span> <span class="hlt">moisture</span> variations, and the generality of these effects across forest types and environments usingmore » four manipulative sites in mature forests. Exposure to relatively long-term (two to six years) <span class="hlt">soil</span> <span class="hlt">moisture</span> reduction decreases tree sap flux sensitivity to daily VPD and relative extractable water ( REW) variations, leading to lower sap flux even under high <span class="hlt">soil</span> <span class="hlt">moisture</span> and optimal VPD. Inversely, trees subjected to long-term irrigation showed a significant increase in their sensitivity to daily VPD and REW, but only at the most water-limited site. The ratio between the relative change in <span class="hlt">soil</span> <span class="hlt">moisture</span> manipulation and the relative change in sap flux sensitivity to VPD and REW variations was similar across sites suggesting common adjustment mechanisms to long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> status across environments for evergreen tree species. Altogether, our results show that long-term changes in <span class="hlt">soil</span> water availability, and subsequent adjustments to these novel conditions, could play a critical and increasingly important role in controlling forest water use in the future.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFMIN33D1066L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFMIN33D1066L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Performance Prediction for the NPOESS Microwave Imager/Sounder (MIS)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, L.; McWilliams, G.</p> <p>2009-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key environmental variable in the global water, energy and carbon cycles and in environmental assessment and prediction. It greatly <span class="hlt">affects</span> a broad range of scientific and operational applications in hydrology, climate studies and agriculture. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is also a desired input parameter to Numerical Weather Prediction (NWP) models since it controls the land-atmosphere interaction, such as dust emission and heating/moistening of the lower atmosphere. It is also a critical battlespace environment variable <span class="hlt">affecting</span> military operations. The <span class="hlt">soil</span> <span class="hlt">moisture</span> content is critically related to trafficability as well as being a vital determinant of thermal and electromagnetic signatures that are vital to the operational ground mission in C4ISR (command, control, communications, computers, intelligence, surveillance, and reconnaissance). The National Polar-orbiting Operational Environmental Satellite System’s (NPOESS) Microwave Imager/Sounder (MIS) instrument is in development, with <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing depth as one of the two Key Performance Parameters (KPPs). The other one is ocean surface wind speed precision. Based on the current design, the MIS sensor shares many channel configurations similar to the WindSat instrument, which provides an opportunity to predict MIS <span class="hlt">soil</span> <span class="hlt">moisture</span> performance using WindSat data. The WindSat land surface algorithm is a physically-based algorithm used to retrieve simultaneously the <span class="hlt">soil</span> <span class="hlt">moisture</span>, land surface temperature and vegetation water content for a range of surface types except for snow, frozen, rainy and flood surfaces. The algorithm has been rigorously validated against global in-situ data and has demonstrated great science potential in study of <span class="hlt">soil</span> <span class="hlt">moisture</span> response to precipitation, ITCZ (Intertropical Convergence Zone) propagation, drought detection, and heat wave evolution. The evaluation results suggest that the WindSat data products meet IORD II threshold <span class="hlt">soil</span> <span class="hlt">moisture</span> requirements. To approximate MIS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ISPAn.II8...97G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ISPAn.II8...97G"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> estimation by ANN using Bistatic Scatterometer data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gupta, D. K.; Kumar, P.; Mishra, V. N.; Prasad, R.</p> <p>2014-11-01</p> <p>The microwave response of bare <span class="hlt">soil</span> surfaces is influenced by a variety of parameters such as surface roughness, vegetation density, <span class="hlt">soil</span> texture and <span class="hlt">soil</span> <span class="hlt">moisture</span>. It makes the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation process more complex. In such condition, the estimation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> using microwave data with fast and less complex computing technique is a significant area of research today. The artificial neural network (ANN) approach has been found more potential in retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span> from microwave sensors as compared to traditional techniques. For this purpose, a back propagation artificial neural network (BPANN) based on Levenberg Marquardt (TRAINLM) algorithm was developed. The measurement of scattering coefficient was carried out over a range of incidence angle from 20° to 70° at 5° steps for both the HH- and VV- polarizations. The BPANN was trained and tested with the experimentally obtained data by using bistatic X-band scatterometer for different values of <span class="hlt">soil</span> <span class="hlt">moistures</span> (12%, 16%, 21% and 25%) at 30° incidence angle. The scattering coefficient and <span class="hlt">soil</span> <span class="hlt">moisture</span> data were interpolated into 20 data sets and these data sets were divided into training data sets (70%) and testing data sets (30%). The performance of the trained BPANN was evaluated by comparing the observed <span class="hlt">soil</span> <span class="hlt">moisture</span> and estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> by developed BPANN using a linear regression analysis (least square fitting) and performance factor Adj_R2. The values of Adj_R2 were found 0.93 and 0.94 for HH- and VV- polarization at 30° incidence angle respectively. The estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> by BPANN with Levenberg Marquardt training algorithm was found better at both HH- and VV- polarizations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/1342334-pore-scale-investigation-response-heterotrophic-respiration-moisture-conditions-heterogeneous-soils','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1342334-pore-scale-investigation-response-heterotrophic-respiration-moisture-conditions-heterogeneous-soils"><span>Pore-scale investigation on the response of heterotrophic respiration to <span class="hlt">moisture</span> conditions in heterogeneous <span class="hlt">soils</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Yan, Zhifeng; Liu, Chongxuan; Todd-Brown, Katherine E.</p> <p>2016-11-15</p> <p>The relationship between microbial respiration rate and <span class="hlt">soil</span> <span class="hlt">moisture</span> content is an important property for understanding and predicting <span class="hlt">soil</span> organic carbon degradation, CO 2 production and emission, and their subsequent effects on climate change. This paper reports a pore-scale modeling study to investigate the response of heterotrophic respiration to <span class="hlt">moisture</span> conditions in <span class="hlt">soils</span> and to evaluate various factors that <span class="hlt">affect</span> this response. X-ray computed tomography was used to derive <span class="hlt">soil</span> pore structures, which were then used for pore-scale model investigation. The pore-scale results were then averaged to calculate the effective respiration rates as a function of water content in <span class="hlt">soils</span>.more » The calculated effective respiration rate first increases and then decreases with increasing <span class="hlt">soil</span> water content, showing a maximum respiration rate at water saturation degree of 0.75 that is consistent with field and laboratory observations. The relationship between the respiration rate and <span class="hlt">moisture</span> content is <span class="hlt">affected</span> by various factors, including pore-scale organic carbon bioavailability, the rate of oxygen delivery, <span class="hlt">soil</span> pore structure and physical heterogeneity, <span class="hlt">soil</span> clay content, and microbial drought resistivity. Simulations also illustrates that a larger fraction of CO 2 produced from microbial respiration can be accumulated inside <span class="hlt">soil</span> cores under higher saturation conditions, implying that CO 2 flux measured on the top of <span class="hlt">soil</span> cores may underestimate or overestimate true <span class="hlt">soil</span> respiration rates under dynamic <span class="hlt">moisture</span> conditions. Overall, this study provides mechanistic insights into the <span class="hlt">soil</span> respiration response to the change in <span class="hlt">moisture</span> conditions, and reveals a complex relationship between heterotrophic microbial respiration rate and <span class="hlt">moisture</span> content in <span class="hlt">soils</span> that is <span class="hlt">affected</span> by various hydrological, geochemical, and biophysical factors.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19910031252&hterms=water+runoff+soil+type&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dwater%2Brunoff%2Bsoil%2Btype','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910031252&hterms=water+runoff+soil+type&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dwater%2Brunoff%2Bsoil%2Btype"><span>Remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> input to a hydrologic model</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Engman, E. T.; Kustas, W. P.; Wang, J. R.</p> <p>1989-01-01</p> <p>The possibility of using detailed spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> maps as input to a runoff model was investigated. The water balance of a small drainage basin was simulated using a simple storage model. Aircraft microwave measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> were used to construct two-dimensional maps of the spatial distribution of the <span class="hlt">soil</span> <span class="hlt">moisture</span>. Data from overflights on different dates provided the temporal changes resulting from <span class="hlt">soil</span> drainage and evapotranspiration. The study site and data collection are described, and the <span class="hlt">soil</span> measurement data are given. The model selection is discussed, and the simulation results are summarized. It is concluded that a time series of <span class="hlt">soil</span> <span class="hlt">moisture</span> is a valuable new type of data for verifying model performance and for updating and correcting simulated streamflow.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.9762M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.9762M"><span>Value of Available Global <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products for Agricultural Monitoring</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mladenova, Iliana; Bolten, John; Crow, Wade; de Jeu, Richard</p> <p>2016-04-01</p> <p>The first operationally derived and publicly distributed global <span class="hlt">soil</span> moil <span class="hlt">moisture</span> product was initiated with the launch of the Advanced Scanning Microwave Mission on the NASA's Earth Observing System Aqua satellite (AMSR-E). AMSR-E failed in late 2011, but its legacy is continued by AMSR2, launched in 2012 on the JAXA Global Change Observation Mission-Water (GCOM-W) mission. AMSR is a multi-frequency dual-polarization instrument, where the lowest two frequencies (C- and X-band) were used for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval. Theoretical research and small-/field-scale airborne campaigns, however, have demonstrated that <span class="hlt">soil</span> <span class="hlt">moisture</span> would be best monitored using L-band-based observations. This consequently led to the development and launch of the first L-band-based mission-the ESA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity (SMOS) mission (2009). In early 2015 NASA launched the second L-band-based mission, the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP). These satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> products have been demonstrated to be invaluable sources of information for mapping water stress areas, crop monitoring and yield forecasting. Thus, a number of agricultural agencies routinely utilize and rely on global <span class="hlt">soil</span> <span class="hlt">moisture</span> products for improving their decision making activities, determining global crop production and crop prices, identifying food restricted areas, etc. The basic premise of applying <span class="hlt">soil</span> <span class="hlt">moisture</span> observations for vegetation monitoring is that the change in <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions will precede the change in vegetation status, suggesting that <span class="hlt">soil</span> <span class="hlt">moisture</span> can be used as an early indicator of expected crop condition change. Here this relationship was evaluated across multiple microwave frequencies by examining the lag rank cross-correlation coefficient between the <span class="hlt">soil</span> <span class="hlt">moisture</span> observations and the Normalized Difference Vegetation Index (NDVI). A main goal of our analysis is to evaluate and inter-compare the value of the different <span class="hlt">soil</span> <span class="hlt">moisture</span> products derived using L-band (SMOS</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830006286','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830006286"><span>Investigation of remote sensing techniques of measuring <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Newton, R. W. (Principal Investigator); Blanchard, A. J.; Nieber, J. L.; Lascano, R.; Tsang, L.; Vanbavel, C. H. M.</p> <p>1981-01-01</p> <p>Major activities described include development and evaluation of theoretical models that describe both active and passive microwave sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>, the evaluation of these models for their applicability, the execution of a controlled field experiment during which passive microwave measurements were acquired to validate these models, and evaluation of previously acquired aircraft microwave measurements. The development of a root zone <span class="hlt">soil</span> water and <span class="hlt">soil</span> temperature profile model and the calibration and evaluation of gamma ray attenuation probes for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles are considered. The analysis of spatial variability of <span class="hlt">soil</span> information as related to remote sensing is discussed as well as the implementation of an instrumented field site for acquisition of <span class="hlt">soil</span> <span class="hlt">moisture</span> and meteorologic information for use in validating the <span class="hlt">soil</span> water profile and <span class="hlt">soil</span> temperature profile models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900028789&hterms=soil+Mexico&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsoil%2BMexico','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900028789&hterms=soil+Mexico&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsoil%2BMexico"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and the persistence of North American drought</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oglesby, Robert J.; Erickson, David J., III</p> <p>1989-01-01</p> <p>Numerical sensitivity experiments on the effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on North American summertime climate are performed using a 12-layer global atmospheric general circulation model. Consideration is given to the hypothesis that reduced <span class="hlt">soil</span> <span class="hlt">moisture</span> may induce and amplify warm, dry summers of midlatitude continental interiors. The simulations resemble the conditions of the summer of 1988, including an extensive drought over much of North America. It is found that a reduction in <span class="hlt">soil</span> <span class="hlt">moisture</span> leads to an increase in surface temperature, lower surface pressure, increased ridging aloft, and a northward shift of the jet stream. It is shown that low-level <span class="hlt">moisture</span> advection from the Gulf of Mexico is important in the maintenance of persistent <span class="hlt">soil</span> <span class="hlt">moisture</span> deficits.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1210153G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1210153G"><span>Multivariate analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> and runoff dynamics for better understanding of catchment <span class="hlt">moisture</span> state</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Graeff, Thomas; Bronstert, Axel; Cunha Costa, Alexandre; Zehe, Erwin</p> <p>2010-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key state that controls runoff formation, infiltration and portioning of radiation into latent and sensible heat flux. The experimental characterisation of near surface <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns and their controls on runoff formation is, however, still largely untapped. Using an intelligent sampling strategy of two TDR clusters installed in the head water of the Wilde Weißeritz catchment (Eastern Ore Mountains, Germany), we investigated how well "the catchment state" may be characterised by means of distributed <span class="hlt">soil</span> <span class="hlt">moisture</span> data observed at the field scale. A grassland site and a forested site both located on gentle slopes were instrumented with two Spatial TDR clusters (STDR) that consist of 39 and 32 coated TDR probes of 60 cm length. The interplay of <span class="hlt">soil</span> <span class="hlt">moisture</span> and runoff formation was interrogated using discharge data from three nested catchments: the Becherbach with a size of 2 km², the Rehefeld catchment (17 km²) and the superordinate Ammelsdorf catchment (49 km²). Multiple regression analysis and information theory including observations of groundwater levels, <span class="hlt">soil</span> <span class="hlt">moisture</span> and rainfall intensity were employed to predict stream flow. On the small scale we found a strong correlation between the average <span class="hlt">soil</span> <span class="hlt">moisture</span> and the runoff coefficients of rainfall-runoff events, which almost explains as much variability as the pre-event runoff. There was, furthermore, a strong correlation between surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and subsurface wetness. With increasing catchment size, the explanatory power of <span class="hlt">soil</span> <span class="hlt">moisture</span> reduced, but it was still in a good accordance to the former results. Combining those results with a recession analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> and discharge we derived a first conceptual model of the dominant runoff mechanisms operating in these catchments, namely subsurface flow, but also by groundwater. The multivariate analysis indicated that the proposed sampling strategy of clustering TDR probes in typical functional units is a promising</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060050765&hterms=Soil+contamination&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DSoil%2Bcontamination','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060050765&hterms=Soil+contamination&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DSoil%2Bcontamination"><span>A Time Series Approach for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kim, Yunjin; vanZyl, Jakob</p> <p>2006-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key parameter in understanding the global water cycle and in predicting natural hazards. Polarimetric radar measurements have been used for estimating <span class="hlt">soil</span> <span class="hlt">moisture</span> of bare surfaces. In order to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> accurately, the surface roughness effect must be compensated properly. In addition, these algorithms will not produce accurate results for vegetated surfaces. It is difficult to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> of a vegetated surface since the radar backscattering cross section is sensitive to the vegetation structure and environmental conditions such as the ground slope. Therefore, it is necessary to develop a method to estimate the effect of the surface roughness and vegetation reliably. One way to remove the roughness effect and the vegetation contamination is to take advantage of the temporal variation of <span class="hlt">soil</span> <span class="hlt">moisture</span>. In order to understand the global hydrologic cycle, it is desirable to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> with one- to two-days revisit. Using these frequent measurements, a time series approach can be implemented to improve the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19780009499','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19780009499"><span>Microwave remote sensing and its application to <span class="hlt">soil</span> <span class="hlt">moisture</span> detection</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Newton, R. W. (Principal Investigator)</p> <p>1977-01-01</p> <p>The author has identified the following significant results. Experimental measurements were utilized to demonstrate a procedure for estimating <span class="hlt">soil</span> <span class="hlt">moisture</span>, using a passive microwave sensor. The investigation showed that 1.4 GHz and 10.6 GHz can be used to estimate the average <span class="hlt">soil</span> <span class="hlt">moisture</span> within two depths; however, it appeared that a frequency less than 10.6 GHz would be preferable for the surface measurement. Average <span class="hlt">soil</span> <span class="hlt">moisture</span> within two depths would provide information on the slope of the <span class="hlt">soil</span> <span class="hlt">moisture</span> gradient near the surface. Measurements showed that a uniform surface roughness similar to flat tilled fields reduced the sensitivity of the microwave emission to <span class="hlt">soil</span> <span class="hlt">moisture</span> changes. Assuming that the surface roughness was known, the approximate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation accuracy at 1.4 GHz calculated for a 25% average <span class="hlt">soil</span> <span class="hlt">moisture</span> and an 80% degree of confidence, was +3% and -6% for a smooth bare surface, +4% and -5% for a medium rough surface, and +5.5% and -6% for a rough surface.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H43G1553S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H43G1553S"><span>Retrieving pace in vegetation growth using precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohoulande Djebou, D. C.; Singh, V. P.</p> <p>2013-12-01</p> <p>The complexity of interactions between the biophysical components of the watershed increases the challenge of understanding water budget. Hence, the perspicacity of the continuum <span class="hlt">soil</span>-vegetation-atmosphere's functionality still remains crucial for science. This study targeted the Texas Gulf watershed and evaluated the behavior of vegetation covers by coupling precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns. Growing season's Normalized Differential Vegetation Index NDVI for deciduous forest and grassland were used over a 23 year period as well as precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The role of time scales on vegetation dynamics analysis was appraised using both entropy rescaling and correlation analysis. This resulted in that <span class="hlt">soil</span> <span class="hlt">moisture</span> at 5 cm and 25cm are potentially more efficient to use for vegetation dynamics monitoring at finer time scale compared to precipitation. Albeit <span class="hlt">soil</span> <span class="hlt">moisture</span> at 5 cm and 25 cm series are highly correlated (R2>0.64), it appeared that 5 cm <span class="hlt">soil</span> <span class="hlt">moisture</span> series can better explain the variability of vegetation growth. A logarithmic transformation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation data increased correlation with NDVI for the different time scales considered. Based on a monthly time scale we came out with a relationship between vegetation index and the couple <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation [NDVI=a*Log(% <span class="hlt">soil</span> <span class="hlt">moisture</span>)+b*Log(Precipitation)+c] with R2>0.25 for each vegetation type. Further, we proposed to assess vegetation green-up using logistic regression model and transinformation entropy using the couple <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation as independent variables and vegetation growth metrics (NDVI, NDVI ratio, NDVI slope) as the dependent variable. The study is still ongoing and the results will surely contribute to the knowledge in large scale vegetation monitoring. Keywords: Precipitation, <span class="hlt">soil</span> <span class="hlt">moisture</span>, vegetation growth, entropy Time scale, Logarithmic transformation and correlation between <span class="hlt">soil</span> <span class="hlt">moisture</span> and NDVI, precipitation and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25386626','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25386626"><span>The effect of row structure on <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval accuracy from passive microwave data.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Xingming, Zheng; Kai, Zhao; Yangyang, Li; Jianhua, Ren; Yanling, Ding</p> <p>2014-01-01</p> <p>Row structure causes the anisotropy of microwave brightness temperature (TB) of <span class="hlt">soil</span> surface, and it also can <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval accuracy when its influence is ignored in the inversion model. To study the effect of typical row structure on the retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span> and evaluate if there is a need to introduce this effect into the inversion model, two ground-based experiments were carried out in 2011. Based on the observed C-band TB, field <span class="hlt">soil</span> and vegetation parameters, row structure rough surface assumption (Q p model and discrete model), including the effect of row structure, and flat rough surface assumption (Q p model), ignoring the effect of row structure, are used to model microwave TB of <span class="hlt">soil</span> surface. Then, <span class="hlt">soil</span> <span class="hlt">moisture</span> can be retrieved, respectively, by minimizing the difference of the measured and modeled TB. The results show that <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval accuracy based on the row structure rough surface assumption is approximately 0.02 cm(3)/cm(3) better than the flat rough surface assumption for vegetated <span class="hlt">soil</span>, as well as 0.015 cm(3)/cm(3) better for bare and wet <span class="hlt">soil</span>. This result indicates that the effect of row structure cannot be ignored for accurately retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span> of farmland surface when C-band is used.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.5589S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.5589S"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurement System For An Improved Flood Warning</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schaedel, W.; Becker, R.</p> <p></p> <p>Precipitation-runoff processes are correlated with the catchment's hydrological pre- conditions that are taken into account in some hydrological models, e.g. by pre- precipitation index. This statistically generated variable is unsuitable in case of ex- treme flood events. Thus a non-statistical estimation of the catchment's preconditions is of tremendous importance for an improvement in reliability of flood warning. This can be achieved by persistent operational observation of the catchment's <span class="hlt">soil</span> mois- ture condition. The <span class="hlt">soil</span> <span class="hlt">moisture</span> acts as a state variable controlling the risk of surface runoff, which is assumed to provoke critical floods. Critical <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions can be identified by measurements in certain areas representative for the catchment. Therefore a measurement arrangement that does not effect the structure of <span class="hlt">soils</span> is realised with twin rod probes. Spatial resolution algorithms result in <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles along the probe rods. In this set up a quasi three dimensional <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution can be interpolated with point measurements of up to 47 twin rod probes per cluster, connected via multiplexer. The large number of probes per cluster is of use for detailed observation of small-scaled <span class="hlt">moisture</span> variability. As regionalized grid cell <span class="hlt">moisture</span> the cluster information calibrates the default, state depending <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution of the catchment. This distribution is explained by diverse <span class="hlt">soil</span> <span class="hlt">moisture</span> influencing properties, which are found by Landsat satellite image. Therefore the im- age is processed with principal component analysis to extract the <span class="hlt">soil</span> <span class="hlt">moisture</span> distri- bution. The distribution is calibrated by the detailed measurements, acting as ground based truth. Linear multiple regression operated on the calibrated distribution identi- fies the mentioned properties. In this fashion the catchment status can be determined and combined with precipitation forecasts, thus allowing for the comprehensive risk calculation of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EurSS..42..797Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EurSS..42..797Y"><span>Microbial destruction of chitin in <span class="hlt">soils</span> under different <span class="hlt">moisture</span> conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yaroslavtsev, A. M.; Manucharova, N. A.; Stepanov, A. L.; Zvyagintsev, D. G.; Sudnitsyn, I. I.</p> <p>2009-07-01</p> <p>The most favorable <span class="hlt">moisture</span> conditions for the microbial destruction of chitin in <span class="hlt">soils</span> are close to the total water capacity. The water content has the most pronounced effect on chitin destruction in <span class="hlt">soils</span> in comparison with other studied substrates. It was found using gas-chromatographic and luminescent-microscopic methods that the maximum specific activity of the respiration of the chitinolytic community was at a rather low redox potential with the <span class="hlt">soil</span> <span class="hlt">moisture</span> close to the total water capacity. The range of <span class="hlt">moisture</span> values under which the most intense microbial transformation of chitin occurred was wider in clayey and clay loamy <span class="hlt">soils</span> as compared with sandy ones. The increase was observed due to the contribution of mycelial bacteria and actinomycetes in the chitinolytic complex as the <span class="hlt">soil</span> <span class="hlt">moisture</span> increased.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730017711','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730017711"><span>Remote monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> using airborne microwave radiometers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kroll, C. L.</p> <p>1973-01-01</p> <p>The current status of microwave radiometry is provided. The fundamentals of the microwave radiometer are reviewed with particular reference to airborne operations, and the interpretative procedures normally used for the modeling of the apparent temperature are presented. Airborne microwave radiometer measurements were made over selected flight lines in Chickasha, Oklahoma and Weslaco, Texas. Extensive ground measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> were made in support of the aircraft mission over the two locations. In addition, laboratory determination of the complex permittivities of <span class="hlt">soil</span> samples taken from the flight lines were made with varying <span class="hlt">moisture</span> contents. The data were analyzed to determine the degree of correlation between measured apparent temperatures and <span class="hlt">soil</span> <span class="hlt">moisture</span> content.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25639106','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25639106"><span>[Response of nitrification/denitrification and their associated microbes to <span class="hlt">soil</span> <span class="hlt">moisture</span> change in paddy <span class="hlt">soil</span>].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Ruo-Xuan; He, Ji-Zheng; Zhang, Li-Mei</p> <p>2014-11-01</p> <p>To investigate the effect of <span class="hlt">moisture</span> change on nitrification and denitrification and their corresponding functional microbes, an acidic paddy <span class="hlt">soil</span> from Taoyuan, Hunan Province was selected as the study object, and <span class="hlt">soil</span> microcosm experiment containing 4 different water holding capacity (WHC) levels (30% WHC, 60% WHC, 90% WHC, and waterlog) was set up in this study. Results showed that no active nitrification and denitrification occurred in 30% WHC treatment as there were no obvious ammonia consumption and nitrate accumulation, while nitrification was active in 60% WHC and 90% WHC treatments as indicated by the obvious accumulation of nitrate in those two treatments. Meanwhile, significant ammonia consumption and N2O emission were only observed in 90% WHC treatment, implying that a much stronger nitrification in 90% WHC treatment than in 60% WHC treatment and the co-occurrence of nitrification and denitrification in 90% WHC treatment. In waterlog treatment, relatively lower N2O emission was detected and no obvious nitrification was detected, corresponding to a significant lower <span class="hlt">soil</span> Eh in this treatment than in the other three non-waterlog treatments. Except the early stage of incubation (7 d), the abundance of nirS, nirK and ammonia-oxidizing bacteria (AOB) amoA genes showed similar responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> change over time. Except the slight decrease in waterlog treatment, the abundances of the three genes increased significantly as the <span class="hlt">soil</span> <span class="hlt">moisture</span> increased, and the highest abundances of nirS, nirK, and amoA gene were observed in 90% WHC treatment in which the highest nitrification and denitrification activity was detected. T-RFLP analysis showed that the community composition of nirS gene-containing denitrifiers changed significantly in response to <span class="hlt">soil</span> <span class="hlt">moisture</span> change after two weeks, and <span class="hlt">soil</span> Eh and C(w) were the main factors <span class="hlt">affecting</span> the community composition of denitrifiers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFM.H11A..08C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFM.H11A..08C"><span>Spatial Variability and Multiscale Errors in Assimilated <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chintalapati, S.; Kumar, P.</p> <p>2003-12-01</p> <p>Coupled land-atmosphere models are increasingly focused on using assimilated near-surface <span class="hlt">soil-moisture</span> to improve the prediction of <span class="hlt">moisture</span> and heat fluxes. Given that these models have a non-linear dependence on the <span class="hlt">soil-moisture</span> state, it should be expected that errors in the observed <span class="hlt">soil-moisture</span> will propagate through the system and influence the predictions of the model. Of particular concern is the situation when model predictions are made at scales that are different from that of the observation scales of the <span class="hlt">soil-moisture</span>. In this case the transformation process of the <span class="hlt">soil-moisture</span> from one scale to another introduces additional errors that will influence the prediction. The purpose of this study is to develop an understanding of the influence of multiscale observational errors in the <span class="hlt">soil-moisture</span> on the prediction of surface fluxes. The following approach is adopted. We use the SGP'97 near-surface <span class="hlt">soil-moisture</span> observation from ESTAR images at 0.8 km (resampled to 1 km). These observations are available for 16 days during the period June 18, 1997 to July 16, 1997. Using these we obtain estimates of the near-surface <span class="hlt">soil-moisture</span> at several scales (1, 2, 4, 8, 16 and 32 km). This is accomplished through a multiscale estimation technique using a mean differenced fractal model [Kumar, 1999]. These multiscale near-surface observations are assimilated using extended Kalman filtering algorithm embedded in the NCAR-LSM (Land Surface Model). The assimilation is performed, once daily on the days when observations are available, at all scales for the entire study period to predict the <span class="hlt">soil-moisture</span> profile and the surface energy fluxes. This allows us to address two key issues: (1) assessment of how the estimation error evolves with different <span class="hlt">moisture</span> conditions as a function of scale, and (2) assessment of the spatial variability of assimilated fields at different scales. From the multiscaling results, we can observe that the estimation errors grow as the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20090038719','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20090038719"><span>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP) Mission</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Entekhabi, Dara; Nijoku, Eni G.; ONeill, Peggy E.; Kellogg, Kent H.; Crow, Wade T.; Edelstein, Wendy N.; Entin, Jared K.; Goodman, Shawn D.; Jackson, Thomas J.; Johnson, Joel; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20090038719'); toggleEditAbsImage('author_20090038719_show'); toggleEditAbsImage('author_20090038719_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20090038719_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20090038719_hide"></p> <p>2009-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP) Mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council s Decadal Survey. SMAP will make global measurements of the <span class="hlt">moisture</span> present at Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state from space will allow significantly improved estimates of water, energy and carbon transfers between land and atmosphere. <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurements are also of great importance in assessing flooding and monitoring drought. SMAP observations can help mitigate these natural hazards, resulting in potentially great economic and social benefits. SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw timing observations will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept would utilize an L-band radar and radiometer. These instruments will share a rotating 6-meter mesh reflector antenna to provide high-resolution and high-accuracy global maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state every two to three days. The SMAP instruments provide direct measurements of surface conditions. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> and estimates of land surface-atmosphere exchanges of water, energy and carbon. SMAP is scheduled for a 2014 launch date</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/43095','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/43095"><span>Core vs. Bulk Samples in <span class="hlt">Soil-Moisture</span> Tension Analyses</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Walter M. Broadfoot</p> <p>1954-01-01</p> <p>The usual laboratory procedure in determining <span class="hlt">soil-moisture</span> tension values is to use "undisturbed" <span class="hlt">soil</span> cores for tensions up to 60 cm. of water and bulk <span class="hlt">soil</span> samples for higher tensions. Low tensions are usually obtained with a tension table and the higher tensions by use of pressure plate apparatus. In tension analysis at the Vicksburg Infiltration Project...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22127183','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22127183"><span>Pupal development of Ceratitis capitata (Diptera: Tephritidae) and Diachasmimorpha longicaudata (Hymenoptera: Braconidae) at different <span class="hlt">moisture</span> values in four <span class="hlt">soil</span> types.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bento, F de M M; Marques, R N; Costa, M L Z; Walder, J M M; Silva, A P; Parra, J R P</p> <p>2010-08-01</p> <p>This study aimed to evaluate adult emergence and duration of the pupal stage of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), and emergence of the fruit fly parasitoid, Diachasmimorpha longicaudata (Ashmead), under different <span class="hlt">moisture</span> conditions in four <span class="hlt">soil</span> types, using <span class="hlt">soil</span> water matric potential. Pupal stage duration in C. capitata was influenced differently for males and females. In females, only <span class="hlt">soil</span> type <span class="hlt">affected</span> pupal stage duration, which was longer in a clay <span class="hlt">soil</span>. In males, pupal stage duration was individually influenced by <span class="hlt">moisture</span> and <span class="hlt">soil</span> type, with a reduction in pupal stage duration in a heavy clay <span class="hlt">soil</span> and in a sandy clay, with longer duration in the clay <span class="hlt">soil</span>. As matric potential decreased, duration of the pupal stage of C. capitata males increased, regardless of <span class="hlt">soil</span> type. C. capitata emergence was <span class="hlt">affected</span> by <span class="hlt">moisture</span>, regardless of <span class="hlt">soil</span> type, and was higher in drier <span class="hlt">soils</span>. The emergence of D. longicaudata adults was individually influenced by <span class="hlt">soil</span> type and <span class="hlt">moisture</span> factors, and the number of emerged D. longicaudata adults was three times higher in sandy loam and lower in a heavy clay <span class="hlt">soil</span>. Always, the number of emerged adults was higher at higher <span class="hlt">moisture</span> conditions. C. capitata and D. longicaudata pupal development was <span class="hlt">affected</span> by <span class="hlt">moisture</span> and <span class="hlt">soil</span> type, which may facilitate pest sampling and allow release areas for the parasitoid to be defined under field conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H21J..01J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H21J..01J"><span>Large Scale Field Campaign Contributions to <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Remote Sensing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jackson, T. J.</p> <p>2007-12-01</p> <p>Large-scale field experiments have been an essential component of <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing for over two decades. They have provided test beds for both the technology and science necessary to develop and refine satellite mission concepts. The high degree of spatial variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> and the relatively coarse resolution of satellite observations present significant challenges for scaling and the design of these field campaigns. Earlier experiments, in particular Washita'92, established the credibility of large scale application of microwave remote sensing. Of particular significance was the demonstration of synthetic aperture radiometry (now the core technology of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity, SMOS, mission), spatial mapping and consistent retrievals, and the temporal information content of repeat observations in deriving <span class="hlt">soil</span> hydraulic properties. Basic concepts were expanded in both space and time in experiments such as SGP97 that attempted to integrate the <span class="hlt">soil</span> <span class="hlt">moisture</span> observations in the broader framework of land surface hydrology. These types of campaigns have expanded globally. Within the U.S. in recent years they have focused on two issues; establishing the foundations of a future active-passive <span class="hlt">soil</span> <span class="hlt">moisture</span> satellite mission and the development and validation of current satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms. Experiments involving a range of spatial domains (point, field, region) and geographical domains have contributed to establishing scaling relationships for both measurements and processes. Future experiments must continue to address the needs of planned <span class="hlt">soil</span> <span class="hlt">moisture</span> missions such as SMOS and Aquarius. As the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission concept continues to develop, there will be an increasing need to test and refine algorithms and in the post-launch time frame, there will be a need for validation products. These needs must be integrated with broader science objectives (to the degree it is possible) in order to secure the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820016659','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820016659"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> variation patterns observed in Hand County, South Dakota</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jones, E. B.; Owe, M.; Schmugge, T. J. (Principal Investigator)</p> <p>1981-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> data were taken during 1976 (April, June, October), 1977 (April, May, June), and 1978 (May, June, July) Hand County, South Dakota as part of the ground truth used in NASA's aircraft experiments to study the use of microwave radiometers for the remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The spatial variability observed on the ground during each of the sampling events was studied. The data reported are the mean gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> contained in three surface horizon depths: 0 to 2.5, 0 to 5 and 0 to 10 cm. The overall <span class="hlt">moisture</span> levels ranged from extremely dry conditions in June 1976 to very wet in May 1978, with a relatively even distribution of values within that range. It is indicated that well drained sites have to be partitioned from imperfectly drained areas when attempting to characterize the general <span class="hlt">moisture</span> profile throughout an area of varying <span class="hlt">soil</span> and cover type conditions. It is also found that the variability in <span class="hlt">moisture</span> content is greatest in the 0 to 2.5 cm measurements and decreases as the measurements are integrated over a greater depth. It is also determined that the sampling intensity of 10 measurements per km is adequate to estimate the mean <span class="hlt">moisture</span> with an uncertainty of + or - 3 percent under average <span class="hlt">moisture</span> conditions in areas of moderate to good drainage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/10610','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/10610"><span>Light, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and tree reproduction in hardwood forest openings.</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Leon S. Minckler; John D. Woerheide; Richard C. Schlesinger</p> <p>1973-01-01</p> <p>Light, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and tree reproduction were measured at five positions in six openings on each of three aspects in southern Illinois. Amount of light received was clearly related to position in the light openings, opening size, and aspect. More <span class="hlt">moisture</span> was available in the centers of the openings, although 4 years after openings were made the differences...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/7767','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/7767"><span>Effects of neutron source type on <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Irving Goldberg; Norman A. MacGillivray; Robert R. Ziemer</p> <p>1967-01-01</p> <p>A number of radioisotopes have recently become commercially available as alternatives to radium-225 in <span class="hlt">moisture</span> gauging devices using alpha-neutron sources for determining <span class="hlt">soil</span> <span class="hlt">moisture</span>, for well logging, and for other industrial applications in which hydrogenous materials are measured.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.B43E..06C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.B43E..06C"><span><span class="hlt">Moisture</span> Controls on <span class="hlt">Soil</span> Respiration Sources in a Pine Forest on Santa Cruz Island, CA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Carbone, M. S.; Ambrose, A.; Boot, C. M.; Dawson, T. E.; Schaeffer, S. M.; Schimel, J.; Still, C. J.; Williams, P.