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Sample records for affect soil moisture

  1. 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.

  2. Macrofauna assemblage composition and soil moisture interact to affect soil ecosystem functions

    NASA Astrophysics Data System (ADS)

    Collison, E. J.; Riutta, T.; Slade, E. M.

    2013-02-01

    Changing climatic conditions and habitat fragmentation are predicted to alter the soil moisture conditions of temperate forests. It is not well understood how the soil macrofauna community will respond to changes in soil moisture, and how changes to species diversity and community composition may affect ecosystem functions, such as litter decomposition and soil fluxes. Moreover, few studies have considered the interactions between the abiotic and biotic factors that regulate soil processes. Here we attempt to disentangle the interactive effects of two of the main factors that regulate soil processes at small scales - moisture and macrofauna assemblage composition. The response of assemblages of three common temperate soil invertebrates (Glomeris marginata Villers, Porcellio scaber Latreille and Philoscia muscorum Scopoli) to two contrasting soil moisture levels was examined in a series of laboratory mesocosm experiments. The contribution of the invertebrates to the leaf litter mass loss of two common temperate tree species of contrasting litter quality (easily decomposing Fraxinus excelsior L. and recalcitrant Quercus robur L.) and to soil CO2 fluxes were measured. Both moisture conditions and litter type influenced the functioning of the invertebrate assemblages, which was greater in high moisture conditions compared with low moisture conditions and on good quality vs. recalcitrant litter. In high moisture conditions, all macrofauna assemblages functioned at equal rates, whereas in low moisture conditions there were pronounced differences in litter mass loss among the assemblages. This indicates that species identity and assemblage composition are more important when moisture is limited. We suggest that complementarity between macrofauna species may mitigate the reduced functioning of some species, highlighting the importance of maintaining macrofauna species richness.

  3. 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

  4. 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

  5. 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.

  6. [Inhibitory effect of DMPP on soil nitrification as affected by soil moisture content, pH and organic matter].

    PubMed

    Xue, Yan; Wu, Zhi-Jie; Zhang, Li-Li; Gong, Ping; Dong, Xin-Xin; Nie, Yan-Xia

    2012-10-01

    A laboratory incubation test with meadow brown soil was conducted to study the inhibitory effect of 3,4-dimethylpyrazole phosphate (DMPP) on soil nitrification as affected by soil moisture content (40%, 60% and 80% of the maximum field capacity), pH (4, 7 and 10), and organic matter (retained and removal). With the decrease of soil moisture content, the degradation of DMPP in soil tended to slow down, and the oxidation of soil NH4+ was more inhibited. At pH 10, more DMPP was remained in soil, and had the greatest inhibitory effect; at pH 7 and pH 4, the DMPP was lesser remained, with a smaller inhibitory effect. The removal of organic matter prolonged the remaining time of DMPP in soil, and decreased the apparent soil nitrification rate significantly.

  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. How do long dry spells affect soil moisture in different forest stands?

    NASA Astrophysics Data System (ADS)

    Heidbüchel, Ingo; Güntner, Andreas; Blume, Theresa

    2017-04-01

    Soil moisture conditions under forests are subject to numerous influences that are directly linked to the tree species composition and age. On the one hand, there are characteristic traits of individual tree species such as the way they funnel intercepted water towards their stems or the way they use water from the soil at different depths and times. On the other hand, there is also the influence of inter- and intra-species competition which may considerably affect the water use behavior of the involved tree species. In order to get insights into these complex relationships we studied spatial and temporal soil moisture patterns under pure and mixed forest stands of beech and pine of different ages in the TERENO observatory in northeastern Germany. We also specifically compared soil moisture conditions in the close vicinity of tree stems with conditions at greater distance from the trees (>1.5 m). The dataset used here derives from 450 sensors measuring soil moisture for 2.5 years at six different soil depths (from 10 cm down to 200 cm). Inspecting the entire time series we found considerable differences between many of the locations (young vs. mature, pine vs. beech, mixed vs. pure). These differences became more or less pronounced during certain weather periods. In particular, we studied the effect of dry spells of different preconditions and length during the three summers 2014, 2015 and 2016. While 2014 was a relatively wet summer, 2015 was dry and warm. Generally speaking, the dry spell in the summer of 2015 led to a decrease in soil moisture differences between locations that was still observable in the following winter and even in the following summer. For example, in the summer of 2014 volumetric water content close to the soil surface under mature pine trees was almost 8% higher compared to beech trees, however, in the dry summer of 2015 this difference disappeared. Contrary to this observation, volumetric water content differences between young stands of

  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. Antecedent soil moisture prior to freezing can affect quantity, composition and stability of soil dissolved organic matter during thaw.

    PubMed

    Wu, Haohao; Xu, Xingkai; Cheng, Weiguo; Fu, Pingqing; Li, Fayun

    2017-07-25

    There are large amounts of dissolved organic matter (DOM) released into the soil during spring thaw, but its bioavailability and components are still unknown. The quantity, composition and stability of DOM in water extracts of forest soils during thaw were studied after two-month freezing with 9 levels of soil moisture ranging from 10% to 90% water-filled pore space (WFPS), by measuring soil carbon dioxide (CO2) flux, biodegradable dissolved organic carbon (BDOC) and nitrogen (BDON), ultraviolet absorbance and parallel factor analysis of fluorescence excitation-emission matrices. Concentrations of BDOC, BDON, DOC and DON were lowest around 30% WFPS and relatively higher and lower soil moisture both increased DOM and BDOM concentrations in thawing soil. With increasing WFPS, the dominant component of soil DOM changed from humic acid-like substances to fulvic acid-like substances and the biological origin of DOM increased gradually. The protein-like component accounted for 8-20% of soil DOM and was affected by vegetation type and WFPS singly and interactively. The results implied that forest soils with more than 50% WFPS before winter freezing could release large amounts of fulvic acid-like DOM, which would be easily biodegraded and emitted as CO2 or run off with ground water during spring snow thaw.

  11. 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

  12. Understanding Soil Moisture

    USDA-ARS?s Scientific Manuscript database

    Understanding soil moisture is critical for landscape irrigation management. This landscaep irrigation seminar will compare volumetric and matric potential soil-moisture sensors, discuss the relationship between their readings and demonstrate how to use these data. Soil water sensors attempt to sens...

  13. Passive microwave soil moisture research

    NASA Technical Reports Server (NTRS)

    Schmugge, T. J.; Oneill, P. E.; Wang, J. R.

    1985-01-01

    The AgRISTARS Soil Moisture Project has made significant progress in the quantification of microwave sensor capabilities for soil moisture remote sensing. The 21-cm wavelength has been verified to be the best single channel for radiometric observations of soil moisture. It has also been found that other remote sensing approaches used in conjunction with L-band passive data are more successful than multiple wavelength microwave radiometry in this application. AgRISTARS studies have also improved current understanding of noise factors affecting the interpretability of microwave emission data. The absorption of soil emission by vegetation has been quantified, although this effect is less important than absorption effects for microwave radiometry.

  14. Soil moisture modeling review

    NASA Technical Reports Server (NTRS)

    Hildreth, W. W.

    1978-01-01

    A determination of the state of the art in soil moisture transport modeling based on physical or physiological principles was made. It was found that soil moisture models based on physical principles have been under development for more than 10 years. However, these models were shown to represent infiltration and redistribution of soil moisture quite well. Evapotranspiration has not been as adequately incorporated into the models.

  15. 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

  16. Passive microwave soil moisture research

    NASA Technical Reports Server (NTRS)

    Schmugge, T.; Oneill, P. E.; Wang, J. R.

    1986-01-01

    During the four years of the AgRISTARS Program, significant progress was made in quantifying the capabilities of microwave sensors for the remote sensing of soil moisture. In this paper, a discussion is provided of the results of numerous field and aircraft experiments, analysis of spacecraft data, and modeling activities which examined the various noise factors such as roughness and vegetation that affect the interpretability of microwave emission measurements. While determining that a 21-cm wavelength radiometer was the best single sensor for soil moisture research, these studies demonstrated that a multisensor approach will provide more accurate soil moisture information for a wider range of naturally occurring conditions.

  17. 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.

  18. 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.

  19. 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...

  20. 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.

  1. Soil moisture and fungi affect seed survival in California grassland annual plants.

    PubMed

    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.

  2. 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

  3. Soil moisture variations affect short-term plant-microbial competition for ammonium, glycine, and glutamate

    PubMed Central

    Månsson, Katarina F; Olsson, Magnus O; Falkengren-Grerup, Ursula; Bengtsson, Göran

    2014-01-01

    We tested whether the presence of plant roots would impair the uptake of ammonium (), glycine, and glutamate by microorganisms in a deciduous forest soil exposed to constant or variable moisture in a short-term (24-h) experiment. The uptake of 15NH4 and dual labeled amino acids by the grass Festuca gigantea L. and soil microorganisms was determined in planted and unplanted soils maintained at 60% WHC (water holding capacity) or subject to drying and rewetting. The experiment used a design by which competition was tested in soils that were primed by plant roots to the same extent in the planted and unplanted treatments. Festuca gigantea had no effect on microbial N uptake in the constant moist soil, but its presence doubled the microbial uptake in the dried and rewetted soil compared with the constant moist. The drying and rewetting reduced by half or more the uptake by F. gigantea, despite more than 60% increase in the soil concentration of . At the same time, the amino acid and -N became equally valued in the plant uptake, suggesting that plants used amino acids to compensate for the lower acquisition. Our results demonstrate the flexibility in plant-microbial use of different N sources in response to soil moisture fluctuations and emphasize the importance of including transient soil conditions in experiments on resource competition between plants and soil microorganisms. Competition between plants and microorganisms for N is demonstrated by a combination of removal of one of the potential competitors, the plant, and subsequent observations of the uptake of N in the organisms in soils that differ only in the physical presence and absence of the plant during a short assay. Those conditions are necessary to unequivocally test for competition. PMID:24772283

  4. 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...

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

    USDA-ARS?s Scientific Manuscript database

    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. Saflufenacil dissolved in acetonitrile was added into pre-incubated samples at the rate of 2000 g ha-1. The amount of...

  6. Studying dynamics of soil moisture patterns

    NASA Astrophysics Data System (ADS)

    Balcerak, Ernie

    2012-11-01

    Soil moisture variations in space and time are important to the hydrological cycle. To better understand the dynamics of the various factors affecting soil moisture patterns, Rosenbaum et al. conducted a comprehensive study in the small Wüstebach catchment in Germany, using a wireless sensor network to monitor soil moisture with high temporal and spatial resolution and broad coverage. They found large variations in spatial soil moisture patterns, which depended on soil depth and catchment wetness. Soil moisture patterns also changed seasonally and during single wetting and drying episodes. The authors showed how soil moisture variations are controlled by a wide range of factors including soil properties, topography, vegetation, groundwater, and rainfall. (Water Resources Research, doi:10.1029/2011WR011518, 2012)

  7. 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.

  8. Soil moisture affects fatty acids and oil quality parameters in peanut

    USDA-ARS?s Scientific Manuscript database

    Drought affects yield of peanut, but its effect on oleic and linoleic acids that influence its oil quality of peanut genotypes with different levels of drought resistance has not been clearly investigated. Therefore, the aims of this research were to determine whether soil water levels could affect...