</p> <p>2009-12-01</p> <p>This presentation will include results from a study in a coastal pine forest/chaparral ecosystem on Santa Cruz Island, California characterized by a Mediterranean type climate. However, in this coastal ecosystem, fog and low-level clouds enhance summertime <span class="hlt">moisture</span> availability through shading and fog-drip. Thus, the summer has many small fog-drip events, while the winter has fewer, larger rain events. Not surprisingly, <span class="hlt">moisture</span> and <span class="hlt">moisture</span> pulses drive carbon fluxes and dynamics in this ecosystem. We used automated measurements of <span class="hlt">soil</span> respiration, <span class="hlt">soil</span> pore space CO2 profiles, pine sap flux, as well as targeted measurements of <span class="hlt">soil</span> nutrient/microbial dynamics, radiocarbon <span class="hlt">soil</span> respiration source partitioning, and manipulations of water to assess how <span class="hlt">moisture/moisture</span> pulses influence ecosystem metabolism over diel, episodic (<span class="hlt">moisture</span> pulse), and seasonal time scales. On the diel time scale, the fraction of <span class="hlt">soil</span> respiration from autotrophic sources was generally greater during the day than at night. Seasonally, changes in <span class="hlt">soil</span> respiration fluxes were largely determined by variation in autotrophic respiration, with autotrophic rates more than doubling in the winter versus summer. Pine sap flux responded to winter rain events but not to summer fog-drip, suggesting that summer fog-water inputs are insufficient to significantly <span class="hlt">affect</span> belowground pine activity. In contrast, summertime fog-drip and cloud shading enhanced <span class="hlt">soil</span> respiration primarily through microbial decomposition, by reducing microbial drought stress in surface <span class="hlt">soil</span>/litter. Finally, <span class="hlt">moisture</span> manipulations in the laboratory showed more <span class="hlt">soil</span> carbon released following one large pulse (rain event) versus multiple smaller pulses (fog events) of <span class="hlt">moisture</span>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H51I1508E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H51I1508E"><span>Automated Quality Control of in Situ <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from the North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database Using NLDAS-2 Products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ek, M. B.; Xia, Y.; Ford, T.; Wu, Y.; Quiring, S. M.</p> <p>2015-12-01</p> <p>The North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models and validating satellite-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms. The NASMD has collected data from over 30 <span class="hlt">soil</span> <span class="hlt">moisture</span> observation networks providing millions of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations in all 50 states as well as Canada and Mexico. It is recognized that the quality of measured <span class="hlt">soil</span> <span class="hlt">moisture</span> in NASMD is highly variable due to the diversity of climatological conditions, land cover, <span class="hlt">soil</span> texture, and topographies of the stations and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy and imprecision in the data set can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure the data is of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System phase 2 (NLDAS-2) Noah <span class="hlt">soil</span> porosity, <span class="hlt">soil</span> temperature, and fraction of liquid and total <span class="hlt">soil</span> <span class="hlt">moisture</span> to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the <span class="hlt">soil</span> is partially frozen. A validation example using NLDAS-2 multiple model <span class="hlt">soil</span> <span class="hlt">moisture</span> products at the 20 cm <span class="hlt">soil</span> layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and West Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1,200 NASMD stations in the near future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27337907','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27337907"><span>[Priming Effects of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on <span class="hlt">Soil</span> Respiration Under Different Tillage Practices].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhang, Yan; Liang, Ai-zhen; Zhang, Xiao-ping; Chen, Sheng-long; Sun, Bing-jie; Liu, Si-yi</p> <p>2016-03-15</p> <p>In the early stage of an incubation experiment, <span class="hlt">soil</span> respiration has a sensitive response to different levels of <span class="hlt">soil</span> <span class="hlt">moisture</span>. To investigate the effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on <span class="hlt">soil</span> respiration under different tillage practices, we designed an incubation trial using air-dried <span class="hlt">soil</span> samples collected from tillage experiment station established on black <span class="hlt">soils</span> in 2001. The tillage experiment consisted of no-tillage (NT), ridge tillage (RT), and conventional tillage (CT). According to field capacity (water-holding capacity, WHC), we set nine <span class="hlt">moisture</span> levels including 30%, 60%, 90%, 120%, 150%, 180%, 210%, 240%, 270% WHC. During the 22-day short-term incubation, <span class="hlt">soil</span> CO₂ emission was measured. In the early stage of incubation, the priming effects occurred under all tillage practices. There were positive correlations between <span class="hlt">soil</span> respiration and <span class="hlt">soil</span> <span class="hlt">moisture</span>. In addition to drought and flood conditions, <span class="hlt">soil</span> CO₂ fluxes followed the order of NT > RT > CT. We fitted the relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> CO₂ fluxes under different tillage practices. In the range of 30%-270% WHC, <span class="hlt">soil</span> CO₂ fluxes and <span class="hlt">soil</span> <span class="hlt">moisture</span> fitted a quadratic regression equation under NT, and linear regression equations under RT and CT. Under the conditions of 30%-210% WHC of both NT and RT, <span class="hlt">soil</span> CO₂ fluxes and <span class="hlt">soil</span> <span class="hlt">moisture</span> were well fitted by the logarithmic equation with fitting coefficient R² = 0.966 and 0.956, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170007932','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170007932"><span>SMAP Level 4 Surface and Root Zone <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reichle, R.; De Lannoy, G.; Liu, Q.; Ardizzone, J.; Kimball, J.; Koster, R.</p> <p>2017-01-01</p> <p>The SMAP Level 4 <span class="hlt">soil</span> <span class="hlt">moisture</span> (L4_SM) product provides global estimates of surface and root zone <span class="hlt">soil</span> <span class="hlt">moisture</span>, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m(exp. 3)m(exp. -3), measured in terms of unbiased root-mean-square-error, for both surface and root zone <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890058149&hterms=mapping+soil&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dmapping%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890058149&hterms=mapping+soil&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dmapping%2Bsoil"><span>Mapping surface <span class="hlt">soil</span> <span class="hlt">moisture</span> with L-band radiometric measurements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, James R.; Shiue, James C.; Schmugge, Thomas J.; Engman, Edwin T.</p> <p>1989-01-01</p> <p>A NASA C-130 airborne remote sensing aircraft was used to obtain four-beam pushbroom microwave radiometric measurements over two small Kansas tall-grass prairie region watersheds, during a dry-down period after heavy rainfall in May and June, 1987. While one of the watersheds had been burned 2 months before these measurements, the other had not been burned for over a year. Surface <span class="hlt">soil-moisture</span> data were collected at the time of the aircraft measurements and correlated with the corresponding radiometric measurements, establishing a relationship for surface <span class="hlt">soil-moisture</span> mapping. Radiometric sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span> variation is higher in the burned than in the unburned watershed; surface <span class="hlt">soil</span> <span class="hlt">moisture</span> loss is also faster in the burned watershed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760020542','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760020542"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and evapotranspiration predictions using Skylab data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Myers, V. I. (Principal Investigator); Moore, D. G.; Horton, M. L.; Russell, M. J.</p> <p>1975-01-01</p> <p>The author has identified the following significant results. Multispectral reflectance and emittance data from the Skylab workshop were evaluated for prediction of evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> for an irrigated region of southern Texas. Wavelengths greater than 2.1 microns were required to spectrally distinguish between wet and dry fallow surfaces. Thermal data provided a better estimate of <span class="hlt">soil</span> <span class="hlt">moisture</span> than did data from the reflective bands. Thermal data were dependent on <span class="hlt">soil</span> <span class="hlt">moisture</span> but not on the type of agricultural land use. The emittance map, when used in conjunction with existing models, did provide an estimate of evapotranspiration rates. Surveys of areas of high <span class="hlt">soil</span> <span class="hlt">moisture</span> can be accomplished with space altitude thermal data. Thermal data will provide a reliable input into irrigation scheduling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110015242','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110015242"><span>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission: Overview</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>O'Neill, Peggy; Entekhabi, Dara; Njoku, Eni; Kellogg, Kent</p> <p>2011-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council?s Decadal Survey [1]. Its mission design consists of L-band radiometer and radar instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state every 2-3 days. The combined active/passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> product will have a spatial resolution of 10 km and a mean latency of 24 hours. In addition, the SMAP surface observations will be combined with advanced modeling and data assimilation to provide deeper root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> and net ecosystem exchange of carbon. SMAP is expected to launch in the late 2014 - early 2015 time frame.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19780021589','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19780021589"><span>Measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> trends with airborne scatterometers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blanchard, B. J. (Principal Investigator)</p> <p>1978-01-01</p> <p>The author had identified the following significant results. Repeated looks at surfaces that maintain constant roughness can provide an estimate of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the surface, when appropriate radar look angles are used. Significant influence due to differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> can be detected in the 13.3 GHz and 1.6 GHz scatterometer returns. Effects of normal crop densities have little influence on the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate, when appropriate look angles are used. It appears that different look angles are optimum for different frequencies to avoid effects from vegetation. Considering the frequency and look angles used on the Seasat-A imaging radar, differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> should produce as much as 9 db difference in return on that system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1412621C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412621C"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> monitoring in Candelaro basin, Southern Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Campana, C.; Gigante, V.; Iacobellis, V.</p> <p>2012-04-01</p> <p>The signature of the hydrologic regime can be investigated, in principle, by recognizing the main mechanisms of runoff generation that take place in the basin and <span class="hlt">affect</span> the seasonal behavior or the rainfall-driven events. In this framework, besides the implementation of hydrological models, a crucial role should be played by direct observation of key state variables such as <span class="hlt">soil</span> <span class="hlt">moisture</span> at different depths and different distances from the river network. In fact, understanding hydrological systems is often limited by the frequency and spatial distribution of observations. Experimental catchments, which are field laboratories with long-term measurements of hydrological variables, are not only sources of data but also sources of knowledge. Wireless distributed sensing platforms are a key technology to address the need for overcoming field limitations such as conflicts between <span class="hlt">soil</span> use and cable connections. A stand-alone wireless network system has been installed for continuous monitoring of <span class="hlt">soil</span> water contents at multiple depths along a transect located in Celone basin (sub-basin of Candelaro basin in Puglia, Southern Italy). The transect consists of five verticals, each one having three <span class="hlt">soil</span> water content sensors at multiple depths: 0,05 m, 0,6 m and 1,2 m below the ground level. The total length of the transect is 307 m and the average distance between the verticals is 77 m. The main elements of the instrumental system installed are: fifteen Decagon 10HS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensors, five Decagon Em50R Wireless Radio Data Loggers, one Rain gauge, one Decagon Data Station and one Campbell CR1000 Data Logger. Main advantages of the system as described and presented in this work are that installation of the wireless network system is fast and easy to use, data retrieval and monitoring information over large spatial scales can be obtained in (near) real-time mode and finally other type of sensors can be connected to the system, also offering wide potentials for future</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..546..393B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..546..393B"><span>Predicting root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> with <span class="hlt">soil</span> properties and satellite near-surface <span class="hlt">moisture</span> data across the conterminous United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.</p> <p>2017-03-01</p> <p>Satellite-based near-surface (0-2 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates have global coverage, but do not capture variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the root zone (up to 100 cm depth) and may be biased with respect to ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data to support the physically based <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Analytical Relationship (SMAR) infiltration model, which estimates root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> with satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> at 43 <span class="hlt">Soil</span> Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using <span class="hlt">soil</span> physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> over broad extents and has</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015SolE....6..595Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015SolE....6..595Y"><span>Responses of vertical <span class="hlt">soil</span> <span class="hlt">moisture</span> to rainfall pulses and land uses in a typical loess hilly area, China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yu, Y.; Wei, W.; Chen, L. D.; Jia, F. Y.; Yang, L.; Zhang, H. D.; Feng, T. J.</p> <p>2015-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays a key role in vegetation restoration and ecosystem stability in arid and semiarid regions. The response of <span class="hlt">soil</span> <span class="hlt">moisture</span> to rainfall pulses is an important hydrological process, which is strongly influenced by land use during the implementation of vegetation restoration. In this study, vertical <span class="hlt">soil</span> <span class="hlt">moisture</span> variations of woodland (Pinus tabulaeformis), native grassland (Stipa bungeana), shrubland (Hippophea rhamnoides), cropland (Triticum aestivum) and artificial grassland (Onobrychis viciaefolia) in five <span class="hlt">soil</span> profiles were monitored in a typical loess hilly area during the 2010 growing season. The results demonstrated that rainfall pulses directly <span class="hlt">affected</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> variation. A multi-peak pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> appeared during the growing season, notably in the surface <span class="hlt">soil</span> layer. Meanwhile, the response of each vegetation type to rainfall was inconsistent, and a time-lag effect before reaching the peak value was detected, following each heavy rainfall event. The response duration of <span class="hlt">soil</span> <span class="hlt">moisture</span>, however, varied markedly with the size of rainfall events. Furthermore, higher <span class="hlt">soil</span> water content was detected in grassland and shrubland. Woodland was characterized by relatively lower <span class="hlt">soil</span> <span class="hlt">moisture</span> values throughout the investigation period. Our research suggests that vegetation restoration efforts should give priority to grassland and shrubland at the research site. We suggest that more studies should be focused on the characteristics of community structure and spatial vegetation distribution on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics, particularly within the grass and shrub ecosystems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H42A..06O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H42A..06O"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Dynamics under Corn, Soybean, and Perennial Kura Clover</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ochsner, T.; Venterea, R. T.</p> <p>2009-12-01</p> <p>Rising global food and energy consumption call for increased agricultural production, whereas rising concerns for environmental quality call for farming systems with more favorable environmental impacts. Improved understanding and management of plant-<span class="hlt">soil</span> water interactions are central to meeting these twin challenges. The objective of this research was to compare the temporal dynamics of <span class="hlt">soil</span> <span class="hlt">moisture</span> under contrasting cropping systems suited for the Midwestern region of the United States. Precipitation, infiltration, drainage, evapotranspiration, <span class="hlt">soil</span> water storage, and freeze/thaw processes were measured hourly for three years in field plots of continuous corn (Zea mays L.), corn/soybean [Glycine max (L.) Merr.] rotation, and perennial kura clover (Trifolium ambiguum M. Bieb.) in southeastern Minnesota. The evapotranspiration from the perennial clover most closely followed the temporal dynamics of precipitation, resulting in deep drainage which was reduced up to 50% relative to the annual crops. <span class="hlt">Soil</span> <span class="hlt">moisture</span> utilization also continued later into the fall under the clover than under the annual crops. In the annual cropping systems, crop sequence influenced the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Soybean following corn and continuous corn exhibited evapotranspiration which was 80 mm less than and deep drainage which was 80 mm greater than that of corn following soybean. These differences occurred primarily during the spring and were associated with differences in early season plant growth between the systems. In the summer, <span class="hlt">soil</span> <span class="hlt">moisture</span> depletion was up to 30 mm greater under corn than soybean. Crop residue also played an important role in the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Higher amounts of residue were associated with reduced <span class="hlt">soil</span> freezing. This presentation will highlight key aspects of the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics for these contrasting cropping systems across temporal scales ranging from hours to years. The links between <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics, crop yields, and nutrient leaching</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..516....6R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..516....6R"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> at local scale: Measurements and simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Romano, Nunzio</p> <p>2014-08-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> refers to the water present in the uppermost part of a field <span class="hlt">soil</span> and is a state variable controlling a wide array of ecological, hydrological, geotechnical, and meteorological processes. The literature on <span class="hlt">soil</span> <span class="hlt">moisture</span> is very extensive and is developing so rapidly that it might be considered ambitious to seek to present the state of the art concerning research into this key variable. Even when covering investigations about only one aspect of the problem, there is a risk of some inevitable omission. A specific feature of the present essay, which may make this overview if not comprehensive at least of particular interest, is that the reader is guided through the various traditional and more up-to-date methods by the central thread of techniques developed to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> interwoven with applications of modeling tools that exploit the observed datasets. This paper restricts its analysis to the evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the local (spatial) scale. Though a somewhat loosely defined term, it is linked here to a characteristic length of the <span class="hlt">soil</span> volume investigated by the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing probe. After presenting the most common concepts and definitions about the amount of water stored in a certain volume of <span class="hlt">soil</span> close to the land surface, this paper proceeds to review ground-based methods for monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span> and evaluates modeling tools for the analysis of the gathered information in various applications. Concluding remarks address questions of monitoring and modeling of <span class="hlt">soil</span> <span class="hlt">moisture</span> at scales larger than the local scale with the related issue of data aggregation. An extensive, but not exhaustive, list of references is provided, enabling the reader to gain further insights into this subject.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H21C1391H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H21C1391H"><span>Seasonal dynamics of tree species specific <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heidbuechel, I.; Blume, T.; Guntner, A.; Dreibrodt, J.; Simard, S.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> patterns in the landscape are largely controlled by <span class="hlt">soil</span> types (pore size distributions), landscape position and precipitation events. But how strong is the influence of vegetation on patterns within a single <span class="hlt">soil</span> type? While we would envision a clear difference in <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns and responses between for example bare <span class="hlt">soil</span>, a pasture and a forest, our conceptual images start to become less clear when we compare different forest stands. Do different tree species cause species specific <span class="hlt">moisture</span> patterns to emerge? Do these patterns change with the seasons? To investigate this question we analyzed data from 15 sensor clusters in the lowlands of north-eastern Germany (within the TERENO observatory) which were instrumented with <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors (5 profiles per site), tensiometers, sap flow sensors, throughfall and stemflow gauges. Data has been collected at these sites since May 2014. While the <span class="hlt">soils</span> under beech trees were more often relatively wet and more often relatively dry, the <span class="hlt">soils</span> under pine trees showed less variability and more often average <span class="hlt">soil</span> <span class="hlt">moisture</span>. These differences could be explained by differences in the complex interactions between throughfall and stemflow on the one hand as well as root water uptake and sap flow patterns on the other hand. Further analysis will explore hydraulic redistribution between <span class="hlt">soil</span> layers and hydraulic lift of groundwater (using root zone water balance methods and stable water isotope samples that were taken at different depths in the <span class="hlt">soil</span>, in the groundwater and from the sapwood). The manifestation of tree species differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns and dynamics is likely to have implications for groundwater recharge, transit times and hydrologic partitioning within the critical zone.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19910037881&hterms=agriculture+soils&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dagriculture%2Bsoils','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910037881&hterms=agriculture+soils&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dagriculture%2Bsoils"><span>Progress in microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Engman, Edwin T.</p> <p>1990-01-01</p> <p>Significant progress has been made in the application of microwave remote sensing for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span>. Both passive and active systems have demonstrated the capability for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span>. However, several questions are still unresolved regarding the optimal instrument configuration and other target characteristics, such as roughness and vegetation. In addition, the most likely disciplines for using these data, agriculture and hydrology, do not currently possess adequate models or procedures for using these new data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H21O..05Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H21O..05Z"><span>Development of an Objective High Spatial Resolution <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Index</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zavodsky, B.; Case, J.; White, K.; Bell, J. R.</p> <p>2015-12-01</p> <p>Drought detection, analysis, and mitigation has become a key challenge for a diverse set of decision makers, including but not limited to operational weather forecasters, climatologists, agricultural interests, and water resource management. One tool that is heavily used is the United States Drought Monitor (USDM), which is derived from a complex blend of objective data and subjective analysis on a state-by-state basis using a variety of modeled and observed precipitation, <span class="hlt">soil</span> <span class="hlt">moisture</span>, hydrologic, and vegetation and crop health data. The NASA Short-term Prediction Research and Transition (SPoRT) Center currently runs a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework. The LIS-Noah is run at 3-km resolution for local numerical weather prediction (NWP) and situational awareness applications at select NOAA/National Weather Service (NWS) forecast offices over the Continental U.S. (CONUS). To enhance the practicality of the LIS-Noah output for drought monitoring and assessing flood potential, a 30+-year <span class="hlt">soil</span> <span class="hlt">moisture</span> climatology has been developed in an attempt to place near real-time <span class="hlt">soil</span> <span class="hlt">moisture</span> values in historical context at county- and/or watershed-scale resolutions. This LIS-Noah <span class="hlt">soil</span> <span class="hlt">moisture</span> climatology and accompanying anomalies is intended to complement the current suite of operational products, such as the North American Land Data Assimilation System phase 2 (NLDAS-2), which are generated on a coarser-resolution grid that may not capture localized, yet important <span class="hlt">soil</span> <span class="hlt">moisture</span> features. Daily <span class="hlt">soil</span> <span class="hlt">moisture</span> histograms are used to identify the real-time <span class="hlt">soil</span> <span class="hlt">moisture</span> percentiles at each grid point according to the county or watershed in which the grid point resides. Spatial plots are then produced that map the percentiles as proxies to the different USDM categories. This presentation will highlight recent developments of this gridded, objective <span class="hlt">soil</span> <span class="hlt">moisture</span> index, comparison to subjective</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24312508','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24312508"><span>Sensitivity of <span class="hlt">soil</span> respiration to variability in <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature in a humid tropical forest.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wood, Tana E; Detto, Matteo; Silver, Whendee L</p> <p>2013-01-01</p> <p>Precipitation and temperature are important drivers of <span class="hlt">soil</span> respiration. The role of <span class="hlt">moisture</span> and temperature are generally explored at seasonal or inter-annual timescales; however, significant variability also occurs on hourly to daily time-scales. We used small (1.54 m(2)), throughfall exclusion shelters to evaluate the role <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature as temporal controls on <span class="hlt">soil</span> CO2 efflux from a humid tropical forest in Puerto Rico. We measured hourly <span class="hlt">soil</span> CO2 efflux, temperature and <span class="hlt">moisture</span> in control and exclusion plots (n = 6) for 6-months. The variance of each time series was analyzed using orthonormal wavelet transformation and Haar-wavelet coherence. We found strong negative coherence between <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> respiration in control plots corresponding to a two-day periodicity. Across all plots, there was a significant parabolic relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> CO2 efflux with peak <span class="hlt">soil</span> respiration occurring at volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> of approximately 0.375 m(3)/m(3). We additionally found a weak positive coherence between CO2 and temperature at longer time-scales and a significant positive relationship between <span class="hlt">soil</span> temperature and CO2 efflux when the analysis was limited to the control plots. The coherence between CO2 and both temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> were reduced in exclusion plots. The reduced CO2 response to temperature in exclusion plots suggests that the positive effect of temperature on CO2 is constrained by <span class="hlt">soil</span> <span class="hlt">moisture</span> availability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3846571','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3846571"><span>Sensitivity of <span class="hlt">Soil</span> Respiration to Variability in <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Temperature in a Humid Tropical Forest</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wood, Tana E.; Detto, Matteo; Silver, Whendee L.</p> <p>2013-01-01</p> <p>Precipitation and temperature are important drivers of <span class="hlt">soil</span> respiration. The role of <span class="hlt">moisture</span> and temperature are generally explored at seasonal or inter-annual timescales; however, significant variability also occurs on hourly to daily time-scales. We used small (1.54 m2), throughfall exclusion shelters to evaluate the role <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature as temporal controls on <span class="hlt">soil</span> CO2 efflux from a humid tropical forest in Puerto Rico. We measured hourly <span class="hlt">soil</span> CO2 efflux, temperature and <span class="hlt">moisture</span> in control and exclusion plots (n = 6) for 6-months. The variance of each time series was analyzed using orthonormal wavelet transformation and Haar-wavelet coherence. We found strong negative coherence between <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> respiration in control plots corresponding to a two-day periodicity. Across all plots, there was a significant parabolic relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> CO2 efflux with peak <span class="hlt">soil</span> respiration occurring at volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> of approximately 0.375 m3/m3. We additionally found a weak positive coherence between CO2 and temperature at longer time-scales and a significant positive relationship between <span class="hlt">soil</span> temperature and CO2 efflux when the analysis was limited to the control plots. The coherence between CO2 and both temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> were reduced in exclusion plots. The reduced CO2 response to temperature in exclusion plots suggests that the positive effect of temperature on CO2 is constrained by <span class="hlt">soil</span> <span class="hlt">moisture</span> availability. PMID:24312508</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26098202','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26098202"><span>Galvanic Cell Type Sensor for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Analysis.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gaikwad, Pramod; Devendrachari, Mruthyunjayachari Chattanahalli; Thimmappa, Ravikumar; Paswan, Bhuneshwar; Raja Kottaichamy, Alagar; Makri Nimbegondi Kotresh, Harish; Thotiyl, Musthafa Ottakam</p> <p>2015-07-21</p> <p>Here we report the first potentiometric sensor for <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis by bringing in the concept of Galvanic cells wherein the redox energies of Al and conducting polyaniline are exploited to design a battery type sensor. The sensor consists of only simple architectural components, and as such they are inexpensive and lightweight, making it suitable for on-site analysis. The sensing mechanism is proved to be identical to a battery type discharge reaction wherein polyaniline redox energy changes from the conducting to the nonconducting state with a resulting voltage shift in the presence of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Unlike the state of the art <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors, a signal derived from the proposed <span class="hlt">moisture</span> sensor is probe size independent, as it is potentiometric in nature and, hence, can be fabricated in any shape or size and can provide a consistent output signal under the strong aberration conditions often encountered in <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis. The sensor is regenerable by treating with 1 M HCl and can be used for multiple analysis with little read out hysteresis. Further, a portable sensor is fabricated which can provide warning signals to the end user when the <span class="hlt">moisture</span> levels in the <span class="hlt">soil</span> go below critically low levels, thereby functioning as a smart device. As the sensor is inexpensive, portable, and potentiometric, it opens up avenues for developing effective and energy efficient irrigation strategies, understanding the heat and water transfer at the atmosphere-land interface, understanding <span class="hlt">soil</span> mechanics, forecasting the risk of natural calamities, and so on.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PCE....66..101B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PCE....66..101B"><span>Calibrating a FDR sensor for <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring in a wetland in Central Kenya</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Böhme, Beate; Becker, Mathias; Diekkrüger, Bernd</p> <p></p> <p>The recent transformation of wetlands into farmland in East Africa is accelerating due to growing food-demand, land shortages, and an increasing unpredictability of climatic conditions for crop production in uplands. However, the conversion of pristine wetlands into sites of production may alter hydrological attributes with negative effects on production potential. Particularly the amount and the dynamics of plant available <span class="hlt">soil</span> <span class="hlt">moisture</span> in the rooting zone of crops determine to a large extent the agricultural production potential of wetlands. Various methods exist to assess <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics with Frequency Domain Reflectometry (FDR) being among the most prominent. However, the suitability of FDR sensors for assessing plant available <span class="hlt">soil</span> <span class="hlt">moisture</span> has to date not been confirmed for wetland <span class="hlt">soils</span> in the region. We monitored the seasonal and spatial dynamics of water availability for crop growth in an inland valley wetland of the Kenyan highlands using a FDR sensor which was site-specifically calibrated. Access tubes were installed within different wetland use types and hydrological situations along valley transects and <span class="hlt">soil</span> properties <span class="hlt">affecting</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> (organic C, texture, and bulk density) were investigated. There was little variation in <span class="hlt">soil</span> attributes between physical positions in the valley, and also between topsoil and subsoil attributes with the exception of organic C contents. With a root mean squared error of 0.073 m3/m3, the developed calibration function of the FDR sensor allows for reasonably accurate <span class="hlt">soil</span> <span class="hlt">moisture</span> prediction for both within-site comparisons and the monitoring of temporal <span class="hlt">soil</span> <span class="hlt">moisture</span> variations. Applying the calibration equation to a time series of profile probe readings over a period of one year illustrated not only the temporal variation of <span class="hlt">soil</span> <span class="hlt">moisture</span>, but also effects of land use.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=243654','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=243654"><span>Effect of <span class="hlt">Soil</span> Type and <span class="hlt">Moisture</span> Availability on the Foraging Behavior of the Formosan Subterranean Termite (Isoptera: Rhinotermitidae)</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>This study examined the influence of <span class="hlt">soil</span> type and <span class="hlt">moisture</span> availability on termite foraging behavior. Physical properties of the <span class="hlt">soil</span> <span class="hlt">affected</span> both tunneling behavior and mud tube construction. Termites tunneled through sand faster than top <span class="hlt">soil</span> and clay. In containers with top <span class="hlt">soil</span> and clay, termi...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=325197','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=325197"><span>Evaluation of the validated <span class="hlt">soil</span> <span class="hlt">moisture</span> product from the SMAP radiometer</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>In this study, we used a multilinear regression approach to retrieve surface <span class="hlt">soil</span> <span class="hlt">moisture</span> from NASA’s <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite data to create a global dataset of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> which is consistent with ESA’s <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite retrieved sur...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=269713','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=269713"><span>The impact of land surface temperature on <span class="hlt">soil</span> <span class="hlt">moisture</span> anomaly detection from passive microwave observations</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>For several years passive microwave observations have been used to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> from the Earth’s surface. Low frequency observations have the most sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span>, therefore the modern <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) and future <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP) ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H41L..08M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H41L..08M"><span>Effects of Regional Topography and Spacecraft Observation Geometry on Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation Accuracies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moghaddam, M.; Akbar, R.; West, R. D.; Colliander, A.; Kim, S.; Dunbar, R. S.</p> <p>2015-12-01</p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active-Passive Mission (SMAP), launched in January 2015, provides near-daily global surface <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates via combined Active Radar and Passive Radiometer observations at various spatial resolutions. The goal of this mission is to enhance our understanding of global carbon and water cycles. This presentation will focus on a comprehensive assessment of the SMAP high resolution radar backscatter data (formally the L1C_S0_HiRes data product) obtained over a 3 km Woody Savanna region in north-central California during a 2.5 month period starting late May 2015. The effects of spacecraft observation geometry (fore- and aft-looks as well as ascending and descending obits) along with regional topography on <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation abilities will be examined. Furthermore surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals, obtained through utilization of different combinations of observation geometries, will be compared to an existing network of in situsensors. Current electromagnetic scattering and emission models do not properly account for surface topography, therefore physical forward model predictions and observations have unaccounted mismatch errors which also <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation accuracies. The goal of this study is to quantify these <span class="hlt">soil</span> <span class="hlt">moisture</span> prediction errors and highlight the need for new and complete Electromagnetic modeling efforts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28632172','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28632172"><span>Fiber Optic Thermo-Hygrometers for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Leone, Marco; Principe, Sofia; Consales, Marco; Parente, Roberto; Laudati, Armando; Caliro, Stefano; Cutolo, Antonello; Cusano, Andrea</p> <p>2017-06-20</p> <p>This work deals with the fabrication, prototyping, and experimental validation of a fiber optic thermo-hygrometer-based <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor, useful for rainfall-induced landslide prevention applications. In particular, we recently proposed a new generation of fiber Bragg grating (FBGs)-based <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors for irrigation purposes. This device was realized by integrating, inside a customized aluminum protection package, a FBG thermo-hygrometer with a polymer micro-porous membrane. Here, we first verify the limitations, in terms of the volumetric water content (VWC) measuring range, of this first version of the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor for its exploitation in landslide prevention applications. Successively, we present the development, prototyping, and experimental validation of a novel, optimized version of a <span class="hlt">soil</span> VWC sensor, still based on a FBG thermo-hygrometer, but able to reliably monitor, continuously and in real-time, VWC values up to 37% when buried in the <span class="hlt">soil</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23579833','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23579833"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> dynamics modeling considering multi-layer root zone.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kumar, R; Shankar, V; Jat, M K</p> <p>2013-01-01</p> <p>The <span class="hlt">moisture</span> uptake by plant from <span class="hlt">soil</span> is a key process for plant growth and movement of water in the <span class="hlt">soil</span>-plant system. A non-linear root water uptake (RWU) model was developed for a multi-layer crop root zone. The model comprised two parts: (1) model formulation and (2) <span class="hlt">moisture</span> flow prediction. The developed model was tested for its efficiency in predicting <span class="hlt">moisture</span> depletion in a non-uniform root zone. A field experiment on wheat (Triticum aestivum) was conducted in the sub-temperate sub-humid agro-climate of Solan, Himachal Pradesh, India. Model-predicted <span class="hlt">soil</span> <span class="hlt">moisture</span> parameters, i.e., <span class="hlt">moisture</span> status at various depths, <span class="hlt">moisture</span> depletion and <span class="hlt">soil</span> <span class="hlt">moisture</span> profile in the root zone, are in good agreement with experiment results. The results of simulation emphasize the utility of the RWU model across different agro-climatic regions. The model can be used for sound irrigation management especially in water-scarce humid, temperate, arid and semi-arid regions and can also be integrated with a water transport equation to predict the solute uptake by plant biomass.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H33F0883K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H33F0883K"><span>Reducing Structural Uncertainty in AMSR2 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Using a Model Combination Approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, S.; Liu, Y.; Johnson, F.; Parinussa, R.; Sharma, A.</p> <p>2014-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important variable in hydrological systems <span class="hlt">affecting</span> the water cycle in the atmosphere, land surface and subsurface. In past decades, a number of passive microwave based <span class="hlt">soil</span> <span class="hlt">moisture</span> products have been used in various fields of the earth sciences. While passive microwave can provide near-real time <span class="hlt">soil</span> <span class="hlt">moisture</span> (global coverage every 1-3 days), its direct applications have been limited due to the coarse spatial resolution (>100 km2) and uncertainties resulting from a number of complex factors that <span class="hlt">affects</span> the radiative transfer model. In this aspect, it is essential to validate the accuracy prior to actual applications and to improve the dataset itself and the retrieval algorithms. As a first step to do this, two remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm and the Land Parameter Retrieval Model (LPRM) are assessed and structural errors noted. The main findings are: 1) LPRM estimates are generally higher than JAXA except for in arid regions. 2) Comparisons with field measurements showed that JAXA has relatively better performance for locations with moderate vegetation density or dry conditions but the retrieved values are generally much lower than field measurements with little variance. 3) The advantage of LPRM is its ability to represent the relationship of <span class="hlt">soil</span> <span class="hlt">moisture</span> with surface temperature. 4) The performance of both products is strongly <span class="hlt">affected</span> by the mean <span class="hlt">soil</span> <span class="hlt">moisture</span>. As it is found that the two products are complementary under the various conditions, a combinatorial approach is presented for improving the accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> dataset. The approach is a linear combination technique which applies a spatio-temporal weighting, calculated based on error statistics of the products, to each product. This combinatorial approach is applied to a year of global dataset and generally shows better performances than the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ISPAr49B2..313S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ISPAr49B2..313S"><span>Validation and Upscaling of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Satellite Products in Romania</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sandric, I.; Diamandi, A.; Oana, N.; Saizu, D.; Vasile, C.; Lucaschi, B.</p> <p>2016-06-01</p> <p>The study presents the validation of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> satellite products for Romania. The validation was performed with in-situ measurements spatially distributed over the country and with in-situ measurements concentrated in on small area. For country level a number of 20 stations from the national meteorological observations network in Romania were selected. These stations have in-situ measurements for <span class="hlt">soil</span> <span class="hlt">moisture</span> in the first 5 cm of the <span class="hlt">soil</span> surface. The stations are more or less distributed in one pixel of SMOS, but it has the advantage that covers almost all the country with a wide range of environmental conditions. Additionally 10 mobile <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements stations were acquired and installed. These are spatially concentrated in one SMOS pixel in order to have a more detailed validation against the <span class="hlt">soil</span> type, <span class="hlt">soil</span> texture, land surface temperature and vegetation type inside one pixel. The results were compared and analyzed for each day, week, season, <span class="hlt">soil</span> type, and <span class="hlt">soil</span> texture and vegetation type. Minimum, maximum, mean and standard deviation were extracted and analyzed for each validation criteria and a hierarchy of those were performed. An upscaling method based on the relations between <span class="hlt">soil</span> <span class="hlt">moisture</span>, land surface temperature and vegetation indices was tested and implemented. The study was financed by the Romanian Space Agency within the framework of ASSIMO project <a href="http://assimo.meteoromania.ro"target="_blank">http://assimo.meteoromania.ro</a>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940012282','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940012282"><span>Evaluation of polarimetric SAR parameters for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shi, Jian-Cheng; Vanzyl, Jakob J.; Engman, Edwin T.</p> <p>1992-01-01</p> <p>Results of ongoing efforts to develop an algorithm for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval from Synthetic Aperture Radar (SAR) imagery are reported. Estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span> are of great importance in numerous environmental studies, including hydrology, meteorology, and agriculture. Previous studies using extensive scatterometer measurements have established the optimum parameters for <span class="hlt">moisture</span> retrieval as C-band HH radar operating at incidence angles between 10 to 15 deg. However, these parameters were not tested or verified with imaging radar systems. The results from different investigators showed considerable variability in the relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and radar backscattering. This variability suggests that those algorithms are site-specific. Furthermore, the small incidence angle requirement limits the spatial application, especially for airborne radar systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4366536','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4366536"><span>Reconciling spatial and temporal <span class="hlt">soil</span> <span class="hlt">moisture</span> effects on afternoon rainfall</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Guillod, Benoit P.; Orlowsky, Boris; Miralles, Diego G.; Teuling, Adriaan J.; Seneviratne, Sonia I.</p> <p>2015-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> impacts on precipitation have been strongly debated. Recent observational evidence of afternoon rain falling preferentially over land parcels that are drier than the surrounding areas (negative spatial effect), contrasts with previous reports of a predominant positive temporal effect. However, whether spatial effects relating to <span class="hlt">soil</span> <span class="hlt">moisture</span> heterogeneity translate into similar temporal effects remains unknown. Here we show that afternoon precipitation events tend to occur during wet and heterogeneous <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions, while being located over comparatively drier patches. Using remote-sensing data and a common analysis framework, spatial and temporal correlations with opposite signs are shown to coexist within the same region and data set. Positive temporal coupling might enhance precipitation persistence, while negative spatial coupling tends to regionally homogenize land surface conditions. Although the apparent positive temporal coupling does not necessarily imply a causal relationship, these results reconcile the notions of <span class="hlt">moisture</span> recycling with local, spatially negative feedbacks. PMID:25740589</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013BGD....10.9213W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013BGD....10.9213W"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> modifies the response of <span class="hlt">soil</span> respiration to temperature in a desert shrub ecosystem</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, B.; Zha, T. S.; Jia, X.; Wu, B.; Zhang, Y. Q.; Qin, S. G.</p> <p>2013-06-01</p> <p>The response of <span class="hlt">soil</span> respiration (Rs) to <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> have been well documented in forests, but data and information from desert shrub ecosystems are limited. <span class="hlt">Soil</span> CO2 efflux from a desert shrub ecosystem was measured continuously with automated chambers in Ningxia, northwest China, from June to October 2012. The responses of Rs to Ts was strongly <span class="hlt">affected</span> diurnally by <span class="hlt">soil</span> <span class="hlt">moisture</span>, with the diel variation in Rs being strongly related to 10 cm <span class="hlt">soil</span> temperature (Ts) at moderate and high <span class="hlt">soil</span> volumetric water content (VWC), but less related to Ts at low VWC. Ts typically lagged Rs by 3-4 h, however, the lag time varied in relation to VWC, with increased lag times at low VWC. Over the seasonal cycle, daily mean Rs was positively correlated with Ts when VWC exceeded 0.08 m3 m-3, but became decoupled from Ts when VWC dropped below this threshold. The annual temperature sensitivity of Rs (Q10) was 1.5. The short-term sensitivity of Rs to Ts, computed using three-day windows, varied significantly over the seasonal cycle; the short-term Q10 was negatively correlated with Ts and positively correlated with VWC. These results suggest the potential for a negative feedback to climate warming in desert ecosystems, related to the impact of low <span class="hlt">soil</span> <span class="hlt">moisture</span> on Rs. The results highlight the biological causes of diel hysteresis between Rs and Ts and the need for carbon cycle models to account for the interacting effects of Ts and VWC as joint determinants of Rs in desert ecosystem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC53D0923D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC53D0923D"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature variability among three plant communities in a High Arctic Lake Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Davis, M. L.; Konkel, J.; Welker, J. M.; Schaeffer, S. M.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature are critical to plant community distribution and <span class="hlt">soil</span> carbon cycle processes in High Arctic tundra. As environmental drivers of <span class="hlt">soil</span> biochemical processes, the predictability of <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature by vegetation zone in High Arctic landscapes has significant implications for the use of satellite imagery and vegetation distribution maps to estimate of <span class="hlt">soil</span> gas flux rates. During the 2017 growing season, we monitored <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature weekly at 48 sites in dry tundra, moist tundra, and wet grassland vegetation zones in a High Arctic lake basin. <span class="hlt">Soil</span> temperature in all three communities reflected fluctuations in air temperature throughout the season. Mean <span class="hlt">soil</span> temperature was highest in the dry tundra community at 10.5±0.6ºC, however, did not differ between moist tundra and wet grassland communities (2.7±0.6 and 3.1±0.5ºC, respectively). Mean volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> differed significantly among all three plant communities with the lowest and highest <span class="hlt">soil</span> <span class="hlt">moisture</span> measured in the dry tundra and wet grassland (30±1.2 and 65±2.7%), respectively. For all three communities, <span class="hlt">soil</span> <span class="hlt">moisture</span> was highest during the early season snow melt. <span class="hlt">Soil</span> <span class="hlt">moisture</span> in wet grassland remained high with no significant change throughout the season, while significant drying occurred in dry tundra. The most significant change in <span class="hlt">soil</span> <span class="hlt">moisture</span> was measured in moist tundra, ranging from 61 to 35%. Our results show different gradients in <span class="hlt">soil</span> <span class="hlt">moisture</span> variability within each plant community where: 1) <span class="hlt">soil</span> <span class="hlt">moisture</span> was lowest in dry tundra with little change, 2) highest in wet grassland with negligible change, and 3) variable in moist tundra which slowly dried but remained moist. Consistently high <span class="hlt">soil</span> <span class="hlt">moisture</span> in wet grassland restricts this plant community to areas with no significant drying during summer. The moist tundra occupies the intermediary areas between wet grassland and dry tundra and experiences the widest range</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020023144&hterms=atmosphere+studies&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Datmosphere%2Bstudies','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020023144&hterms=atmosphere+studies&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Datmosphere%2Bstudies"><span>Thresholds in Atmosphere-<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Interactions: Results from Climate Model Studies</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oglesby, Robert J.; Marshall, Susan; Erickson, David J., III; Roads, John O.; Robertson, Franklin R.; Arnold, James E. (Technical Monitor)</p> <p>2001-01-01</p> <p>The potential predictability of the effects of warm season <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies over the central U.S. has been investigated using a series of GCM (Global Climate Model) experiments with the NCAR (National Center for Atmospheric Research) CCM3 (Community Climate Model version 3)/LSM (Land Surface Model). Three different types of experiments have been made, all starting in either March (representing precursor conditions) or June (conditions at the onset of the warm season): (1) 'anomaly' runs with large, exaggerated initial <span class="hlt">soil</span> <span class="hlt">moisture</span> reductions, aimed at evaluating the physical mechanisms by which <span class="hlt">soil</span> <span class="hlt">moisture</span> can <span class="hlt">affect</span> the atmosphere; (2) 'predictability' runs aimed at evaluating whether typical <span class="hlt">soil</span> <span class="hlt">moisture</span> initial anomalies (indicative of year-to-year variability) can have a significant effect, and if so, for how long; (3) 'threshold' runs aimed at evaluating if a <span class="hlt">soil</span> <span class="hlt">moisture</span> anomaly must be of a specific size (i.e., a threshold crossed) before a significant impact on the atmosphere is seen. The 'anomaly' runs show a large, long-lasting response in <span class="hlt">soil</span> <span class="hlt">moisture</span> and also quantities such as surface temperature, sea level pressure, and precipitation; effects persist for at least a year. The 'predictability' runs, on the other hand, show very little impact of the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies on the subsequent evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> and other atmospheric parameters; internal variability is most important, with the initial state of the atmosphere (representing remote effects such as SST anomalies) playing a more minor role. The 'threshold' runs, devised to help resolve the dichotomy in 'anomaly' and 'predictability' results, suggest that, at least in CCM3/LSM, the vertical profile of <span class="hlt">soil</span> <span class="hlt">moisture</span> is the most important factor, and that deep <span class="hlt">soil</span> zone anomalies exert a more powerful, long-lasting effect than do anomalies in the near surface <span class="hlt">soil</span> zone. We therefore suggest that <span class="hlt">soil</span> <span class="hlt">moisture</span> feedbacks may be more important in explaining prolonged</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014BGeo...11.6173C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014BGeo...11.6173C"><span>Does <span class="hlt">soil</span> <span class="hlt">moisture</span> overrule temperature dependence of <span class="hlt">soil</span> respiration in Mediterranean riparian forests?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chang, C. T.; Sabaté, S.; Sperlich, D.; Poblador, S.; Sabater, F.; Gracia, C.</p> <p>2014-11-01</p> <p><span class="hlt">Soil</span> respiration (SR) is a major component of ecosystems' carbon cycles and represents the second largest CO2 flux in the terrestrial biosphere. <span class="hlt">Soil</span> temperature is considered to be the primary abiotic control on SR, whereas <span class="hlt">soil</span> <span class="hlt">moisture</span> is the secondary control factor. However, <span class="hlt">soil</span> <span class="hlt">moisture</span> can become the dominant control on SR in very wet or dry conditions. Determining the trigger that makes <span class="hlt">soil</span> <span class="hlt">moisture</span> as the primary control factor of SR will provide a deeper understanding on how SR changes under the projected future increase in droughts. Specific objectives of this study were (1) to investigate the seasonal variations and the relationship between SR and both <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> in a Mediterranean riparian forest along a groundwater level gradient; (2) to determine <span class="hlt">soil</span> <span class="hlt">moisture</span> thresholds at which SR is controlled by <span class="hlt">soil</span> <span class="hlt">moisture</span> rather than by temperature; (3) to compare SR responses under different tree species present in a Mediterranean riparian forest (Alnus glutinosa, Populus nigra and Fraxinus excelsior). Results showed that the heterotrophic <span class="hlt">soil</span> respiration rate, groundwater level and 30 cm integral <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM30) decreased significantly from the riverside moving uphill and showed a pronounced seasonality. SR rates showed significant differences between tree species, with higher SR for P. nigra and lower SR for A. glutinosa. The lower threshold of <span class="hlt">soil</span> <span class="hlt">moisture</span> was 20 and 17% for heterotrophic and total SR, respectively. Daily mean SR rate was positively correlated with <span class="hlt">soil</span> temperature when <span class="hlt">soil</span> <span class="hlt">moisture</span> exceeded the threshold, with Q10 values ranging from 1.19 to 2.14; nevertheless, SR became decoupled from <span class="hlt">soil</span> temperature when <span class="hlt">soil</span> <span class="hlt">moisture</span> dropped below these thresholds.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014BGD....11.7991C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014BGD....11.7991C"><span>Does <span class="hlt">soil</span> <span class="hlt">moisture</span> overrule temperature dependency of <span class="hlt">soil</span> respiration in Mediterranean riparian forests?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chang, C.-T.; Sabaté, S.; Sperlich, D.; Poblador, S.; Sabater, F.; Gracia, C.</p> <p>2014-06-01</p> <p><span class="hlt">Soil</span> respiration (SR) is a major component of ecosystem's carbon cycle and represents the second largest CO2 flux of the terrestrial biosphere. <span class="hlt">Soil</span> temperature is considered to be the primary control on SR whereas <span class="hlt">soil</span> <span class="hlt">moisture</span> as the secondary control factor. However, <span class="hlt">soil</span> <span class="hlt">moisture</span> can become the dominant control on SR in very wet or dry conditions. Determining the trigger that switches-on <span class="hlt">soil</span> <span class="hlt">moisture</span> as the primary control factor of SR will provide a deeper understanding on how SR changes under projected future increased droughts. Specific objectives of this study were (1) to investigate the seasonal variations and the relationship between SR and both <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> in a Mediterranean riparian forest along a groundwater level gradient; (2) to determine <span class="hlt">soil</span> <span class="hlt">moisture</span> thresholds at which SR is rather controlled by <span class="hlt">soil</span> <span class="hlt">moisture</span> than by temperature; (3) to compare SR responses under different tree species present in a Mediterranean riparian forest (Alnus, glutinosa, Populus nigra and Fraxinus excelsior). Results showed that the heterotrophic <span class="hlt">soil</span> respiration rate, groundwater level and 30 cm integral <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM30) decreased significantly from riverside to uphill and showed a pronounced seasonality. SR rates showed significant differences among tree species, with higher SR for P. nigra and lower SR for A. glutinosa. The lower threshold of <span class="hlt">soil</span> <span class="hlt">moisture</span> was 20 and 17% for heterotrophic and total SR respectively. Daily mean SR rate was positively correlated with <span class="hlt">soil</span> temperature when <span class="hlt">soil</span> <span class="hlt">moisture</span> exceeded the threshold, with Q10 values ranging from 1.19 to 2.14; nevertheless, SR became decoupled from <span class="hlt">soil</span> temperature when <span class="hlt">soil</span> <span class="hlt">moisture</span> dropped below these thresholds.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SPIE10426E..0JH','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SPIE10426E..0JH"><span>Creating <span class="hlt">soil</span> <span class="hlt">moisture</span> maps based on radar satellite imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hnatushenko, Volodymyr; Garkusha, Igor; Vasyliev, Volodymyr</p> <p>2017-10-01</p> <p>The presented work is related to a study of mapping <span class="hlt">soil</span> <span class="hlt">moisture</span> basing on radar data from Sentinel-1 and a test of adequacy of the models constructed on the basis of data obtained from alternative sources. Radar signals are reflected from the ground differently, depending on its properties. In radar images obtained, for example, in the C band of the electromagnetic spectrum, <span class="hlt">soils</span> saturated with <span class="hlt">moisture</span> usually appear in dark tones. Although, at first glance, the problem of constructing <span class="hlt">moisture</span> maps basing on radar data seems intuitively clear, its implementation on the basis of the Sentinel-1 data on an industrial scale and in the public domain is not yet available. In the process of mapping, for verification of the results, measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> obtained from logs of the network of climate stations NOAA US Climate Reference Network (USCRN) were used. This network covers almost the entire territory of the United States. The passive microwave radiometers of Aqua and SMAP satellites data are used for comparing processing. In addition, other supplementary cartographic materials were used, such as maps of <span class="hlt">soil</span> types and ready <span class="hlt">moisture</span> maps. The paper presents a comparison of the effect of the use of certain methods of roughening the quality of radar data on the result of mapping <span class="hlt">moisture</span>. Regression models were constructed showing dependence of backscatter coefficient values Sigma0 for calibrated radar data of different spatial resolution obtained at different times on <span class="hlt">soil</span> <span class="hlt">moisture</span> values. The obtained <span class="hlt">soil</span> <span class="hlt">moisture</span> maps of the territories of research, as well as the conceptual solutions about automation of operations of constructing such digital maps, are presented. The comparative assessment of the time required for processing a given set of radar scenes with the developed tools and with the ESA SNAP product was carried out.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1918155S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1918155S"><span>Synergistic method for boreal <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> freeze retrievals using active and passive microwave instruments</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smolander, Tuomo; Lemmetyinen, Juha; Rautiainen, Kimmo; Schwank, Mike; Pulliainen, Jouni</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> freezing are important for diverse hydrological, biogeochemical, and climatological applications. They <span class="hlt">affect</span> surface energy balance, surface and subsurface water flow, and exchange rates of carbon with the atmosphere. <span class="hlt">Soil</span> freezing controls important biogeochemical processes, like photosynthetic activity of plants and microbial activity within <span class="hlt">soils</span>. Permafrost covers approximately 24% of the land surface in the Northern Hemisphere and seasonal freezing occurs on approximately 51% of the area. The retrieval method presented is based on an inversion technique and applies a semiempirical backscattering model that describes the dependence of radar backscattering of forest as a function of stem volume, <span class="hlt">soil</span> permittivity, the extinction coefficient of forest canopy, surface roughness, incidence angle, and radar frequency. It gives an estimate of <span class="hlt">soil</span> permittivity using active microwave measurements. Applying a Bayesian assimilation scheme, it is also possible to use other <span class="hlt">soil</span> permittivity retrievals to regulate this estimate to combine for example low resolution passive observations with high resolution active observations for a synergistic retrieval. This way the higher variance in the active retrieval can be constricted with the passive retrieval when at the same time the spatial resolution of the product is improved compared to the passive-only retrieval. The retrieved <span class="hlt">soil</span> permittivity estimate can be used to detect <span class="hlt">soil</span> freeze/thaw state by considering the <span class="hlt">soil</span> to be frozen when the estimate is below a threshold value. The permittivity retrieval can also be used to estimate the relative <span class="hlt">moisture</span> of the <span class="hlt">soil</span>. The method was tested using SAR (Synthetic Aperture Radar) measurements from ENVISAT ASAR instrument for the years 2010-2012 and from Sentinel-1 satellite for the years 2015-2016 in Sodankylä area in Northern Finland. The synergistic method was tested combining the SAR measurements with a SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity) radiometer</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.8427B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.8427B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> retrieval from Sentinel-1 satellite data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Benninga, Harm-Jan; van der Velde, Rogier; Su, Zhongbo</p> <p>2016-04-01</p> <p>Reliable up-to-date information on the current water availability and models to evaluate management scenarios are indispensable for skilful water management. The Sentinel-1 radar satellite programme provides an opportunity to monitor water availability (as surface <span class="hlt">soil</span> <span class="hlt">moisture</span>) from space on an operational basis at unprecedented fine spatial and temporal resolutions. However, the influences of <span class="hlt">soil</span> roughness and vegetation cover complicate the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> states from radar data. In this contribution, we investigate the sensitivity of Sentinel-1 radar backscatter to <span class="hlt">soil</span> <span class="hlt">moisture</span> states and vegetation conditions. The analyses are based on 105 Sentinel-1 images in the period from October 2014 to January 2016 covering the Twente region in the Netherlands. This area is almost flat and has a heterogeneous landscape, including agricultural (mainly grass, cereal and corn), forested and urban land covers. In-situ measurements at 5 cm depth collected from the Twente <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring network are used as reference. This network consists of twenty measurement stations (most of them at agricultural fields) distributed across an area of 50 km × 40 km. The Normalized Difference Vegetation Index (NDVI) derived from optical images is adopted as proxy to represent seasonal variability in vegetation conditions. The results from this sensitivity study provide insight into the potential capability of Sentinel-1 data for the estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> states and they will facilitate the further development of operational retrieval methods. An operationally applicable <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval method requires an algorithm that is usable without the need for area specific model calibration with detailed field information (regarding roughness and vegetation). Because it is not yet clear which method provides the most reliable <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from Sentinel-1 data, multiple <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval methods will be studied in which the fine spatiotemporal</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=156896&keyword=sun&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=156896&keyword=sun&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>ELEVATED TEMPERATURE, <span class="hlt">SOIL</span> <span class="hlt">MOISTURE</span> AND SEASONALITY BUT NOT CO2 <span class="hlt">AFFECT</span> CANOPY ASSIMILATION AND SYSTEM RESPIRATION IN SEEDLING DOUGLAS-FIR ECOSYSTEMS</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>We investigated the effects of elevated atmospheric CO2 and air temperature on C cycling in trees and associated <span class="hlt">soil</span> system, focusing on canopy CO2 assimilation (Asys) and system CO2 loss through respiration (Rsys). We hypothesized that both elevated CO2 and elevated temperature...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/1988','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/1988"><span><span class="hlt">Soil</span> CO2 evolution and root respiration in 11 year-old Loblolly Pine (Pinus taeda) Plantations as <span class="hlt">Affected</span> by <span class="hlt">Moisture</span> and Nutrient Availability</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Chris A. Maier; L.W. Kress</p> <p>2000-01-01</p> <p>We measured <span class="hlt">soil</span> CO2 evolution rates with (Sff) and without (Sms) the forest floor litter and root respiration monthly in 11-year-old loblolly pine (Pinus taeda L.) plantations during the fourth year of fertilization and irrigation treatments. Values of Sff...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3804323','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3804323"><span>Influence of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on <span class="hlt">Soil</span> Gas Vapor Concentration for Vapor Intrusion</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Shen, Rui; Pennell, Kelly G.; Suuberg, Eric M.</p> <p>2013-01-01</p> <p>Abstract Mathematical models have been widely used in analyzing the effects of various environmental factors in the vapor intrusion process. <span class="hlt">Soil</span> <span class="hlt">moisture</span> content is one of the key factors determining the subsurface vapor concentration profile. This manuscript considers the effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles on the <span class="hlt">soil</span> gas vapor concentration away from any surface capping by buildings or pavement. The “open field” <span class="hlt">soil</span> gas vapor concentration profile is observed to be sensitive to the <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution. The van Genuchten relations can be used for describing the <span class="hlt">soil</span> <span class="hlt">moisture</span> retention curve, and give results consistent with the results from a previous experimental study. Other modeling methods that account for <span class="hlt">soil</span> <span class="hlt">moisture</span> are evaluated. These modeling results are also compared with the measured subsurface concentration profiles in the U.S. EPA vapor intrusion database. PMID:24170970</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70030715','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70030715"><span>Dependence of <span class="hlt">soil</span> respiration on <span class="hlt">soil</span> temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> in successional forests in Southern China</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tang, X.-L.; Zhou, G.-Y.; Liu, S.-G.; Zhang, D.-Q.; Liu, S.-Z.; Li, Ji; Zhou, C.-Y.</p> <p>2006-01-01</p> <p>The spatial and temporal variations in <span class="hlt">soil</span> respiration and its relationship with biophysical factors in forests near the Tropic of Cancer remain highly uncertain. To contribute towards an improvement of actual estimates, <span class="hlt">soil</span> respiration rates, <span class="hlt">soil</span> temperature, and <span class="hlt">soil</span> <span class="hlt">moisture</span> were measured in three successional subtropical forests at the Dinghushan Nature Reserve (DNR) in southern China from March 2003 to February 2005. The overall objective of the present study was to analyze the temporal variations of <span class="hlt">soil</span> respiration and its biophysical dependence in these forests. The relationships between biophysical factors and <span class="hlt">soil</span> respiration rates were compared in successional forests to test the hypothesis that these forests responded similarly to biophysical factors. The seasonality of <span class="hlt">soil</span> respiration coincided with the seasonal climate pattern, with high respiration rates in the hot humid season (April-September) and with low rates in the cool dry season (October-March). <span class="hlt">Soil</span> respiration measured at these forests showed a clear increasing trend with the progressive succession. Annual mean (±SD) <span class="hlt">soil</span> respiration rate in the DNR forests was (9.0 ± 4.6) Mg CO2-C/hm2per year, ranging from (6.1 ± 3.2) Mg CO2-C/hm2per year in early successional forests to (10.7 ± 4.9) Mg CO2-C/hm2 per year in advanced successional forests. <span class="hlt">Soil</span> respiration was correlated with both <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span>. The T/M model, where the two biophysical variables are driving factors, accounted for 74%-82% of <span class="hlt">soil</span> respiration variation in DNR forests. Temperature sensitivity decreased along progressive succession stages, suggesting that advanced-successional forests have a good ability to adjust to temperature. In contrast, <span class="hlt">moisture</span> increased with progressive succession processes. This increase is caused, in part, by abundant respirators in advanced-successional forest, where more <span class="hlt">soil</span> <span class="hlt">moisture</span> is needed to maintain their activities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=316516','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=316516"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> monitoring methods: Strengths and limitations</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>All <span class="hlt">soil</span> water content sensors require <span class="hlt">soil</span>-specific calibration – but calibration of capacitance sensors, whether in the laboratory or in the field, doesn’t ensure accuracy in the field. EM fields from capacitance sensors do not uniformly interrogate the <span class="hlt">soil</span> and are influenced by <span class="hlt">soil</span> structure – ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.7557K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.7557K"><span>SMOS <span class="hlt">Soil</span> <span class="hlt">moisture</span> Cal val activities</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kerr, Y.; Mialon, A.; Bitar, A. Al; Leroux, D.; Richaume, P.; Gruhier, C.; Berthon, L.; Novello, N.; Rudiger, C.; Bircher, S.; Wigneron, J. P.; Ferrazzoli, P.; Rahmoune, R.</p> <p>2012-04-01</p> <p>SMOS, successfully launched on November 2, 2009, uses an L Band radiometer with aperture synthesis to achieve a good spatial resolution.. It was developed and made under the leadership of the European Space Agency (ESA) as an Earth Explorer Opportunity mission. It is a joint program with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L band 2D interferometric,radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the vegetation and with the atmosphere being almost transparent, it enables us to infer both <span class="hlt">soil</span> <span class="hlt">moisture</span> and vegetation water content. SMOS achieves an unprecedented spatial resolution of 50 km at L-band maximum (43 km on average) with multi angular-dual polarized (or fully polarized) brightness temperatures over the globe and with a revisit time smaller than 3 days. SMOS is now acquiring data and has undergone the commissioning phase. The data quality exceeds what was expected, showing very good sensitivity and stability. The data is however very much impaired by man made emission in the protected band, leading to degraded measurements in several areas including parts of Europe and China. Many different international teams are now performing cal val activities in various parts of the world, with notably large field campaigns either on the long time scale or over specific targets to address the specific issues. These campaigns take place in various parts of the world and in different environments, from the Antarctic plateau to the deserts, from rain forests to deep oceans. SMOS is a new sensor, making new measurements and paving the way for new applications. It requires a detailed analysis of the data so as to validate both the approach and the quality of the retrievals, and allow for monitoring and the evolution of the sensor. To achieve such goals it is very important to link efficiently ground</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-PIA19879.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-PIA19879.html"><span>NASA SMAPVEX 15 Field Campaign Measures <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Over Arizona</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-09-09</p> <p>NASA's SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive) satellite observatory conducted a field experiment as part of its <span class="hlt">soil</span> <span class="hlt">moisture</span> data product validation program in southern Arizona on Aug. 2-18, 2015. The images here represent the distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> over the SMAPVEX15 (SMAP Validation Experiment 2015) experiment domain, as measured by the Passive Active L-band System (PALS) developed by NASA's Jet Propulsion Laboratory, Pasadena, California, which was installed onboard a DC-3 aircraft operated by Airborne Imaging, Inc. Blue and green colors denote wet conditions and dry conditions are marked by red and orange. The black lines show the nominal flight path of PALS. The measurements show that on the first day, the domain surface was wet overall, but had mostly dried down by the second measurement day. On the third day, there was a mix of <span class="hlt">soil</span> wetness. The heterogeneous <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution over the domain is typical for the area during the North American Monsoon season and provides excellent conditions for SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> product validation and algorithm enhancement. The images are based on brightness temperature measured by the PALS instrument gridded on a grid with 0.6-mile (1-kilometer) pixel size. They do not yet compensate for surface characteristics, such as vegetation and topography. That work is currently in progress. http://photojournal.jpl.nasa.gov/catalog/PIA19879</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25358174','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25358174"><span>[<span class="hlt">Soil</span> <span class="hlt">moisture</span> estimation model based on multiple vegetation index].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wu, Hai-long; Yu, Xin-xiao; Zhang, Zhen-ming; Zhang, Yan</p> <p>2014-06-01</p> <p>Estimating <span class="hlt">soil</span> <span class="hlt">moisture</span> conveniently and exactly is a hot issues in water resource monitoring among agriculture and forestry. Estimating <span class="hlt">soil</span> <span class="hlt">moisture</span> based on vegetation index has been recognized and applied widely. 8 vegetation indexes were figured out based on the hyper-spectral data measured by portable spectrometer. The higher correlation indexes among 8 vegetation indexes and surface vegetation temperature were selected by Gray Relative Analysis method (GRA). Then, these selected indexes were analyzed using Multiple Linear Regression to establish <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation model based on multiple vegetation indexes, and the model accuracy was evaluated. The accuracy evaluation indicated that the fitting was satisfied and the significance was 0.000 (P < 0.001). High correlation was turned out between estimated and measured <span class="hlt">soil</span> <span class="hlt">moisture</span> with R2 reached 0.636 1 and RMSE 2.149 9. This method introduced multiple vegetation indexes into <span class="hlt">soil</span> water content estimating over micro scale by non-contact measuring method using portable spectrometer. The exact estimation could be an appropriate replacement for remote sensing inversion and direct measurement. The model could estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> quickly and accurately, and provide theory and technology reference for water resource management in agriculture and forestry.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4884879','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4884879"><span>Effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on seasonal variation in indoor radon concentration: modelling and measurements in 326 Finnish houses</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Arvela, H.; Holmgren, O.; Hänninen, P.</p> <p>2016-01-01</p> <p>The effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on seasonal variation in <span class="hlt">soil</span> air and indoor radon is studied. A brief review of the theory of the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on <span class="hlt">soil</span> air radon has been presented. The theoretical estimates, together with <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements over a period of 10 y, indicate that variation in <span class="hlt">soil</span> <span class="hlt">moisture</span> evidently is an important factor <span class="hlt">affecting</span> the seasonal variation in <span class="hlt">soil</span> air radon concentration. Partitioning of radon gas between the water and air fractions of <span class="hlt">soil</span> pores is the main factor increasing <span class="hlt">soil</span> air radon concentration. On two example test sites, the relative standard deviation of the calculated monthly average <span class="hlt">soil</span> air radon concentration was 17 and 26 %. Increased <span class="hlt">soil</span> <span class="hlt">moisture</span> in autumn and spring, after the snowmelt, increases <span class="hlt">soil</span> gas radon concentrations by 10–20 %. In February and March, the <span class="hlt">soil</span> gas radon concentration is in its minimum. <span class="hlt">Soil</span> temperature is also an important factor. High <span class="hlt">soil</span> temperature in summer increased the calculated <span class="hlt">soil</span> gas radon concentration by 14 %, compared with winter values. The monthly indoor radon measurements over period of 1 y in 326 Finnish houses are presented and compared with the modelling results. The model takes into account radon entry, climate and air exchange. The measured radon concentrations in autumn and spring were higher than expected and it can be explained by the seasonal variation in the <span class="hlt">soil</span> <span class="hlt">moisture</span>. The variation in <span class="hlt">soil</span> <span class="hlt">moisture</span> is a potential factor <span class="hlt">affecting</span> markedly to the high year-to-year variation in the annual or seasonal average radon concentrations, observed in many radon studies. PMID:25899611</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25899611','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25899611"><span>Effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on seasonal variation in indoor radon concentration: modelling and measurements in 326 Finnish houses.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Arvela, H; Holmgren, O; Hänninen, P</p> <p>2016-02-01</p> <p>The effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on seasonal variation in <span class="hlt">soil</span> air and indoor radon is studied. A brief review of the theory of the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on <span class="hlt">soil</span> air radon has been presented. The theoretical estimates, together with <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements over a period of 10 y, indicate that variation in <span class="hlt">soil</span> <span class="hlt">moisture</span> evidently is an important factor <span class="hlt">affecting</span> the seasonal variation in <span class="hlt">soil</span> air radon concentration. Partitioning of radon gas between the water and air fractions of <span class="hlt">soil</span> pores is the main factor increasing <span class="hlt">soil</span> air radon concentration. On two example test sites, the relative standard deviation of the calculated monthly average <span class="hlt">soil</span> air radon concentration was 17 and 26%. Increased <span class="hlt">soil</span> <span class="hlt">moisture</span> in autumn and spring, after the snowmelt, increases <span class="hlt">soil</span> gas radon concentrations by 10-20 %. In February and March, the <span class="hlt">soil</span> gas radon concentration is in its minimum. <span class="hlt">Soil</span> temperature is also an important factor. High <span class="hlt">soil</span> temperature in summer increased the calculated <span class="hlt">soil</span> gas radon concentration by 14%, compared with winter values. The monthly indoor radon measurements over period of 1 y in 326 Finnish houses are presented and compared with the modelling results. The model takes into account radon entry, climate and air exchange. The measured radon concentrations in autumn and spring were higher than expected and it can be explained by the seasonal variation in the <span class="hlt">soil</span> <span class="hlt">moisture</span>. The variation in <span class="hlt">soil</span> <span class="hlt">moisture</span> is a potential factor <span class="hlt">affecting</span> markedly to the high year-to-year variation in the annual or seasonal average radon concentrations, observed in many radon studies. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170002025&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170002025&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsoil"><span>Assessment of the SMAP Passive <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chan, Steven K.; Bindlish, Rajat; O'Neill, Peggy E.; Njoku, Eni; Jackson, Tom; Colliander, Andreas; Chen, Fan; Burgin, Mariko; Dunbar, Scott; Piepmeier, Jeffrey; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20170002025'); toggleEditAbsImage('author_20170002025_show'); toggleEditAbsImage('author_20170002025_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20170002025_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20170002025_hide"></p> <p>2016-01-01</p> <p>The National Aeronautics and Space Administration (NASA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product became the only operational Level 2 <span class="hlt">soil</span> <span class="hlt">moisture</span> product for SMAP. The product provides <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates posted on a 36 kilometer Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals averaged over the CVSs was 0.038 cubic meter per cubic meter unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 cubic meter per cubic meter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H24E..02D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H24E..02D"><span>Advances, experiences, and prospects of the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dorigo, W.; van Oevelen, P. J.; Drusch, M.; Wagner, W.; Scipal, K.; Mecklenburg, S.</p> <p>2012-12-01</p> <p>In 2009, the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network (ISMN; http:www.ipf.tuwien.ac.at) was initiated as a platform to support calibration and validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> products from remote sensing and land surface models, and to advance studies on the behavior of <span class="hlt">soil</span> <span class="hlt">moisture</span> over space and time. This international initiative is fruit of continuing coordinative efforts of the Global Energy and Water Cycle Experiment (GEWEX) in cooperation with the Group of Earth Observation (GEO) and the Committee on Earth Observation Satellites (CEOS). The decisive financial incentive was given by the European Space Agency (ESA) who considered the establishment of the network critical for optimizing the <span class="hlt">soil</span> <span class="hlt">moisture</span> products from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission. The ISMN collects and harmonizes ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets from a large variety of individually operating networks and makes them available through a centralized data portal. Meanwhile, almost 6000 <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets from over 1300 sites, distributed among 34 networks worldwide, are contained in the database. The steadily increasing number of organizations voluntarily contributing to the ISMN, and the rapidly increasing number of studies based on the network show that the portal has been successful in reaching its primary goal to promote easy data accessibility to a wide variety of users. Recently, several updates of the system were performed to keep up with the increasing data amount and traffic, and to meet the requirements of many advanced users. Many datasets from operational networks (e.g., SCAN, the US Climate Reference Network, COSMOS, and ARM) are now assimilated and processed in the ISMN on a fully automated basis in near-real time. In addition, a new enhanced quality control system is currently being implemented. This presentation gives an overview of these recent developments, presents some examples of important scientific results based on the ISMN, and sketches an outlook for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=332686','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=332686"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> sampling and decision frameworks for agriculture</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Sampling of <span class="hlt">soil</span> <span class="hlt">moisture</span> involves temporal and spatial components. The spatial component can be further expanded into a vertical and horizontal array of observations that are required to understand the dynamics of processes occurring with the <span class="hlt">soil</span> profile. The decision frameworks for agriculture re...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=316894','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=316894"><span>Using a <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation network for satellite validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>A long term in situ network for the study of <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation was deployed in north central Iowa, in cooperation between USDA and NASA. A total of 20 dual precipitation gages were established across a watershed landscape with an area of approximately 600 km2. In addition, four <span class="hlt">soil</span> mo...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20568626','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20568626"><span>Effect of <span class="hlt">soil</span> type and <span class="hlt">moisture</span> availability on the foraging behavior of the Formosan subterranean termite (Isoptera: Rhinotermitidae).