  9. 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

  10. 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

  11. SOIL moisture data intercomparison

    NASA Astrophysics Data System (ADS)

    Kerr, Yann; Rodriguez-Frenandez, Nemesio; Al-Yaari, Amen; Parens, Marie; Molero, Beatriz; Mahmoodi, Ali; Mialon, Arnaud; Richaume, Philippe; Bindlish, Rajat; Mecklenburg, Susanne; Wigneron, Jean-Pierre

    2016-04-01

    The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. Subsequently, the satellite has been in operation for over 6 years while the retrieval algorithms from Level 1 to Level 2 underwent significant evolutions as knowledge improved. Other approaches for retrieval at Level 2 over land were also investigated while Level 3 and 4 were initiated. In this présentation these improvements are assessed by inter-comparisons of the current Level 2 (V620) against the previous version (V551) and new products either using neural networks or Level 3. In addition a global evaluation of different SMOS soil moisture (SM) products is performed comparing products with those of model simulations and other satellites (AMSR E/ AMSR2 and ASCAT). Finally, all products were evaluated against in situ measurements of soil moisture (SM). The study demonstrated that the V620 shows a significant improvement (including those at level1 improving level2)) with respect to the earlier version V551. Results also show that neural network based approaches can yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved while RFI sources in several areas of the world are significantly reduced. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

  12. 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.

  13. 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 ...

  14. 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

  15. Early Soil Moisture Field Experiments

    NASA Astrophysics Data System (ADS)

    Schmugge, T.

    2008-12-01

    Before the large scale field experiments described in the call for papers, there were a number of experiments devoted to a single parameter, e.g. soil moisture. In the early 1970's, before the launch of the first microwave radiometer by NASA, there were a number of aircraft experiments to determine utility of these sensors for land observations. For soil moisture, these experiments were conducted in southwestern United States over irrigated agricultural areas which could provide a wide range of moisture conditions on a given day. The radiometers covered the wavelength range from 0.8 to 21 cm. These experiments demonstrated that it is possible to observe soil moisture variations remotely using a microwave radiometer with a sensitivity of about 3 K / unit of soil moisture. The results also showed that the longer wavelengths were better, with a radiometer at the 21 cm wavelength giving the best results. These positive results led to the development of Push Broom Microwave Radiometer (PBMR) and the Electrically Scanned Thinned Array Radiometer (ESTAR) instruments at the 21-cm wavelength. They have been used extensively in the large-scale experiments such as HAPEX-MOBILHY, FIFE, Monsoon90, SMEX, etc. The multi-beam nature of these instruments makes it possible to obtain more extensive coverage and thus to map spatial variations of surface soil moisture. Examples of the early results along with the more recent soil moisture maps will be presented.

  16. Microwave Remote Sensing of Soil Moisture

    NASA Technical Reports Server (NTRS)

    Schmugge, T. J.

    1985-01-01

    Because of the large contrast between the dielectric constant of liquid water and that of dry soil at microwave wavelength, there is a strong dependence of the thermal emission and radar backscatter from the soil on its moisture content. This dependence provides a means for the remote sensing of the moisture content in a surface layer approximately 5 cm thick. The feasibility of these techniques is demonstrated from field, aircraft and spacecraft platforms. The soil texture, surface roughness, and vegetative cover affect the sensitivity of the microwave response to moisture variations with vegetation being the most important. It serves as an attenuating layer which can totally obscure the surface. Research indicates that it is possible to obtain five or more levels of moisture discrimination and that a mature corn crop is the limiting vegetation situation.

  17. 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...

  18. [Soil moisture dynamics under artificial Caragana microphylla shrub].

    PubMed

    Alamusa; Jiang, Deming; Fan, Shixiang; Luo, Yongming

    2002-12-01

    Applying the methods of deducing time series from vegetation space alignment, we analyzed the spatial and temporal variation features of soil moisture under artificial Caragana microphylla shrubs built in 1984, 1987, 1995, 1999. The results showed that affected by mechanical composition of mobile sandy dunes, the soil of sandy land was mainly composed of sandy particle, and the particles of > 0.01 mm were accounted for 97%. The withered moisture was 1.55%. The field waterhold capacity was 5.5%, and the available moisture storage was 3.95%. With the increase of the dominance of fix-sand vegetation, the moisture content of soil under artificial Caragana microphylla shrubs was decreased. The soil moisture of vegetation built in 1984 was lower than that built in 1999. The soil moisture conditions of four stages vegetation were continued depressing from April to June in a year, the lowest point presenced in June, and then gradually increased from July to October. The vertical change of soil moisture showed the tendency of increasing with soil depth. The soil moisture decreased by the degrees of early built vegetation (1984, 1987). Especially in 70 cm soil depth, the moisture content of soil decreased obviously. Caragana microphylla shrubs absorbed water and aggravated the shortage of soil moisture content near the root system, which affected the component of vegetation in Caragana microphylla shrubs. The species of herbaceous plants and annual plants increased during the growth of Caragana microphylla shrub.

  19. Remote sensing of soil moisture

    NASA Technical Reports Server (NTRS)

    Schmugge, T.

    1976-01-01

    The surface emissivity and reflectivity of soil are strong functions of its moisture content. Changes in emissivity, observed by passive microwave techniques (radiometry), and changes in reflectivity, observed by active microwave techniques (radar), can provide information on the moisture content of the 0 to 5 cm surface layer. In addition, the thermal inertia of the surface layer, which can be remotely sensed by observing the diurnal range of surface temperature, is an indicator of soil moisture content. The thermal infrared approach to remote sensing of soil moisture has little utility in the presence of cloud cover, but provides soil moisture data at high spatial resolutions and thermal data which are a potentially useful indicator of crop status. Microwave techniques can penetrate cloud covers. The passive technique has been demonstrated by both aircraft and spacecraft instruments, but spatial resolution is limited by the size of the antenna which can be flown. Active microwave systems offer the possibility of better spatial resolution, but have yet to be demonstrated from aircraft or spacecraft platforms.

  20. The role of biological soil crusts on soil moisture

    NASA Astrophysics Data System (ADS)

    Chamizo, S.; Cantón, Y.; Lázaro, R.; Rodriguez-Caballero, E.; Domingo, F.

    2012-04-01

    In water-limited ecosystems, water becomes the most important driver for plant productivity. In these systems, spatial distribution of water resources is not random but organized into a mosaic of water-depletion areas linked to water-accumulation areas. In other words, water is transferred from interplant patches that act as source areas to vegetation patches that act as sinks of this resource. Thus, structure and functioning of interplant patches have a decisive role in water redistribution and distribution patterns of vegetation. Soil surface in the interplant spaces of most arid and semiarid ecosystems is covered by biological soil crusts (BSCs). These organisms regulate water fluxes into and through soils and play major roles in local hydrological processes. In the last years, the role of these organisms in infiltration and runoff has gained increased importance and a better knowledge about their effects on these processes has been acquired. However, the role of BSCs in other important components of the water balance such as evaporation or soil moisture has been scarcely studied, so that their effects on these processes remain unknown. The objective of this work is to examine the influence of BSCs on soil moisture regimes in the top profile of the soil in two semiarid ecosystems of SE Spain with contrasting soil texture and where BSCs are well-represented. Soil moisture content at 0.03 and 0.10 m was monitored under two representative types of BSCs, a dark cyanobacteria-dominated BSC and a light-coloured lichen-dominated BSC, and in soils where these BSCs were removed by scraping, at both study sites. Our results show that, under high water conditions, removal of BSCs leads to a decrease in soil moisture compared to soils covered by BSCs. Decrease in soil moisture due to BSC removal namely affects moisture in the upper layer of the soil (0.03 m), but has little impact in deeper soil (0.10 m). Evaporation is also generally faster in soils with no BSCs than in

  1. Remote sensing of soil moisture - Recent advances

    NASA Technical Reports Server (NTRS)

    Schmugge, T. J.

    1983-01-01

    Recent advancements in microwave remote sensing of soil moisture include a method for estimating the dependence of the soil dielectric constant on its texture, the use of a percent of field capacity to express soil moisture magnitudes independently of soil texture, methods of estimating soil moisture sampling depth, and models for describing the effect of surface roughness on microwave response in terms of surface height variance and horizontal correlation length, as well as the verification of radiative transfer model predictions of microwave emission from soils and methods for the estimation of vegetation effects on the microwave response to soil moisture. Such researches have demonstrated that it is possible to remotely sense soil moisture in the 0-5 cm soil surface layer, and simulation studies have indicated how remotely sensed surface soil moisture may be used to estimate evapotranspiration rates and root-zone soil moisture.

  2. Estimation of Surface Soil Moisture Using Fractal

    NASA Astrophysics Data System (ADS)

    Chen, Yen Chang; He, Chun Hsuan

    2016-04-01

    This study establishes the relationship between surface soil moisture and fractal dimension. The surface soil moisture is one of important factors in the hydrological cycle of surface evaporation. It could be used in many fields, such as reservoir management, early drought warning systems, irrigation scheduling and management, and crop yield estimations. Soil surface cracks due to dryness can be used to describe drought conditions. Soil cracking phenomenon and moisture have a certain relationship, thus this study makes used the fractal theory to interpret the soil moisture represented by soil cracks. The fractal dimension of surface soil cracking is a measure of the surface soil moisture. Therefore fractal dimensions can also be used to indicate how dry of the surface soil is. This study used the sediment in the Shimen Reservoir to establish the fractal dimension and soil moisture relation. The soil cracking is created under the control of temperature and thickness of surface soil layers. The results show the increase in fractal dimensions is accompanied by a decreases in surface soil moisture. However the fractal dimensions will approach a constant even the soil moisture continually decreases. The sigmoid function is used to fit the relation of fractal dimensions and surface soil moistures. The proposed method can be successfully applied to estimate surface soil moisture. Only a photo taken from the field is needed and is sufficient to provide the fractal dimension. Consequently, the surface soil moisture can be estimated quickly and accurately.

  3. 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...

  4. Soil Moisture State and Hydrologic Process

    NASA Astrophysics Data System (ADS)

    Western, A. W.; Grayson, R. B.; Blöschl, G.; Wilson, D.; Longobardi, A.; Villani, P.; Duncan, M.