</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cornelius, Mary L; Osbrink, Weste L A</p> <p>2010-06-01</p> <p>This study examined the influence of <span class="hlt">soil</span> type and <span class="hlt">moisture</span> availability on termite foraging behavior. Physical properties of the <span class="hlt">soil</span> <span class="hlt">affected</span> both tunneling behavior and shelter tube construction. Termites tunneled through sand faster than top <span class="hlt">soil</span> and clay. In containers with top <span class="hlt">soil</span> and clay, termites built shelter tubes on the sides of the containers. In containers with sand, termites built shelter tubes directly into the air and covered the sides of the container with a layer of sand. The interaction of <span class="hlt">soil</span> type and <span class="hlt">moisture</span> availability <span class="hlt">affected</span> termite movement, feeding, and survival. In assays with moist <span class="hlt">soils</span>, termites were more likely to aggregate in top <span class="hlt">soil</span> over potting <span class="hlt">soil</span> and peat moss. However, termites were more likely to move into containers with dry peat moss and potting <span class="hlt">soil</span> than containers with dry sand and clay. Termites were also significantly more likely to move into containers with dry potting <span class="hlt">soil</span> than dry top <span class="hlt">soil</span>. In the assay with dry <span class="hlt">soils</span>, termite mortality was high even though termites were able to travel freely between moist sand and dry <span class="hlt">soil</span>, possibly due to desiccation caused by contact with dry <span class="hlt">soil</span>. Evaporation from potting <span class="hlt">soil</span> and peat moss resulted in significant mortality, whereas termites were able to retain enough <span class="hlt">moisture</span> in top <span class="hlt">soil</span>, sand, and clay to survive for 25 d. The interaction of <span class="hlt">soil</span> type and <span class="hlt">moisture</span> availability influences the distribution of foraging termites in microhabitats.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC43C1083L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC43C1083L"><span>Improving simulation of <span class="hlt">soil</span> <span class="hlt">moisture</span> in China using modified meteorological forcing, land surface information and CLM4.5</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, J.; Xie, Z.</p> <p>2017-12-01</p> <p>The quality of simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> using land surface models (LSM) is largely dependent on the accuracy of meteorological forcing data, land surface information data and advanced LSM. In this study, a modified (high-accuracy) meteorological forcing data and land surface information(land cover and <span class="hlt">soil</span> texture) data were used in advanced LSM version 4.5 of Community Land Model(CLM4.5) , to explore how the new meteorological forcing data, land surface information data and advanced LSM <span class="hlt">affected</span> the <span class="hlt">soil</span> <span class="hlt">moisture</span> modeling over mainland China. Six simulations were conducted using different meteorological forcing data, different land surface information data and different LSMs over mainland China during 1982-2007. The simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> from all six experiments was then compared to in situ measured <span class="hlt">soil</span> <span class="hlt">moisture</span> from 411 stations in eight climate subregions across mainland China. The results showed that the six simulations could capture the spatial and seasonal variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> in most cases with some mean bias. The simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> using observation-based meteorological forcing data, land surface information and CLM4.5 was closer to the in situ measured <span class="hlt">soil</span> <span class="hlt">moisture</span>, exhibited a higher correlation coefficient, smaller root mean square error and more consistent in time variation compared with other five simulations in most cases. This simulation reduced significantly the uncertainty arising from meteorological forcing, land surface information and LSM, and improved effectively the simulation of <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.8883L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.8883L"><span>Comparisons of Satellite <span class="hlt">Soil</span> <span class="hlt">Moisture</span>, an Energy Balance Model Driven by LST Data and Point Measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Laiolo, Paola; Gabellani, Simone; Rudari, Roberto; Boni, Giorgio; Puca, Silvia</p> <p>2013-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays a fundamental role in the partitioning of mass and energy fluxes between land surface and atmosphere, thereby influencing climate and weather, and it is important in determining the rainfall-runoff response of catchments; moreover, in hydrological modelling and flood forecasting, a correct definition of <span class="hlt">moisture</span> conditions is a key factor for accurate predictions. Different sources of information for the estimation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> state are currently available: satellite data, point measurements and model predictions. All are <span class="hlt">affected</span> by intrinsic uncertainty. Among different satellite sensors that can be used for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation three major groups can be distinguished: passive microwave sensors (e.g., SSMI), active sensors (e.g. SAR, Scatterometers), and optical sensors (e.g. Spectroradiometers). The last two families, mainly because of their temporal and spatial resolution seem the most suitable for hydrological applications In this work <span class="hlt">soil</span> <span class="hlt">moisture</span> point measurements from 10 sensors in the Italian territory are compared of with the satellite products both from the HSAF project SM-OBS-2, derived from the ASCAT scatterometer, and from ACHAB, an operative energy balance model that assimilate LST data derived from MSG and furnishes daily an evaporative fraction index related to <span class="hlt">soil</span> <span class="hlt">moisture</span> content for all the Italian region. Distributed comparison of the ACHAB and SM-OBS-2 on the whole Italian territory are performed too.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000038011&hterms=water+available+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dwater%2Bavailable%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038011&hterms=water+available+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dwater%2Bavailable%2Bsoil"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span>: The Hydrologic Interface Between Surface and Ground Waters</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Engman, Edwin T.</p> <p>1997-01-01</p> <p>A hypothesis is presented that many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. The specific hydrologic processes that may be detected include groundwater recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential evapotranspiration (ET), and information about the hydrologic properties of <span class="hlt">soils</span>. In basin and hillslope hydrology, <span class="hlt">soil</span> <span class="hlt">moisture</span> is the interface between surface and ground waters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.H53E0532M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.H53E0532M"><span>An Investigation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Dynamics Using FLUXNET Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miller, G. R.; Baldocchi, D. D.</p> <p>2005-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> data, obtained from four FLUXNET sites in the US, were examined using an ecohydrological framework. Sites were selected for the analysis to provide a range of plant functional type, climate, and <span class="hlt">soil</span> grain size distribution. Data at a selected site included at least two years of measurements of volumetric <span class="hlt">soil</span> water content, air temperature, precipitation, atmospheric pressure, net radiation, and latent heat flux. The Rosetta database program, based on pedo-transfer functions, was used to generate water retention curves from site <span class="hlt">soil</span> grain size distributions. Using these curves and plant parameters found in the literature, ranges for each critical <span class="hlt">soil</span> <span class="hlt">moisture</span> point were determined. For all sites, the hydroscopic point (Sh) and wilting point (Sw) had the smallest range, while more uncertainty was associated with the stress point (S*) and field content (Sfc). <span class="hlt">Soil</span> <span class="hlt">moisture</span> trends revealed the importance of measuring water content at several depths throughout the rooting zone; <span class="hlt">soil</span> <span class="hlt">moisture</span> at the surface (above 10 cm) was around 20 to 30 percent less than that at 50 to 60 cm. Frequently, the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> would fall below Sw while remaining between S* and Sw at deeper intervals. While daily variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> was high due to the timing of precipitation events, yearly variability was lower than anticipated. However, a broader range of years should be examined to confirm this finding. A steady state <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics model was used to generate <span class="hlt">soil</span> <span class="hlt">moisture</span> probability density functions (pdfs) at each site. The model was altered to accommodate the year-round growing seasons at two of the sites, a compromise between a fully transient model and the typical steady state model. The modeled pdfs were then compared to histograms generated from the measured data. Model accuracy depended heavily on proper parameter selection. Most parameters could be found using available FLUXNET data for the site, however, S* and Sfc were not known with</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMNH53B3890E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMNH53B3890E"><span>Improving government decision making in response to floods using <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Escobar, V. M.; Schumann, G.; Torak, L. J.</p> <p>2014-12-01</p> <p>NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission, due to launch January 2015, will provide global observations of the Earth's surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, providing high accuracy, resolution and continuous global coverage. This paper seeks to show how SMAP data can be used in flood applications to improve flood warning/planning operations for the Upper Mississippi River basin. The Mississippi River ranks as the fourth longest and tenth largest river in the world and is noted as one of the most altered rivers in the United States. The Mississippi River has a very long track record of flood events, with the 2011 event being a unique event due to large volumes of snow melt and heavy spring rain in the Upper Mississippi basin. Understanding and modeling these processes and combining them with relevant satellite observations such as <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions could help alleviate some of the risk to flooding by identifying when infiltration to <span class="hlt">soils</span> is cut off causing excessive runoff. The objective of the analysis is to improve our understanding of how satellite-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> will impact basin scaled/multi state decision processes linked to emergency planning and preparedness, such as FEMA FloodSMART. Using the snow hydrology model SNOW-17 (NWS) coupled to a large-scale two-dimensional floodplain inundation model LISFLOOD-FP, the study evaluates how different <span class="hlt">soil</span> <span class="hlt">moisture</span> states can be captured by satellites to enable a multi-state decision process focused on flood risk and planning. The study develops a scenario that applies historical <span class="hlt">soil</span> <span class="hlt">moisture</span> data from past events to monitor basin <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions and yields a percent value of the saturation status. Scenario analysis is particularly important for decision makers such as emergency responders and insurers as their operations depend on their ability to gauge and appropriately assess risk. This analysis will enables insurers to develop mitigation strategies and contingency plans for such events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150014256','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150014256"><span>Assessment of SMOS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval Parameters Using Tau-Omega Algorithms for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit Estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika</p> <p>2014-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> data for land applications. However, the efficient retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> for hydrological applications depends on optimally choosing the <span class="hlt">soil</span> and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval from SMOS brightness temperature are performed to estimate <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval. In tau-omega, the <span class="hlt">soil</span> <span class="hlt">moisture</span> is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22803450','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22803450"><span>[Spatial heterogeneity of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its relationships with environmental factors at small catchment level].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Shi, Zhi-Hua; Zhu, Hua-De; Chen, Jia; Fang, Nu-Fang; Ai, Lei</p> <p>2012-04-01</p> <p>Taking the Wulongchi catchment of Danjiangkou in central China as a case, the <span class="hlt">soil</span> <span class="hlt">moisture</span> regime in the observation period from April to October, 2008 was divided into different dry-wet time periods by two way indicator species analysis (TWINSPAN), and the environmental factors that had significant effects on the spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> in different dry-wet time periods were selected by forward selection and Monte Carlo tests. The redundancy analysis (RDA) was adopted to identify the relationships between the distribution pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> and the environmental factors in different time periods, and the partial RDA was applied to quantitatively analyze the effects of environmental factors, spatial variables, and their interactions on the variation pattern of the <span class="hlt">soil</span> <span class="hlt">moisture</span>. The <span class="hlt">soil</span> <span class="hlt">moisture</span> regime in the observation period was divided into 7 types, and grouped into 4 time periods, i. e. , dry, semi-arid, semi-humid, and humid. In dry period, land use type was the dominant factor <span class="hlt">affecting</span> the spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span>, and the <span class="hlt">soil</span> thickness, relative elevation, profile curvature, <span class="hlt">soil</span> bulk density, and <span class="hlt">soil</span> organic matter content also had significant effects. In semi-arid period, <span class="hlt">soil</span> thickness played dominant role, and land use type, topographic wetness index, <span class="hlt">soil</span> bulk density, and profile curvature had significant effects. In semi-humid period, topographic wetness index was the most important <span class="hlt">affecting</span> factor, and the land use type and the sine value of aspect played significant roles. In humid period, the topographic compound index and the sine value of aspect were the dominant factors, whereas the relative elevation and catchment area were the important factors. In the four time periods, there was a better consistency between the spatial distribution pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> and the environmental ecological gradient. From dry period to humid period, the independent effects of environmental factors on <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.3692Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.3692Z"><span>Biological <span class="hlt">soil</span> crust succession impact on <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature in the sub-surface along a rainfall gradient</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zaady, E.; Yizhaq, H.; Ashkenazy, Y.</p> <p>2012-04-01</p> <p>Biological <span class="hlt">soil</span> crusts produce mucilage sheets of polysaccharides that cover the <span class="hlt">soil</span> surface. This hydrophobic coating can seal the <span class="hlt">soil</span> micro-pores and thus cause reduction of water permeability and may influence <span class="hlt">soil</span> temperature. This study evaluates the impact of crust composition on sub-surface water and temperature over time. We hypothesized that the successional stages of biological <span class="hlt">soil</span> crusts, <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature differently along a rainfall gradient throughout the year. Four experimental sites were established along a rainfall gradient in the western Negev Desert. At each site three treatments; crust removal, pure sand (moving dune) and natural crusted were monitored. Crust successional stage was measured by biophysiological and physical measurements, <span class="hlt">soil</span> water permeability by field mini-Infiltrometer, <span class="hlt">soil</span> <span class="hlt">moisture</span> by neutron scattering probe and temperature by sensors, at different depths. Our main interim conclusions from the ongoing study along the rainfall gradient are: 1. the biogenic crust controls water infiltration into the <span class="hlt">soil</span> in sand dunes, 2. infiltration was dependent on the composition of the biogenic crust. It was low for higher successional stage crusts composed of lichens and mosses and high with cyanobacterial crust. Thus, infiltration rate controlled by the crust is inverse to the rainfall gradient. Continuous disturbances to the crust increase infiltration rates, 3. despite the different rainfall amounts at the sites, <span class="hlt">soil</span> <span class="hlt">moisture</span> content below 50 cm is almost the same. We therefore predict that climate change in areas that are becoming dryer (desertification) will have a positive effect on <span class="hlt">soil</span> water content and vice versa.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFM.H54C..01C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFM.H54C..01C"><span>Horizontal and vertical variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> in savanna ecosystems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caylor, K.; D'Odorico, P.; Rodriguez-Iturbe, I.</p> <p>2004-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key hydrological variable that mediates the interactions between climate, <span class="hlt">soil</span>, and vegetation dynamics in water-limited ecosystems. Because of the importance of water limitation in savannas, a number of theoretical models of tree-grass coexistence have been developed which differ in their underlying assumptions about the ways in which trees and grasses access and use <span class="hlt">soil</span> <span class="hlt">moisture</span>. However, clarification of the mechanisms that allow savanna vegetation to persist as a mixture of grasses and trees remains a vexing problem in both hydrological and vegetation science. A particular challenge is the fact that the spatial pattern of vegetation is both a cause and effect of variation in water availability in semiarid ecosystems. At landscape to regional scales, climatic and geologic constraints on <span class="hlt">soil</span> <span class="hlt">moisture</span> availability are primary determinants of vegetation structural pattern. However, at local to landscape scales the patchy vegetation structural mosaic serves to redistribute the availability of <span class="hlt">soil</span> <span class="hlt">moisture</span> in ways that have important consequences for structural dynamics and community composition. In this regard, the emerging field of ecohydrology is well suited to investigate questions concerning couplings between the patchy structural mosaic of savanna vegetation and the kinds self-organizing dynamics known to exist in other light and nutrient-limited vegetation systems. Here we address the role of patchy vegetation structure through the use of a lumped model of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics that accounts for the effect of tree canopy on the lateral and vertical distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The model includes mechanisms for the drying of the ground surface due to <span class="hlt">soil</span> evaporation in the sites with no tree cover, and for the lateral water uptake due to root invading areas with no canopy cover located in the proximity of trees. The model, when applied to a series of sites along a rainfall gradient in southern Africa, is able to explain the cover</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJAEO..48...85R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJAEO..48...85R"><span>Multiyear monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> over Iran through satellite and reanalysis <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rahmani, Abdolaziz; Golian, Saeed; Brocca, Luca</p> <p>2016-06-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> (SM) plays a fundamental role for many hydrological applications including water resources, drought analysis, agriculture, and climate variability and extremes. SM is not measured in most parts of Iran and limited measurements do not meet sufficient temporal and spatial resolution. Hence, due to ease of operation, their global coverage and demonstrated accuracy, use of remote sensing SM products is almost the only way for deriving SM information in Iran. In the present research, surface SM (SSM) datasets at six subregions of Iran with different climate conditions were extracted from two satellite-based passive (SMOSL3) and active + passive (ESA CCI SM) microwave observations, and two reanalysis (ERA-Interim and ERA-Interim/Land) products. Time series of averaged monthly mean SSM products and in situ ground precipitation and temperature measurements were derived for each subregion. Results revealed that, generally, all SSM products were in good agreement with each other with correlation coefficients higher than 0.5. The better agreement was found in the Northeast and Southwest region with average correlation values equal to 0.88 and 0.91, respectively. It should be noted that the SSM datasets are characterized by different periods and lengths. Hence, results should be assessed with cautious. Moreover, most SSM products have strong correlations with maximum, minimum and average temperature as well as with total monthly precipitation. Also, trend analysis showed no trend for time series of monthly SSM over all subregions in the two periods 1980-1999 and 2000-2014. The only exceptions were the Southeast subregion for ERA-Interim and Center and Northwest subregions for the ESA CCI SM for which a negative trend was detected for the period 2000-2014. Finally, the Standardized <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Index (SSI) calculated from ERA-Interim, ERA-I/Land and ESA CCI SM datasets showed that the Center and Southeast regions suffered from the most severe and longest</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8904L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8904L"><span>Spatial variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieved by SMOS satellite</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lukowski, Mateusz; Marczewski, Wojciech; Usowicz, Boguslaw; Rojek, Edyta; Slominski, Jan; Lipiec, Jerzy</p> <p>2015-04-01</p> <p>Standard statistical methods assume that the analysed variables are independent. Since the majority of the processes observed in the nature are continuous in space and time, this assumption introduces a significant limitation for understanding the examined phenomena. In classical approach, valuable information about the locations of examined observations is completely lost. However, there is a branch of statistics, called geostatistics, which is the study of random variables, but taking into account the space where they occur. A common example of so-called "regionalized variable" is <span class="hlt">soil</span> <span class="hlt">moisture</span>. Using in situ methods it is difficult to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution because it is often significantly diversified. Thanks to the geostatistical methods, by employing semivariance analysis, it is possible to get the information about the nature of spatial dependences and their lengths. Since the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity mission launch in 2009, the estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial distribution for regional up to continental scale started to be much easier. In this study, the SMOS L2 data for Central and Eastern Europe were examined. The statistical and geostatistical features of <span class="hlt">moisture</span> distributions of this area were studied for selected natural <span class="hlt">soil</span> phenomena for 2010-2014 including: freezing, thawing, rainfalls (wetting), drying and drought. Those <span class="hlt">soil</span> water "states" were recognized employing ground data from the agro-meteorological network of ground-based stations SWEX and SMUDP2 data from SMOS. After pixel regularization, without any upscaling, the geostatistical methods were applied directly on Discrete Global Grid (15-km resolution) in ISEA 4H9 projection, on which SMOS observations are reported. Analysis of spatial distribution of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span>, carried out for each data set, in most cases did not show significant trends. It was therefore assumed that each of the examined distributions of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the adopted scale satisfies</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950048080&hterms=Moisture&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D10%26Ntt%3DMoisture','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950048080&hterms=Moisture&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D10%26Ntt%3DMoisture"><span>Use of midlatitude <span class="hlt">soil</span> <span class="hlt">moisture</span> and meteorological observations to validate <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations with biosphere and bucket models</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Robock, Alan; Vinnikov, Konstantin YA.; Schlosser, C. Adam; Speranskaya, Nina A.; Xue, Yongkang</p> <p>1995-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> observations in sites with natural vegetation were made for several decades in the former Soviet Union at hundreds of stations. In this paper, the authors use data from six of these stations from different climatic regimes, along with ancillary meteorological and actinometric data, to demonstrate a method to validate <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations with biosphere and bucket models. Some early and current general circulation models (GCMs) use bucket models for <span class="hlt">soil</span> hydrology calculations. More recently, the Simple Biosphere Model (SiB) was developed to incorporate the effects of vegetation on fluxes of <span class="hlt">moisture</span>, momentum, and energy at the earth's surface into <span class="hlt">soil</span> hydrology models. Until now, the bucket and SiB have been verified by comparison with actual <span class="hlt">soil</span> <span class="hlt">moisture</span> data only on a limited basis. In this study, a Simplified SiB (SSiB) <span class="hlt">soil</span> hydrology model and a 15-cm bucket model are forced by observed meteorological and actinometric data every 3 h for 6-yr simulations at the six stations. The model calculations of <span class="hlt">soil</span> <span class="hlt">moisture</span> are compared to observations of <span class="hlt">soil</span> <span class="hlt">moisture</span>, literally 'ground truth,' snow cover, surface albedo, and net radiation, and with each other. For three of the stations, the SSiB and 15-cm bucket models produce good simulations of seasonal cycles and interannual variations of <span class="hlt">soil</span> <span class="hlt">moisture</span>. For the other three stations, there are large errors in the simulations by both models. Inconsistencies in specification of field capacity may be partly responsible. There is no evidence that the SSiB simulations are superior in simulating <span class="hlt">soil</span> <span class="hlt">moisture</span> variations. In fact, the models are quite similar since SSiB implicitly has a bucket embedded in it. One of the main differences between the models is in the treatment of runoff due to melting snow in the spring -- SSiB incorrectly puts all the snowmelt into runoff. While producing similar <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations, the models produce very different surface latent and sensible heat fluxes, which</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.7073H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.7073H"><span>Physically plausible prescription of land surface model <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hauser, Mathias; Orth, René; Thiery, Wim; Seneviratne, Sonia</p> <p>2016-04-01</p> <p>Land surface hydrology is an important control of surface weather and climate, especially under extreme dry or wet conditions where it can amplify heat waves or floods, respectively. Prescribing <span class="hlt">soil</span> <span class="hlt">moisture</span> in land surface models is a valuable technique to investigate this link between hydrology and climate. It has been used for example to assess the influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> on temperature variability, mean and extremes (Seneviratne et al. 2006, 2013, Lorenz et al., 2015). However, perturbing the <span class="hlt">soil</span> <span class="hlt">moisture</span> content artificially can lead to a violation of the energy and water balances. Here we present a new method for prescribing <span class="hlt">soil</span> <span class="hlt">moisture</span> which ensures water and energy balance closure by using only water from runoff and a reservoir term. If water is available, the method prevents <span class="hlt">soil</span> <span class="hlt">moisture</span> decrease below climatological values. Results from simulations with the Community Land Model (CLM) indicate that our new method allows to avoid <span class="hlt">soil</span> <span class="hlt">moisture</span> deficits in many regions of the world. We show the influence of the irrigation-supported <span class="hlt">soil</span> <span class="hlt">moisture</span> content on mean and extreme temperatures and contrast our findings with that of earlier studies. Additionally, we will assess how long into the 21st century the new method will be able to maintain present-day climatological <span class="hlt">soil</span> <span class="hlt">moisture</span> levels for different regions. Lorenz, R., Argüeso, D., Donat, M.G., Pitman, A.J., den Hurk, B.V., Berg, A., Lawrence, D.M., Chéruy, F., Ducharne, A., Hagemann, S. and Meier, A., 2015. Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. Journal of Geophysical Research: Atmospheres. Seneviratne, S.I., Lüthi, D., Litschi, M. and Schär, C., 2006. Land-atmosphere coupling and climate change in Europe. Nature, 443(7108), pp.205-209. Seneviratne, S.I., Wilhelm, M., Stanelle, T., Hurk, B., Hagemann, S., Berg, A., Cheruy, F., Higgins, M.E., Meier, A., Brovkin, V. and Claussen, M., 2013. Impact of <span class="hlt">soil</span> <span class="hlt">moisture</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSM.A34B..04V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSM.A34B..04V"><span>Sensitivity of the NCEP CFS to Localized <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Anomalies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>van den Dool, H. M.; Saha, S.</p> <p>2006-05-01</p> <p>It has been speculated for a very long time that one of the few sources of short-term climate prediction skill may be related to continental <span class="hlt">soil</span> <span class="hlt">moisture</span>. The question is just how nature reacts to <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies? Observations of Temperature and Precipitation can shed some light, but because observations of Evaporation, Runoff and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> are spotty at best, we need to use model integrations for a more complete understanding. We make use of the NCEP CFS hindcast data (Saha et al 2006) as our control. This data set is also used to define the long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> and snow climatology over a 25 year period. We made new runs with the same CFS set-up, everything else exactly as before, except that globally the climatology defined above for the <span class="hlt">soil</span> <span class="hlt">moisture</span> and snow is given as initial condition. We ran 5 members for each year from 1981-2005, starting from April 28, 29, 30, and May 1, 2. In total we have 125 runs. Each run goes through northern summer until September 1, i.e. 4 month integration. During this period <span class="hlt">soil</span> <span class="hlt">moisture</span> and snow are interactive. Only the runs for the years 2000-2004 (25 runs) are used for comparison to some new runs where a "patch" was added to the initial climatological <span class="hlt">soil</span> <span class="hlt">moisture</span>. A patch is a <span class="hlt">soil</span> <span class="hlt">moisture</span> anomaly that is zero globally, except for a small area (like a circle with 10 degree radius) where the <span class="hlt">soil</span> <span class="hlt">moisture</span> anomaly is large (300mm in the center). The goal is to study the impact of a localized source of water over land, and to make attribution of its impact easier. We have looked at three positions for the patch centered at i) US (38N,100W), ii) India (25N,75E) and iii) Africa (12.5N,10E), and the result in terms of difference between the two sets of runs will be given. If time permits, all integrations may be rerun with the Noah land model, which has replaced the old OSU land model in NCEP operations. Saha and Co-Authors, 2006: The NCEP Climate Forecast System, J. Climate, in press.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..44.6124B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..44.6124B"><span>Uncertain <span class="hlt">soil</span> <span class="hlt">moisture</span> feedbacks in model projections of Sahel precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berg, Alexis; Lintner, Benjamin R.; Findell, Kirsten; Giannini, Alessandra</p> <p>2017-06-01</p> <p>Given the uncertainties in climate model projections of Sahel precipitation, at the northern edge of the West African Monsoon, understanding the factors governing projected precipitation changes in this semiarid region is crucial. This study investigates how long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> changes projected under climate change may feedback on projected changes of Sahel rainfall, using simulations with and without <span class="hlt">soil</span> <span class="hlt">moisture</span> change from five climate models participating in the Global Land Atmosphere Coupling Experiment-Coupled Model Intercomparison Project phase 5 experiment. In four out of five models analyzed, <span class="hlt">soil</span> <span class="hlt">moisture</span> feedbacks significantly influence the projected West African precipitation response to warming; however, the sign of these feedbacks differs across the models. These results demonstrate that reducing uncertainties across model projections of the West African Monsoon requires, among other factors, improved mechanistic understanding and constraint of simulated land-atmosphere feedbacks, even at the large spatial scales considered here.<abstract type="synopsis"><title type="main">Plain Language SummaryClimate model projections of Sahel rainfall remain notoriously uncertain; understanding the physical processes responsible for this uncertainty is thus crucial. Our study focuses on analyzing the feedbacks of <span class="hlt">soil</span> <span class="hlt">moisture</span> changes on model projections of the West African Monsoon under global warming. <span class="hlt">Soil</span> <span class="hlt">moisture</span>-atmosphere interactions have been shown in prior studies to play an important role in this region, but the potential feedbacks of long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> changes on projected precipitation changes have not been investigated specifically. To isolate these feedbacks, we use targeted simulations from five climate models, with and without <span class="hlt">soil</span> <span class="hlt">moisture</span> change. Importantly, we find that climate models exhibit <span class="hlt">soil</span> <span class="hlt">moisture</span>-precipitation feedbacks of different sign in this region: in some models <span class="hlt">soil</span> <span class="hlt">moisture</span> changes amplify precipitation changes</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.9120W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.9120W"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Extremes Observed by METOP ASCAT: Was 2012 an Exceptional Year?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wagner, Wolfgang; Paulik, Christoph; Hahn, Sebastian; Melzer, Thomas; Parinussa, Robert; de Jeu, Richard; Dorigo, Wouter; Chung, Daniel; Enenkel, Markus</p> <p>2013-04-01</p> <p>In summer 2012 the international press reported widely about the severe drought that had befallen large parts of the United States. Yet, the US drought was only one of several major droughts that occurred in 2012: Southeastern Europe, Central Asia, Brazil, India, Southern Australia and several other regions suffered from similarly dry <span class="hlt">soil</span> conditions. This raises the question whether 2012 was an exceptionally dry year? In this presentation we will address this question by analyzing global <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns as observed by the Advanced Scatterometer (ASCAT) flown on board of the METOP-A satellite. We firstly compare the 2012 ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> data to all available ASCAT measurements acquired by the instrument since the launch of METOP-A in November 2006. Secondly, we compare the 2012 data to a long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> data set derived by merging the ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> data with other active and passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals as described by Liu et al. (2012) and Wagner et al. (2012) (see also http://www.esa-soilmoisture-cci.org/). A first trend analysis of the latter long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> data set carried out by Dorigo et al. (2012) has revealed that over the period 1988-2010 significant trends were observed over 27 % of the area covered by the data set, of which 73 % were negative (<span class="hlt">soil</span> drying) and only 27 % were positive (<span class="hlt">soil</span> wetting). In this presentation we will show how the inclusion of the years 2011 and 2012 <span class="hlt">affects</span> the areal extent and strengths of these significant trends. REFERENCES Dorigo, W., R. de Jeu, D. Chung, R. Parinussa, Y. Liu, W. Wagner, D. Fernández-Prieto (2012) Evaluating global trends (1988-2010) in harmonized multi-satellite surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, Geophysical Research Letters, 39, L18405, 1-7. Liu, Y.Y., W.A. Dorigo, R.M. Parinussa, R.A.M. de Jeu, W. Wagner, M.F. McCabe, J.P. Evans, A.I.J.M. van Dijk (2012) Trend-preserving blending of passive and active microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals, Remote Sensing of Environment</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.B23E0248P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.B23E0248P"><span>Longwall Coal Mining and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Changes in Southwestern Pennsylvania</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pfeil-McCullough, E. K.; Bain, D.</p> <p>2014-12-01</p> <p>Subsidence from longwall coal mining impacts the surface and sub-surface hydrology in overlying areas. During longwall mining, coal is completely removed in large rectangular panels and the overlying rock collapses into the void. Though the hydrologic effects of longwall mining subsidence have been studied in arid systems, in humid-temperate regions these effects are not well understood. In particular, it is not clear how longwall mining will impact <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns. Utilizing simple <span class="hlt">soil</span> water modeling frameworks (ArcGIS-based Water Balance Toolbox) and the locations of recent long wall mining, potential impacts on <span class="hlt">soil</span> water availability were predicted at the landscape scale. For example, in areas overlying panel edges, <span class="hlt">soil</span> available water capacities (AWC) were altered based on several scenarios of AWC change and interactions between aspect driven <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes and the mining perturbation were explored over a five year period (2008-2013). The regular patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> arising from insolation contrasts, when interacting with broad-scale longwall mining impacts, are predicted to cause complicated patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> change. These predictions serve as a means to guide field campaigns necessary to understand longwall mining's hydrologic impacts in wetter climates</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760025537','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760025537"><span>Remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> with microwave radiometers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schmugge, T.; Wilheit, T.; Webster, W., Jr.; Gloerson, P.</p> <p>1976-01-01</p> <p>Results are presented that were derived from measurements made by microwave radiometers during the March 1972 and February 1973 flights of National Aeronautics and Space Administration (NASA) Convair-9900 aircraft over agricultural test sites in the southwestern part of United States. The purpose of the missions was to study the use of microwave radiometers for the remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The microwave radiometers covered the 0.8- to 21-cm wavelength range. The results show a good linear correlation between the observed microwave brightness temperature and <span class="hlt">moisture</span> content of the 0- to 1-cm layer of the <span class="hlt">soil</span>. The results at the largest wavelength (21 cm) show the greatest sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span> variations and indicate the possibility of sensing these variations through a vegetative canopy. The effect of <span class="hlt">soil</span> texture on the emission from the <span class="hlt">soil</span> was also studied and it was found that this effect can be compensated for by expressing <span class="hlt">soil</span> <span class="hlt">moisture</span> as a percent of field capacity for the <span class="hlt">soil</span>. The results were compared with calculations based on a radiative transfer model for layered dielectrics and the agreement is very good at the longer wavelengths. At the shorter wavelengths, surface roughness effects are larger and the agreement becomes poorer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28309510','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28309510"><span>Some effects of <span class="hlt">soil-moisture</span> availability on above-ground production and reproductive allocation in Larrea tridentata (DC) Cov.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cunningham, G L; Syvertsen, J P; Reynolds, J F; Willson, J M</p> <p>1979-01-01</p> <p>Data from the US/IBP Desert Biome validation studies indicate that above-ground production and biomass allocated to reproduction in Larrea tridentata vary from one year to another depending upon the timing and extent of <span class="hlt">soil-moisture</span> availability. In an attempt to verify these observations and determine to what extent water availability can <span class="hlt">affect</span> total aboveground production and reproductive allocation in this widely distributed warm desert shrub, a series of <span class="hlt">soil-moisture</span> augmentation experiments were conducted. High levels of <span class="hlt">soil</span> <span class="hlt">moisture</span> had a greater effect on reproductive allocation than on total above-ground production. Enhanced <span class="hlt">soil</span> <span class="hlt">moisture</span> during the period of active growth increased total above-ground production and reduced the percentage of biomass allocated to reproduction. Enhanced <span class="hlt">soil</span> <span class="hlt">moisture</span> during the normal periods of little or no growth did not increase total above-ground production.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H21I1597B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H21I1597B"><span>Enhancing SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals via Superresolution Techniques</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beale, K. D.; Ebtehaj, A. M.; Romberg, J. K.; Bras, R. L.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key state variable that modulates land-atmosphere interactions and its high-resolution global scale estimates are essential for improved weather forecasting, drought prediction, crop management, and the safety of troop mobility. Currently, NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active/Passive (SMAP) satellite provides a global picture of <span class="hlt">soil</span> <span class="hlt">moisture</span> variability at a resolution of 36 km, which is prohibitive for some hydrologic applications. The goal of this research is to enhance the resolution of SMAP passive microwave retrievals by a factor of 2 to 4 using modern superresolution techniques that rely on the knowledge of high-resolution land surface models. In this work, we explore several super-resolution techniques including an empirical dictionary method, a learned dictionary method, and a three-layer convolutional neural network. Using a year of global high-resolution land surface model simulations as training set, we found that we are able to produce high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> maps that outperform the original low-resolution observations both qualitatively and quantitatively. In particular, on a patch-by-patch basis we are able to produce estimates of high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> maps that improve on the original low-resolution patches by on average 6% in terms of mean-squared error, and 14% in terms of the structural similarity index.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170006035','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170006035"><span>Combined Radar-Radiometer Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Roughness Estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Akbar, Ruzbeh; Cosh, Michael H.; O'Neill, Peggy E.; Entekhabi, Dara; Moghaddam, Mahta</p> <p>2017-01-01</p> <p>A robust physics-based combined radar-radiometer, or Active-Passive, surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and roughness estimation methodology is presented. <span class="hlt">Soil</span> <span class="hlt">moisture</span> and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval, the importance of consistent forward emission and scattering development is discussed and presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29657350','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29657350"><span>Combined Radar-Radiometer Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Roughness Estimation.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Akbar, Ruzbeh; Cosh, Michael H; O'Neill, Peggy E; Entekhabi, Dara; Moghaddam, Mahta</p> <p>2017-07-01</p> <p>A robust physics-based combined radar-radiometer, or Active-Passive, surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and roughness estimation methodology is presented. <span class="hlt">Soil</span> <span class="hlt">moisture</span> and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval, the importance of consistent forward emission and scattering development is discussed and presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5672948','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5672948"><span>Relating coccidioidomycosis (valley fever) incidence to <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Coopersmith, E. J.; Bell, J. E.; Benedict, K.; Shriber, J.; McCotter, O.; Cosh, M. H.</p> <p>2017-01-01</p> <p>Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp., in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets, it has not been feasible to perform a direct analysis of the relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower <span class="hlt">soil</span> <span class="hlt">moisture</span> in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to <span class="hlt">soil</span> <span class="hlt">moisture</span> levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location. PMID:29124249</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020066567','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020066567"><span>Impact of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Initialization on Seasonal Weather Prediction</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal D.; Suarez, Max J.; Houser, Paul (Technical Monitor)</p> <p>2002-01-01</p> <p>The potential role of <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997-2001, we generated two 16-member ensembles of 3-month simulations. The first, "AMIP-style" ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with "realistic" <span class="hlt">soil</span> <span class="hlt">moisture</span> contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation data set and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization has a statistically significant impact on summertime precipitation and temperature over only a handful of continental regions. These regions agree, to first order, with regions that satisfy three conditions: (1) a tendency toward large initial <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies, (2) a strong sensitivity of evaporation to <span class="hlt">soil</span> <span class="hlt">moisture</span>, and (3) a strong sensitivity of precipitation to evaporation. The degree to which the initialization improves forecasts relative to observations is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820003640','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820003640"><span>An evaluation of the spatial resolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> information</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hardy, K. R.; Cohen, S. H.; Rogers, L. K.; Burke, H. H. K.; Leupold, R. C.; Smallwood, M. D.</p> <p>1981-01-01</p> <p>Rainfall-amount patterns in the central regions of the U.S. were assessed. The spatial scales of surface features and their corresponding microwave responses in the mid western U.S. were investigated. The usefulness for U.S. government agencies of <span class="hlt">soil</span> <span class="hlt">moisture</span> information at scales of 10 km and 1 km. was ascertained. From an investigation of 494 storms, it was found that the rainfall resulting from the passage of most types of storms produces patterns which can be resolved on a 10 km scale. The land features causing the greatest problem in the sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> over large agricultural areas with a radiometer are bodies of water. Over the mid-western portions of the U.S., water occupies less than 2% of the total area, the consequently, the water bodies will not have a significant impact on the mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Over most of the areas, measurements at a 10-km resolution would adequately define the distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Crop yield models and hydrological models would give improved results if <span class="hlt">soil</span> <span class="hlt">moisture</span> information at scales of 10 km was available.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMEP41C0921A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMEP41C0921A"><span>Downscaling Coarse Scale Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product using Machine Learning</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abbaszadeh, P.; Moradkhani, H.; Yan, H.</p> <p>2016-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of <span class="hlt">soil</span> <span class="hlt">moisture</span> at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> information that is currently used for land data assimilation applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H13I1518M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H13I1518M"><span>Compact polarimetric synthetic aperture radar for monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span> condition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Merzouki, A.; McNairn, H.; Powers, J.; Friesen, M.</p> <p>2017-12-01</p> <p>Coarse resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> maps are currently operationally delivered by ESA's SMOS and NASA's SMAP passive microwaves sensors. Despite this evolution, operational <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring at the field scale remains challenging. A number of factors contribute to this challenge including the complexity of the retrieval that requires advanced SAR systems with enhanced temporal revisit capabilities. Since the launch of RADARSAT-2 in 2007, Agriculture and Agri-Food Canada (AAFC) has been evaluating the accuracy of these data for estimating surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Thus, a hybrid (multi-angle/multi-polarization) retrieval approach was found well suited for the planned RADARSAT Constellation Mission (RCM) considering the more frequent relook expected with the three satellite configuration. The purpose of this study is to evaluate the capability of C-band CP data to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> over agricultural fields, in anticipation of the launch of RCM. In this research we introduce a new CP approach based on the IEM and simulated RCM CP mode intensities from RADARSAT-2 images acquired at different dates. The accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval from the proposed multi-polarization and hybrid methods will be contrasted with that from a more conventional quad-pol approach, and validated against in situ measurements by pooling data collected over AAFC test sites in Ontario, Manitoba and Saskatchewan, Canada.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29124249','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29124249"><span>Relating coccidioidomycosis (valley fever) incidence to <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Coopersmith, E J; Bell, J E; Benedict, K; Shriber, J; McCotter, O; Cosh, M H</p> <p>2017-04-17</p> <p>Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp. , in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets, it has not been feasible to perform a direct analysis of the relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower <span class="hlt">soil</span> <span class="hlt">moisture</span> in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to <span class="hlt">soil</span> <span class="hlt">moisture</span> levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H51R..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H51R..02L"><span>Four Decades of Microwave Satellite <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observations: Product validation and inter-satellite comparisons</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lanka, K.; Pan, M.; Wanders, N.; Kumar, D. N.; Wood, E. F.</p> <p>2017-12-01</p> <p>. Although the retrievals from the SMOS mission are <span class="hlt">affected</span> by issues such as RFI, the accuracy is still comparable to or better than that of AMSR-E and ASCAT sensors. All <span class="hlt">soil</span> <span class="hlt">moisture</span> products have indicated better agreement with the ISMN data than the VICSM, which indicate that they produce <span class="hlt">soil</span> <span class="hlt">moisture</span> with better accuracy than the VICSM over the CONUS.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24836136','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24836136"><span>Fate of 14C-labeled dissolved organic matter in paddy and upland <span class="hlt">soils</span> in responding to <span class="hlt">moisture</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chen, Xiangbi; Wang, Aihua; Li, Yang; Hu, Lening; Zheng, Hua; He, Xunyang; Ge, Tida; Wu, Jinshui; Kuzyakov, Yakov; Su, Yirong</p> <p>2014-08-01</p> <p><span class="hlt">Soil</span> organic matter (SOM) content in paddy <span class="hlt">soils</span> is higher than that in upland <span class="hlt">soils</span> in tropical and subtropical China. The dissolved organic matter (DOM) concentration, however, is lower in paddy <span class="hlt">soils</span>. We hypothesize that <span class="hlt">soil</span> <span class="hlt">moisture</span> strongly controls the fate of DOM, and thereby leads to differences between the two agricultural <span class="hlt">soils</span> under contrasting management regimens. A 100-day incubation experiment was conducted to trace the fate and biodegradability of DOM in paddy and upland <span class="hlt">soils</span> under three <span class="hlt">moisture</span> levels: 45%, 75%, and 105% of the water holding capacity (WHC). (14)C labeled DOM, extracted from the (14)C labeled rice plant material, was incubated in paddy and upland <span class="hlt">soils</span>, and the mineralization to (14)CO2 and incorporation into microbial biomass were analyzed. Labile and refractory components of the initial (14)C labeled DOM and their respective half-lives were calculated by a double exponential model. During incubation, the mineralization of the initial (14)C labeled DOM in the paddy <span class="hlt">soils</span> was more <span class="hlt">affected</span> by <span class="hlt">moisture</span> than in the upland <span class="hlt">soils</span>. The amount of (14)C incorporated into the microbial biomass (2.4-11.0% of the initial DOM-(14)C activity) was less <span class="hlt">affected</span> by <span class="hlt">moisture</span> in the paddy <span class="hlt">soils</span> than in the upland <span class="hlt">soils</span>. At any of the <span class="hlt">moisture</span> levels, 1) the mineralization of DOM to (14)CO2 within 100 days was 1.2-2.1-fold higher in the paddy <span class="hlt">soils</span> (41.9-60.0% of the initial DOM-(14)C activity) than in the upland <span class="hlt">soils</span> (28.7-35.7%), 2) (14)C activity remaining in solution was significantly lower in the paddy <span class="hlt">soils</span> than in the upland <span class="hlt">soils</span>, and 3) (14)C activity remaining in the same agricultural <span class="hlt">soil</span> solution was not significantly different among the three <span class="hlt">moisture</span> levels after 20 days. Therefore, <span class="hlt">moisture</span> strongly controls DOM fate, but <span class="hlt">moisture</span> was not the key factor in determining the lower DOM in the paddy <span class="hlt">soils</span> than in the upland <span class="hlt">soils</span>. The UV absorbance of DOM at 280 nm indicates less aromaticity of DOM from the paddy <span class="hlt">soils</span> than from the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..551..203K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..551..203K"><span>Automated general temperature correction method for dielectric <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kapilaratne, R. G. C. Jeewantinie; Lu, Minjiao</p> <p>2017-08-01</p> <p>An effective temperature correction method for dielectric sensors is important to ensure the accuracy of <span class="hlt">soil</span> water content (SWC) measurements of local to regional-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring networks. These networks are extensively using highly temperature sensitive dielectric sensors due to their low cost, ease of use and less power consumption. Yet there is no general temperature correction method for dielectric sensors, instead sensor or site dependent correction algorithms are employed. Such methods become ineffective at <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring networks with different sensor setups and those that cover diverse climatic conditions and <span class="hlt">soil</span> types. This study attempted to develop a general temperature correction method for dielectric sensors which can be commonly used regardless of the differences in sensor type, climatic conditions and <span class="hlt">soil</span> type without rainfall data. In this work an automated general temperature correction method was developed by adopting previously developed temperature correction algorithms using time domain reflectometry (TDR) measurements to ThetaProbe ML2X, Stevens Hydra probe II and Decagon Devices EC-TM sensor measurements. The rainy day effects removal procedure from SWC data was automated by incorporating a statistical inference technique with temperature correction algorithms. The temperature correction method was evaluated using 34 stations from the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring Network and another nine stations from a local <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring network in Mongolia. <span class="hlt">Soil</span> <span class="hlt">moisture</span> monitoring networks used in this study cover four major climates and six major <span class="hlt">soil</span> types. Results indicated that the automated temperature correction algorithms developed in this study can eliminate temperature effects from dielectric sensor measurements successfully even without on-site rainfall data. Furthermore, it has been found that actual daily average of SWC has been changed due to temperature effects of dielectric sensors with a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9685H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9685H"><span>Operational Irrigation Scheduling for Citrus Trees with <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data Assimilation and Weather Forecast</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Han, Xujun; Hendricks Franssen, Harrie-Jan; Martínez Alzamora, Fernando; Ángel Jiménez Bello, Miguel; Chanzy, André; Vereecken, Harry</p> <p>2015-04-01</p> <p>Agricultural areas in the Mediterranean are expected to face more drought stress in the future due to climate change and human activities. Irrigation scheduling is necessary to allocate the optimal water amount at the right time period to avoid unnecessary water losses. An operational data assimilation framework was set-up to combine model predictions and <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements in an optimal way for characterizing the <span class="hlt">soil</span> water status of the root zone. Irrigation amounts for the next days are optimized on the basis of the <span class="hlt">soil</span> water status of the root zone and meteorological ensemble predictions. In these experiments, the uncertainties of atmospheric forcings and <span class="hlt">soil</span> properties were considered. The uncertain model forcings were taken from an ensemble of weather forecasts by ECMWF, and delivered by MeteoFrance in this project. The improved <span class="hlt">soil</span> <span class="hlt">moisture</span> profile was used to calculate the irrigation requirement taking into account the root distribution of citrus trees in the subsurface. The approach was tested operationally for the experimental site near Picassent, Valencia, Spain. Three fields were irrigated according to our approach in the years 2013 and 2014. Three others were irrigated traditionally, based on FAO-criteria. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was measured by FDR probes at 10 cm and 30 cm depth at various fields and these real time data were assimilated by the Local Ensemble Transform Kalman Filter (LETKF) into the Community Land Model (CLM) to improve the estimation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> profile. The measured <span class="hlt">soil</span> <span class="hlt">moisture</span> was assimilated five times per day before the start of the next drip irrigation. The final results (total amount of irrigated water, stem water potential and citrus production) show that our strategy resulted in significantly less irrigated water compared to the FAO-irrigated fields, but without indications of increased water stress. <span class="hlt">Soil</span> <span class="hlt">moisture</span> contents did not decline over time in our approach, stem water potential measurements did not</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JHyd..498...89K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JHyd..498...89K"><span>Patterns and scaling properties of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> in an agricultural landscape: An ecohydrological modeling study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Korres, W.; Reichenau, T. G.; Schneider, K.</p> <p>2013-08-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable in hydrology, meteorology and agriculture. <span class="hlt">Soil</span> <span class="hlt">moisture</span>, and surface <span class="hlt">soil</span> <span class="hlt">moisture</span> in particular, is highly variable in space and time. Its spatial and temporal patterns in agricultural landscapes are <span class="hlt">affected</span> by multiple natural (precipitation, <span class="hlt">soil</span>, topography, etc.) and agro-economic (<span class="hlt">soil</span> management, fertilization, etc.) factors, making it difficult to identify unequivocal cause and effect relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> and its driving variables. The goal of this study is to characterize and analyze the spatial and temporal patterns of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (top 20 cm) in an intensively used agricultural landscape (1100 km2 northern part of the Rur catchment, Western Germany) and to determine the dominant factors and underlying processes controlling these patterns. A second goal is to analyze the scaling behavior of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns in order to investigate how spatial scale <span class="hlt">affects</span> spatial patterns. To achieve these goals, a dynamically coupled, process-based and spatially distributed ecohydrological model was used to analyze the key processes as well as their interactions and feedbacks. The model was validated for two growing seasons for the three main crops in the investigation area: Winter wheat, sugar beet, and maize. This yielded RMSE values for surface <span class="hlt">soil</span> <span class="hlt">moisture</span> between 1.8 and 7.8 vol.% and average RMSE values for all three crops of 0.27 kg m-2 for total aboveground biomass and 0.93 for green LAI. Large deviations of measured and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> can be explained by a change of the infiltration properties towards the end of the growing season, especially in maize fields. The validated model was used to generate daily surface <span class="hlt">soil</span> <span class="hlt">moisture</span> maps, serving as a basis for an autocorrelation analysis of spatial patterns and scale. Outside of the growing season, surface <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns at all spatial scales depend mainly upon <span class="hlt">soil</span> properties. Within the main growing season, larger scale</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1816968T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1816968T"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> sensitivity of autotrophic and heterotrophic forest floor respiration in boreal xeric pine and mesic spruce forests</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ťupek, Boris; Launiainen, Samuli; Peltoniemi, Mikko; Heikkinen, Jukka; Lehtonen, Aleksi</p> <p>2016-04-01</p> <p>Litter decomposition rates of the most process based <span class="hlt">soil</span> carbon models <span class="hlt">affected</span> by environmental conditions are linked with <span class="hlt">soil</span> heterotrophic CO2 emissions and serve for estimating <span class="hlt">soil</span> carbon sequestration; thus due to the mass balance equation the variation in measured litter inputs and measured heterotrophic <span class="hlt">soil</span> CO2 effluxes should indicate <span class="hlt">soil</span> carbon stock changes, needed by <span class="hlt">soil</span> carbon management for mitigation of anthropogenic CO2 emissions, if sensitivity functions of the applied model suit to the environmental conditions e.g. <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span>. We evaluated the response forms of autotrophic and heterotrophic forest floor respiration to <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> in four boreal forest sites of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) by a <span class="hlt">soil</span> trenching experiment during year 2015 in southern Finland. As expected both autotrophic and heterotrophic forest floor respiration components were primarily controlled by <span class="hlt">soil</span> temperature and exponential regression models generally explained more than 90% of the variance. <span class="hlt">Soil</span> <span class="hlt">moisture</span> regression models on average explained less than 10% of the variance and the response forms varied between Gaussian for the autotrophic forest floor respiration component and linear for the heterotrophic forest floor respiration component. Although the percentage of explained variance of <span class="hlt">soil</span> heterotrophic respiration by the <span class="hlt">soil</span> <span class="hlt">moisture</span> was small, the observed reduction of CO2 emissions with higher <span class="hlt">moisture</span> levels suggested that <span class="hlt">soil</span> <span class="hlt">moisture</span> response of <span class="hlt">soil</span> carbon models not accounting for the reduction due to excessive <span class="hlt">moisture</span> should be re-evaluated in order to estimate right levels of <span class="hlt">soil</span> carbon stock changes. Our further study will include evaluation of process based <span class="hlt">soil</span> carbon models by the annual heterotrophic respiration and <span class="hlt">soil</span> carbon stocks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H13K1735K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H13K1735K"><span>Impacts of Irrigation on <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Scaling Properties and Downscaling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ko, A.; Mascaro, G.; Vivoni, E. R.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> (θ) exhibits high spatial variability due to the combined effect of natural and anthropogenic factors. Among the latter group, irrigation can introduce significant heterogeneity in the spatial variability of θ, thus modifying the statistical properties typically observed in natural landscapes. This, in turn, can <span class="hlt">affect</span> the application of downscaling models of coarse satellite θ products based on the hypothesis of spatial homogeneity of θ distribution. In this study, the impact of irrigation on the scale invariance properties of θ and the application of a multifractal downscaling algorithm are analyzed using ground- and aircraft-based θ measurements from the National Airborne Field Experiments 2005 (NAFE05) and 2006 (NAFE06) campaigns conducted in two sites in Australia. After identifying irrigated areas through vegetation indices derived from Landsat 5 Thematic Mapper scenes, we investigate the presence of scale invariance from 32 km to 1 km in three scenarios, including (1) the original θ fields and in cases where θ in irrigated pixels was (2) replaced with missing data or (3) interpolated from neighboring pixels. We found that irrigation has a larger impact on the scale invariance properties in a large and compact agricultural district in the NAFE06 region, while it has a negligible influence on the sparser districts of NAFE05. The θ fields of scenario 3 are then used to calibrate a downscaling model based on spatially-homogeneous multifractal cascades as a function of coarse predictors. The model capability to reproduce the θ variability across scales is assessed by comparing ensembles of disaggregated field with the small-scale θ airborne observations and, for the first time, with ground θ measurements. Model performances are adequate in most cases in both experiments, although some deficiencies are found in regions with a larger presence of irrigated fields, suggesting the need to further refine the technique for detection of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170001399','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170001399"><span>Correlation Between <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Dust Emissions: An Investigation for Global Climate Modeling</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fredrickson, Carley; Tan, Qian</p> <p>2017-01-01</p> <p>This work is using the newly available NASA SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement data to evaluate its impact on the atmospheric dust emissions. Dust is an important component of atmospheric aerosols, which <span class="hlt">affects</span> both climate and air quality. In this work, we focused on semi-desert regions, where dust emissions show seasonal variations due to <span class="hlt">soil</span> <span class="hlt">moisture</span> changes, i.e. in Sahel of Africa. We first identified three Aerosol Robotic Network (AERONET) sites in the Sahel (IER_Cinzana, Banizoumbou, and Zinder_Airport). We then utilized measurements of aerosol optical depth (AOD), fine mode fraction, size distribution, and single-scattering albedo and its wave-length dependence to select dust plumes from the available measurements We matched the latitude and longitude of the AERONET station to the corresponding SMAP data cell in the years 2015 and 2016, and calculated their correlation coefficient. Additionally, we looked at the correlation coefficient with a three-day and a five-day shift to check the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> on dust plumes with some time delay. Due to the arid nature of Banizoumbou and Zinder_Airport, no correlation was found to exist between local <span class="hlt">soil</span> <span class="hlt">moisture</span> and dust aerosol load. While IER_Cinzana had <span class="hlt">soil</span> <span class="hlt">moisture</span> levels above the satellite threshold of 0.02cm3/cm3, R-value approaching zero indicated no presence of a correlation. On the other hand, Ilorin demonstrated a significant negative correlation between aerosol optical depth and <span class="hlt">soil</span> <span class="hlt">moisture</span>. When isolating the analysis to Ilorin's dry season, a negative correlation of -0.593 was the largest dust-isolated R-value recorded, suggesting that <span class="hlt">soil</span> <span class="hlt">moisture</span> is driven the dust emission in this semi-desert region during transitional season.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/46775','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/46775"><span>Sensitivity of <span class="hlt">soil</span> respiration to variability in <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature in a humid tropical forest</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Tana Wood; M. Detto; W.L. Silver</p> <p>2013-01-01</p> <p>Precipitation and temperature are important drivers of <span class="hlt">soil</span> respiration. The role of <span class="hlt">moisture</span> and temperature are generally explored at seasonal or inter-annual timescales; however, significant variability also occurs on hourly to daily time-scales. We used small (1.54 m2), throughfall exclusion shelters to evaluate the role <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature as temporal...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H21I1587H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H21I1587H"><span>Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Memory Estimated from Models and SMAP Observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>He, Q.; Mccoll, K. A.; Li, C.; Lu, H.; Akbar, R.; Pan, M.; Entekhabi, D.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> memory(SMM), which is loosely defined as the time taken by <span class="hlt">soil</span> to forget an anomaly, has been proved to be important in land-atmosphere interaction. There are many metrics to calculate the SMM timescale, for example, the timescale based on the time-series autocorrelation, the timescale ignoring the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series and the timescale which only considers <span class="hlt">soil</span> <span class="hlt">moisture</span> increment. Recently, a new timescale based on `Water Cycle Fraction' (Kaighin et al., 2017), in which the impact of precipitation on <span class="hlt">soil</span> <span class="hlt">moisture</span> memory is considered, has been put up but not been fully evaluated in global. In this study, we compared the surface SMM derived from SMAP observations with that from land surface model simulations (i.e., the SMAP Nature Run (NR) provided by the Goddard Earth Observing System, version 5) (Rolf et al., 2014). Three timescale metrics were used to quantify the surface SMM as: T0 based on the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series autocorrelation, deT0 based on the detrending <span class="hlt">soil</span> <span class="hlt">moisture</span> time series autocorrelation, and tHalf based on the Water Cycle Fraction. The comparisons indicate that: (1) there are big gaps between the T0 derived from SMAP and that from NR (2) the gaps get small for deT0 case, in which the seasonality of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> was removed with a moving average filter; (3) the tHalf estimated from SMAP is much closer to that from NR. The results demonstrate that surface SMM can vary dramatically among different metrics, while the memory derived from land surface model differs from the one from SMAP observation. tHalf, with considering the impact of precipitation, may be a good choice to quantify surface SMM and have high potential in studies related to land atmosphere interactions. References McColl. K.A., S.H. Alemohammad, R. Akbar, A.G. Konings, S. Yueh, D. Entekhabi. The Global Distribution and Dynamics of Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span>, Nature Geoscience, 2017 Reichle. R., L. Qing, D.L. Gabrielle, A. Joe. The "SMAP_Nature_v03" Data</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.4163E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.4163E"><span>Synergies and complementarities between ASCAT and SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Escorihuela, Maria Jose; Quintana, Pere; Merlin, Olivier</p> <p>2014-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a critical variable in many kinds of applications including agriculture, water management, meteorology or climatology. This is especially true in the Mediterranean context, where <span class="hlt">soil</span> <span class="hlt">moisture</span> plays an important role in water resources management and hydrometeorological risks such as floods and droughts. Unfortunately, this variable is not widely observed in situ, so we lack data on its time evolution and spatial structure. Remote sensing has been used to estimate surface <span class="hlt">soil</span> <span class="hlt">moisture</span> because it provides comprehensive data over large surfaces. In this study we compared two different surface <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing products; one derived from active microwave data of the ASCAT scatterometer instrument onboard METOP and the other from passive microwave data of the SMOS mission the first dedicated to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span>. SMOS measuring frequency (1.4 GHz) is theoretically more suited to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> than ASCAT measuring frequency (5.255 GHz) because of its lower vegetation effects. On the other hand, ASCAT- like instruments have been providing measurements for more than 2 decades and have been a key input in building the CCI <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variable. In order to get the best global <span class="hlt">soil</span> <span class="hlt">moisture</span> products it is thus essential to understand their respective performances and restrictions. The comparison has been carried out in Catalonia where we have implemented the SURFEX/ISBA land-surface model, which we forced with the SAFRAN meteorological analysis system. A downscaling algorithm has been also implemented and validated over the area to provide SMOS derived <span class="hlt">soil</span> <span class="hlt">moisture</span> fields at 1 km spatial resolution. Catalonia is located in the northeast of the Iberian Peninsula and its climate is typically Mediterranean, mild in winter and warm in summer. The Pyrenees and the neighbouring areas have a high-altitude climate, with minimum temperatures below 0º C, annual rainfall above 1000 mm and abundant snow during the winter. Along the coast</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760017595','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760017595"><span>Results of <span class="hlt">soil</span> <span class="hlt">moisture</span> flights during April 1974</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schmugge, T. J.; Blanchard, B. J.; Burke, W. J.; Paris, J. F.; Swang, J. R.</p> <p>1976-01-01</p> <p>The results presented here are derived from measurements made during the April 5 and 6, 1974 flights of the NASA P-3A aircraft over the Phoenix, Arizona agricultural test site. The purpose of the mission was to study the use of microwave techniques for the remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. These results include infrared (10-to 12 micrometers) 2.8-cm and 21-cm brightness temperatures for approximately 90 bare fields. These brightness temperatures are compared with surface measurements of the <span class="hlt">soil</span> <span class="hlt">moisture</span> made at the time of the overflights. These data indicate that the combination of the sum and difference of the vertically and the horizontally polarized brightness temperatures yield information on both the <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000116624','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000116624"><span>BOREAS HYD-6 Ground Gravimetric <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carroll, Thomas; Knapp, David E. (Editor); Hall, Forrest G. (Editor); Peck, Eugene L.; Smith, David E. (Technical Monitor)</p> <p>2000-01-01</p> <p>The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-6 team collected several data sets related to the <span class="hlt">moisture</span> content of <span class="hlt">soil</span> and overlying humus layers. This data set contains percent <span class="hlt">soil</span> <span class="hlt">moisture</span> ground measurements. These data were collected on the ground along the various flight lines flown in the Southern Study Area (SSA) and Northern Study Area (NSA) during 1994 by the gamma ray instrument. The data are available in tabular ASCII files. The HYD-06 ground gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..4411860L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..4411860L"><span>Irrigation Signals Detected From SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lawston, Patricia M.; Santanello, Joseph A.; Kumar, Sujay V.</p> <p>2017-12-01</p> <p>Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled land-atmosphere system. One way to improve irrigation representation in models is to assimilate <span class="hlt">soil</span> <span class="hlt">moisture</span> observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new enhanced <span class="hlt">soil</span> <span class="hlt">moisture</span> product from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive satellite is able to capture irrigation signals over three semiarid regions in the western United States. This marks an advancement in Earth-observing satellite skill and the ability to monitor human impacts on the water cycle.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1911670C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1911670C"><span>Advantages of using satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates over precipitation products to assess regional vegetation water availability and activity</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Tiexi</p> <p>2017-04-01</p> <p>To improve the understanding of water-vegetation relationships, direct comparative studies assessing the utility of satellite remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span>, gridded precipitation products, and land surface model output are needed. A case study was investigated for a water-limited, lateral inflow receiving area in northeastern Australia during December 2008 to May 2009. In January 2009, monthly precipitation showed strong positive anomalies, which led to strong positive <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies. The precipitation anomalies disappeared within a month. In contrast, the <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies persisted for months. Positive anomalies of Normalized Difference Vegetation Index (NDVI) appeared in February, in response to water supply, and then persisted for several months. In addition to these temporal characteristics, the spatial patterns of NDVI anomalies were more similar to <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns than to those of precipitation and land surface model output. The long memory of <span class="hlt">soil</span> <span class="hlt">moisture</span> mainly relates to the presence of clay-rich <span class="hlt">soils</span>. Modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> from four of five global land surface models failed to capture the memory length of <span class="hlt">soil</span> <span class="hlt">moisture</span> and all five models failed to present the influence of lateral inflow. This case study indicates that satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> is a better predictor of vegetation water availability than precipitation in environments having a memory of several months and thus is able to persistently <span class="hlt">affect</span> vegetation dynamics. These results illustrate the usefulness of satellite remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> in ecohydrology studies. This case study has the potential to be used as a benchmark for global land surface model evaluations. The advantages of using satellite remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> over gridded precipitation products are mainly expected in lateral-inflow and/or clay-rich regions worldwide.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H33K1729L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H33K1729L"><span>Impact of Tropical Cyclones on <span class="hlt">Soil</span> <span class="hlt">Moisture</span> over East Asia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liess, S.</p> <p>2016-12-01</p> <p>A simulation of a series of three strong typhoons (Frankie, Gloria, and Herb) during the 1996 typhoon season shows that the Weather Research and Forecasting (WRF) model is representing the general characteristics of each typhoon, including sharp right turns by Gloria and Herb over the Philippine Sea. These sharp right turns can be attributed to tropical easterly waves and they are responsible for landfall over Taiwan, instead of following the general direction toward the Philippines. A second simulation where the typhoon signal is removed before landfall over East Asia shows that both rainfall and <span class="hlt">soil</span> <span class="hlt">moisture</span> is increased by up to 30% in coastal regions after landfall, mostly to the north of the landfall region. However, despite the noisier signal in rainfall, significant increases in <span class="hlt">soil</span> <span class="hlt">moisture</span> related to the paths of the simulated typhoons occur as far west as western China and Myanmar. Strong winds associated with the typhoons can also increase local evaporation and thus locally reduce <span class="hlt">soil</span> <span class="hlt">moisture</span>, especially south of the landfall region. Detailed observations of hydrologic variables such as <span class="hlt">soil</span> <span class="hlt">moisture</span> are needed to evaluate these model studies not only over coastal regions but also further inland where typhoon signals are weaker but local <span class="hlt">moisture</span> availability is still influenced by increased rainfall and stronger winds.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25085217','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25085217"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span>--a regulator of arbuscular mycorrhizal fungal community assembly and symbiotic phosphorus uptake.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Deepika, Sharma; Kothamasi, David</p> <p>2015-01-01</p> <p>Multiple species of arbuscular mycorrhizal fungi (AMF) can colonize roots of an individual plant species but factors which determine the selection of a particular AMF species in a plant root are largely unknown. The present work analysed the effects of drought, flooding and optimal <span class="hlt">soil</span> <span class="hlt">moisture</span> (15-20 %) on AMF community composition and structure in Sorghum vulgare roots, using PCR-RFLP. Rhizophagus irregularis (isolate BEG 21), and rhizosphere <span class="hlt">soil</span> (mixed inoculum) of Heteropogon contortus, a perennial C4 grass, collected from the semi-arid Delhi ridge, were used as AMF inocula. <span class="hlt">Soil</span> <span class="hlt">moisture</span> functioned as an abiotic filter and <span class="hlt">affected</span> AMF community assembly inside plant roots by regulating AMF colonization and phylotype diversity. Roots of plants in flooded <span class="hlt">soils</span> had lowest AMF diversity whilst root AMF diversity was highest under the <span class="hlt">soil</span> <span class="hlt">moisture</span> regime of 15-20 %. Although plant biomass was not <span class="hlt">affected</span>, root P uptake was significantly influenced by <span class="hlt">soil</span> <span class="hlt">moisture</span>. Plants colonized with R. irregularis or mixed AMF inoculum showed higher root P uptake than non-mycorrhizal plants in drought and control treatments. No differences in root P levels were found in the flooded treatment between plants colonized with R. irregularis and non-mycorrhizal plants, whilst under the same treatment, root P uptake was lower in plants colonized with mixed AMF inoculum than in non-mycorrhizal plants.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170010214','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170010214"><span>Version 3 of the SMAP Level 4 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reichle, Rolf; Liu, Qing; Ardizzone, Joe; Crow, Wade; De Lannoy, Gabrielle; Kolassa, Jana; Kimball, John; Koster, Randy</p> <p>2017-01-01</p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Level 4 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root zone (0-100 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as related land surface states and fluxes from 31 March 2015 to present with a latency of 2.5 days. The ensemble-based L4_SM algorithm is a variant of the Goddard Earth Observing System version 5 (GEOS-5) land data assimilation system and ingests SMAP L-band (1.4 GHz) Level 1 brightness temperature observations into the Catchment land surface model. The <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis is non-local (spatially distributed), performs downscaling from the 36-km resolution of the observations to that of the model, and respects the relative uncertainties of the modeled and observed brightness temperatures. Prior to assimilation, a climatological rescaling is applied to the assimilated brightness temperatures using a 6 year record of SMOS observations. A new feature in Version 3 of the L4_SM data product is the use of 2 years of SMAP observations for rescaling where SMOS observations are not available because of radio frequency interference, which expands the impact of SMAP observations on the L4_SM estimates into large regions of northern Africa and Asia. This presentation investigates the performance and data assimilation diagnostics of the Version 3 L4_SM data product. The L4_SM <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates meet the 0.04 m3m3 (unbiased) RMSE requirement. We further demonstrate that there is little bias in the <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis. Finally, we illustrate where the assimilation system overestimates or underestimates the actual errors in the system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ThApC.132....1L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ThApC.132....1L"><span>Evaluation of a simple, point-scale hydrologic model in simulating <span class="hlt">soil</span> <span class="hlt">moisture</span> using the Delaware environmental observing system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Legates, David R.; Junghenn, Katherine T.</p> <p>2018-04-01</p> <p>Many local weather station networks that measure a number of meteorological variables (i.e. , mesonetworks) have recently been established, with <span class="hlt">soil</span> <span class="hlt">moisture</span> occasionally being part of the suite of measured variables. These mesonetworks provide data from which detailed estimates of various hydrological parameters, such as precipitation and reference evapotranspiration, can be made which, when coupled with simple surface characteristics available from <span class="hlt">soil</span> surveys, can be used to obtain estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The question is Can meteorological data be used with a simple hydrologic model to estimate accurately daily <span class="hlt">soil</span> <span class="hlt">moisture</span> at a mesonetwork site? Using a state-of-the-art mesonetwork that also includes <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements across the US State of Delaware, the efficacy of a simple, modified Thornthwaite/Mather-based daily water balance model based on these mesonetwork observations to estimate site-specific <span class="hlt">soil</span> <span class="hlt">moisture</span> is determined. Results suggest that the model works reasonably well for most well-drained sites and provides good qualitative estimates of measured <span class="hlt">soil</span> <span class="hlt">moisture</span>, often near the accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> instrumentation. The model exhibits particular trouble in that it cannot properly simulate the slow drainage that occurs in poorly drained <span class="hlt">soils</span> after heavy rains and interception loss, resulting from grass not being short cropped as expected also adversely <span class="hlt">affects</span> the simulation. However, the model could be tuned to accommodate some non-standard siting characteristics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23359920','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23359920"><span>[Relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> and needle-fall in Masson pine forests in acid rain region of Chongqing, Southwest China].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Yi-Hao; Wang, Yan-Hui; Li, Zhen-Hua; Yu, Peng-Tao; Xiong, Wei; Hao, Jia; Duan, Jian</p> <p>2012-10-01</p> <p>From March 2009 to November 2011, an investigation was conducted on the spatiotemporal variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its effects on the needle-fall in Masson pine (Pinus massoniana) forests in acid rain region of Chongqing, Southeast China, with the corresponding <span class="hlt">soil</span> <span class="hlt">moisture</span> thresholds determined. No matter the annual precipitation was abundant, normal or less than average, the seasonal variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the forests could be obviously divided into four periods, i.e., sufficient (before May), descending (from June to July), drought (from August to September), and recovering (from October to November). With increasing <span class="hlt">soil</span> depth, the <span class="hlt">soil</span> <span class="hlt">moisture</span> content increased after an initial decrease, but the difference of the <span class="hlt">soil</span> <span class="hlt">moisture</span> content among different <span class="hlt">soil</span> layers decreased with decreasing annual precipitation. The amount of monthly needle-fall in the forests in growth season was significantly correlated with the water storage in root zone (0-60 cm <span class="hlt">soil</span> layer), especially in the main root zone (20-50 cm <span class="hlt">soil</span> layer). <span class="hlt">Soil</span> field capacity (or capillary porosity) and 82% of field capacity (or 80% of capillary porosity) were the main <span class="hlt">soil</span> <span class="hlt">moisture</span> thresholds <span class="hlt">affecting</span> the litter-fall. It was suggested that in acid rain region, Masson pine forest was easily to suffer from water deficit stress, especially in dry-summer period. The water deficit stress, together with already existed acid rain stress, would further threaten the health of the Masson forest.