    It has long been recognized that soil moisture has a key role in controlling evapo- transpiration during dryer periods, as well as runoff processes, particularly saturation excess runoff. The temporal and spatial variability of moisture can be an important influence on the temporal and spatial characteristics of these processes. More recently, the role of soil moisture in controlling lateral flow processes has re- ceived close attention, with switching between persistent dry and wet states leading to switches between controls on spatial patterns of soil moisture and consequent changes in runoff behaviour. In this paper we will review results on the spatial and temporal variability of soil moisture at the small catchment scale, concentrating in particular on dominant controls and temporal changes in dominant controls. We will discuss the climatic and catchment characteristics under which switching between dominant controls is likely. We will also present results relating spatial soil moisture behaviour to soil moisture state and relating rainfall-runoff response to moisture state: in particular we investi- gated the relationships between the basin soil moisture dynamic and the occurrence of very extreme flood events. The spatial probability density function of soil moisture is bounded by wilting point and porosity. This bounding combined with catchment processes leads to a strong link between spatial variance and spatial mean soil mois- ture, with an initial increase in variance followed by a decrease as mean soil moisture increases from wilting point to saturation. Changes in the spatial control of soil mois- ture and the relationship between soil moisture and terrain also occur as the spatial controls on the soil moisture pattern change in response to mean soil moisture. Strong links between the changes in the spatial characteristics of soil moisture will be demon- strated and the potential of measurements of soil moisture to provide information on catchment state