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.6315G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.6315G"><span>Crop yield monitoring in the Sahel using root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies derived from SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gibon, François; Pellarin, Thierry; Alhassane, Agali; Traoré, Seydou; Baron, Christian</p> <p>2017-04-01</p> <p>West Africa is greatly vulnerable, especially in terms of food sustainability. Mainly based on rainfed agriculture, the high variability of the rainy season strongly impacts the crop production driven by the <span class="hlt">soil</span> water availability in the <span class="hlt">soil</span>. To monitor this water availability, classical methods are based on daily precipitation measurements. However, the raingauge network suffers from the poor network density in Africa (1/10000km2). Alternatively, real-time satellite-derived precipitations can be used, but they are known to suffer from large uncertainties which produce significant error on crop yield estimations. The present study proposes to use root <span class="hlt">soil</span> <span class="hlt">moisture</span> rather than precipitation to evaluate crop yield variations. First, a local analysis of the spatiotemporal impact of water deficit on millet crop production in Niger was done, from in-situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements (AMMA-CATCH/OZCAR (French Critical Zone exploration network)) and in-situ millet yield survey. Crop yield measurements were obtained for 10 villages located in the Niamey region from 2005 to 2012. The mean production (over 8 years) is 690 kg/ha, and ranges from 381 to 872 kg/ha during this period. Various statistical relationships based on <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates were tested, and the most promising one (R>0.9) linked the 30-cm <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies from mid-August to mid-September (grain filling period) to the crop yield anomalies. Based on this local study, it was proposed to derive regional statistical relationships using 30-cm <span class="hlt">soil</span> <span class="hlt">moisture</span> maps over West Africa. The selected approach was to use a simple hydrological model, the Antecedent Precipitation Index (API), forced by real-time satellite-based precipitation (CMORPH, PERSIANN, TRMM3B42). To reduce uncertainties related to the quality of real-time rainfall satellite products, SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements were assimilated into the API model through a Particular Filter algorithm. Then, obtained <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies were</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70162540','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70162540"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and biogeochemical factors influence the distribution of annual Bromus species</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Belnap, Jayne; Stark, John Thomas; Rau, Benjamin; Allen, Edith B.; Phillips, Sue</p> <p>2016-01-01</p> <p>Abiotic factors have a strong influence on where annual Bromus species are found. At the large regional scale, temperature and precipitation extremes determine the boundaries of Bromusoccurrence. At the more local scale, <span class="hlt">soil</span> characteristics and climate influence distribution, cover, and performance. In hot, dry, summer-rainfall-dominated deserts (Sonoran, Chihuahuan), little or noBromus is found, likely due to timing or amount of <span class="hlt">soil</span> <span class="hlt">moisture</span> relative to Bromus phenology. In hot, winter-rainfall-dominated deserts (parts of the Mojave Desert), Bromus rubens is widespread and correlated with high phosphorus availability. It also responds positively to additions of nitrogen alone or with phosphorus. On the Colorado Plateau, with higher <span class="hlt">soil</span> <span class="hlt">moisture</span> availability, factors limiting Bromus tectorum populations vary with life stage: phosphorus and water limit germination, potassium and the potassium/magnesium ratio <span class="hlt">affect</span> winter performance, and water and potassium/magnesium <span class="hlt">affect</span> spring performance. Controlling nutrients also change with elevation. In cooler deserts with winter precipitation (Great Basin, Columbia Plateau) and thus even greater <span class="hlt">soil</span> <span class="hlt">moisture</span> availability, B. tectorum populations are controlled by nitrogen, phosphorus, or potassium. Experimental nitrogen additions stimulate Bromus performance. The reason for different nutrients limiting in dissimilar climatic regions is not known, but it is likely that site conditions such as <span class="hlt">soil</span> texture (as it <span class="hlt">affects</span> water and nutrient availability), organic matter, and/or chemistry interact in a manner that regulates nutrient availability and limitations. Under future drier, hotter conditions,Bromus distribution is likely to change due to changes in the interaction between <span class="hlt">moisture</span> and nutrient availability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24889286','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24889286"><span>Short-term precipitation exclusion alters microbial responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> in a wet tropical forest.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Waring, Bonnie G; Hawkes, Christine V</p> <p>2015-05-01</p> <p>Many wet tropical forests, which contain a quarter of global terrestrial biomass carbon stocks, will experience changes in precipitation regime over the next century. <span class="hlt">Soil</span> microbial responses to altered rainfall are likely to be an important feedback on ecosystem carbon cycling, but the ecological mechanisms underpinning these responses are poorly understood. We examined how reduced rainfall <span class="hlt">affected</span> <span class="hlt">soil</span> microbial abundance, activity, and community composition using a 6-month precipitation exclusion experiment at La Selva Biological Station, Costa Rica. Thereafter, we addressed the persistent effects of field <span class="hlt">moisture</span> treatments by exposing <span class="hlt">soils</span> to a controlled <span class="hlt">soil</span> <span class="hlt">moisture</span> gradient in the lab for 4 weeks. In the field, compositional and functional responses to reduced rainfall were dependent on initial conditions, consistent with a large degree of spatial heterogeneity in tropical forests. However, the precipitation manipulation significantly altered microbial functional responses to <span class="hlt">soil</span> <span class="hlt">moisture</span>. Communities with prior drought exposure exhibited higher respiration rates per unit microbial biomass under all conditions and respired significantly more CO2 than control <span class="hlt">soils</span> at low <span class="hlt">soil</span> <span class="hlt">moisture</span>. These functional patterns suggest that changes in microbial physiology may drive positive feedbacks to rising atmospheric CO2 concentrations if wet tropical forests experience longer or more intense dry seasons in the future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H51J..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H51J..08S"><span>The effect of surface sealing on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in a semiarid hillslope</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sela, S.; Svoray, T.; Assouline, S.</p> <p>2010-12-01</p> <p>Understanding the mechanisms underlying hillslope <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics and vegetation patchiness remains a current challenge in hydrology, especially in ungauged watersheds. In dry areas, these mechanisms include the formation of surface seals, that although directly <span class="hlt">affects</span> infiltration and evaporation fluxes, researchers usually disregard its development when predicting <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns. The role of these seals in shaping spatial and temporal patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span>, considered as the primary limiting factor for dry area plant distribution, is still an open research gap. At the LTER Lehavim site, in the center of Israel (31020' N, 34045' E), a typical hillslope (0.115 Km2) was chosen offering different aspects and a classic geomorphologic banding. Annual rainfall is 290 mm, the <span class="hlt">soils</span> are brown lithosols and arid brown loess and the dominant rock formations are Eocenean limestone and chalk with patches of calcrete. The vegetation is characterised by scattered dwarf shrubs (dominant species Sarcopoterium spinosum) and patches of herbaceous vegetation, mostly annuals, are spread between rocks and dwarf shrubs. An extensive spatial database of <span class="hlt">soil</span> hydraulic and environmental parameters (e.g. slope, radiation, bulk density) was measured in the field and was interpolated to continuous maps using geostatistical techniques and physically-based models. To explore the effect of <span class="hlt">soil</span> surface sealing, the Mualem and Assouline (1989) equations, describing the change in hydraulic parameters resulting from <span class="hlt">soil</span> seal formation, were applied explicitly in space to the entire hillslope. Two simple indices were developed to describe local evaporation rates and the contribution of water from rock outcrops to the downslope <span class="hlt">soil</span> patches. This spatio-temporal database was used to characterise 1187 cells serving as an input to a numeric model (Hydrus 1D) solving the flow equations to predict <span class="hlt">soil</span> water content at the single storm and the seasonal scales. Predictions were</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1332724','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1332724"><span><span class="hlt">Soil</span> Temperature and <span class="hlt">Moisture</span> Profile (STAMP) System Handbook</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Cook, David R.</p> <p>2016-11-01</p> <p>The <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> profile system (STAMP) provides vertical profiles of <span class="hlt">soil</span> temperature, <span class="hlt">soil</span> water content (<span class="hlt">soil</span>-type specific and loam type), plant water availability, <span class="hlt">soil</span> conductivity, and real dielectric permittivity as a function of depth below the ground surface at half-hourly intervals, and precipitation at one-minute intervals. The profiles are measured directly by in situ probes at all extended facilities of the SGP climate research site. The profiles are derived from measurements of <span class="hlt">soil</span> energy conductivity. Atmospheric scientists use the data in climate models to determine boundary conditions and to estimate the surface energy flux. The data are alsomore » useful to hydrologists, <span class="hlt">soil</span> scientists, and agricultural scientists for determining the state of the <span class="hlt">soil</span>. The STAMP system replaced the SWATS system in early 2016.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JHyd..495..150J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JHyd..495..150J"><span>Spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its temporal stability within profiles on a loessial slope in northwestern China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jia, Yu-Hua; Shao, Ming-An; Jia, Xiao-Xu</p> <p>2013-07-01</p> <p> effects on the temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Among selected <span class="hlt">soil</span> properties, saturated hydraulic conductivity, bulk density and <span class="hlt">soil</span> organic carbon all significantly <span class="hlt">affected</span> the SDRDs. These observations are expected to add valuable information to the theory of temporal stability and for the practices of <span class="hlt">soil</span> <span class="hlt">moisture</span> management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=247140','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=247140"><span>Effect of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on Fumigant Emissions from a Loam <span class="hlt">Soil</span></span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Emissions of <span class="hlt">soil</span> fumigants must be minimized in order to protect air quality in California. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important factor that can be managed at a relatively low cost prior to <span class="hlt">soil</span> fumigation to reduce emissions. A previous study indicated that increasing <span class="hlt">soil</span> water content up to field capac...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/19797','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/19797"><span>A comparison of <span class="hlt">soil-moisture</span> loss from forested and clearcut areas in West Virginia</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Charles A. Troendle</p> <p>1970-01-01</p> <p><span class="hlt">Soil-moisture</span> losses from forested and clearcut areas were compared on the Fernow Experimental Forest. As expected, hardwood forest <span class="hlt">soils</span> lost most <span class="hlt">moisture</span> while revegetated clearcuttings, clearcuttings, and barren areas lost less, in that order. <span class="hlt">Soil-moisture</span> losses from forested <span class="hlt">soils</span> also correlated well with evapotranspiration and streamflow.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1616875A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1616875A"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> under contrasted atmospheric conditions in Eastern Spain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Azorin-Molina, César; Cerdà, Artemi; Vicente-Serrano, Sergio M.</p> <p>2014-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays a key role on the recently abandoned agriculture land where determine the recovery and the erosion rates (Cerdà, 1995), on the <span class="hlt">soil</span> water repellency degree (Bodí et al., 2011) and on the hydrological cycle (Cerdà, 1999), the plant development (García Fayos et al., 2000) and the seasonality of the geomorphological processes (Cerdà, 2002). Moreover, <span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key factor on the semiarid land (Ziadat and Taimeh, 2013), on the productivity of the land (Qadir et al., 2013) and <span class="hlt">soils</span> treated with amendments (Johnston et al., 2013) and on <span class="hlt">soil</span> reclamation on drained saline-sodic <span class="hlt">soils</span> (Ghafoor et al., 2012). In previous study (Azorin-Molina et al., 2013) we investigated the intraannual evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> in <span class="hlt">soils</span> under different land managements in the Valencia region, Eastern Spain, and concluded that <span class="hlt">soil</span> <span class="hlt">moisture</span> recharges are much controlled by few heavy precipitation events; 23 recharge episodes during 2012. Most of the <span class="hlt">soil</span> <span class="hlt">moisture</span> recharge events occurred during the autumn season under Back-Door cold front situations. Additionally, sea breeze front episodes brought isolated precipitation and <span class="hlt">moisture</span> to mountainous areas within summer (Azorin-Molina et al., 2009). We also evidenced that the intraanual evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> changes are positively and significatively correlated (at p<0.01) with the amount of measured precipitation. In this study we analyze the role of other crucial atmospheric parameters (i.e., temperature, relative humidity, global solar radiation, and wind speed and wind direction) in the intraanual evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span>; focussing our analyses on the <span class="hlt">soil</span> <span class="hlt">moisture</span> discharge episodes. Here we present 1-year of <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements at two experimental sites in the Valencia region, one representing rainfed orchard typical from the Mediterranean mountains (El Teularet-Sierra de Enguera), and a second site corresponding to an irrigated orange crop (Alcoleja). Key Words: <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Discharges</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009aogs...11...95C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009aogs...11...95C"><span>Evapotranspiration Estimates for a Stochastic <span class="hlt">Soil-Moisture</span> Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chaleeraktrakoon, Chavalit; Somsakun, Somrit</p> <p>2009-03-01</p> <p>Potential evapotranspiration is information that is necessary for applying a widely used stochastic model of <span class="hlt">soil</span> <span class="hlt">moisture</span> (I. Rodriguez Iturbe, A. Porporato, L. Ridolfi, V. Isham and D. R. Cox, Probabilistic modelling of water balance at a point: The role of climate, <span class="hlt">soil</span> and vegetation, Proc. Roy. Soc. London A455 (1999) 3789-3805). An objective of the present paper is thus to find a proper estimate of the evapotranspiration for the stochastic model. This estimate is obtained by comparing the calculated <span class="hlt">soil-moisture</span> distribution resulting from various techniques, such as Thornthwaite, Makkink, Jensen-Haise, FAO Modified Penman, and Blaney-Criddle, with an observed one. The comparison results using five sequences of daily <span class="hlt">soil-moisture</span> for a dry season from November 2003 to April 2004 (Udornthani Province, Thailand) have indicated that all methods can be used if the weather information required is available. This is because their <span class="hlt">soil-moisture</span> distributions are alike. In addition, the model is shown to have its ability in approximately describing the phenomenon at a weekly or biweekly time scale which is desirable for agricultural engineering applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=324790','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=324790"><span>Projected irrigation requirements for upland crops using <span class="hlt">soil</span> <span class="hlt">moisture</span> model under climate change in South Korea</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>An increase in abnormal climate change patterns and unsustainable irrigation in uplands cause drought and <span class="hlt">affect</span> agricultural water security, crop productivity, and price fluctuations. In this study, we developed a <span class="hlt">soil</span> <span class="hlt">moisture</span> model to project irrigation requirements (IR) for upland crops under cl...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.1134F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.1134F"><span>Effects of land preparation and artificial vegetation on <span class="hlt">soil</span> <span class="hlt">moisture</span> variation in a loess hilly catchment of China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feng, Tianjiao; Wei, Wei; Chen, Liding; Yu, Yang</p> <p>2017-04-01</p> <p>In the dryland regions, <span class="hlt">soil</span> <span class="hlt">moisture</span> is the main factor to determine vegetation growth and ecosystem restoration. Land preparation and vegetation restoration are the principal means for improving <span class="hlt">soil</span> water content(SWC). Thus, it is important to analyze the coupling role of these two means on <span class="hlt">soil</span> <span class="hlt">moisture</span>. In this study, <span class="hlt">soil</span> <span class="hlt">moisture</span> were monitored at a semi-arid loess hilly catchment of China, during the growing season of 2014 and 2015. Four different land preparation methods (level ditches, fish-scale pits, adverse grade tablelands and level benches)and vegetation types(Prunus armeniaca, Platycladus orientalis, Platycladus orientalis and Caragana microphylla) were included in the experimental design. Our results showed that: (1)<span class="hlt">Soil</span> <span class="hlt">moisture</span> content differed across land preparation types, which is higher for fish-scale pits and decreased in the order of level ditches and adverse grade tablelands.(2) Rainwater harvesting capacity of fish-scale pits is greater than adverse grade tablelands. However the water holding capacity is much higher at <span class="hlt">soils</span> prepared with the adverse grade tablelands method than the ones prepared by fish-scale pits methods. (3) When land preparation method is similar, vegetation play a key role in <span class="hlt">soil</span> <span class="hlt">moisture</span> variation. For example, the mean <span class="hlt">soil</span> <span class="hlt">moisture</span> under a Platycladus orientalis field is 26.72% higher than a Pinus tabulaeformis field, with the same land preparation methods. (4)<span class="hlt">Soil</span> <span class="hlt">moisture</span> in deeper <span class="hlt">soil</span> layers is more <span class="hlt">affected</span> by changes in the vegetation cover while <span class="hlt">soil</span> <span class="hlt">moisture</span> in the shallower layers is more <span class="hlt">affected</span> by the variation in the land preparation methods. Therefore, we suggest that vegetation types such as: Platycladus orientalisor as well as <span class="hlt">soil</span> preparation methods such as level ditch and fish-scale pit are the most appropriate vegetation cover and land preparation methods for landscape restoration in semi-arid loess hilly area. This conclusion was made based on the vegetation type and land preparation with the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26710640','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26710640"><span>[Factors influencing <span class="hlt">soil</span> <span class="hlt">moisture</span> at different scales of the Lhasa River basin, China].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fu, Guo-zhen; Bai, Wan-qi; Yao, Li-nao</p> <p>2015-07-01</p> <p>The scale difference and role of factors influencing <span class="hlt">soil</span> <span class="hlt">moisture</span> regime are the basis of scale dependency study. This study, which selected farmland <span class="hlt">soils</span> in the Lhasa River basin of the Tibetan Plateau as the research object, identified the main factors <span class="hlt">affecting</span> the <span class="hlt">soil</span> <span class="hlt">moisture</span> using ecological redundancy analysis (RDA) and statistic analysis methods, based on data obtained by remote sensing technology and field surveys. The <span class="hlt">soil</span> layers of 0-20, 20-40 and 40-60 cm were collected with the <span class="hlt">soil</span>-drilling method at each of 115 sampling sites distributed in the whole basin of the Lhasa River and 49 sampling sites in one of its sub-watersheds. The results showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> content in the Lhasa River basin, under the influence of climate and altitude, increased from southwest to northeast, and was higher in the lower <span class="hlt">soil</span> layer than the upper layer due to water supplement by lateral seepage of the river. At sub-watershed scale, farmland <span class="hlt">soil</span> water content decreased with increasing the altitude and slope, and <span class="hlt">soil</span> water storage capacity decreased with increasing the gravel content. The results were a significant support for the farmland expansion to higher altitude, adjustment of cropping structure, land consolidation, and construction of irrigation facilities in the region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016cosp...41E1188L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016cosp...41E1188L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Remote Sensing with GNSS-R at the Valencia Anchor Station. The SOMOSTA (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Station) Experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lopez-Baeza, Ernesto</p> <p>2016-07-01</p> <p>In this paper, the SOMOSTA (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring Station) experiment on <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring byGlobal Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced. L-band microwaves have very good advantages in <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing, for being unaffected by clouds and the atmosphere, and for the ability to penetrate vegetation. During this experimental campaign, the ESA GNSS-R Oceanpal antenna was installed on the same tower as the ESA ELBARA-II passive microwave radiometer, both measuring instruments having similar field of view. This experiment is fruitfully framed within the ESA - China Programme of Collaboration on GNSS-R. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and two down-looking antennas for receiving LHCP (left-hand circular polarisation) and RHCP (right-hand circular polarisation) reflected signals from the <span class="hlt">soil</span> surface. We could collect data from the three different antennas through the two channels of Oceanpal and, in addition, calibration could be performed to reduce the impact from the differing channels. Reflectivity was thus measured and <span class="hlt">soil</span> <span class="hlt">moisture</span> could be retrieved by the L- MEB (L-band Microwave Emission of the Biosphere) model considering the effect of vegetation optical thickness and <span class="hlt">soil</span> roughness. By contrasting GNSS-R and ELBARA-II radiometer data, a negative correlation existed between reflectivity measured by GNSS-R and brightness temperature measured by the radiometer. The two parameters represent reflection and absorption of the <span class="hlt">soil</span>. <span class="hlt">Soil</span> <span class="hlt">moisture</span> retrieved by both L-band remote sensing methods shows good agreement. In addition, correspondence with in-situ measurements and rainfall is also good.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.3734S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.3734S"><span>De-noising of microwave satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> time series</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Su, Chun-Hsu; Ryu, Dongryeol; Western, Andrew; Wagner, Wolfgang</p> <p>2013-04-01</p> <p>The use of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data for scientific and operational hydrologic, meteorological and climatological applications is advancing rapidly due to increasing capability and temporal coverage of current and future missions. However evaluation studies of various existing remotely-sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> products from these space-borne microwave sensors, which include AMSR-E (Advanced Microwave Scanning Radiometer) on Aqua satellite, SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity) mission and ASCAT (Advanced Scatterometer) on MetOp-A satellite, found them to be significantly different from in-situ observations, showing large biases and different dynamic ranges and temporal patterns (e.g., Albergel et al., 2012; Su et al., 2012). Moreover they can have different error profiles in terms of bias, variance and correlations and their performance varies with land surface characteristics (Su et al., 2012). These severely impede the effort to use <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from multiple sensors concurrently in land surface modelling, cross-validation and multi-satellite blending. The issue of systematic errors present in data sets should be addressed prior to renormalisation of the data for blending and data assimilation. Triple collocation estimation technique has successfully yielded realistic error estimates (Scipal et al., 2008), but this method relies on availability of large number of coincident data from multiple independent satellite data sets. In this work, we propose, i) a conceptual framework for distinguishing systematic periodic errors in the form of false spectral resonances from non-systematic errors (stochastic noise) in remotely-sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data in the frequency domain; and ii) the use of digital filters to reduce the variance- and correlation-related errors in satellite data. In this work, we focus on the VUA-NASA (Vrije Universiteit Amsterdam with NASA) AMSR-E, CATDS (Centre National d'Etudes Spatiales, CNES) SMOS and TUWIEN (Vienna University of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/20486','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/20486"><span>Unsaturated <span class="hlt">soil</span> <span class="hlt">moisture</span> drying and wetting diffusion coefficient measurements in the laboratory.</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2009-09-01</p> <p>ABSTRACTTransient <span class="hlt">moisture</span> flow in an unsaturated <span class="hlt">soil</span> in response to suction changes is controlled by the unsaturated <span class="hlt">moisture</span> diffusion coefficient. The <span class="hlt">moisture</span> diffusion coefficient can be determined by measuring suction profiles over time. The l...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27337651','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27337651"><span><span class="hlt">Moisture</span> effect in prompt gamma measurements from <span class="hlt">soil</span> samples.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Naqvi, A A; Khiari, F Z; Liadi, F A; Khateeb-Ur-Rehman; Raashid, M A; Isab, A H</p> <p>2016-09-01</p> <p>The variation in intensity of 1.78MeV silicon, 6.13MeV oxygen, and 2.22MeV hydrogen prompt gamma rays from <span class="hlt">soil</span> samples due to the addition of 5.1, 7.4, 9.7, 11.9 and 14.0wt% water was studied for 14MeV incident neutron beams utilizing a LaBr3:Ce gamma ray detector. The intensities of 1.78MeV and 6.13MeV gamma rays from silicon and oxygen, respectively, decreased with increasing sample <span class="hlt">moisture</span>. The intensity of 2.22MeV hydrogen gamma rays increases with <span class="hlt">moisture</span>. The decrease in intensity of silicon and oxygen gamma rays with <span class="hlt">moisture</span> concentration indicates a loss of 14MeV neutron flux, while the increase in intensity of 2.22MeV gamma rays with <span class="hlt">moisture</span> indicates an increase in thermal neutron flux due to increasing concentration of <span class="hlt">moisture</span>. The experimental intensities of silicon, oxygen and hydrogen prompt gamma rays, measured as a function of <span class="hlt">moisture</span> concentration in the <span class="hlt">soil</span> samples, are in good agreement with the theoretical results obtained through Monte Carlo calculations. Copyright © 2016 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28886067','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28886067"><span>A wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor powered by solar energy.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Mingliang; Lv, Mouchao; Deng, Zhong; Zhai, Guoliang</p> <p>2017-01-01</p> <p>In a variety of agricultural activities, such as irrigation scheduling and nutrient management, <span class="hlt">soil</span> water content is regarded as an essential parameter. Either power supply or long-distance cable is hardly available within field scale. For the necessity of monitoring <span class="hlt">soil</span> water dynamics at field scale, this study presents a wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor based on the impedance transform of the frequency domain. The sensor system is powered by solar energy, and the data can be instantly transmitted by wireless communication. The sensor electrodes are embedded into the bottom of a supporting rod so that the sensor can measure <span class="hlt">soil</span> water contents at different depths. An optimal design with time executing sequence is considered to reduce the energy consumption. The experimental results showed that the sensor is a promising tool for monitoring <span class="hlt">moisture</span> in large-scale farmland using solar power and wireless communication.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19810058820&hterms=Capacity+Mapping&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D10%26Ntt%3DCapacity%2BMapping','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19810058820&hterms=Capacity+Mapping&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D10%26Ntt%3DCapacity%2BMapping"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> applications of the heat capacity mapping mission</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Heilman, J. L.; Moore, D. G.</p> <p>1981-01-01</p> <p>Results are presented of ground, aircraft and satellite investigations conducted to evaluate the potential of the Heat Capacity Mapping Mission (HCMM) to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span> and the depth of shallow ground water. The investigations were carried out over eastern South Dakota to evaluate the relation between directly measured <span class="hlt">soil</span> temperatures and water content at various stages of canopy development, aircraft thermal scanner measurements of apparent canopy temperature and the reliability of actual HCMM data. The results demonstrate the possibility of evaluating <span class="hlt">soil</span> <span class="hlt">moisture</span> on the basis of HCMM apparent canopy temperature and day-night <span class="hlt">soil</span> temperature difference measurements. Limitations on the use of thermal data posed by environmental factors which influence energy balance interactions, including phase transformations, wind patterns, topographic variations and atmospheric constituents are pointed out.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830027190','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830027190"><span>Advanced microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> studies. [Big Sioux River Basin, Iowa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dalsted, K. J.; Harlan, J. C.</p> <p>1983-01-01</p> <p>Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: <span class="hlt">soil</span> map and land cover data; direct <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (<span class="hlt">soil</span> features and land cover) hold promise for qualitative assessment of <span class="hlt">soil</span> <span class="hlt">moisture</span> and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5590862','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5590862"><span>A wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor powered by solar energy</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lv, Mouchao; Deng, Zhong; Zhai, Guoliang</p> <p>2017-01-01</p> <p>In a variety of agricultural activities, such as irrigation scheduling and nutrient management, <span class="hlt">soil</span> water content is regarded as an essential parameter. Either power supply or long-distance cable is hardly available within field scale. For the necessity of monitoring <span class="hlt">soil</span> water dynamics at field scale, this study presents a wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor based on the impedance transform of the frequency domain. The sensor system is powered by solar energy, and the data can be instantly transmitted by wireless communication. The sensor electrodes are embedded into the bottom of a supporting rod so that the sensor can measure <span class="hlt">soil</span> water contents at different depths. An optimal design with time executing sequence is considered to reduce the energy consumption. The experimental results showed that the sensor is a promising tool for monitoring <span class="hlt">moisture</span> in large-scale farmland using solar power and wireless communication. PMID:28886067</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715618E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715618E"><span>Analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> probability in a tree cropped watershed</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Espejo-Perez, Antonio Jesus; Giraldez Cervera, Juan Vicente; Pedrera, Aura; Vanderlinden, Karl</p> <p>2015-04-01</p> <p>Probability density functions (pdfs) of <span class="hlt">soil</span> <span class="hlt">moisture</span> were estimated for an experimental watershed in Southern Spain, cropped with olive trees. Measurements were made using a capacitance sensors network from June 2011 until May 2013. The network consisted of 22 profiles of sensors, installed close to the tree trunk under the canopy and in the adjacent inter-row area, at 11 locations across the watershed to assess the influence of rain interception and root-water uptake on the <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution. A bimodal pdf described the <span class="hlt">moisture</span> dynamics at the 11 sites, both under and in-between the trees. Each mode represented the <span class="hlt">moisture</span> status during either the dry or the wet period of the year. The observed histograms could be decomposed into a Lognormal pdf for dry period and a Gaussian pdf for the wet period. The pdfs showed a larger variation among the different locations at inter-row positions, as compared to under the canopy, reflecting the strict control of the vegetation on <span class="hlt">soil</span> <span class="hlt">moisture</span>. At both positions this variability was smaller during the wet season than during the dry period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H31F..02N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H31F..02N"><span>Connectivity Dynamics in Hillslope <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Flow</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nieber, J. L.; Sidle, R. C.; Steenhuis, T. S.</p> <p>2009-12-01</p> <p>During the last few decades hydrology field studies on hillslopes conducted in the U.S., Japan, Canada, and New Zealand have shown that the lateral flow conducted by macropores within sloping <span class="hlt">soils</span> occurs when the <span class="hlt">soil</span> reaches specific threshold levels of saturation. In recent work it has also been demonstrated by numerical solution of the steady-state form of the Richards equation that a population of arbitrarily oriented macropores of different size and types can significantly increase the flow capacity of the <span class="hlt">soil</span> profile for lateral flow even when these macropores are not connected to each other. It is observed from these numerical simulations that when the <span class="hlt">soil</span> profile reaches the threshold wetness, the population of disconnected macropores begins to form a network of preferential flow pathways, and this network becomes more developed and complex as wetness increases. In these previous numerical simulations, the <span class="hlt">soil</span> matrix was treated as a homogeneous block, containing a population of macropores of arbitrary orientation and size, underlain by an impervious layer. Field observations in Japan however have shown that <span class="hlt">soils</span> may contain low-permeability layers that lead to perched saturated conditions within the profile thereby creating conditions conducive to activation and expansion of the preferential flow network. In the present work we examine the effect of internal <span class="hlt">soil</span> layering on the production of localized saturated zones, development of multiple seepage faces, and the expansion of preferential flow networks. We also examine the temporal development of preferential flow networks and thereby derive inferences about the effect of initial conditions and rainfall depth on the thresholds of preferential flow path development. Water table response to steady rainfall for a hillslope <span class="hlt">soil</span> containing disconnected macropores.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29076022','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29076022"><span>The impact of cerium oxide nanoparticles on the physiology of soybean (Glycine max (L.) Merr.) under different <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cao, Zhiming; Rossi, Lorenzo; Stowers, Cheyenne; Zhang, Weilan; Lombardini, Leonardo; Ma, Xingmao</p> <p>2018-01-01</p> <p>The ongoing global climate change raises concerns over the decreasing <span class="hlt">moisture</span> content in agricultural <span class="hlt">soils</span>. Our research investigated the physiological impact of two types of cerium oxide nanoparticles (CeO 2 NPs) on soybean at different <span class="hlt">moisture</span> content levels. One CeO 2 NP was positively charged on the surface and the other negatively charged due to the polyvinylpyrrolidone (PVP) coating. The results suggest that the effect of CeO 2 NPs on plant photosynthesis and water use efficiency (WUE) was dependent upon the <span class="hlt">soil</span> <span class="hlt">moisture</span> content. Both types of CeO 2 NPs exhibited consistently positive impacts on plant photosynthesis at the <span class="hlt">moisture</span> content above 70% of field capacity (θ fc ). Similar positive impact of CeO 2 NPs was not observed at 55% θ fc , suggesting that the physiological impact of CeO 2 NPs was dependent upon the <span class="hlt">soil</span> <span class="hlt">moisture</span> content. The results also revealed that V Cmax (maximum carboxylation rate) was <span class="hlt">affected</span> by CeO 2 NPs, indicating that CeO 2 NPs <span class="hlt">affected</span> the Rubisco activity which governs carbon assimilation in photosynthesis. In conclusion, CeO 2 NPs demonstrated significant impacts on the photosynthesis and WUE of soybeans and such impacts were <span class="hlt">affected</span> by the <span class="hlt">soil</span> <span class="hlt">moisture</span> content. Graphical abstract <span class="hlt">Soil</span> <span class="hlt">moisture</span> content <span class="hlt">affects</span> plant cerium oxide nanoparticle interactions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ESASP.729E..15J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ESASP.729E..15J"><span>Estimation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Under Vegetation Cover at Multiple Frequencies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jadghuber, Thomas; Hajnsek, Irena; Weiß, Thomas; Papathanassiou, Konstantinos P.</p> <p>2015-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> under vegetation cover was estimated by a polarimetric, iterative, generalized, hybrid decomposition and inversion approach at multiple frequencies (X-, C- and L-band). Therefore the algorithm, originally designed for longer wavelength (L-band), was adapted to deal with the short wavelength scattering scenarios of X- and C-band. The Integral Equation Method (IEM) was incorporated together with a pedo-transfer function of Dobson et al. to account for the peculiarities of short wavelength scattering at X- and C-band. DLR's F-SAR system acquired fully polarimetric SAR data in X-, C- and L-band over the Wallerfing test site in Lower Bavaria, Germany in 2014. Simultaneously, <span class="hlt">soil</span> and vegetation measurements were conducted on different agricultural test fields. The results indicate a spatially continuous inversion of <span class="hlt">soil</span> <span class="hlt">moisture</span> in all three frequencies (inversion rates >92%), mainly due to the careful adaption of the vegetation volume removal including a physical constraining of the decomposition algorithm. However, for X- and C-band the inversion results reveal <span class="hlt">moisture</span> pattern inconsistencies and in some cases an incorrectly high inversion of <span class="hlt">soil</span> <span class="hlt">moisture</span> at X-band. The validation with in situ measurements states a stable performance of 2.1- 7.6vol.% at L-band for the entire growing period. At C- and X-band a reliable performance of 3.7-13.4vol.% in RMSE can only be achieved after distinct filtering (X- band) leading to a loss of almost 60% in spatial inversion rate. Hence, a robust inversion for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation under vegetation cover can only be conducted at L-band due to a constant availability of the <span class="hlt">soil</span> signal in contrast to higher frequencies (X- and C-band).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMEP43E..08N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMEP43E..08N"><span>Impacts of Potential Evapotranspiration and Precipitation Patterns on Downscaling <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Regions with Large Topographic Relief</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Niemann, J. D.; Cowley, G. S.; Green, T. R.; Seyfried, M. S.; Jones, A. S.; Grazaitis, P. J.</p> <p>2016-12-01</p> <p>Mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> is important for many applications such as flood forecasting, <span class="hlt">soil</span> protection, and crop management. <span class="hlt">Soil</span> <span class="hlt">moisture</span> can be estimated at coarse resolutions (e.g., 10-40 km grid cells) using active/passive microwave remote-sensing methods, but information at that resolution is poorly suited for many applications. The Equilibrium <span class="hlt">Moisture</span> from Topography, Vegetation, and <span class="hlt">Soil</span> (EMT+VS) model downscales coarse-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> using fine-resolution topographic, vegetation, and <span class="hlt">soil</span> information to produce fine-resolution (10-30 m) estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The EMT+VS model has been shown to perform well at catchments with low topographic relief (25-124 m), but it has not been applied to larger regions with substantial topographic relief. Large ranges of elevation can produce wide variations in the potential evapotranspiration (PET) and precipitation, which might <span class="hlt">affect</span> the fine-resolution patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span>. In this research, PET and precipitation downscaling methods are developed and included in a generalized EMT+VS model, and the effects of spatial variations in these variables on the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates are investigated. The methods are applied to the 239 km2 Reynolds Creek Watershed in southern Idaho, which has 1200 m of relief, for dates in late spring and summer. The PET and precipitation downscaling methods are able to capture the main features in the average spatial patterns of both variables, and the fine-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates improve when these downscaling methods are used in the EMT+VS model. The PET downscaling provides a larger improvement in the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates than precipitation downscaling, likely because the PET pattern is more persistent through time than the precipitation pattern.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H33F1402K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H33F1402K"><span>Effect of Vegetation Patterns on SAR derived Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Distribution</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Koyama, C. N.; Schneider, K.</p> <p>2012-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> can be regarded as one of the important life sustaining entities on our planet. Among its various functions, the first is probably to enable the growth of vegetation on the land surface. Apart from this, water stored in <span class="hlt">soils</span> plays many other important roles in the global water (and energy) cycle. In the past decades, radar imaging has proven its potential to quantitatively estimate the near surface water content of <span class="hlt">soils</span> at high spatial resolutions. The use of active microwave data to measure surface <span class="hlt">soil</span> <span class="hlt">moisture</span> requires the consideration of several factors like e.g. <span class="hlt">soil</span> texture, surface roughness, and vegetation. Among these factors, the presence of a vegetation cover is perhaps the major impediment to accurate quantitative retrievals of <span class="hlt">soil</span> <span class="hlt">moisture</span>. On the one hand, the vegetation has a disturbing effect on the radar reflectivity and thus causes errors in the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval which is generally based on theoretical or experimental relationships between the dielectric properties of the <span class="hlt">soil</span> surface and the radar backscattering coefficient. On the other hand, the spatial distribution of vegetation with e.g. different crop types with different transpiration coefficients and different phenological development, etc, can cause large variations in the plant water consumption and thus has a significant impact on the <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns. We have developed methods to estimate the amount of biomass for different crop types and the underlying surface <span class="hlt">soil</span> water content directly from polarimetric L-band SAR images. While the horizontally-transmit horizontally-receive co-polarization (hh) is most sensitive towards the dielectric <span class="hlt">soil</span> properties, the horizontally-transmit vertically-receive cross-polarization (hv) is much more sensitive towards the backscattering from the vegetation canopy. In addition the polarimetric observables entropy (H), alpha angle (α), and the total reflected power (span), all of which are highly <span class="hlt">affected</span> by the canopy</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H31G1485Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H31G1485Z"><span>Contributions of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Vegetation Components to Polarized Emission Based on the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission Measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhao, T.; Talebi, S.; Li, S.; Entekhabi, D.; Lu, H.; Shi, J.; Akbar, R.; Wang, Z.; Weng, H.; Mccoll, K. A.</p> <p>2016-12-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) is an Earth satellite mission providing polarized L-band brightness temperature measurements with 6AM and 6PM equatorial crossing times. The brightness temperature measurements over land respond to land and water mixing across the landscape. Over land the <span class="hlt">soil</span> dielectric constant and the vegetation structure and biomass cause variations in brightness temperature. The physical temperature of the landscape components and their emissivity determine the polarized brightness temperature. During the morning crossing when the physical temperature of the components are nearly equal, the difference of the polarizations normalized by the sum is independent of physical temperature. In this study, we use the Polarization Ratio (PR) as a measurement of surface emission because it does not depend on physical temperature and potentially is also a signature of <span class="hlt">soil</span> <span class="hlt">moisture</span> and vegetation. To decompose the PR signal into vegetation and <span class="hlt">soil</span> components, SMAP Level 2 radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> products at 36-km are directly used. Radar observations are used as a measurement of vegetation, including cross-polarized backscattering coefficients and the Radar Vegetation Index (RVI). Regressions between these satellite observations are conducted. The regression coefficients are used to estimate percentage variance explained. Results show there is a positive correlation between PR and <span class="hlt">soil</span> <span class="hlt">moisture</span> and an inverse correlation exists between PR and the cross polarization of radar signal or RVI that corresponds to vegetation. In light to moderate vegetation regions, there is a substantial explained-variance between PR and <span class="hlt">soil</span> <span class="hlt">moisture</span>. But in dense vegetation the correlation is weak because the vegetation causes depolarization and reduces the dynamic range of the PR.