  5. 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('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/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('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=Wade&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DWade','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170003709&hterms=Wade&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DWade"><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://ntrs.nasa.gov/search.jsp?R=19840014042&hterms=evapotranspiration+normal&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Devapotranspiration%2Bnormal','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19840014042&hterms=evapotranspiration+normal&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Devapotranspiration%2Bnormal"><span>Role of <span class="hlt">soil</span> <span class="hlt">moisture</span> in maintaining droughts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sud, Y. C.; Smith, W. E.</p> <p>1984-01-01</p> <p>The influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the persistence of an ongoing drought was investigated. The case study of drought of the summer of 1980 was selected. The difference in the simulation of two identical twin runs: one with the climatological normal <span class="hlt">soil</span> <span class="hlt">moisture</span> and the other with anomalous <span class="hlt">soil</span> <span class="hlt">moisture</span> for drought conditions, were examined on the mean monthly circulation. It is found that a reduction in <span class="hlt">soil</span> <span class="hlt">moisture</span> did produce a corresponding reduction in precipitation. The pattern of the rainfall anomaly however, was not identical to the <span class="hlt">soil</span> <span class="hlt">moisture</span> (evapotranspiration) anomaly but had a good resemblance with observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19840014042&hterms=drought&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Ddrought','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19840014042&hterms=drought&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Ddrought"><span>Role of <span class="hlt">soil</span> <span class="hlt">moisture</span> in maintaining droughts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sud, Y. C.; Smith, W. E.</p> <p>1984-01-01</p> <p>The influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the persistence of an ongoing drought was investigated. The case study of drought of the summer of 1980 was selected. The difference in the simulation of two identical twin runs: one with the climatological normal <span class="hlt">soil</span> <span class="hlt">moisture</span> and the other with anomalous <span class="hlt">soil</span> <span class="hlt">moisture</span> for drought conditions, were examined on the mean monthly circulation. It is found that a reduction in <span class="hlt">soil</span> <span class="hlt">moisture</span> did produce a corresponding reduction in precipitation. The pattern of the rainfall anomaly however, was not identical to the <span class="hlt">soil</span> <span class="hlt">moisture</span> (evapotranspiration) anomaly but had a good resemblance with observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.B41E0251G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.B41E0251G"><span>Modeling <span class="hlt">soil</span> <span class="hlt">moisture</span> memory 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>Gou, S.; Miller, G. R.</p> <p>2011-12-01</p> <p>Antecedent <span class="hlt">soil</span> conditions create an ecosystem's "memory" of past rainfall events. Such <span class="hlt">soil</span> <span class="hlt">moisture</span> memory effects may be observed over a range of timescales, from daily to yearly, and lead to feedbacks between hydrological and ecosystem processes. In this study, we modeled the <span class="hlt">soil</span> <span class="hlt">moisture</span> memory effect on savanna ecosystems in California, Arizona, and Africa, using a system dynamics model created to simulate the ecohydrological processes at the plot-scale. The model was carefully calibrated using <span class="hlt">soil</span> <span class="hlt">moisture</span> and evapotranspiration data collected at three study sites. The model was then used to simulate scenarios with various initial <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions and antecedent precipitation regimes, in order to study the <span class="hlt">soil</span> <span class="hlt">moisture</span> memory effects on the evapotranspiration of understory and overstory species. Based on the model results, <span class="hlt">soil</span> texture and antecedent precipitation regime impact the redistribution of water within <span class="hlt">soil</span> layers, potentially causing deeper <span class="hlt">soil</span> layers to influence the ecosystem for a longer time. Of all the study areas modeled, <span class="hlt">soil</span> <span class="hlt">moisture</span> memory of California savanna ecosystem site is replenished and dries out most rapidly. Thus <span class="hlt">soil</span> <span class="hlt">moisture</span> memory could not maintain the high rate evapotranspiration for more than a few days without incoming rainfall event. On the contrary, <span class="hlt">soil</span> <span class="hlt">moisture</span> memory of Arizona savanna ecosystem site lasts the longest time. The plants with different root depths respond to different memory effects; shallow-rooted species mainly respond to the <span class="hlt">soil</span> <span class="hlt">moisture</span> memory in the shallow <span class="hlt">soil</span>. The growing season of grass is largely depended on the <span class="hlt">soil</span> <span class="hlt">moisture</span> memory of the top 25cm <span class="hlt">soil</span> layer. Grass transpiration is sensitive to the antecedent precipitation events within daily to weekly timescale. Deep-rooted plants have different responses since these species can access to the deeper <span class="hlt">soil</span> <span class="hlt">moisture</span> memory with longer time duration <span class="hlt">Soil</span> <span class="hlt">moisture</span> memory does not have obvious impacts on the phenology of woody plants</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19899452','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19899452"><span>[Effects of <span class="hlt">soil</span> thickness on spatiotemporal pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> in 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>Chen, Jia; Shi, Zhi-Hua; Li, Lu; Luo, Xuan</p> <p>2009-07-01</p> <p>Based on the fixed-spot observation, this paper analyzed the effects of <span class="hlt">soil</span> thicknesses on the spatiotemporal pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the Wulongchi catchment of Danjiangkou, China. The <span class="hlt">soil</span> <span class="hlt">moisture</span> content increased soon after precipitation events, followed by a decline as the <span class="hlt">soil</span> dried down, whilst its spatial heterogeneity exhibited an opposite pattern. The profile-averaged <span class="hlt">soil</span> <span class="hlt">moisture</span> content differed significantly with <span class="hlt">soil</span> thickness. The <span class="hlt">soil</span> with a thickness of 20 cm had lower profile-averaged <span class="hlt">moisture</span> content whose variation trend was similar to that of precipitation and varied obviously among seasons; medium thickness (20-40 cm) <span class="hlt">soil</span> had medium level of profile-averaged <span class="hlt">moisture</span> content whose seasonal variation was moderately and <span class="hlt">affected</span> by the characteristics of precipitation; while the <span class="hlt">soil</span> with a thicknesses of > 40 cm had higher profile-averaged <span class="hlt">moisture</span> content whose seasonal variation was relatively small. The profile distribution pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> was determined by the integrated effects of precipitation, evapotranspiration, and leakage, exhibiting increasing-type at semi-humid stage, waving-type at humid stage, and both of the two types at arid stage. There was a significant positive correlation between profile-averaged <span class="hlt">soil</span> <span class="hlt">moisture</span> content and <span class="hlt">soil</span> thickness, and the correlation coefficient was 0.630-0.855. The <span class="hlt">moisture</span> content in 0-15 cm <span class="hlt">soil</span> layer had less correlation with <span class="hlt">soil</span> thickness, but the <span class="hlt">moisture</span> content in 20-55 cm <span class="hlt">soil</span> layer was significantly correlated with <span class="hlt">soil</span> thickness.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19980045323&hterms=dubois&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddubois','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19980045323&hterms=dubois&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddubois"><span>On <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval and Target Decomposition</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</p> <p>1996-01-01</p> <p>In an earlier study, an empirical model was developed to infer <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness from radar data. The inversion technique was extensively tested over bare surfaces by comparing the estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> to in situ measurements. The overall RMS error in the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate was found to be 3.5% and the RMS error in the RMS height estimate was less than 0.35 cm absolute for bare or slightly vegetated surfaces. However, inversion results indicate that significant amounts of vegetation cause the algorithm to underestimate <span class="hlt">soil</span> <span class="hlt">moisture</span> and overestimate RMS height. Among the areas over which the inversion cannot be applied, the areas with intermediate vegetation cover are of particular interest as both the vegetation and the underlying bare surface <span class="hlt">affect</span> the backscatter. This paper concentrates mostly on these areas. Using the full polarimetric information and the Cloude target decomposition approach. Three different components of the target backscattering can be isolated. One of these three components can be identified as the surface component in the case of intermediate vegetation cover. Once the surface component of the scattering is isolated, the bare surface inversion can then be applied.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19970010773&hterms=dubois&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddubois','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19970010773&hterms=dubois&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddubois"><span>On <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval and Target Decomposition</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</p> <p>1996-01-01</p> <p>In an earlier study, an empirical model was developed to infer <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness from radar data. The inversion technique was extensively tested over bare surfaces by comparing the estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> to in situ measurements. The overall root mean square (RMS) error in the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate was found to be about 3.5% and the RMS error in the RMS height estimate was less than 0.35 cm absolute for bare or slightly vegetated surfaces. However, inversion results indicate that significant amounts of vegetation cause the algorithm to underestimate <span class="hlt">soil</span> <span class="hlt">moisture</span> and overestimate RMS height. Among the areas over which the inversion cannot be applied, the areas with intermediate vegetation cover are of particular interest as both the vegetation and the underlying bare surface <span class="hlt">affect</span> the backscatter. This paper concentrates mostly on these areas. Using the full polarimetric information and the Cloude target decomposition approach, three different components of the target backscattering can be isolated. One of these three components can be identified as the surface component in the case of intermediate vegetation cover. Once the surface component of the scattering is isolated, the bare surface inversion can then be applied.</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://www.ars.usda.gov/research/publications/publication/?seqNo115=254669','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=254669"><span>Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Assimilation with SWAT</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 one of the most critical state variables in hydrologic modeling. Certain studies have demonstrated that assimilating observed surface <span class="hlt">soil</span> <span class="hlt">moisture</span> into a hydrologic model results in improved predictions of profile <span class="hlt">soil</span> water content. With the <span class="hlt">Soil</span> and Water Assessment Tool (SWAT), ...</p> </li> <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/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><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('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('https://www.osti.gov/scitech/biblio/400790','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/400790"><span>Depression of <span class="hlt">soil</span> <span class="hlt">moisture</span> freezing point</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Fedorov, V.I.</p> <p>1996-12-01</p> <p>Certain criteria for freezing temperature of clay <span class="hlt">soil</span> have been found which are a relative <span class="hlt">moisture</span> content at the <span class="hlt">soil</span> liquid limit (W/W{sub L}) and maximum hydroscopic <span class="hlt">moisture</span> (W/W{sub h}). On the strength of test data it has been established that the relative <span class="hlt">moisture</span> content at the <span class="hlt">soil</span> liquid limit (W/W{sub L}) may also serve as a criterion on compression pressure and resistance against shearing for <span class="hlt">soil</span> paste with no structural binding. Linear correlation between the <span class="hlt">moisture</span> content of natural <span class="hlt">soil</span> and its paste -- the equation of <span class="hlt">moisture</span> balance -- has been found which specifies a thermodynamic balance condition. The equation of <span class="hlt">moisture</span> balance represents a whole set of properties for a certain type of <span class="hlt">soil</span>, such as strength and compressibility. In this respect, it may be considered as a ``<span class="hlt">Soil</span> equation`` which allows for further prognosis of its properties.</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.osti.gov/scitech/biblio/930948','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/930948"><span>How do elevated [CO<sub>2</sub>], warming, and reduced precipitation interact to <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> and LAI in an old field ecosystem?</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Dermody, Orla; Weltzin, Jake; Engel, Elizabeth C.; Allen, Phillip; Norby, Richard J</p> <p>2007-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content and leaf area index (LAI) are properties that will be particularly important in mediating whole system responses to the combined effects of elevated atmospheric [CO2], warming and altered precipitation. Warming and drying will likely reduce <span class="hlt">soil</span> <span class="hlt">moisture</span>, and this effect may be exacerbated when these factors are combined. However, elevated [CO2] may increase <span class="hlt">soil</span> <span class="hlt">moisture</span> contents and when combined with warming and drying may partially compensate for their effects. The response of LAI to elevated [CO2] and warming will be closely tied to <span class="hlt">soil</span> <span class="hlt">moisture</span> status and may mitigate or exacerbate the effects of global change on <span class="hlt">soil</span> <span class="hlt">moisture</span>. Using open-top chambers (4-m diameter), the interactive effects of elevated [CO2], warming, and differential irrigation on <span class="hlt">soil</span> <span class="hlt">moisture</span> availability were examined in the OCCAM (Old-Field Community Climate and Atmospheric Manipulation) experiment at Oak Ridge National Laboratory in eastern Tennessee. Warming consistently reduced <span class="hlt">soil</span> <span class="hlt">moisture</span> contents and this effect was exacerbated by reduced irrigation. However, elevated [CO2] partially compensated for the effects of warming and drying on <span class="hlt">soil</span> <span class="hlt">moisture</span>. Changes in LAI were closely linked to <span class="hlt">soil</span> <span class="hlt">moisture</span> status. LAI was determined using an AccuPAR ceptometer and both the leaf area duration (LAD) and canopy size were increased by irrigation and elevated [CO2]. The climate of the southeastern United States is predicted to be warmer and drier in the future. This research suggests that although elevated [CO2] will partially ameliorate the effects of warming and drying, losses of <span class="hlt">soil</span> <span class="hlt">moisture</span> will increase from old field ecosystems in the future.</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://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('https://www.ncbi.nlm.nih.gov/pubmed/15119602','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15119602"><span>Diffusion and emissions of 1,3-dichloro propene in Florida sandy <span class="hlt">soil</span> in microplots <span class="hlt">affected</span> by <span class="hlt">soil</span> <span class="hlt">moisture</span>, organic matter, and plastic film.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Thomas, John E; Allen, L Hartwell; McCormack, Leslie A; Vu, Joseph C; Dickson, Donald W; Ou, Li-Tse</p> <p>2004-04-01</p> <p>The main objective of this study was to determine the influence of <span class="hlt">soil</span> <span class="hlt">moisture</span>, organic matter amendment and plastic cover (a virtually impermeable film, VIF) on diffusion and emissions of (Z)- and (E)-1,3-dichloropropene (1,3-D) in microplots of Florida sandy <span class="hlt">soil</span> (Arredondo fine sand). Upward diffusion of the two isomers in the Arredondo <span class="hlt">soil</span> without a plastic cover was greatly influenced by <span class="hlt">soil</span>-water content and (Z)-1,3-D diffused faster than (E)-1,3-D. In less than 5 h after 1,3-D injection to 30 cm depth, (Z)- and (E)-1,3-D in air dry <span class="hlt">soil</span> had diffused to a 10 cm depth, whereas diffusion for the two isomers was negligible in near-water-saturated <span class="hlt">soil</span>, even 101 h after injection. The diffusion rate of (Z)- and (E)-1,3-D in near-field-capacity <span class="hlt">soil</span> was between the rates in the two water regimes. Yard waste compost (YWC) amendment greatly reduced diffusion of (Z)- and (E)-1,3-D, even in air-dry <span class="hlt">soil</span>. Although upward diffusion of (Z)- and (E)-1,3-D in <span class="hlt">soil</span> with VIF cover was slightly less than in the corresponding bare <span class="hlt">soil</span>; the cover promoted retention of vapors of the two isomers in <span class="hlt">soil</span> pore air in the shallow subsurface. More (Z)-1,3-D vapor was found initially in <span class="hlt">soil</span> pore air than (E)-1,3-D although the difference declined thereafter. As a result of rapid upward movement in air-dry bare <span class="hlt">soil</span>, (Z)- and (E)-1,3-D were rapidly volatilized into the atmosphere, but emissions from the near-water-saturated <span class="hlt">soil</span> were minimal. Virtually impermeable film and YWC amendment retarded emissions. This study indicated that adequate <span class="hlt">soil</span> water in this sandy <span class="hlt">soil</span> is needed to prevent rapid emissions, but excess <span class="hlt">soil</span> water slows diffusion of (Z)- and (E)-1,3-D. Thus, management for optimum water in <span class="hlt">soil</span> is critical for pesticidal efficacy and the environment.</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://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/2011AGUFM.H44B..06H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H44B..06H"><span>Is Regional Root Reinforcement Controlled by <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variability?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hales, T.; Ford, C. R.</p> <p>2011-12-01</p> <p>Climate change will alter the amount, type (i.e., snow vs. rain), and timing of precipitation that controls many hazardous Earth surface processes, including debris flows. Most GCMs agree that as climate warms the frequency of extreme precipitation will increase across the globe. Debris flow events triggered by heavy precipitation will likely also increase. Precipitation also <span class="hlt">affects</span> the resistance to debris flow initiation by controlling belowground plant hydraulic architecture (e.g. root frequency, diameter distribution, tensile strength). Quantifying the links between precipitation, below ground properties, and the processes that initiate debris flows are therefore critical to understanding future hazard. To explore these links, we conducted a field experiment in the Coweeta Hydrologic Laboratory by excavating 12 <span class="hlt">soil</span> pits (~1 m3), from two topographies (noses, hollows), and two tree species (Liriodendron tulipifera and Betula lenta). For each species and topography, we collected all biomass from five <span class="hlt">soil</span> depths and measured <span class="hlt">soil</span> <span class="hlt">moisture</span> at 30, 60, and 90cm depth. For each depth we also measured root tensile strength, root cellulose content. Where we collected <span class="hlt">soil</span> <span class="hlt">moisture</span> data, we also measured root and <span class="hlt">soil</span> hydraulic conductivity. Our data show a link between <span class="hlt">soil</span> <span class="hlt">moisture</span> content and root biomass distribution; root biomass is more evenly distributed through the <span class="hlt">soil</span> column in hollows compared to noses. This relationship is consistent with the hypothesis that more consistent <span class="hlt">soil</span> <span class="hlt">moisture</span> in hollows allows plant roots to access resources from deeper within the <span class="hlt">soil</span> column. This physiologic control has a significant effect on root cohesion, with trees on noses (or lower average <span class="hlt">soil</span> <span class="hlt">moisture</span>) providing greater root cohesion close to the surface, but considerably less cohesion at depth. Root tensile strength correlated with local daily <span class="hlt">soil</span> <span class="hlt">moisture</span> rather than the long term differences represented by noses and hollows. Daily <span class="hlt">soil</span> <span class="hlt">moisture</span> <span class="hlt">affected</span> the amount</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JGRD..11517116Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JGRD..11517116Y"><span><span class="hlt">Soil</span>-atmosphere exchange potential of NO and N2O in different land use types of Inner Mongolia as <span class="hlt">affected</span> by <span class="hlt">soil</span> temperature, <span class="hlt">soil</span> <span class="hlt">moisture</span>, freeze-thaw, and drying-wetting events</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yao, Zhisheng; Wu, Xing; Wolf, Benjamin; Dannenmann, Michael; Butterbach-Bahl, Klaus; Brüggemann, Nicolas; Chen, Weiwei; Zheng, Xunhua</p> <p>2010-09-01</p> <p>Changes in precipitation and temperature in Asian continental steppelands may <span class="hlt">affect</span> <span class="hlt">soil</span> physical, chemical and biological processes that control the biosphere-atmosphere exchange of N-trace gases. The changes include regional desertification, global warming and strong El Niño events that impact the large steppe land area in China and Mongolia. The area is so large that feedbacks to the global greenhouse gas balance may occur. In this study we investigated how changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature, and especially drying-rewetting and freeze-thaw events, <span class="hlt">affect</span> nitric oxide (NO) and nitrous oxide (N2O) fluxes from large intact <span class="hlt">soil</span> cores taken from representative land use/cover types in the region of the Xilin River catchment, Inner Mongolia. These <span class="hlt">soil</span> cores were incubated under varying conditions with respect to temperature (ranging from -10 to 15°C) and simulated rainfall (25, 45 and 65 mm). Following drying-rewetting and freeze-thaw transitions, we observed pulses of NO and N2O emissions from the <span class="hlt">soils</span> of typical steppe, mountain meadow, sand dune and marshland. A comparable trend in <span class="hlt">soil</span> CO2 emissions and <span class="hlt">soil</span> air N2O concentrations indicated that the high substrate availability and rapid recovery of microbial activity after <span class="hlt">soil</span> wetting and thawing resulted in high gas fluxes. Across the whole temperature range, NO and N2O fluxes from all <span class="hlt">soils</span>, except for N2O emissions from marshland <span class="hlt">soils</span>, showed a positive exponential relationship with <span class="hlt">soil</span> temperature. A combination of <span class="hlt">soil</span> temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> explained most of the observed variations in NO (up to 74-90%) and N2O (up to 67-89%) fluxes for individual <span class="hlt">soils</span>. Spatial differences in NO emissions between land use/cover types could be explained by differences in <span class="hlt">soil</span> organic carbon and pH, whereas spatial variations of N2O fluxes were primarily correlated with differences in <span class="hlt">soil</span> microbial biomass. On the basis of the incubation under controlled conditions, the average annual flux, weighted by</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> <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> </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('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('https://ntrs.nasa.gov/search.jsp?R=20050181940&hterms=values&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dvalues','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20050181940&hterms=values&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dvalues"><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('http://hdl.handle.net/2060/19720004649','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19720004649"><span>Summary: Remote sensing <span class="hlt">soil</span> <span class="hlt">moisture</span> research</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schmer, F. A.; Werner, H. D.; Waltz, F. A.</p> <p>1970-01-01</p> <p>During the 1969 and 1970 growing seasons research was conducted to investigate the relationship between remote sensing imagery and <span class="hlt">soil</span> <span class="hlt">moisture</span>. The research was accomplished under two completely different conditions: (1) cultivated cropland in east central South Dakota, and (2) rangeland in western South Dakota. Aerial and ground truth data are being studied and correlated in order to evaluate the <span class="hlt">moisture</span> supply and water use. Results show that remote sensing is a feasible method for monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/1015737','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1015737"><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, three <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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.B43A0242D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.B43A0242D"><span>The Presence of Plants Alters the Effect of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on <span class="hlt">Soil</span> C Decomposition in Two Different <span class="hlt">Soil</span> Types</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dijkstra, F. A.; Cheng, W.</p> <p>2005-12-01</p> <p>While it is well known that <span class="hlt">soil</span> <span class="hlt">moisture</span> directly <span class="hlt">affects</span> microbial activity and <span class="hlt">soil</span> C decomposition, it is unclear if the presence of plants alters these effects through rhizosphere processes. We studied <span class="hlt">soil</span> <span class="hlt">moisture</span> effects on <span class="hlt">soil</span> C decomposition with and without sunflower and soybean. Plants were grown in two different <span class="hlt">soil</span> types with <span class="hlt">soil</span> <span class="hlt">moisture</span> contents of 45 and 85% of field capacity in a greenhouse experiment. We continuously labeled plants with depleted 13C, which allowed us to separate plant-derived CO2-C from original <span class="hlt">soil</span>-derived CO2-C in <span class="hlt">soil</span> respiration measurements. We observed an overall increase in <span class="hlt">soil</span>-derived CO2-C efflux in the presence of plants (priming effect) in both <span class="hlt">soils</span> with on average a greater priming effect in the high <span class="hlt">soil</span> <span class="hlt">moisture</span> treatment (60% increase in <span class="hlt">soil</span>-derived CO2-C compared to control) than in the low <span class="hlt">soil</span> <span class="hlt">moisture</span> treatment (37% increase). Greater plant biomass in the high <span class="hlt">soil</span> <span class="hlt">moisture</span> treatment contributed to greater priming effects, but priming effects remained significantly higher after correcting for plant biomass. Possibly, root exudation of labile C may have increased more than plant biomass and may have become more effective in stimulating microbial decomposition in the higher <span class="hlt">soil</span> <span class="hlt">moisture</span> treatment. Our results indicate that changing <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions can significantly alter rhizosphere effects on <span class="hlt">soil</span> C decomposition.</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://www.osti.gov/scitech/biblio/6285017','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/6285017"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> variability within remote sensing pixels</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Charpentier, M.A.; Groffman, P.M. )</p> <p>1992-11-30</p> <p>This work is part of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), an international land-surface-atmosphere experiment aimed at improving the way climate models represent energy, water, heat, and carbon exchanges, and improving the utilization of satellite based remote sensing to monitor such parameters. This paper addresses the question of <span class="hlt">soil</span> <span class="hlt">moisture</span> variation within the field of view of a remote sensing pixel. Remote sensing is the only practical way to sense <span class="hlt">soil</span> <span class="hlt">moisture</span> over large areas, but it is known that there can be large variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> within the field of view of a pixel. The difficulty with this is that many processes, such as gas exchange between surface and atmosphere can vary dramatically with <span class="hlt">moisture</span> content, and a small wet spot, for example, can have a dramatic impact on such processes, and thereby bias remote sensing data results. Here the authors looked at the impact of surface topography on the level of <span class="hlt">soil</span> <span class="hlt">moisture</span>, and the interaction of both on the variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensed by a push broom microwave radiometer (PBMR). In addition the authors looked at the question of whether variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> within pixel size areas could be used to assign errors to PBMR generated <span class="hlt">soil</span> <span class="hlt">moisture</span> data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70159491','USGSPUBS'); return false;" href="http://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('http://pubs.er.usgs.gov/publication/70034255','USGSPUBS'); return false;" href="http://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/2017MeScT..28b4002E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MeScT..28b4002E"><span>On-irrigator pasture <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Eng-Choon Tan, Adrian; Richards, Sean; Platt, Ian; Woodhead, Ian</p> <p>2017-02-01</p> <p>In this paper, we presented the development of a proximal <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor that measured the <span class="hlt">soil</span> <span class="hlt">moisture</span> content of dairy pasture directly from the boom of an irrigator. The proposed sensor was capable of <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements at an accuracy of  ±5% volumetric <span class="hlt">moisture</span> content, and at meter scale ground area resolutions. The sensor adopted techniques from the ultra-wideband radar to enable measurements of ground reflection at resolutions that are smaller than the antenna beamwidth of the sensor. An experimental prototype was developed for field measurements. Extensive field measurements using the developed prototype were conducted on grass pasture at different ground conditions to validate the accuracy of the sensor in performing <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H51F1274K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H51F1274K"><span>The Effects of Wildfire 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>Kanarek, M.; Cardenas, M.