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26087288','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26087288"><span>Changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> drive <span class="hlt">soil</span> methane uptake along a fire regeneration chronosequence in a eucalypt forest landscape.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fest, Benedikt; Wardlaw, Tim; Livesley, Stephen J; Duff, Thomas J; Arndt, Stefan K</p> <p>2015-11-01</p> <p>Disturbance associated with severe wildfires (WF) and WF simulating harvest operations can potentially alter <span class="hlt">soil</span> methane (CH4 ) oxidation in well-aerated forest <span class="hlt">soils</span> due to the effect on <span class="hlt">soil</span> properties linked to diffusivity, methanotrophic activity or changes in methanotrophic bacterial community structure. However, changes in <span class="hlt">soil</span> CH4 flux related to such disturbances are still rarely studied even though WF frequency is predicted to increase as a consequence of global climate change. We measured in-situ <span class="hlt">soil</span>-atmosphere CH4 exchange along a wet sclerophyll eucalypt forest regeneration chronosequence in Tasmania, Australia, where the time since the last severe fire or harvesting disturbance ranged from 9 to >200 years. On all sampling occasions, mean CH4 uptake increased from most recently disturbed sites (9 year) to sites at stand 'maturity' (44 and 76 years). In stands >76 years since disturbance, we observed a decrease in <span class="hlt">soil</span> CH4 uptake. A similar age dependency of potential CH4 oxidation for three <span class="hlt">soil</span> layers (0.0-0.05, 0.05-0.10, 0.10-0.15 m) could be observed on incubated <span class="hlt">soils</span> under controlled laboratory conditions. The differences in <span class="hlt">soil</span> CH4 uptake between forest stands of different age were predominantly driven by differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> status, which <span class="hlt">affected</span> the diffusion of atmospheric CH4 into the <span class="hlt">soil</span>. The observed <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern was likely driven by changes in interception or evapotranspiration with forest age, which have been well described for similar eucalypt forest systems in south-eastern Australia. Our results imply that there is a large amount of variability in CH4 uptake at a landscape scale that can be attributed to stand age and <span class="hlt">soil</span> <span class="hlt">moisture</span> differences. An increase in severe WF frequency in response to climate change could potentially increase overall forest <span class="hlt">soil</span> CH4 sinks. © 2015 John Wiley & Sons Ltd.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19790001913&hterms=structure+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dstructure%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19790001913&hterms=structure+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dstructure%2Bsoil"><span>High resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> radiometer. [large space structures</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wilheit, T. T.</p> <p>1978-01-01</p> <p>An electrically scanned pushbroom phased antenna array is described for a microwave radiometer which can provide agriculturally meaningful measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The antenna size of 100 meters at 1400 MHz or 230 meters at 611 MHz requires several shuttle launches and orbital assembly. Problems inherent to the size of the structure and specific instrument problems are discussed as well as the preliminary design.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=239469','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=239469"><span>SMAPVEX08: <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Validation Experiment 2008</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Mission (SMAP) is currently addressing issues related to the development and selection of retrieval algorithms as well as refining the mission design and instruments. Some of these issues require resolution as soon as possible. Several forums had identified specific ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170002761','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170002761"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Mission Status and Science Performance</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Yueh, Simon H.; Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni; Entin, Jared K.</p> <p>2016-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational since mid-April 2015. The SMAP radiometer has been operating flawlessly, but the radar transmitter ceased operation on July 7. This paper provides a status summary of the calibration and validation of the SMAP instruments and the quality assessment of its <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw products. Since the loss of the radar in July, the SMAP project has been conducting two parallel activities to enhance the resolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> products. One of them explores the Backus Gilbert optimum interpolation and de-convolution techniques based on the oversampling characteristics of the SMAP radiometer. The other investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic radar data to obtain <span class="hlt">soil</span> <span class="hlt">moisture</span> products at about 1 to 3 kilometers resolution. In addition, SMAP's L-band data have found many new applications, including vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-PIA18058.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-PIA18058.html"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Mission Produces First Global Radar Map</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-04-21</p> <p>With its antenna now spinning at full speed, NASA new <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive SMAP observatory has successfully re-tested its science instruments and generated its first global maps, a key step to beginning routine science operations in May, 2015</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-PIA18057.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-PIA18057.html"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Mission Produces First Global Radiometer Map</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-04-21</p> <p>With its antenna now spinning at full speed, NASA new <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive SMAP observatory has successfully re-tested its science instruments and generated its first global maps, a key step to beginning routine science operations in May, 2015</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=323268','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=323268"><span>GCOM-W <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature algorithms and validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Passive microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=294399','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=294399"><span>U.S National cropland <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring using SMAP</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Crop condition information is critical for public and private sector decision making that concerns agricultural policy, food production, food security, and food commodity prices. Crop conditions change quickly due to various growing condition events, such as temperature extremes, <span class="hlt">soil</span> <span class="hlt">moisture</span> defic...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=337101','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=337101"><span>SMAP radiometer-based <span class="hlt">soil</span> <span class="hlt">moisture</span> products status and validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission has been providing L-band brightness temperature measurements of the globe since 2015. These are used with retrieval algorithms to generate global products every 2-3 days. SMAP has recently implemented several new products to enhance both the spat...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H51E1329K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H51E1329K"><span>Statistical techniques to extract information during SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kolassa, J.; Reichle, R. H.; Liu, Q.; Alemohammad, S. H.; Gentine, P.</p> <p>2017-12-01</p> <p>Statistical techniques permit the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA614851','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA614851"><span>Effect of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on Chlorine Deposition (POSTPRINT)</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-01-01</p> <p>distribution unlimited. 13. SUPPLEMENTARY NOTES The original document contains color images. 14. ABSTRACT The effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on chlorine (Cl2...conditions but additional experimental investi- ations were needed [4]. Experimental measurements of Cl2 uptake n aerosol particles [5,6], alfalfa grass [7</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA555920','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA555920"><span>Remote Sensing of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Using Airborne Hyperspectral Data</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2011-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals for Forecasting Rainfall-Runoff Partitioning ," Geophysical Research Letters, 32(18):L 18401 [doi: 10.1029/2005GL023543...Influences on the Remote Estimation of Evapotranspiration Using Multiple Satellite Sensors," Remote Sensing of Envi- ronment, 105(4):271-285. Milfred, C</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRD..122.2239C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRD..122.2239C"><span>Uncertainties of <span class="hlt">soil</span> <span class="hlt">moisture</span> in historical simulations and future projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cheng, Shanjun; Huang, Jianping; Ji, Fei; Lin, Lei</p> <p>2017-02-01</p> <p>Uncertainties of <span class="hlt">soil</span> <span class="hlt">moisture</span> in historical simulations (1920-2005) and future projections (2006-2080) were investigated by using the outputs from the Coupled Model Intercomparison Project Phase 5 and Community Earth System Model. The results showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> climatology varies greatly among models despite the good agreement between the ensemble mean of simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> and the Global Land Data Assimilation System data. The uncertainties of initial conditions and model structure showed similar spatial patterns and magnitudes, with high uncertainties in dry regions and low uncertainties in wet regions. In addition, the long-term variability of model structure uncertainty rapidly decreased before 1980 and increased thereafter, but the uncertainty in initial conditions showed an upward trend over the entire time span. The model structure and initial conditions can cause uncertainties at all time scales. Despite these large uncertainties, almost all of the simulations showed significant decreasing linear trends in <span class="hlt">soil</span> <span class="hlt">moisture</span> for the 21st century, especially in the Mediterranean region, northeast and southwest South America, southern Africa, and southwestern USA.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=291075','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=291075"><span>The <span class="hlt">soil</span> <span class="hlt">moisture</span> active passive (SMAP) mission and validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite will be launched by the National Aeronautics and Space Administration in October 2014. This satellite is the culmination of basic research and applications development over the past thirty years. During most of this period, research and development o...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=315838','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=315838"><span>SMAP Validation and Accuracy Assessment of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Introduction: The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission was launched in January, 2015 and will begin its calibration and validation (Cal/Val) phase in May, 2015. This will begin with a focus on instrument measurements, brightness temperature and backscatter, and evolve to the geophysical produ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=317084','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=317084"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Satellite Status and Recent Validation Results</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission was launched in January, 2015 and began its calibration and validation (cal/val) phase in May, 2015. Cal/Val will begin with a focus on instrument measurements, brightness temperature and backscatter, and evolve to the geophysical products that include...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=268159','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=268159"><span>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) applications activity</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission is one of the first-tier satellite missions recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space. The SMAP mission 1 is under development by NASA and is scheduled for launch late in 2014. The SMAP mea...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26672277','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26672277"><span>[Bare <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Inversion Model Based on Visible-Shortwave Infrared Reflectance].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zheng, Xiao-po; Sun, Yue-jun; Qin, Qi-ming; Ren, Hua-zhong; Gao, Zhong-ling; Wu, Ling; Meng, Qing-ye; Wang, Jin-liang; Wang, Jian-hua</p> <p>2015-08-01</p> <p><span class="hlt">Soil</span> is the loose solum of land surface that can support plants. It consists of minerals, organics, atmosphere, <span class="hlt">moisture</span>, microbes, et al. Among its complex compositions, <span class="hlt">soil</span> <span class="hlt">moisture</span> varies greatly. Therefore, the fast and accurate inversion of <span class="hlt">soil</span> <span class="hlt">moisture</span> by using remote sensing is very crucial. In order to reduce the influence of <span class="hlt">soil</span> type on the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span>, this paper proposed a normalized spectral slope and absorption index named NSSAI to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span>. The modeling of the new index contains several key steps: Firstly, <span class="hlt">soil</span> samples with different <span class="hlt">moisture</span> level were artificially prepared, and <span class="hlt">soil</span> reflectance spectra was consequently measured using spectroradiometer produced by ASD Company. Secondly, the <span class="hlt">moisture</span> absorption spectral feature located at shortwave wavelengths and the spectral slope of visible wavelengths were calculated after analyzing the regular spectral feature change patterns of different <span class="hlt">soil</span> at different <span class="hlt">moisture</span> conditions. Then advantages of the two features at reducing <span class="hlt">soil</span> types' effects was synthesized to build the NSSAI. Thirdly, a linear relationship between NSSAI and <span class="hlt">soil</span> <span class="hlt">moisture</span> was established. The result showed that NSSAI worked better (correlation coefficient is 0.93) than most of other traditional methods in <span class="hlt">soil</span> <span class="hlt">moisture</span> extraction. It can weaken the influences caused by <span class="hlt">soil</span> types at different <span class="hlt">moisture</span> levels and improve the bare <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H51K1342R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H51K1342R"><span>Optimizing <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sampling Locations for Validation Networks for SMAP</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roshani, E.; Berg, A. A.; Lindsay, J.</p> <p>2013-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive satellite (SMAP) is scheduled for launch on Oct 2014. Global efforts are underway for establishment of <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring networks for both the pre- and post-launch validation and calibration of the SMAP products. In 2012 the SMAP Validation Experiment, SMAPVEX12, took place near Carman Manitoba, Canada where nearly 60 fields were sampled continuously over a 6 week period for <span class="hlt">soil</span> <span class="hlt">moisture</span> and several other parameters simultaneous to remotely sensed images of the sampling region. The locations of these sampling sites were mainly selected on the basis of accessibility, <span class="hlt">soil</span> texture, and vegetation cover. Although these criteria are necessary to consider during sampling site selection, they do not guarantee optimal site placement to provide the most efficient representation of the studied area. In this analysis a method for optimization of sampling locations is presented which combines the state-of-art multi-objective optimization engine (non-dominated sorting genetic algorithm, NSGA-II), with the kriging interpolation technique to minimize the number of sampling sites while simultaneously minimizing the differences between the <span class="hlt">soil</span> <span class="hlt">moisture</span> map resulted from the kriging interpolation and <span class="hlt">soil</span> <span class="hlt">moisture</span> map from radar imaging. The algorithm is implemented in Whitebox Geospatial Analysis Tools, which is a multi-platform open-source GIS. The optimization framework is subject to the following three constraints:. A) sampling sites should be accessible to the crew on the ground, B) the number of sites located in a specific <span class="hlt">soil</span> texture should be greater than or equal to a minimum value, and finally C) the number of sampling sites with a specific vegetation cover should be greater than or equal to a minimum constraint. The first constraint is implemented into the proposed model to keep the practicality of the approach. The second and third constraints are considered to guarantee that the collected samples from each <span class="hlt">soil</span> texture categories</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26635219','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26635219"><span><span class="hlt">Moisture</span> content-<span class="hlt">affected</span> electrokinetic remediation of Cr(VI)-contaminated clay by a hydrocalumite barrier.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Xu, Yunfeng; Xu, Xiangjian; Hou, Hetian; Zhang, Jia; Zhang, Dayi; Qian, Guangren</p> <p>2016-04-01</p> <p>An electrokinetic-permeable reaction barrier (EK-PRB) system was introduced in this study with hydrocalumite as the barrier material. The combined system effectively remediated the Cr(VI)-contaminated clay after a 72-h treatment, and the Cr(VI) removal efficiency increased with the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> content. Further evidence was found that the changing <span class="hlt">soil</span> pH value and current density were highly associated with the initial <span class="hlt">moisture</span> content, showing its important roles in the Cr(VI) removal process. Additionally, the total Cr removal efficiency was much lower than that of Cr(VI) owing to the partial conversion of Cr(VI) to Cr(III) in the electrokinetic remediation process. Under high <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions (40%), the removal efficiency of Cr(VI) and total Cr was 96.6 and 67.3%, respectively. Further analysis also revealed the new mineral phase, chromate hydrocalumite, for Cr fixation in the hydrocalumite barrier, which was significantly <span class="hlt">affected</span> by the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> content. Our results showed that the EK-PRB system with a hydrocalumite barrier is highly promising with great potential for the effective remediation of Cr(VI)-contaminated clay and engineering implementation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20180002232','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20180002232"><span>Improving <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Girotto, Manuela</p> <p>2018-01-01</p> <p>Observations from recent <span class="hlt">soil</span> <span class="hlt">moisture</span> dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) <span class="hlt">soil</span> <span class="hlt">moisture</span> profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily <span class="hlt">affects</span> (in absolute terms) deeper <span class="hlt">moisture</span> storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of <span class="hlt">soil</span> <span class="hlt">moisture</span> (i.e., surface and deeper water storages).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19..100D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19..100D"><span>Detection of <span class="hlt">soil</span> <span class="hlt">moisture</span> impact in convective initiation in the central region of Mexico</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dolores, Edgar; Caetano, Ernesto</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is important for understanding hydrological cycle variability in many regions. Local surface heat and <span class="hlt">moisture</span> fluxes represent a major source of convective rainfall in Mexico during the summer, driven by positive evaporation-precipitation feedback. The effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> are directly reflected in the limitation of evapotranspiration, <span class="hlt">affecting</span> the development of the planetary boundary layer and, therefore, the initiation and intensity of convective precipitation. This study presents preliminary analysis of the role of <span class="hlt">soil</span> <span class="hlt">moisture</span> in convective initiations in central Mexico, for which a methodology for the detection of convective initiations similar to Taylor (2015) has been considered. The results show that the <span class="hlt">moisture</span> fluxes from the surface influence the development of convection favored by mesoscale circulations at low levels. Initiations are more frequent in regions less humid than their surroundings with the very strong signal during the month of September. The knowledge of the <span class="hlt">soil</span> predisposition to allow the development of deep convection suggests an alternative tool for the prediction of convective rains in Mexico.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.epa.gov/watersense/watersense-soil-moisture-based-control-technologies-notice-intent-noi','PESTICIDES'); return false;" href="https://www.epa.gov/watersense/watersense-soil-moisture-based-control-technologies-notice-intent-noi"><span>WaterSense <span class="hlt">Soil</span> <span class="hlt">Moisture</span>-Based Control Technologies Notice of Intent (NOI)</span></a></p> <p><a target="_blank" href="http://www.epa.gov/pesticides/search.htm">EPA Pesticide Factsheets</a></p> <p></p> <p></p> <p>By directly measuring the amount of <span class="hlt">moisture</span> in the <span class="hlt">soil</span>, <span class="hlt">soil</span> <span class="hlt">moisture</span>-based control technologies tailor irrigation schedules to meet landscape water needs based on seasonal patterns, as well as prevailing conditions in the landscape.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=254216&keyword=soil+AND+moisture&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=254216&keyword=soil+AND+moisture&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns in contrasting habitats in the Willamette Valley, Oregon</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Changing seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes caused by global warming may alter plant community composition in sensitive habitats such as wetlands and oak savannas. To evaluate such changes, an understanding of typical seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes is necessary. The primary objective...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011762','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011762"><span>Evaluation of SMAP Level 2 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Algorithms Using SMOS Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann; Shi, J. C.</p> <p>2011-01-01</p> <p>The objectives of the SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive) mission are global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and land freeze/thaw state at 10 km and 3 km resolution, respectively. SMAP will provide <span class="hlt">soil</span> <span class="hlt">moisture</span> with a spatial resolution of 10 km with a 3-day revisit time at an accuracy of 0.04 m3/m3 [1]. In this paper we contribute to the development of the Level 2 <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithm that is based on passive microwave observations by exploiting <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity (SMOS) satellite observations and products. SMOS brightness temperatures provide a global real-world, rather than simulated, test input for the SMAP radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithm. Output of the potential SMAP algorithms will be compared to both in situ measurements and SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products. The investigation will result in enhanced SMAP pre-launch algorithms for <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760009508','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760009508"><span>Ground truth report 1975 Phoenix microwave experiment. [Joint <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blanchard, B. J.</p> <p>1975-01-01</p> <p>Direct measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> obtained in conjunction with aircraft data flights near Phoenix, Arizona in March, 1975 are summarized. The data were collected for the Joint <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H11D1202Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H11D1202Y"><span>Aspect-related Vegetation Differences Amplify <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variability in Semiarid Landscapes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yetemen, O.; Srivastava, A.; Kumari, N.; Saco, P. M.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> variability (SMV) in semiarid landscapes is <span class="hlt">affected</span> by vegetation, <span class="hlt">soil</span> texture, climate, aspect, and topography. The heterogeneity in vegetation cover that results from the effects of microclimate, terrain attributes (slope gradient, aspect, drainage area etc.), <span class="hlt">soil</span> properties, and spatial variability in precipitation have been reported to act as the dominant factors modulating SMV in semiarid ecosystems. However, the role of hillslope aspect in SMV, though reported in many field studies, has not received the same degree of attention probably due to the lack of extensive large datasets. Numerical simulations can then be used to elucidate the contribution of aspect-driven vegetation patterns to this variability. In this work, we perform a sensitivity analysis to study on variables driving SMV using the CHILD landscape evolution model equipped with a spatially-distributed solar-radiation component that couples vegetation dynamics and surface hydrology. To explore how aspect-driven vegetation heterogeneity contributes to the SMV, CHILD was run using a range of parameters selected to reflect different scenarios (from uniform to heterogeneous vegetation cover). Throughout the simulations, the spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> and vegetation cover are computed to estimate the corresponding coefficients of variation. Under the uniform spatial precipitation forcing and uniform <span class="hlt">soil</span> properties, the factors <span class="hlt">affecting</span> the spatial distribution of solar insolation are found to play a key role in the SMV through the emergence of aspect-driven vegetation patterns. Hence, factors such as catchment gradient, aspect, and latitude, define water stress and vegetation growth, and in turn <span class="hlt">affect</span> the available <span class="hlt">soil</span> <span class="hlt">moisture</span> content. Interestingly, changes in <span class="hlt">soil</span> properties (porosity, root depth, and pore-size distribution) over the domain are not as effective as the other factors. These findings show that the factors associated to aspect-related vegetation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ESASP.713E..40J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ESASP.713E..40J"><span>Polarimetric Decompositions for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval from Vegetated <span class="hlt">Soils</span> in TERENO Observatories</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jagdhuber, Thomas; Hajnsek, Irena; Papathanassiou, Konstantinos P.</p> <p>2013-08-01</p> <p>A refined hybrid polarimetric decomposition and inversion method for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation under vegetation is investigated for its potential to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> from vegetated <span class="hlt">soils</span> in TERENO observatories. The refined algorithm is applied on L- band fully polarimetric data acquired by DLR's novel F-SAR sensor. Two flight and field measurement campaigns were conducted in 2011 and 2012 for the TERENO Harz, Eifel and DEMMIN observatories, located all across Germany. The applied algorithm reveals distinct potential to invert <span class="hlt">soil</span> <span class="hlt">moisture</span> with inversion rates higher than >98% for a variety of crop types, phenological conditions and for pronounced topography. A quality assessment is conducted by validation with FDR, TDR and a wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> network. The RMSE stays below 6.1vol.% for the different test sites and data takes including a variety of vegetation types in different phenological stages.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/of/2008/1100/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/of/2008/1100/"><span>Modeling <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in the Mojave Desert</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Miller, David M.; Hughson, Debra; Schmidt, Kevin M.</p> <p>2008-01-01</p> <p>The Mojave Desert is an arid region of southeastern California and parts of Nevada, Arizona, and Utah; the desert occupies more than 25,000 square miles (fig. 1). Ranging from below sea level to over 5,000 feet (1,524 m) in elevation, the Mojave Desert is considered a ?high desert.? On the west and southwest it is bounded by the Sierra Nevada, the San Gabriel, and the San Bernardino Mountains. These imposing mountains intercept <span class="hlt">moisture</span> traveling inland from the Pacific Ocean, producing arid conditions characterized by extreme fluctuations in daily temperatures, strong seasonal winds, and an average annual precipitation of less than six inches. The Mojave Desert lies farther south and at a lower elevation than the cooler Great Basin Desert and grades southward into the even lower and hotter Sonoran Desert.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27459857','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27459857"><span>The <span class="hlt">soil</span> microbiome at the Gi-FACE experiment responds to a <span class="hlt">moisture</span> gradient but not to CO2 enrichment.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>de Menezes, Alexandre B; Müller, Christoph; Clipson, Nicholas; Doyle, Evelyn</p> <p>2016-09-01</p> <p>The <span class="hlt">soil</span> bacterial community at the Giessen free-air CO2 enrichment (Gi-FACE) experiment was analysed by tag sequencing of the 16S rRNA gene. No substantial effects of CO2 levels on bacterial community composition were detected. However, the <span class="hlt">soil</span> <span class="hlt">moisture</span> gradient at Gi-FACE had a significant effect on bacterial community composition. Different groups within the Acidobacteria and Verrucomicrobia phyla were <span class="hlt">affected</span> differently by <span class="hlt">soil</span> <span class="hlt">moisture</span> content. These results suggest that modest increases in atmospheric CO2 may cause only minor changes in <span class="hlt">soil</span> bacterial community composition and indicate that the functional responses of the <span class="hlt">soil</span> community to CO2 enrichment previously reported at Gi-FACE are due to factors other than changes in bacterial community composition. The effects of the <span class="hlt">moisture</span> gradient revealed new information about the relationships between poorly known Acidobacteria and Verrucomicrobia and <span class="hlt">soil</span> <span class="hlt">moisture</span> content. This study contrasts with the relatively small number of other temperate grassland free-air CO2 enrichment microbiome studies in the use of moderate CO2 enrichment and the resulting minor changes in the <span class="hlt">soil</span> microbiome. Thus, it will facilitate the development of further climate change mitigation studies. In addition, the <span class="hlt">moisture</span> gradient found at Gi-FACE contributes new knowledge in <span class="hlt">soil</span> microbial ecology, particularly regarding the abundance and <span class="hlt">moisture</span> relationships of the <span class="hlt">soil</span> Verrucomicrobia.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.B51C0444H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.B51C0444H"><span>Linking Spatial and Temporal Patterns of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> with Upland <span class="hlt">Soil</span> Iron Reduction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hodges, C. A.; Markewitz, D.; Thompson, A.</p> <p>2015-12-01</p> <p>Iron minerals play important roles in governing <span class="hlt">soil</span> nutrient availability and carbon dynamics. Periods of intermittent anoxia (low-oxygen) in upland <span class="hlt">soils</span> can drive microbial reduction and dissolution of iron minerals. However, quantifying ecosystem-scale iron reduction in upland <span class="hlt">soils</span> is challenging. The key condition necessary for <span class="hlt">soil</span> iron reduction is water saturation of <span class="hlt">soil</span> micropores, even if the entire <span class="hlt">soil</span> profile is not flooded. We assessed <span class="hlt">soil</span> <span class="hlt">moisture</span> and texture across three first-order watersheds at the Calhoun Critical Zone Observatory in South Carolina, USA over one year using electromagnetic induction (EMI). From these point measurements, we have created monthly maps of interpolated <span class="hlt">soil</span> <span class="hlt">moisture</span>. From the EMI data, we found that locations that remain relatively wet or dry throughout the year are not related to hill-slope position but to differences in <span class="hlt">soil</span> texture along a catena. Across a gradient of <span class="hlt">soil</span> <span class="hlt">moisture</span> and texture (based on <span class="hlt">soil</span> conductivity from the EMI probe) we installed passive redox sensors and conducted in situ iron reduction experiments. This data will be presented and the relationships between iron reduction, the spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span>/clay content, and the significance of these data with respect to <span class="hlt">soil</span> carbon cycling will be discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1916428C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1916428C"><span>Observing and modeling links between <span class="hlt">soil</span> <span class="hlt">moisture</span>, microbes and CH4 fluxes from forest <span class="hlt">soils</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Christiansen, Jesper; Levy-Booth, David; Barker, Jason; Prescott, Cindy; Grayston, Sue</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key driver of methane (CH4) fluxes in forest <span class="hlt">soils</span>, both of the net uptake of atmospheric CH4 and emission from the <span class="hlt">soil</span>. Climate and land use change will alter spatial patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as temporal variability impacting the net CH4 exchange. The impact on the resultant net CH4 exchange however is linked to the underlying spatial and temporal distribution of the <span class="hlt">soil</span> microbial communities involved in CH4 cycling as well as the response of the <span class="hlt">soil</span> microbial community to environmental changes. Significant progress has been made to target specific CH4 consuming and producing <span class="hlt">soil</span> organisms, which is invaluable in order to understand the microbial regulation of the CH4 cycle in forest <span class="hlt">soils</span>. However, it is not clear as to which extent <span class="hlt">soil</span> <span class="hlt">moisture</span> shapes the structure, function and abundance of CH4 specific microorganisms and how this is linked to observed net CH4 exchange under contrasting <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes. Here we report on the results from a research project aiming to understand how the CH4 net exchange is shaped by the interactive effects <span class="hlt">soil</span> <span class="hlt">moisture</span> and the spatial distribution CH4 consuming (methanotrophs) and producing (methanogens). We studied the growing season variations of in situ CH4 fluxes, microbial gene abundances of methanotrophs and methanogens, <span class="hlt">soil</span> hydrology, and nutrient availability in three typical forest types across a <span class="hlt">soil</span> <span class="hlt">moisture</span> gradient in a temperate rainforest on the Canadian Pacific coast. Furthermore, we conducted laboratory experiments to determine whether the net CH4 exchange from hydrologically contrasting forest <span class="hlt">soils</span> responded differently to changes in <span class="hlt">soil</span> <span class="hlt">moisture</span>. Lastly, we modelled the microbial mediation of net CH4 exchange along the <span class="hlt">soil</span> <span class="hlt">moisture</span> gradient using structural equation modeling. Our study shows that it is possible to link spatial patterns of in situ net exchange of CH4 to microbial abundance of CH4 consuming and producing organisms. We also show that the microbial</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AtmEn..66...52R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AtmEn..66...52R"><span>Effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the diurnal pattern of pesticide emission: Comparison of simulations with field measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reichman, Rivka; Yates, Scott R.; Skaggs, Todd H.; Rolston, Dennis E.</p> <p>2013-02-01</p> <p>Pesticide volatilization from agricultural <span class="hlt">soils</span> is one of the main pathways in which pesticides are dispersed in the environment and <span class="hlt">affects</span> ecosystems including human welfare. Thus, it is necessary to have accurate knowledge of the various physical and chemical mechanisms that <span class="hlt">affect</span> volatilization rates from field <span class="hlt">soils</span>. A verification of the influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> modeling on the simulated volatilization rate, <span class="hlt">soil</span> temperature and <span class="hlt">soil</span>-water content is presented. Model simulations are compared with data collected in a field study that measured the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on diazinon volatilization. These data included diurnal changes in volatilization rate, <span class="hlt">soil</span>-water content, and <span class="hlt">soil</span> temperature measured at two depths. The simulations were performed using a comprehensive non-isothermal model, two water retention functions, and two <span class="hlt">soil</span> surface resistance functions, resulting in four tested models. Results show that the degree of similarity between volatilization curves simulated using the four models depended on the initial water content. Under fairly wet conditions, the simulated curves mainly differ in the magnitude of their deviation from the measured values. However, under intermediate and low <span class="hlt">moisture</span> conditions, the simulated curves also differed in their pattern (shape). The model prediction accuracy depended on <span class="hlt">soil</span> <span class="hlt">moisture</span>. Under normal practices, where initial <span class="hlt">soil</span> <span class="hlt">moisture</span> is about field capacity or higher, a combination of Brooks and Corey water retention and the van de Grind and Owe <span class="hlt">soil</span> surface resistance functions led to the most accurate predictions. However, under extremely dry conditions, when <span class="hlt">soil</span>-water content in the top 1 cm is below the volumetric threshold value, the use of a full-range water retention function increased prediction accuracy. The different models did not <span class="hlt">affect</span> the <span class="hlt">soil</span> temperature predictions, and had a minor effect on the predicted <span class="hlt">soil</span>-water content of Yolo silty clay <span class="hlt">soil</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=318456','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=318456"><span>Biochar can positively influence <span class="hlt">soil</span> <span class="hlt">moisture</span> relations</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>One major issue related to climate change is the potential to improve <span class="hlt">soil</span> water relations in light of changes in future precipitation patterns or reductions in water availability in drier portions of the world (such as the western US). It appears that biochar may play a positive role, but that rol...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820016662','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820016662"><span>Microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements and analysis</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Newton, R. W.; Howell, T. A.; Nieber, J. L.; Vanbavel, C. H. M. (Principal Investigator)</p> <p>1980-01-01</p> <p>An effort to develop a model that simulates the distribution of water content and of temperature in bare <span class="hlt">soil</span> is documented. The field experimental set up designed to acquire the data to test this model is described. The microwave signature acquisition system (MSAS) field measurements acquired in Colby, Kansas during the summer of 1978 are pesented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H21C1068K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H21C1068K"><span>Impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> on land-atmosphere interaction - a study on stemflow</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kuo, T.; Chen, J.</p> <p>2013-12-01</p> <p>Hydrological cycle inside a forest ecosystem is complicated. Rainfall entering the forest is redistributed via several pathways before reaching the forest floor. Some of the rainwater is intercepted by the canopy and some become throughfall. Water intercepted by the canopy and branches can flow to the forest floor as stemflow. Furthermore, in contrast to the slow penetration through the top <span class="hlt">soil</span>, the stemflow can quickly reach deep <span class="hlt">soil</span> and water table via the root system. Stemflow has been found to vary as a function of plant species, seasonality, meteorological conditions, rainfall intensity and canopy structure (Levia and Frost, 2003). It can significantly influence runoff generation (Neave and Abrahams, 2002), groundwater recharge (Taniguchi et al., 1996), and spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> (Chang and Matzner, 2000; Liang et al., 2007). The stemflow mechanism has not been considered as part of the land-surface processes in most atmospheric models. So, in this effort we parameterize the stemflow effect into a land-surface module -- the Simplified Simple Biosphere (SSiB) model, and analyze how it <span class="hlt">affects</span> <span class="hlt">soil</span> <span class="hlt">moisture</span>, and if this effect is significant enough to influence atmospheric processes. We first applied the SSiB model to simulate offline the sensitivity of <span class="hlt">soil</span> <span class="hlt">moisture</span> to stemflow under different rainfall intensity. Then the SSiB with stemflow effect is incorporated into the Weather Research and Forecasting (WRF) model to simulate stemflow effect on <span class="hlt">moisture</span> exchange between <span class="hlt">soil</span> and the atmosphere. The case selected is a summer convection event which lasted for five consecutive days, under weak synoptic weather conditions. The results indicated that stemflow acts like a bypass highway which allows <span class="hlt">soil</span> water to quickly enter deep layer. As a result, upper layer <span class="hlt">soil</span> <span class="hlt">moisture</span> is decreased, leading to a stronger surface heating and thus atmospheric instability, consequently more intense rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24647610','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24647610"><span>Effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the temperature sensitivity of <span class="hlt">soil</span> heterotrophic respiration: a laboratory incubation study.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhou, Weiping; Hui, Dafeng; Shen, Weijun</p> <p>2014-01-01</p> <p>The temperature sensitivity (Q10) of <span class="hlt">soil</span> heterotrophic respiration (Rh) is an important ecological model parameter and may vary with temperature and <span class="hlt">moisture</span>. While Q10 generally decreases with increasing temperature, the <span class="hlt">moisture</span> effects on Q10 have been controversial. To address this, we conducted a 90-day laboratory incubation experiment using a subtropical forest <span class="hlt">soil</span> with a full factorial combination of five <span class="hlt">moisture</span> levels (20%, 40%, 60%, 80%, and 100% water holding capacity--WHC) and five temperature levels (10, 17, 24, 31, and 38°C). Under each <span class="hlt">moisture</span> treatment, Rh was measured several times for each temperature treatment to derive Q10 based on the exponential relationships between Rh and temperature. Microbial biomass carbon (MBC), microbial community structure and <span class="hlt">soil</span> nutrients were also measured several times to detect their potential contributions to the <span class="hlt">moisture</span>-induced Q10 variation. We found that Q10 was significantly lower at lower <span class="hlt">moisture</span> levels (60%, 40% and 20% WHC) than at higher <span class="hlt">moisture</span> level (80% WHC) during the early stage of the incubation, but became significantly higher at 20%WHC than at 60% WHC and not significantly different from the other three <span class="hlt">moisture</span> levels during the late stage of incubation. In contrast, <span class="hlt">soil</span> Rh had the highest value at 60% WHC and the lowest at 20% WHC throughout the whole incubation period. Variations of Q10 were significantly associated with MBC during the early stages of incubation, but with the fungi-to-bacteria ratio during the later stages, suggesting that changes in microbial biomass and community structure are related to the <span class="hlt">moisture</span>-induced Q10 changes. This study implies that global warming's impacts on <span class="hlt">soil</span> CO2 emission may depend upon <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. With the same temperature rise, wetter <span class="hlt">soils</span> may emit more CO2 into the atmosphere via heterotrophic respiration.