</p> <p>2013-12-01</p> <p><span class="hlt">Moisture</span> dynamics in the critical zone have significant implications for a variety of hydrologic processes, from water availability to plants to infiltration and groundwater recharge rates. These processes are perturbed by events such as wildfires, which may have long-lasting impacts. In September 2011, the most destructive wildfire in Texas history occurred in and around Bastrop State Park, which was significantly <span class="hlt">affected</span>; thus we take advantage of a rare opportunity to study <span class="hlt">soil</span> <span class="hlt">moisture</span> under such burned conditions. A 165 m long transect bridging burned and unburned areas was established within the 'Lost Pines' of the park. <span class="hlt">Soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature were monitored and estimated using a variety of methods, including 2D electrical resistivity imaging (using dipole-dipole and Schlumberger configurations), surface permittivity measurements (ThetaProbe), permittivity-based <span class="hlt">soil</span> <span class="hlt">moisture</span> profiling (PR2 profile probes), and installation of thermistors. Field measurements were collected at approximately one-month intervals to study temporal and seasonal effects on <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature in this area. Greater <span class="hlt">soil</span> <span class="hlt">moisture</span> and lower resistivity were found near the surface at the heavily burned end of the transect, where trees have been largely killed by the fire and grasses now dominate, and very low near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and higher resistivity were found at the opposite end, which is still populated by pine trees. These variations can likely be attributed to the vegetative variations between the two ends of the transect, with trees consuming more water at one end and the ground cover of grasses and mosses consuming less water and helping reduce evaporation at the burned end. Higher clay content at the burned end of the transect could also be a factor in greater <span class="hlt">soil</span> <span class="hlt">moisture</span> retention there. Given the higher <span class="hlt">moisture</span> content throughout the <span class="hlt">soil</span> profile at the heavily burned end of the transect, this could be an indication of greater infiltration</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015E%26ES...25a2014B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015E%26ES...25a2014B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> monitoring for crop management</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Boyd, Dale</p> <p>2015-07-01</p> <p>The 'Risk management through <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring' project has demonstrated the capability of current technology to remotely monitor and communicate real time <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The project investigated whether capacitance probes would assist making informed pre- and in-crop decisions. Crop potential and cropping inputs are increasingly being subject to greater instability and uncertainty due to seasonal variability. In a targeted survey of those who received regular correspondence from the Department of Primary Industries it was found that i) 50% of the audience found the information generated relevant for them and less than 10% indicted with was not relevant; ii) 85% have improved their knowledge/ability to assess <span class="hlt">soil</span> <span class="hlt">moisture</span> compared to prior to the project, with the most used indicator of <span class="hlt">soil</span> <span class="hlt">moisture</span> still being rain fall records; and iii) 100% have indicated they will continue to use some form of the technology to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span> levels in the future. It is hoped that continued access to this information will assist informed input decisions. This will minimise inputs in low decile years with a low <span class="hlt">soil</span> <span class="hlt">moisture</span> base and maximise yield potential in more favourable conditions based on <span class="hlt">soil</span> <span class="hlt">moisture</span> and positive seasonal forecasts</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H31D1418D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H31D1418D"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> spatial patterns at three different scales explored using three different <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement devices</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.; Ochsner, T. E.</p> <p>2016-12-01</p> <p>Knowledge of <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial patterns and the development of <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement techniques are dependent on each other. On the one hand, devices with a wide range of footprint sizes or support volumes have been developed, which make it possible to study <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial patterns at many different scales. On the other hand, none of the existing devices have the capability to reveal all the diverse scales of spatial patterns in <span class="hlt">soil</span> <span class="hlt">moisture</span> because of limitations related to footprint size, measurement precision, or time required for taking measurements. Comparing the potentials and limits of <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement devices across scales may help illuminate some of the hidden mysteries of <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial scaling. This research aims to compare spatial patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> as revealed by three different measurement techniques at three spatial scales. Near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> was measured along three transects with similar numbers of measurements but vastly different lengths (18 m, 150 km and 7200 km) using sensors with differing footprint sizes: a point-scale dielectric sensor (ML3 Theta probe, Delta T Devices), a field-scale cosmic-ray neutron rover, and a 36-km scale satellite product (SMAP L2), respectively. Spatial variance and autocorrelation of <span class="hlt">soil</span> <span class="hlt">moisture</span> will be calculated and compared across the three transects. The scaling properties of <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial patterns will be studied based on the results of the comparisons as well.</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/2017GeoRL..44.3682M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..44.3682M"><span>Global characterization of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> drydowns</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.; Wang, Wei; Peng, Bin; Akbar, Ruzbeh; Short Gianotti, Daniel J.; Lu, Hui; Pan, Ming; Entekhabi, Dara</p> <p>2017-04-01</p> <p>Loss terms in the land water budget (including drainage, runoff, and evapotranspiration) are encoded in the shape of <span class="hlt">soil</span> <span class="hlt">moisture</span> "drydowns": the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series directly following a precipitation event, during which the infiltration input is zero. The rate at which drydowns occur—here characterized by the exponential decay time scale τ—is directly related to the shape of the loss function and is a key characteristic of global weather and climate models. In this study, we use 1 year of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive mission to characterize τ globally. Consistent with physical reasoning, the observations show that τ is lower in regions with sandier <span class="hlt">soils</span>, and in regions that are more arid. To our knowledge, these are the first global estimates of τ—based on observations alone—at scales relevant to weather and climate models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.3523S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.3523S"><span>Is <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization important for seasonal to decadal predictions?</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>2014-05-01</p> <p>The state of <span class="hlt">soil</span> <span class="hlt">moisture</span> can can have a significant impact on regional climate conditions for short time scales up to several months. However, focusing on seasonal to decadal time scales, it is not clear whether the predictive skill of global a Earth System Model might be enhanced by assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> data or improving the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions with respect to observations. As a first attempt to provide answers to this question, we set up an experiment to investigate the life time (memory) of extreme <span class="hlt">soil</span> <span class="hlt">moisture</span> states in the coupled land-atmosphere model ECHAM6-JSBACH, which is part of the Max Planck Institute for Meteorology's Earth System Model (MPI-ESM). This experiment consists of an ensemble of 3 years simulations which are initialized with extreme wet and dry <span class="hlt">soil</span> <span class="hlt">moisture</span> states for different seasons and years. Instead of using common thresholds like wilting point or critical <span class="hlt">soil</span> <span class="hlt">moisture</span>, the extreme states were extracted from a reference simulation to ensure that they are within the range of simulated climate variability. As a prerequisite for this experiment, the <span class="hlt">soil</span> hydrology in JSBACH was improved by replacing the bucket-type <span class="hlt">soil</span> hydrology scheme with a multi-layer scheme. This new scheme is a more realistic representation of the <span class="hlt">soil</span>, including percolation and diffusion fluxes between up to five separate layers, the limitation of bare <span class="hlt">soil</span> evaporation to the uppermost <span class="hlt">soil</span> layer and the addition of a long term water storage below the root zone in regions with deep <span class="hlt">soil</span>. While the hydrological cycle is not strongly <span class="hlt">affected</span> by this new scheme, it has some impact on the simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> memory which is mostly strengthened due to the additional deep layer water storage. Ensemble statistics of the initialization experiment indicate perturbation lengths between just a few days up to several seasons for some regions. In general, the strongest effects are seen for wet initialization during northern winter over cold and humid</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000004216&hterms=soil+sampling+technique&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsoil%2Bsampling%2Btechnique','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000004216&hterms=soil+sampling+technique&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsoil%2Bsampling%2Btechnique"><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('http://pubs.er.usgs.gov/publication/70187027','USGSPUBS'); return false;" href="http://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('http://adsabs.harvard.edu/abs/2016EGUGA..18.2626A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2626A"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Prediction in the <span class="hlt">Soil</span>, Vegetation and Snow (SVS) Scheme</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alavi, Nasim; Bélair, Stéphane; Fortin, Vincent; Zhang, Shunli; Husain, Syed; Carrera, Marco; Abrahamowicz, Maria</p> <p>2016-04-01</p> <p>A new land surface scheme has been developed at Environment of Canada to provide surface fluxes of momentum, heat and <span class="hlt">moisture</span> for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the <span class="hlt">soil</span>, vegetation and snow (SVS) scheme in estimating surface and root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> is evaluated against the ISBA (Interactions between Surface, Biosphere, and Atmosphere) scheme currently used operationally within GEM for numerical weather prediction. In addition, the sensitivity of SVS <span class="hlt">soil</span> <span class="hlt">moisture</span> results to <span class="hlt">soil</span> texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ as well as brightness temperature data from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> more accurately, and compared to ISBA results in higher correlations with observations and reduced errors. The sensitivity tests carried out during this study revealed that SVS <span class="hlt">soil</span> <span class="hlt">moisture</span> results are not <span class="hlt">affected</span> significantly by the <span class="hlt">soil</span> texture data from different sources. The vegetation data source, however, has a major impact on the <span class="hlt">soil</span> <span class="hlt">moisture</span> results predicted by SVS, and accurate specification of vegetation characteristics is crucial for accurate <span class="hlt">soil</span> <span class="hlt">moisture</span> prediction.</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> </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('http://adsabs.harvard.edu/abs/2012BGeo....9.1173M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012BGeo....9.1173M"><span>The <span class="hlt">moisture</span> response of <span class="hlt">soil</span> heterotrophic respiration: interaction with <span class="hlt">soil</span> properties</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moyano, F. E.; Vasilyeva, N.; Bouckaert, L.; Cook, F.; Craine, J.; Curiel Yuste, J.; Don, A.; Epron, D.; Formanek, P.; Franzluebbers, A.; Ilstedt, U.; Kätterer, T.; Orchard, V.; Reichstein, M.; Rey, A.; Ruamps, L.; Subke, J.-A.; Thomsen, I. K.; Chenu, C.</p> <p>2012-03-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is of primary importance for predicting the evolution of <span class="hlt">soil</span> carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the <span class="hlt">soil</span> functions used to model the heterotrophic respiration response to <span class="hlt">moisture</span> have limited empirical support and introduce an uncertainty of at least 4% in global <span class="hlt">soil</span> carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data-driven analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span>-respiration relations based on 90 <span class="hlt">soils</span>. With the use of linear models we show how the relationship between <span class="hlt">soil</span> heterotrophic respiration and different measures of <span class="hlt">soil</span> <span class="hlt">moisture</span> is consistently <span class="hlt">affected</span> by <span class="hlt">soil</span> properties. The empirical models derived include main effects and <span class="hlt">moisture</span> interaction effects of <span class="hlt">soil</span> texture, organic carbon content and bulk density. When compared to other functions currently used in different <span class="hlt">soil</span> biogeochemical models, we observe that our results can correct biases and reconcile differences within and between such functions. Ultimately, accurate predictions of the response of <span class="hlt">soil</span> carbon to future climate scenarios will require the integration of <span class="hlt">soil</span>-dependent <span class="hlt">moisture</span>-respiration functions coupled with realistic representations of <span class="hlt">soil</span> water dynamics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011BGD.....811577M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011BGD.....811577M"><span>The <span class="hlt">moisture</span> response of <span class="hlt">soil</span> heterotrophic respiration: interaction with <span class="hlt">soil</span> properties</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moyano, F. E.; Vasilyeva, N.; Bouckaert, L.; Cook, F.; Craine, J.; Curiel Yuste, J.; Don, A.; Epron, D.; Formanek, P.; Franzluebbers, A.; Ilstedt, U.; Kätterer, T.; Orchard, V.; Reichstein, M.; Rey, A.; Ruamps, L.; Subke, J.-A.; Thomsen, I. K.; Chenu, C.</p> <p>2011-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is of primary importance for predicting the evolution of <span class="hlt">soil</span> carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the <span class="hlt">soil</span> functions used to model the heterotrophic respiration response to <span class="hlt">moisture</span> have limited empirical support and introduce an uncertainty of at least 4 % in global <span class="hlt">soil</span> carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data-driven analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span>-respiration relations based on 90 <span class="hlt">soils</span>. With the use of linear models we show how the relationship between <span class="hlt">soil</span> heterotrophic respiration and different measures of <span class="hlt">soil</span> <span class="hlt">moisture</span> is consistently <span class="hlt">affected</span> by <span class="hlt">soil</span> properties. The empirical models derived include main and <span class="hlt">moisture</span> interaction effects of <span class="hlt">soil</span> texture, organic carbon content and bulk density. When compared to other functions currently used in different <span class="hlt">soil</span> biogeochemical models, we observe that our results can correct biases and reconcile differences within and between such functions. Ultimately, accurate predictions of the response of <span class="hlt">soil</span> carbon to future climate scenarios will require the integration of <span class="hlt">soil</span>-dependent <span class="hlt">moisture</span>-respiration functions coupled with realistic representations of <span class="hlt">soil</span> water dynamics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003HESS....7..937M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003HESS....7..937M"><span>Microwave radiometric measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> in Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Macelloni, G.; Paloscia, S.; Pampaloni, P.; Santi, E.; Tedesco, M.</p> <p></p> <p>Within the framework of the MAP and RAPHAEL projects, airborne experimental campaigns were carried out by the IFAC group in 1999 and 2000, using a multifrequency microwave radiometer at L, C and X bands (1.4, 6.8 and 10 GHz). The aim of the experiments was to collect <span class="hlt">soil</span> <span class="hlt">moisture</span> and vegetation biomass information on agricultural areas to give reliable inputs to the hydrological models. It is well known that microwave emission from <span class="hlt">soil</span>, mainly at L-band (1.4 GHz), is very well correlated to its <span class="hlt">moisture</span> content. Two experimental areas in Italy were selected for this project: one was the Toce Valley, Domodossola, in 1999, and the other, the agricultural area of Cerbaia, close to Florence, where flights were performed in 2000. Measurements were carried out on bare <span class="hlt">soils</span>, corn and wheat fields in different growth stages and on meadows. Ground data of <span class="hlt">soil</span> <span class="hlt">moisture</span> (SMC) were collected by other research teams involved in the experiments. From the analysis of the data sets, it has been confirmed that L-band is well related to the SMC of a rather deep <span class="hlt">soil</span> layer, whereas C-band is sensitive to the surface SMC and is more <span class="hlt">affected</span> by the presence of surface roughness and vegetation, especially at high incidence angles. An algorithm for the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span>, based on the sensitivity to <span class="hlt">moisture</span> of the brightness temperature at C-band, has been tested using the collected data set. The results of the algorithm, which is able to correct for the effect of vegetation by means of the polarisation index at X-band, have been compared with <span class="hlt">soil</span> <span class="hlt">moisture</span> data measured on the ground. Finally, the sensitivity of emission at different frequencies to the <span class="hlt">soil</span> <span class="hlt">moisture</span> profile was investigated. Experimental data sets were interpreted by using the Integral Equation Model (IEM) and the outputs of the model were used to train an artificial neural network to reproduce the <span class="hlt">soil</span> <span class="hlt">moisture</span> content at different depths.