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3960259','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3960259"><span>Effects of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on the Temperature Sensitivity of <span class="hlt">Soil</span> Heterotrophic Respiration: A Laboratory Incubation Study</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhou, Weiping; Hui, Dafeng; Shen, Weijun</p> <p>2014-01-01</p> <p>The temperature sensitivity (Q10) of <span class="hlt">soil</span> heterotrophic respiration (Rh) is an important ecological model parameter and may vary with temperature and <span class="hlt">moisture</span>. While Q10 generally decreases with increasing temperature, the <span class="hlt">moisture</span> effects on Q10 have been controversial. To address this, we conducted a 90-day laboratory incubation experiment using a subtropical forest <span class="hlt">soil</span> with a full factorial combination of five <span class="hlt">moisture</span> levels (20%, 40%, 60%, 80%, and 100% water holding capacity - WHC) and five temperature levels (10, 17, 24, 31, and 38°C). Under each <span class="hlt">moisture</span> treatment, Rh was measured several times for each temperature treatment to derive Q10 based on the exponential relationships between Rh and temperature. Microbial biomass carbon (MBC), microbial community structure and <span class="hlt">soil</span> nutrients were also measured several times to detect their potential contributions to the <span class="hlt">moisture</span>-induced Q10 variation. We found that Q10 was significantly lower at lower <span class="hlt">moisture</span> levels (60%, 40% and 20% WHC) than at higher <span class="hlt">moisture</span> level (80% WHC) during the early stage of the incubation, but became significantly higher at 20%WHC than at 60% WHC and not significantly different from the other three <span class="hlt">moisture</span> levels during the late stage of incubation. In contrast, <span class="hlt">soil</span> Rh had the highest value at 60% WHC and the lowest at 20% WHC throughout the whole incubation period. Variations of Q10 were significantly associated with MBC during the early stages of incubation, but with the fungi-to-bacteria ratio during the later stages, suggesting that changes in microbial biomass and community structure are related to the <span class="hlt">moisture</span>-induced Q10 changes. This study implies that global warming’s impacts on <span class="hlt">soil</span> CO2 emission may depend upon <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. With the same temperature rise, wetter <span class="hlt">soils</span> may emit more CO2 into the atmosphere via heterotrophic respiration. PMID:24647610</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24743980','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24743980"><span><span class="hlt">Soil</span> microbial community responses to antibiotic-contaminated manure under different <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Reichel, Rüdiger; Radl, Viviane; Rosendahl, Ingrid; Albert, Andreas; Amelung, Wulf; Schloter, Michael; Thiele-Bruhn, Sören</p> <p>2014-01-01</p> <p>Sulfadiazine (SDZ) is an antibiotic frequently administered to livestock, and it alters microbial communities when entering <span class="hlt">soils</span> with animal manure, but understanding the interactions of these effects to the prevailing climatic regime has eluded researchers. A climatic factor that strongly controls microbial activity is <span class="hlt">soil</span> <span class="hlt">moisture</span>. Here, we hypothesized that the effects of SDZ on <span class="hlt">soil</span> microbial communities will be modulated depending on the <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. To test this hypothesis, we performed a 49-day fully controlled climate chamber pot experiments with <span class="hlt">soil</span> grown with Dactylis glomerata (L.). Manure-amended pots without or with SDZ contamination were incubated under a dynamic <span class="hlt">moisture</span> regime (DMR) with repeated drying and rewetting changes of >20 % maximum water holding capacity (WHCmax) in comparison to a control <span class="hlt">moisture</span> regime (CMR) at an average <span class="hlt">soil</span> <span class="hlt">moisture</span> of 38 % WHCmax. We then monitored changes in SDZ concentration as well as in the phenotypic phospholipid fatty acid and genotypic 16S rRNA gene fragment patterns of the microbial community after 7, 20, 27, 34, and 49 days of incubation. The results showed that strongly changing water supply made SDZ accessible to mild extraction in the short term. As a result, and despite rather small SDZ effects on community structures, the PLFA-derived microbial biomass was suppressed in the SDZ-contaminated DMR <span class="hlt">soils</span> relative to the CMR ones, indicating that dynamic <span class="hlt">moisture</span> changes accelerate the susceptibility of the <span class="hlt">soil</span> microbial community to antibiotics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H53J1615A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H53J1615A"><span>Downscaling SMAP Radiometer <span class="hlt">Soil</span> <span class="hlt">Moisture</span> over the CONUS using <span class="hlt">Soil</span>-Climate Information and Ensemble Learning</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abbaszadeh, P.; Moradkhani, H.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> contributes significantly towards the improvement of weather and climate forecast and understanding terrestrial ecosystem processes. It is known as a key hydrologic variable in the agricultural drought monitoring, flood modeling and irrigation management. While satellite retrievals can provide an unprecedented information on <span class="hlt">soil</span> <span class="hlt">moisture</span> at global-scale, the products are generally at coarse spatial resolutions (25-50 km2). This often hampers their use in regional or local studies, which normally require a finer resolution of the data set. This work presents a new framework based on an ensemble learning method while using <span class="hlt">soil</span>-climate information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer <span class="hlt">soil</span> <span class="hlt">moisture</span> over the Continental U.S. (CONUS) at 1 km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets in addition to <span class="hlt">soil</span> texture information and topography data among others were used. The downscaled product was validated against in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements collected from a limited number of core validation sites and several hundred sparse <span class="hlt">soil</span> <span class="hlt">moisture</span> networks throughout the CONUS. The obtained results indicated a great potential of the proposed methodology to derive the fine resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> information applicable for fine resolution hydrologic modeling, data assimilation and other regional studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27594213','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27594213"><span>The sensitivity of <span class="hlt">soil</span> respiration to <span class="hlt">soil</span> temperature, <span class="hlt">moisture</span>, and carbon supply at the global scale.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hursh, Andrew; Ballantyne, Ashley; Cooper, Leila; Maneta, Marco; Kimball, John; Watts, Jennifer</p> <p>2017-05-01</p> <p><span class="hlt">Soil</span> respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, <span class="hlt">moisture</span>, carbon supply, and other site characteristics are known to regulate <span class="hlt">soil</span> respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of <span class="hlt">soil</span> <span class="hlt">moisture</span>, <span class="hlt">soil</span> temperature, primary productivity, and <span class="hlt">soil</span> carbon estimates with observations of annual Rs from the Global <span class="hlt">Soil</span> Respiration Database (SRDB). We find that calibrating models with parabolic <span class="hlt">soil</span> <span class="hlt">moisture</span> functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. <span class="hlt">Soil</span> temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> carbon emerge as dominant predictors of Rs. We identify regions where typical temperature-driven responses are further mediated by <span class="hlt">soil</span> <span class="hlt">moisture</span>, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because <span class="hlt">soil</span> <span class="hlt">moisture</span> integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global-scale models tend to smooth its spatial heterogeneity by aggregating factors that increase <span class="hlt">moisture</span> variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108 Pg yr -1 , but also highlight regions of uncertainty</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GMD....10.1665H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GMD....10.1665H"><span>Investigating <span class="hlt">soil</span> <span class="hlt">moisture</span>-climate interactions with prescribed <span class="hlt">soil</span> <span class="hlt">moisture</span> experiments: an assessment with the Community Earth System Model (version 1.2)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hauser, Mathias; Orth, René; Seneviratne, Sonia I.</p> <p>2017-04-01</p> <p>Land surface hydrology is an important control of surface weather and climate. A valuable technique to investigate this link is the prescription of <span class="hlt">soil</span> <span class="hlt">moisture</span> in land surface models, which leads to a decoupling of the atmosphere and land processes. Diverse approaches to prescribe <span class="hlt">soil</span> <span class="hlt">moisture</span>, as well as different prescribed <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions have been used in previous studies. Here, we compare and assess four methodologies to prescribe <span class="hlt">soil</span> <span class="hlt">moisture</span> and investigate the impact of two different estimates of the climatological seasonal cycle used to prescribe <span class="hlt">soil</span> <span class="hlt">moisture</span>. Our analysis shows that, though in appearance similar, the different approaches require substantially different long-term <span class="hlt">moisture</span> inputs and lead to different temperature signals. The smallest influence on temperature and the water balance is found when prescribing the median seasonal cycle of deep <span class="hlt">soil</span> liquid water, whereas the strongest signal is found when prescribing <span class="hlt">soil</span> liquid and <span class="hlt">soil</span> ice using the mean seasonal cycle. These results indicate that induced net water-balance perturbations in experiments investigating <span class="hlt">soil</span> <span class="hlt">moisture</span>-climate coupling are important contributors to the climate response, in addition to the intended impact of the decoupling. These results help to guide the set-up of future experiments prescribing <span class="hlt">soil</span> <span class="hlt">moisture</span>, as for instance planned within the Land Surface, Snow and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Model Intercomparison Project (LS3MIP).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003JHyd..280...13T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003JHyd..280...13T"><span>Inferring the location of catchment characteristic <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring sites. Covariance structures in the temporal domain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thierfelder, Tomas K.; Grayson, Rodger B.; von Rosen, Dietrich; Western, Andrew W.</p> <p>2003-09-01</p> <p>Information shortage is a fundamental constraint in catchment hydrology that severely <span class="hlt">affects</span> the possibilities for secure inference of the generic hydrologic landscape, as well as for secure validation of physically deduced distributed models. The introduction of databases with high enough spatiotemporal resolution to properly reflect generic hydrological catchment characteristics may therefore be considered as an inferential breakthrough. The work presented here is part of a project where observations from such an Australian catchment (the Tarrawarra) are utilised to estimate the discrepancy for individual <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring sites in reflecting generic catchment characteristics. With low enough discrepancy, observation sites may be considered as catchment characteristic <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring (CASMM) sites, thus capturing unbiased catchment characteristics and being well suited to represent the catchment in a monitoring effort. In this particular study, covariance structures in the temporal domain are inferred in order to enable subsequent enquiries regarding CASMM discrepancies. This is accomplished with ARMAX filters applied to the conditional auto- and cross-covariance structures that connect observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> to the temporal variation of meteorology. The results suggest that weekly observations of Tarrawarra <span class="hlt">soil</span> <span class="hlt">moisture</span> are quite consistent realisations of first order auto-regressive processes, which means that the present state of <span class="hlt">soil</span> <span class="hlt">moisture</span> is generally acquired through the past week. With auto-correlative effects filtered out, cross-correlative meteorological effects on Tarrawarra <span class="hlt">soil</span> <span class="hlt">moisture</span> are identified and generally represented by the present week's accumulation of rainfall, the present week's accumulation of global radiation, and the previous week's maximum wind speed. After successive filtering of conditional cross-correlative effects, residual time-series observations may be considered as temporally independent, and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..120.9955N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.9955N"><span>Spatial representativeness of <span class="hlt">soil</span> <span class="hlt">moisture</span> using in situ, remote sensing, and land reanalysis data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nicolai-Shaw, Nadine; Hirschi, Martin; Mittelbach, Heidi; Seneviratne, Sonia I.</p> <p>2015-10-01</p> <p>This study investigates the spatial representativeness of the temporal dynamics of absolute <span class="hlt">soil</span> <span class="hlt">moisture</span> and its temporal anomalies over North America based on a range of data sets. We use three main data sources: in situ observations, the remote-sensing-based data set of the European Space Agency Climate Change Initiative on the Essential Climate Variable <span class="hlt">soil</span> <span class="hlt">moisture</span> (ECV-SM), and land surface model estimates from European Centre for Medium-Range Weather Forecasts's ERA-Land. The intercomparisons of the three <span class="hlt">soil</span> <span class="hlt">moisture</span> data sources are performed at the in situ locations as well as for the full-gridded products. The applied method allows us to quantify the spatial footprint of <span class="hlt">soil</span> <span class="hlt">moisture</span>. At the in situ locations it is shown that for absolute <span class="hlt">soil</span> <span class="hlt">moisture</span> the ECV-SM and ERA-Land products perform similarly, while for the temporal anomalies the ECV-SM product shows more similarity in spatial representativeness with the in situ data. When taking into account all grid cells of the ECV-SM and ERA-Land products to calculate spatial representativeness, we find the largest differences in spatial representativeness for the absolute values. The differences in spatial representativeness between the single products can be related to some of their intrinsic characteristics, i.e., for ECV-SM low similarities are found in topographically complex terrain and areas with dense vegetation, while for ERA-Land the smoothed model topography and surface properties <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> and its spatial representativeness. Additionally, we show that the applied method is robust and can be used to analyze existing networks to provide insight into the locations in which higher station density would be of most benefit.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H43F1017B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H43F1017B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> dynamics and their effect on bioretention performance in Northeast Ohio</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bush, S. A.; Jefferson, A.; Jarden, K.; Kinsman-Costello, L. E.; Grieser, J.</p> <p>2014-12-01</p> <p> time and peak flow, are altered relative to a control street. This analysis suggests that street-scale implementation of bioretention can reduce the impact of impervious surface on stormflows, but more information is needed to fully understand how <span class="hlt">soil</span> <span class="hlt">moisture</span> of the bioretentions <span class="hlt">affects</span> inter-storm variability in performance.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1919199M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1919199M"><span>Sensitivity of <span class="hlt">soil</span> <span class="hlt">moisture</span> analyses to contrasting background and observation error scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Munoz-Sabater, Joaquín; de Rosnay, Patricia; Albergel, Clément; Isaksen, Lars</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a crucial variable for numerical weather prediction. Accurate, global initialization of <span class="hlt">soil</span> <span class="hlt">moisture</span> is obtained through data assimilation systems. However analyses depend largely on the way observations and background errors are defined. In this paper a wide range of short experiments with contrasted specification of the observation error and <span class="hlt">soil</span> <span class="hlt">moisture</span> background were conducted. As observations, screen-level variables and brightness temperatures from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission were used. The region of interest was North America given the good availability of in-situ observations. The impact of these experiments on <span class="hlt">soil</span> <span class="hlt">moisture</span> and the atmospheric layer near the surface were evaluated. The results highlighted the importance of assimilating sensitive observations to <span class="hlt">soil</span> <span class="hlt">moisture</span> for air temperature and humidity forecasts. The benefits on the <span class="hlt">soil</span> water content were more noticeable with increasing the SMOS observation error and with the introduction of <span class="hlt">soil</span> texture dependency in the <span class="hlt">soil</span> <span class="hlt">moisture</span> background error.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241335&keyword=soil+AND+moisture&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241335&keyword=soil+AND+moisture&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Identification of optimal <span class="hlt">soil</span> hydraulic functions and parameters for predicting <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>We examined the accuracy of several commonly used <span class="hlt">soil</span> hydraulic functions and associated parameters for predicting observed <span class="hlt">soil</span> <span class="hlt">moisture</span> data. We used six combined methods formed by three commonly used <span class="hlt">soil</span> hydraulic functions – i.e., Brooks and Corey (1964) (BC), Campbell (19...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/5151','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/5151"><span>Using <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> to predict forest <span class="hlt">soil</span> nitrogen mineralization</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Jennifer D. Knoepp; Wayne T. Swank</p> <p>2002-01-01</p> <p>Due to the importance of N in forest productivity ecosystem and nutrient cycling research often includes measurement of <span class="hlt">soil</span> N transformation rates as indices of potential availability and ecosystem losses of N. We examined the feasibility of using <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> content to predict <span class="hlt">soil</span> N mineralization rates (Nmin) at the Coweeta Hydrologic Laboratory...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=335031','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=335031"><span>Evaluating <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Two satellites are currently monitoring surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) from L-band observations: SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity), a European Space Agency (ESA) satellite that was launched on November 2, 2009 and SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive), a National Aeronautics and Space Administration...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=330724','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=330724"><span>Rainfall estimation by inverting SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates: a comparison of different methods over Australia</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the <span class="hlt">soil</span> <span class="hlt">moisture</span> product from the European Space Agency’s <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) is used...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=304546','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=304546"><span>Evaluation of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products over the CanEx-SM10 area</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) Earth observation satellite was launched in November 2009 to provide global <span class="hlt">soil</span> <span class="hlt">moisture</span> and ocean salinity measurements based on L-Band passive microwave measurements. Since its launch, different versions of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products processors have be...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=263658','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=263658"><span>SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> validation with U.S. in situ newworks</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> at large scale has been performed using several satellite-based passive microwave sensors using a variety of retrieval methods. The most recent source of <span class="hlt">soil</span> <span class="hlt">moisture</span> is the European Space Agency <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission. Since it is a new sensor u...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=318185','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=318185"><span>Potential of bias correction for downscaling passive microwave and <span class="hlt">soil</span> <span class="hlt">moisture</span> data</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Passive microwave satellites such as SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity) or SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive) observe brightness temperature (TB) and retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disag...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=275143','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=275143"><span>Canadian experiment for <span class="hlt">soil</span> <span class="hlt">moisture</span> in 2010 (CanEx-SM10): Overview and preliminary results</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The Canadian Experiment for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada from 31 May to 16 June, 2010. Its main objective was to contribute to the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission validation and the pre-launch assessment of the proposed <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=328373','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=328373"><span>Precipitation estimation using L-Band and C-Band <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>An established methodology for estimating precipitation amounts from satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals is applied to L-band products from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterome...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=269392','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=269392"><span>SMOS/SMAP synergy for SMAP level 2 <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithm evaluation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP)satellite has been proposed to provide global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product at 40-km spatial resolution. This product and the sup...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=330813','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=330813"><span>Validation of SMAP surface <span class="hlt">soil</span> <span class="hlt">moisture</span> products with core validation sites</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm performance. Those sites provide well-calibrated in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements within SMAP product grid pixels for diver...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=320076','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=320076"><span>Estimating error cross-correlations in <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets using extended collocation analysis</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Consistent global <span class="hlt">soil</span> <span class="hlt">moisture</span> records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> data into water balance models or merging multi-source <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals int...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=244275','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=244275"><span>Remote sensing of an agricultural <span class="hlt">soil</span> <span class="hlt">moisture</span> network in Walnut Creek, Iowa</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The calibration and validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing products is complicated by the logistics of installing a <span class="hlt">soil</span> <span class="hlt">moisture</span> network for a long term period in an active landscape. Usually <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors are added to existing precipitation networks which have as a singular requiremen...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H33A0843S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33A0843S"><span>The impact of Precipitation and Grassland Vegetation on <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Dynamics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salve, R.; Sudderth, E. A.; St. Clair, S. B.; Torn, M. S.</p> <p>2009-12-01</p> <p>The primary objective of this study was to assess the impact of grassland vegetation and precipitation (defined by the temporal pattern of water deposition and cumulative rainfall) on near-surface hydrology. Using a randomized block design experiment in a greenhouse, we monitored <span class="hlt">soil-moisture</span> dynamics in mesocosms planted with three types of grassland vegetation found in California (mixed California grassland, avena grass monoculture, and erodium forb monoculture). We observed that above ground biomass production was strongly influenced by rainfall amount, with most productivity in the mid-level rainfall treatment. <span class="hlt">Soil</span> <span class="hlt">moisture</span> content (SMC) was best predicted by rainfall, stage of plant growth, and the interaction between these two parameters. Surprisingly, SMC did not depend on species composition of the grassland. The role of ET in drying the <span class="hlt">soil</span> was influenced by the interaction between growth stage and rainfall, and to a lesser extend by the interaction between vegetation type and growth stage. When combined, seasonal precipitation and vegetation influenced the near-surface hydrology in ways that cannot be predicted from manipulation of a single variable. These results emphasize the importance of the interactive effects of precipitation and vegetation on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics, and the potential for feedbacks since <span class="hlt">soil</span> <span class="hlt">moisture</span> <span class="hlt">affects</span> vegetation. This study was supported by the Program for Ecosystem Research, Office of Science, U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27600157','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27600157"><span>Individual contributions of climate and vegetation change to <span class="hlt">soil</span> <span class="hlt">moisture</span> trends across multiple spatial scales.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Feng, Huihui</p> <p>2016-09-07</p> <p>Climate and vegetation change are two dominating factors for <span class="hlt">soil</span> <span class="hlt">moisture</span> trend. However, their individual contributions remain unknown due to their complex interaction. Here, I separated their contributions through a trajectory-based method across the global, regional and local scales. Our results demonstrated that climate change accounted for 98.78% and 114.64% of the global drying and wetting trend. Vegetation change exhibited a relatively weak influence (contributing 1.22% and -14.64% of the global drying and wetting) because it occurred in a limited area on land. Regionally, the impact of vegetation change cannot be neglected, which contributed -40.21% of the <span class="hlt">soil</span> <span class="hlt">moisture</span> change in the wetting zone. Locally, the contributions strongly correlated to the local environmental characteristics. Vegetation negatively <span class="hlt">affected</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> trends in the dry and sparsely vegetated regions and positively in the wet and densely vegetated regions. I conclude that individual contributions of climate and vegetation change vary at the global, regional and local scales. Climate change dominates the <span class="hlt">soil</span> <span class="hlt">moisture</span> trends, while vegetation change acts as a regulator to drying or wetting the <span class="hlt">soil</span> under the changing climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUSM.B73B..03O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUSM.B73B..03O"><span>Influence of Antecedent <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Conditions and Substrate Quality on the Magnitude and Timing of N2O Emissions From Riparian <span class="hlt">Soil</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Owens, J. L.; Macrae, M. L.; Bourbonniere, R. A.; Petrone, R. M.; Schiff, S. L.</p> <p>2009-05-01</p> <p>Nitrous oxide (N2O) is a greenhouse gas with a large global warming potential. Consequently there is concern over increased concentrations of atmospheric N2O. Denitrification and nitrification are the primary sources of N2O emissions from agricultural <span class="hlt">soils</span> and riparian wetlands within these systems. These processes are regulated by <span class="hlt">soil</span> <span class="hlt">moisture</span>, oxygen levels in <span class="hlt">soil</span> pores, <span class="hlt">soil</span> substrate/nutrient supply (e.g. carbon (C) and nitrogen (N)), pH, and temperature. <span class="hlt">Soil</span> <span class="hlt">moisture</span> history may also be a key determinant of N2O flux timing and magnitude through its influence on <span class="hlt">soil</span> turnover processes and therefore available nutrient pools. However, the linkages between these controls as well as their relative influence on N2O fluxes are poorly understood. This research uses an experimental approach to examine the combined influences of <span class="hlt">soil</span> <span class="hlt">moisture</span> and nutrient availability (as <span class="hlt">affected</span> by <span class="hlt">soil</span> antecedent <span class="hlt">moisture</span> history) on N2O fluxes from riparian <span class="hlt">soil</span>. <span class="hlt">Soil</span> cores were collected from both an upland (loam <span class="hlt">soil</span>) location and a lowland (organic <span class="hlt">soil</span>) location in an agricultural riparian wetland in Southern Ontario for this experiment. In the laboratory, intact <span class="hlt">soil</span> cores were subject to <span class="hlt">moisture</span> cycles (wet-dry-wet; dry-wet-dry) over a six-week period to examine how N2O fluxes and <span class="hlt">soil</span> available nutrient pools changed throughout different types of <span class="hlt">moisture</span> cycles. Preliminary results indicate that antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> influences the timing and magnitude of N2O flux due to its influence on both <span class="hlt">soil</span> available nutrient content and likely O2 availability; however, these relationships differ for the two <span class="hlt">soil</span> types. Larger N2O fluxes were observed from upland <span class="hlt">soils</span> on a drying trend as opposed to a wetting trend. In contrast, larger N2O fluxes were observed from <span class="hlt">soils</span> on a wetting trend rather than a drying trend from lowland <span class="hlt">soil</span>. In addition, the timing of the onset and cessation of N2O fluxes differed both with <span class="hlt">soil</span> type and the direction of the <span class="hlt">moisture</span> cycle (i</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H51F1265L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H51F1265L"><span>Assessing Landscape-Scale <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Distribution Using Auxiliary Sensing Technologies and Multivariate Geostatistics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Landrum, C.; Castrignanò, A.; Mueller, T.; Zourarakis, D.; Zhu, J.</p> <p>2013-12-01</p> <p>It is important to assess <span class="hlt">soil</span> <span class="hlt">moisture</span> to develop strategies to better manage its availability and use. At the landscape scale, <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution derives from an integration of hydrologic, pedologic and geomorphic processes that cause <span class="hlt">soil</span> <span class="hlt">moisture</span> variability (SMV) to be time, space, and scale-dependent. Traditional methods to assess SMV at this scale are often costly, labor intensive, and invasive, which can lead to inadequate sampling density and spatial coverage. Fusing traditional sampling techniques with georeferenced auxiliary sensing technologies, such as geoelectric sensing and LiDAR, provide an alternative approach. Because geoelectric and LiDAR measurements are sensitive to <span class="hlt">soil</span> properties and terrain features that <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> variation, they are often employed as auxiliary measures to support less dense direct sampling. Georeferenced proximal sensing acquires rapid, real-time, high resolution data over large spatial extents that is enriched with spatial, temporal and scale-dependent information. Data fusion becomes important when proximal sensing is used in tandem with more sparse direct sampling. Multicollocated factorial cokriging (MFC) is one technique of multivariate geostatistics to fuse multiple data sources collected at different sampling scales to study the spatial characteristics of environmental properties. With MFC sparse <span class="hlt">soil</span> observations are supported by more densely sampled auxiliary attributes to produce more consistent spatial descriptions of scale-dependent parameters <span class="hlt">affecting</span> SMV. This study uses high resolution geoelectric and LiDAR data as auxiliary measures to support direct <span class="hlt">soil</span> sampling (n=127) over a 40 hectare Central Kentucky (USA) landscape. Shallow and deep apparent electrical resistivity (ERa) were measured using a Veris 3100 in tandem with <span class="hlt">soil</span> <span class="hlt">moisture</span> sampling on three separate dates with ascending <span class="hlt">soil</span> <span class="hlt">moisture</span> contents ranging from plant wilting point to field capacity. Terrain features were produced</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830004221','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830004221"><span>Measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> using remote sensing multisensor radiation techniques</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Waite, W. P. (Principal Investigator)</p> <p>1982-01-01</p> <p>Theoretical modeling as well as laboratory and field measurement were coupled with analysis of aircraft data obtained from controlled sites in an effort to enhance understanding of the microwave response due to <span class="hlt">soil</span> <span class="hlt">moisture</span> so as to specify sensor parameters and develop inversion algorithms. Models to predict the complex dielectric constant were produced which led to the interpretation of the results in terms of a matrix potential rather than simply <span class="hlt">moisture</span> content. Similar advances were made in the development of coherent and incoherent radiative transfer models and rough surface scattering models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715065F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715065F"><span>Upscaling of <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements in NW Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ferraris, Stefano; Canone, Davide; Previati, Maurizio; Brunod, Christian; Ratto, Sara; Cauduro, Marco</p> <p>2015-04-01</p> <p>There is large mismatch in spatial scale between the climate and meteorological model grid, and the scale of <span class="hlt">soil</span> and vegetation measurements. Remote sensing data can help to fit the model scale, but they cannot provide rootzone data. In this work some <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets are analysed for the sake of providing larger scale estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and water and energy fluxes. The first dataset refers to a plain site near Torino, where measurements are taken since 1997 (Baudena et al., 2012), and a mountain site close to the town. The second one is a dataset in the mountains of Valle d'Aosta (Brocca et al., 2013), where 4 years of data are available. The use of digital elevation models and vegetation maps is shown in this work. Some <span class="hlt">soil</span> processes (e.g. Whalley et al., 2012) are usually disregarded, but in this work their possible impact is considered. References L. Brocca, A. Tarpanelli, T. Moramarco, F. Melone, S.M. Ratto, M. Cauduro, S. Ferraris, N. Berni, F. Ponziani, W. Wagner, T. Melzer (2013). <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation in Alpine Catchments through Modeling and Satellite Observations VADOSE ZONE JOURNAL, vol. 8-2, p. 1-10, doi:10.2136/vzj2012.0102 M. Baudena, I. Bevilacqua, D. Canone, S. Ferraris, M. Previati, A. Provenzale (2012). <span class="hlt">Soil</span> water dynamics at a midlatitude test site: Field measurements and box modeling approaches. JOURNAL OF HYDROLOGY, vol. 414-415, p. 329-340, ISSN: 0022-1694, doi: 10.1016/j.jhydrol.2011.11.009 W.R. Whalley, G.P. Matthews, S. Ferraris (2012). The effect of compaction and shear deformation of saturated <span class="hlt">soil</span> on hydraulic conductivity. <span class="hlt">SOIL</span> & TILLAGE RESEARCH, vol. 125, p. 23-29, ISSN: 0167-1987</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000038131&hterms=IEEE&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DIEEE','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038131&hterms=IEEE&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DIEEE"><span>Microwave Remote Sensing of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> for Estimation of <span class="hlt">Soil</span> Properties</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mattikalli, Nandish M.; Engman, Edwin T.; Jackson, Thomas J.</p> <p>1997-01-01</p> <p>Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics was derived using microwave remote sensing, and employed to estimate <span class="hlt">soil</span> physical and hydraulic properties. The L-band ESTAR radiometer was employed in an airborne campaign over the Little Washita watershed, Oklahoma during June 10-18, 1992. Brightness temperature (TB) data were employed in a <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion algorithm which corrected for vegetation and <span class="hlt">soil</span> effects. Analyses of spatial TB and <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics during the dry-down period revealed a direct relationship between changes in TB, <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> texture. Extensive regression analyses were carried out which yielded statistically significant quantitative relationships between ratio of percent sand to percent clay (RSC, a term derived to quantify <span class="hlt">soil</span> texture) and saturated hydraulic conductivity (Ksat) in terms of change components of TB and surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Validation of results indicated that both RSC and Ksat can be estimated with reasonable accuracy. These findings have potential applications for deriving spatial distributions of RSC and Ksat over large areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29432925','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29432925"><span>Applicability of common stomatal conductance models in maize under varying <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Qiuling; He, Qijin; Zhou, Guangsheng</p> <p>2018-07-01</p> <p>In the context of climate warming, the varying <span class="hlt">soil</span> <span class="hlt">moisture</span> caused by precipitation pattern change will <span class="hlt">affect</span> the applicability of stomatal conductance models, thereby <span class="hlt">affecting</span> the simulation accuracy of carbon-nitrogen-water cycles in ecosystems. We studied the applicability of four common stomatal conductance models including Jarvis, Ball-Woodrow-Berry (BWB), Ball-Berry-Leuning (BBL) and unified stomatal optimization (USO) models based on summer maize leaf gas exchange data from a <span class="hlt">soil</span> <span class="hlt">moisture</span> consecutive decrease manipulation experiment. The results showed that the USO model performed best, followed by the BBL model, BWB model, and the Jarvis model performed worst under varying <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. The effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> made a difference in the relative performance among the models. By introducing a water response function, the performance of the Jarvis, BWB, and USO models improved, which decreased the normalized root mean square error (NRMSE) by 15.7%, 16.6% and 3.9%, respectively; however, the performance of the BBL model was negative, which increased the NRMSE by 5.3%. It was observed that the models of Jarvis, BWB, BBL and USO were applicable within different ranges of <span class="hlt">soil</span> relative water content (i.e., 55%-65%, 56%-67%, 37%-79% and 37%-95%, respectively) based on the 95% confidence limits. Moreover, introducing a water response function, the applicability of the Jarvis and BWB models improved. The USO model performed best with or without introducing the water response function and was applicable under varying <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. Our results provide a basis for selecting appropriate stomatal conductance models under drought conditions. Copyright © 2018 Elsevier B.V. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JHyd..543..270Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..543..270Z"><span>Effects of rainfall intensity and intermittency on woody vegetation cover and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> in dryland ecosystems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Ding-Hai; Li, Xin-Rong; Zhang, Feng; Zhang, Zhi-Shan; Chen, Yong-Le</p> <p>2016-12-01</p> <p>Identifying the relationship between the stochastic daily rainfall regime and the dynamics of plants and <span class="hlt">soil</span> <span class="hlt">moisture</span> is fundamental for the sustainable management of dryland ecosystems in a context of global climate change. An eco-hydrological model that couples the dynamics of woody vegetation cover and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> (typically with a depth interval of 30-150 cm) was used to investigate the effect of stochastic intensity and the intermittency of precipitation on <span class="hlt">soil</span> <span class="hlt">moisture</span> in this deep interval, which <span class="hlt">affects</span> woody vegetation cover. Our results suggest that the precipitation intensity and intermittency play an important role in the dynamics of wood vegetation cover and deep <span class="hlt">soil</span> <span class="hlt">moisture</span>. In arid and semiarid regions, as the annual precipitation increased, the rate of woody vegetation cover increased as a power-law function, and the deep <span class="hlt">soil</span> <span class="hlt">moisture</span> increased exponentially. For a given annual rainfall, there were positive correlations between the rainfall intensity (or rainfall intermittency) and both the woody vegetation cover and deep <span class="hlt">soil</span> <span class="hlt">moisture</span>. The positive correlations between wood vegetation cover and both rainfall intensity and intermittency may decrease with increases in the precipitation intensity or precipitation intermittency. The positive correlations between deep <span class="hlt">soil</span> <span class="hlt">moisture</span> and both rainfall intensity and rainfall intermittency increase as the precipitation intensity or precipitation intermittency increases. Moreover, these positive correlations may increase with increases in the mean annual rainfall. Our results emphasize the importance of daily precipitation variations in controlling the responses of woody vegetation cover and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> to climate variations in arid and semiarid regions. Our model can aid the understanding of rainfall processes and indicates that increases in rainfall intensity or rainfall intermittency may lead to an increase in woody vegetation cover and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> given an invariable annual</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013BGeo...10.3963J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013BGeo...10.3963J"><span>Responses of <span class="hlt">soil</span> respiration and its temperature/<span class="hlt">moisture</span> sensitivity to precipitation in three subtropical forests in southern China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, H.; Deng, Q.; Zhou, G.; Hui, D.; Zhang, D.; Liu, S.; Chu, G.; Li, J.</p> <p>2013-06-01</p> <p>Both long-term observation data and model simulations suggest an increasing chance of serious drought in the dry season and extreme flood in the wet season in southern China, yet little is known about how changes in precipitation pattern will <span class="hlt">affect</span> <span class="hlt">soil</span> respiration in the region. We conducted a field experiment to study the responses of <span class="hlt">soil</span> respiration to precipitation manipulations - precipitation exclusion to mimic drought, double precipitation to simulate flood, and ambient precipitation as control (abbr. EP, DP and AP, respectively) - in three subtropical forests in southern China. The three forest sites include Masson pine forest (PF), coniferous and broad-leaved mixed forest (MF) and monsoon evergreen broad-leaved forest (BF). Our observations showed that altered precipitation strongly influenced <span class="hlt">soil</span> respiration, not only through the well-known direct effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on plant and microbial activities, but also by modification of both <span class="hlt">moisture</span> and temperature sensitivity of <span class="hlt">soil</span> respiration. In the dry season, <span class="hlt">soil</span> respiration and its temperature sensitivity, as well as fine root and <span class="hlt">soil</span> microbial biomass, showed rising trends with precipitation increases in the three forest sites. Contrarily, the <span class="hlt">moisture</span> sensitivity of <span class="hlt">soil</span> respiration decreased with precipitation increases. In the wet season, different treatments showed different effects in three forest sites. The EP treatment decreased fine root biomass, <span class="hlt">soil</span> microbial biomass, <span class="hlt">soil</span> respiration and its temperature sensitivity, but enhanced <span class="hlt">soil</span> <span class="hlt">moisture</span> sensitivity in all three forest sites. The DP treatment significantly increased <span class="hlt">soil</span> respiration, fine root and <span class="hlt">soil</span> microbial biomass in the PF only, and no significant change was found for the <span class="hlt">soil</span> temperature sensitivity. However, the DP treatment in the MF and BF reduced <span class="hlt">soil</span> temperature sensitivity significantly in the wet season. Our results indicated that <span class="hlt">soil</span> respiration would decrease in the three subtropical forests if <span class="hlt">soil</span> <span class="hlt">moisture</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMIN34A..02M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMIN34A..02M"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensing Controller and Optimal Estimator (<span class="hlt">Soil</span>SCaPE): An in-situ Wireless Sensor Network for Validation of Spaceborne <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimates (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moghaddam, M.; Liu, M.; Wu, X.; Li, K.; Burgin, M.; Goykhman, Y.; Wang, Q.; Shuman, D.; Nayyar, A.; Teneketzis, D.; Entekhabi, D.</p> <p>2010-12-01</p> <p>We develop technologies for dynamic and near-real-time validation of space-borne <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements, in particular those from the NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP) mission. <span class="hlt">Soil</span> <span class="hlt">moisture</span> fields are functions of variables that change over time scales of minutes to days or weeks, and across the range of spatial scales from a few meters to several kilometers. We develop a sensor placement policy based on nonstationary spatial statistics of <span class="hlt">soil</span> <span class="hlt">