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060041690&hterms=dubois&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddubois','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060041690&hterms=dubois&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddubois"><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('https://ntrs.nasa.gov/search.jsp?R=20150012008&hterms=GOODMAN&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DGOODMAN','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150012008&hterms=GOODMAN&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DGOODMAN"><span>NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Observatory</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kellogg, Kent; Thurman, Sam; Edelstein, Wendy; Spencer, Michael; Chen, Gun-Shing; Underwood, Mark; Njoku, Eni; Goodman, Shawn; Jai, Benhan</p> <p>2013-01-01</p> <p>The SMAP mission will produce high-resolution and accurate global maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its freeze/thaw state using data from a non-imaging synthetic aperture radar and a radiometer, both operating at L-band.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19740046300&hterms=different+types+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Ddifferent%2Btypes%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19740046300&hterms=different+types+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Ddifferent%2Btypes%2Bsoil"><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>1974-01-01</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- to 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 mounted atop a 75-ft truck-mounted boom was used to measure the return at ten frequency points across the 4- to 8-GHz band, at eight different look angles (0 through 70 deg), and for all polarization combinations. A total of 17 sets of data were collected covering the range from 4 to 36% <span class="hlt">soil</span> <span class="hlt">moisture</span> content by weight. 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, however, over the range from 15 to 30% <span class="hlt">moisture</span> content for all angles, frequencies, polarizations and surface conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150012008&hterms=Goodman&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DGoodman','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150012008&hterms=Goodman&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DGoodman"><span>NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Observatory</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kellogg, Kent; Thurman, Sam; Edelstein, Wendy; Spencer, Michael; Chen, Gun-Shing; Underwood, Mark; Njoku, Eni; Goodman, Shawn; Jai, Benhan</p> <p>2013-01-01</p> <p>The SMAP mission will produce high-resolution and accurate global maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its freeze/thaw state using data from a non-imaging synthetic aperture radar and a radiometer, both operating at L-band.</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/2009EGUGA..11.7671C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.7671C"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> estimation with limited <span class="hlt">soil</span> characterization for decision making</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chanzy, A.; Richard, G.; Boizard, H.; Défossez, P.</p> <p>2009-04-01</p> <p>Many decisions in agriculture are conditional to <span class="hlt">soil</span> <span class="hlt">moisture</span>. For instance in wet conditions, farming operations as <span class="hlt">soil</span> tillage, organic waste spreading or harvesting may lead to degraded results and/or induce <span class="hlt">soil</span> compaction. The development of a tool that allows the estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> is useful to help farmers to organize their field work in a context where farm size tends to increase as well as the need to optimize the use of expensive equipments. <span class="hlt">Soil</span> water transfer models simulate <span class="hlt">soil</span> <span class="hlt">moisture</span> vertical profile evolution. These models are highly sensitive to site dependant parameters. A method to implement the mechanistic <span class="hlt">soil</span> water and heat flow model (the TEC model) in a context of limited information (<span class="hlt">soil</span> texture, climatic data, <span class="hlt">soil</span> organic carbon) is proposed [Chanzy et al., 2008]. In this method the most sensitive model inputs were considered i.e. <span class="hlt">soil</span> hydraulic properties, <span class="hlt">soil</span> <span class="hlt">moisture</span> profile initialization and the lower boundary conditions. The accuracy was estimated by implementing the method on several experimental cases covering a range of <span class="hlt">soils</span>. Simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> results were compared to <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements. The obtained accuracy in surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (0-30 cm) was 0.04 m3/m3. When a few <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements are available (collected for instance by the farmer using a portable <span class="hlt">moisture</span> sensor), significant improvement in <span class="hlt">soil</span> <span class="hlt">moisture</span> accuracy is obtained by assimilating the results into the model. Two assimilation strategies were compared and led to comparable results: a sequential approach, where the measurement were used to correct the simulated <span class="hlt">moisture</span> profile when measurements are available and a variational approach which take <span class="hlt">moisture</span> measurements to invert the TEC model and so retrieve <span class="hlt">soil</span> hydraulic properties of the surface layer. The assimilation scheme remains however heavy in terms of computing time and so, for operational purposed fast code should be taken to simulate the <span class="hlt">soil</span> <span class="hlt">moisture</span> as with the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760020566','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760020566"><span><span class="hlt">Soil-moisture</span> ground truth, 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.</p> <p>1976-01-01</p> <p><span class="hlt">Soil</span> types were determined from the <span class="hlt">Soil</span> Survey of Hand County, South Dakota. The <span class="hlt">soil</span> types encountered on the <span class="hlt">soil</span> <span class="hlt">moisture</span> lines are summarized. The actual <span class="hlt">soil</span> <span class="hlt">moisture</span> data are presented. The data have been divided by range, township and section. The <span class="hlt">soil</span> <span class="hlt">moisture</span> data obtained in fields of winter wheat and spring wheat are briefly summarized.</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('http://adsabs.harvard.edu/abs/2012EGUGA..1414192B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1414192B"><span>Assessing the <span class="hlt">soil</span> texture specific sensitivity of simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> to projected climate change by SVAT modelling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bormann, H.</p> <p>2012-04-01</p> <p>Climate change is assumed to have a regionally specific impact on the <span class="hlt">soil</span> <span class="hlt">moisture</span> regime. The impact of climate change on the <span class="hlt">soil</span> <span class="hlt">moisture</span> can be expected to depend on the <span class="hlt">soil</span> texture. Since <span class="hlt">soil</span> <span class="hlt">moisture</span> observations are not available operationally, models can be used to elaborate such sensitivity. In this study, a <span class="hlt">soil</span> vegetation atmosphere transfer scheme (SVAT) was applied to virtual <span class="hlt">soil</span> columns to assess the <span class="hlt">soil</span> texture specific sensitivity of simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> to projected climate change. For each of the 31 <span class="hlt">soil</span> texture classes of the German <span class="hlt">soil</span> texture classification, long term simulations were carried out based on observed and scenario based climate data representing five different climate regions in Germany. The simulation results indicate that <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes considerably differ from region to region and among different <span class="hlt">soil</span> texture classes. Different <span class="hlt">soil</span> texture classes showed different sensitivities of <span class="hlt">soil</span> <span class="hlt">moisture</span> with respect to projected climate change. While differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> between current conditions and SRES climate scenarios were largest for silt <span class="hlt">soils</span>, they were smallest for clay <span class="hlt">soils</span> for continental as well as humid climates. Sand and loam <span class="hlt">soils</span> behaved intermediately, showing a moderate sensitivity. The results also showed that <span class="hlt">soil</span> texture specific sensitivity of <span class="hlt">soil</span> <span class="hlt">moisture</span> to climate change was largest for <span class="hlt">soils</span> which were not <span class="hlt">affected</span> by groundwater (no capillary rise). With an increasing influence of groundwater, differences between <span class="hlt">soil</span> texture classes decreased. In contrast, increasing vegetation density, rooting depths and transpiration demand induced an increasing sensitivity of <span class="hlt">soil</span> <span class="hlt">moisture</span> to climate change except for continental climates. This study indicates that validated, physical based <span class="hlt">soil</span> hydrological models serve as suitable tools to assess the response of <span class="hlt">soil</span> <span class="hlt">moisture</span> to changing climate conditions. Based on virtual <span class="hlt">soil</span> columns, modelling experiments systematically reveal <span class="hlt">soil</span> texture</p> </li> <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('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> <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://adsabs.harvard.edu/abs/2013EGUGA..15.2784H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.2784H"><span>Impact of the <span class="hlt">soil</span> hydrology scheme on simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> memory in a GCM</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hagemann, Stefan; Stacke, Tobias</p> <p>2013-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span>-atmosphere feedback effects play an important role in several regions of the globe. For some of these regions, <span class="hlt">soil</span> <span class="hlt">moisture</span> memory may contribute significantly to the development of the regional climate. Identifying those regions can help to improve predictability in seasonal to decadal climate forecasts. The present study investigates how different setups of the <span class="hlt">soil</span> hydrology scheme <span class="hlt">affect</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> memory simulated by the global climate model of the Max Planck Institute for Meteorology (MPI-M), ECHAM6/JSBACH. First, the standard setup applied for the CMIP5 exercise is used, in which <span class="hlt">soil</span> water is represented by a single <span class="hlt">soil</span> <span class="hlt">moisture</span> reservoir. Second, a new five <span class="hlt">soil</span> layer hydrology scheme is utilized where the previous bucket <span class="hlt">soil</span> <span class="hlt">moisture</span> now corresponds to the root zone <span class="hlt">soil</span> <span class="hlt">moisture</span>. In the standard setup, transpiration may access the whole <span class="hlt">soil</span> <span class="hlt">moisture</span> that is exceeding the wilting point over vegetated areas. However, in the five layer scheme, <span class="hlt">soil</span> water below the root zone cannot be accessed by transpiration directly, but only be transported upwards into the root zone by diffusion following the Richard's equation. Thus, this below the root zone, which is not present in the standard setup, can act as buffer in the transition between wet and dry periods. A second notable difference between the two setups is the formulation of bare <span class="hlt">soil</span> evaporation. In the standard setup, it may only occur if the whole <span class="hlt">soil</span> <span class="hlt">moisture</span> bucket is almost completely saturated, while in the new setup, it depends only on the saturation of the upper most <span class="hlt">soil</span> layer. As the latter is much thinner than the root zone (bucket), bare <span class="hlt">soil</span> evaporation can occur more frequently, especially after rainfall events. For the second setup, two further variants are considered: one where the bare <span class="hlt">soil</span> evaporation was modified and one where a new parameter dataset of <span class="hlt">soil</span> water holding capacities was used. <span class="hlt">Soil</span> <span class="hlt">moisture</span> memory of the different setups will be analysed from global</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19930064033&hterms=hydrologic+modeling&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dhydrologic%2Bmodeling','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930064033&hterms=hydrologic+modeling&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dhydrologic%2Bmodeling"><span>Hydrologic applications of SAR derived <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>1992-01-01</p> <p>The MACHYDRO-90 was a multi-sensor aircraft campaign conducted to study drainage basin hydrology and the role of <span class="hlt">soil</span> <span class="hlt">moisture</span> in defining hydrologic characteristics and patterns. The results from the synthetic aperture radar (SAR) are presented. Data were collected over a period in which the <span class="hlt">soil</span> conditions changed from dry to wet and then through a drying period which was close to ideal. Radar backscatter data are compared to detailed <span class="hlt">soil</span> <span class="hlt">moisture</span> samples taken to define <span class="hlt">soil</span> <span class="hlt">moisture</span> gradients within a watershed. The analysis also includes 40-MHz bandwidth SAR data, which provide very high spatial resolution. It is shown these data can be interpreted for hydrology and their application to hydrologic modeling is discussed.</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> </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('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/2014AGUFM.H13D1134C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H13D1134C"><span>Field-Scale <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensing Using GPS Reflections: Description of the PBO H2O <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chew, C. C.; Small, E. E.; Larson, K. M.</p> <p>2014-12-01</p> <p>Data from NSF's EarthScope Plate Boundary Observatory (PBO), and similar GPS networks worldwide, can be used to monitor the terrestrial water cycle. GPS satellites transmit L-band microwave signals, which are <span class="hlt">affected</span> by water at Earth's surface. GPS signals take two paths: (1) the "direct" signal travels from the satellite to the antenna; (2) the "reflected" signal interacts with the Earth's surface before travelling to the antenna. The direct signal is used by geophysicists to measure position of the antenna, while the effects of reflected signals are generally ignored. Recently, our group has developed a technique to retrieve terrestrial water cycle variables from GPS reflections. The sensing footprint is intermediate in scale between in situ and remote sensing observations. <span class="hlt">Soil</span> <span class="hlt">moisture</span>, snow depth, and an index of vegetation water content are estimated from data collected at over 400 PBO sites. The products are updated daily and are available online. This presentation provides a description of the <span class="hlt">soil</span> <span class="hlt">moisture</span> product. Near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> is estimated at more than 100 sites in the PBO H2O network. At each site, a geodetic-quality GPS antenna records the interference pattern between the direct and ground-reflected GPS signals in signal-to-noise ratio (SNR) interferograms. The ground-reflected GPS signal is altered by changes in the permittivity of the ground surface, which is primarily a function of its water content. Temporal changes in the SNR interferogram, primarily its phase, are indicative of changes in <span class="hlt">soil</span> <span class="hlt">moisture</span>. SNR phase data are converted to <span class="hlt">soil</span> <span class="hlt">moisture</span> using relationships determined using an electrodynamic model. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is not retrieved when there is snow or significant vegetation (> ~1 kg m-2 of vegetation water), as both <span class="hlt">affect</span> SNR phase. When there is moderate vegetation, a correction is applied to the phase data before conversion to <span class="hlt">soil</span> <span class="hlt">moisture</span>. The effect of vegetation on SNR phase and the exact relationship between SNR</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFMIN43B1184L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFMIN43B1184L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation Using Hyperspectral SWIR Imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lewis, D.</p> <p>2007-12-01</p> <p>The U.S. Geological Survey (USGS) is engaged with the U.S. Department of Agriculture's (USDA) Agricultural Research Service (ARS) and the University of Georgia's National Environmentally Sound Production Agriculture Laboratory (NESPAL) both in Tifton, Georgia, USA, to develop transformations for medium and high resolution remotely sensed images to generate <span class="hlt">moisture</span> indicators for <span class="hlt">soil</span>. The Institute for Technology Development (ITD) is located at the Stennis Space Center in southern Mississippi and has developed hyperspectral sensor systems that, when mounted in aircraft, collect electromagnetic reflectance data of the terrain. The sensor suite consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near InfraRed (VNIR) and Short Wave InfraRed (SWIR). The USDA/ ARS' Southeast Watershed Research Laboratory has probes that measure and record <span class="hlt">soil</span> <span class="hlt">moisture</span>. Data taken from the ITD SWIR sensor and the USDA/ARS <span class="hlt">soil</span> <span class="hlt">moisture</span> meters were analyzed to study the informatics relationships between SWIR data and measured <span class="hlt">soil</span> <span class="hlt">moisture</span>. The geographic locations of 29 <span class="hlt">soil</span> <span class="hlt">moisture</span> meters provided by the USDA/ARS are in the vicinity of Tifton, Georgia. Using USGS Digital Ortho Quads (DOQ), flightlines were drawn over the 29 <span class="hlt">soil</span> <span class="hlt">moisture</span> meters. The SWIR sensor was installed into an aircraft. The coordinates for the flightlines were also loaded into the navigational system of the aircraft. This airborne platform was used to collect the data over these flightlines. In order to prepare the data set for analysis, standard preprocessing was performed. These standard processes included sensor calibration, spectral subsetting, and atmospheric calibration. All 60 bands of the SWIR data were collected for each line in the image data, 15 bands of which were stripped from the data set leaving 45 bands of information in the wavelength range of 906 to 1705 nanometers. All the image files were calibrated using the regression equations</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://hdl.handle.net/2060/19760023549','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760023549"><span><span class="hlt">Soil-moisture</span> ground truth, 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.</p> <p>1976-01-01</p> <p><span class="hlt">Soil</span> samples were taken in the field and carefully preserved in taped metal containers for later laboratory gravimetric analysis to determine <span class="hlt">soil-moisture</span> content. The typical sampling pattern used in this mission is illustrated, and the <span class="hlt">soil</span> types encountered on the <span class="hlt">soil-moisture</span> lines are summarized. The actual <span class="hlt">soil-moisture</span> data were tabulated by range, township and section. <span class="hlt">Soil-moisture</span> data obtained in fields of winter wheat and spring wheat are briefly summarized.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=299628','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=299628"><span>Evaluation of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors</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 evaluated the measurement accuracy and repeatability of the EC-5 and 5TM <span class="hlt">soil</span> volumetric water content (SVWC) sensors, MPS-2 and 200SS <span class="hlt">soil</span> water potential (SWP) sensors, and 200TS <span class="hlt">soil</span> temperature sensor. Six 183cm x 183cm x 71cm wooden compartments were built inside a greenhouse, and e...</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/2011AGUFM.H11G1150N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H11G1150N"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation Using Inexpensive Radios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Niemeier, J. J.; Kruger, A.</p> <p>2011-12-01</p> <p>Technological advances and changes in licensing have made small, inexpensive radio modules commonplace. Today, these radios are used in a large number of wireless data- and control applications. A novel approach is to view such radio modules not only as communication devices, but also as small, inexpensive sources of radio frequency (RF) energy, which are useful for devising unconventional sensors. We have explored the possibility of using buried radios and the resulting RF links as distributed <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors. We conducted a number of experiments that record the RF attenuation of the links over time. Estimating RF attenuation is straightforward, since the radio modules provide a received signal strength indication (RSSI). To provide reference data, we installed several time-domain reflectometry (TDR) <span class="hlt">soil</span> <span class="hlt">moisture</span> probes with accompanying temperature probes to monitor changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature. We collocated tipping bucket rain gauges for monitoring rain events. We employed RF modules that operate at 900 MHz. Rather than burying the radios, we lowered the antennas into 2.5 cm PVC pipes that we drove into the ground to a depth of 60 cm. We seal both ends of the PVC pipe to prevent water from entering the tube. Our experimental data shows a clear relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and RF attenuation. We developed a simple, yet effective, mathematical model to relate changes in RF attenuation to changes in <span class="hlt">soil</span> <span class="hlt">moisture</span>. One can easily achieve reliable links 2-3 m long, and we believe the technique holds promise as an economical method for distributed/integrated <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation.</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('https://ntrs.nasa.gov/search.jsp?R=19930063678&hterms=Soil+science&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Bscience','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930063678&hterms=Soil+science&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Bscience"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> needs in earth sciences</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>1992-01-01</p> <p>The author reviews the development of passive and active microwave techniques for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> with respect to how the data may be used. New science programs such as the EOS, the GEWEX Continental-Scale International Project (GCIP) and STORM, a mesoscale meteorology and hydrology project, will have to account for <span class="hlt">soil</span> <span class="hlt">moisture</span> either as a storage in water balance computations or as a state variable in-process modeling. The author discusses future <span class="hlt">soil</span> <span class="hlt">moisture</span> needs such as frequency of measurement, accuracy, depth, and spatial resolution, as well as the concomitant model development that must proceed concurrently if the development in microwave technology is to have a major impact in these areas.</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/2013EGUGA..15.9657H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.9657H"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span>-temperature coupling: revisited using remote sensing <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>Hirschi, Martin; Mueller, Brigitte; Dorigo, Wouter; Seneviratne, Sonia I.</p> <p>2013-04-01</p> <p>Hot extremes have been shown to be induced by antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> deficits and drought conditions in several regions (e.g., Mueller and Seneviratne, 2012). While most previous studies on this topic relied on modeling results or precipitation-based <span class="hlt">soil</span> <span class="hlt">moisture</span> information (in particular the standardized precipitation index, SPI), we use here a new merged remote sensing (RS) <span class="hlt">soil</span> <span class="hlt">moisture</span> product combining data from active and passive microwave sensors to investigate the relation between the number of hot days (NHD) and preceding <span class="hlt">soil</span> <span class="hlt">moisture</span> deficits. Overall, the global patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span>-NHD correlations from RS data and from SPI as used in previous studies agree relatively well, suggesting that these patterns are partly independent of the chosen dataset. Nonetheless, the strength of the relationship appears underestimated with RS-based <span class="hlt">soil</span> mois- ture data compared to SPI-based estimates, in particular in previously iden- tified regions of strong <span class="hlt">soil</span> <span class="hlt">moisture</span>-temperature coupling. This is mainly due to the fact that the temporal hydrological variability is less pronounced in the RS data than the SPI estimates in these regions, and that pronounced (dry or wet) anomalies appear underestimated. Further, complementary anal- yses with data from the Global Land Data Assimilation System (GLDAS) suggest that the differences between the RS-based <span class="hlt">soil</span> <span class="hlt">moisture</span>-NHD and the precipitation-based SPI-NHD coupling estimates are not primarily due to the use of <span class="hlt">soil</span> <span class="hlt">moisture</span> instead of SPI, or to the shallow depth of the RS- based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. Mueller, B., and S. I. Seneviratne (2012). Hot days induced by precipitation deficits at the global scale. Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1204330109.</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://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-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-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> </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/2014Geomo.207..141W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014Geomo.207..141W"><span>Freeze/thaw and <span class="hlt">soil</span> <span class="hlt">moisture</span> effects on wind erosion</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, L.; Shi, Z. H.; Wu, G. L.; Fang, N. F.</p> <p>2014-02-01</p> <p>Wind erosion is very pronounced in semiarid regions during late winter-early spring and has major impacts on regional desertification and agriculture. In order to identify the effects of freeze/thaw and <span class="hlt">soil</span> <span class="hlt">moisture</span> on wind erosion, wind tunnel experiments were conducted to compare wind erosion effects under various <span class="hlt">soil</span> <span class="hlt">moisture</span> gradients in frozen and thawed <span class="hlt">soil</span>. The variation of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> after wind erosion and the effective <span class="hlt">soil</span> particle size distribution was tested to explain the differences. The results showed that surface <span class="hlt">soil</span> <span class="hlt">moisture</span> content decreased in thawed <span class="hlt">soil</span> and increased in frozen <span class="hlt">soil</span> after wind erosion. The mean weight diameter, which increased with increasing <span class="hlt">soil</span> <span class="hlt">moisture</span>, was smaller in thawed <span class="hlt">soil</span> than in frozen <span class="hlt">soil</span>. The wind-driven sediment flux of frozen and thawed <span class="hlt">soil</span> both decreased with increasing <span class="hlt">moisture</span>, owing to the heavier <span class="hlt">soil</span> particle weight and stronger interparticle bonding forces. The critical <span class="hlt">soil</span> <span class="hlt">moisture</span> content for suppressing wind erosion was around 2.34% for frozen <span class="hlt">soil</span> and around 2.61% for thawed <span class="hlt">soil</span>. The wind-driven sediment flux of thawed <span class="hlt">soil</span> was always larger than that of frozen <span class="hlt">soil</span> at the same <span class="hlt">moisture</span> content, but this difference became negligible at <span class="hlt">moisture</span> contents above 3.38%. We may speculate that wind erosion will be more severe in the future because of the lower <span class="hlt">soil</span> <span class="hlt">moisture</span> content and fewer <span class="hlt">soil</span> freezing days as a result of global warming.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=215777','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=215777"><span>Estimating rootzone <span class="hlt">soil</span> <span class="hlt">moisture</span> by assimilating both microwave based surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and thermal based <span class="hlt">soil</span> <span class="hlt">moisture</span> proxy 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>A number of synthetic data assimilation experiments are carried out at the USDA Economic and Environmental Enhancement (OPE3) site in Beltsville, Maryland. As a first case, only surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals are assimilated into a land surface model using the Ensemble Kalman filter (EnKF). This...</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.ncbi.nlm.nih.gov/pubmed/22463129','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22463129"><span>Heated distributed temperature sensing for field scale <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>Striegl, Arlen M; Loheide, Steven P</p> <p>2012-01-01</p> <p>Characterizing both spatial and temporal <span class="hlt">soil</span> <span class="hlt">moisture</span> (θ) dynamics at site scales is difficult with existing technologies. To address this shortcoming, we developed a distributed <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing system that employs a distributed temperature sensing system to monitor thermal response at 2 m intervals along the length of a buried cable which is subjected to heat pulses. The cable temperature response to heating, which is strongly dependent on <span class="hlt">soil</span> <span class="hlt">moisture</span>, was empirically related to colocated, dielectric-based θ measurements at three locations. Spatially distributed, and temporally continuous estimates of θ were obtained in dry conditions (θ≤ 0.31) using this technology (root mean square error [RMSE] = 0.016), but insensitivity of the instrument response curve adversely <span class="hlt">affected</span> accuracy under wet conditions (RMSE = 0.050). © 2012, The Author(s). Ground Water © 2012, National Ground Water Association.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1918804V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1918804V"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> monitoring with GPS reflected signals.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Van Baelen, Joël; Presset, Benoît; Arnaudet, Emilien; Morel, Laurent</p> <p>2017-04-01</p> <p>In the context of necessary reductions of inputs in agricultural practices (fertilizers, sanitary treatments, irrigation), monitoring of low level atmospheric water vapor and <span class="hlt">soil</span> <span class="hlt">moisture</span> has become a major issue. Nowadays, it is well known that GPS positioning with a network of receivers can also yield estimates of tropospheric parameters which in turn provide reliable estimates of atmospheric water vapor. Furthermore, when a dense network of GPS stations exists, GPS signals can be used to perform tomography in order to retrieve the three dimensional distribution of water vapour density. A more recent aspect of the GPS applications is also to investigate the technique of reflectivity (i.e., the monitoring of ground reflected rays) to monitor and retrieve the <span class="hlt">soil</span> <span class="hlt">moisture</span> variations. In this work, we will present preliminary results of a dedicated campaign to study low level water vapour retrieval and, more particularily, <span class="hlt">soil</span> <span class="hlt">moisture</span> variation identification and estimation as demonstrated in the figure below. A strong correlation exists between <span class="hlt">soil</span> humidity and GPS reflected signal phase variations, while we also pursue ways to investigate the most influential factors which help determine the most suited satellite passage to potentially provide ways to estimate the <span class="hlt">soil</span> <span class="hlt">moisture</span> content fluctuations. PIC</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=215186','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=215186"><span>An adaptive ensemble Kalman filter for <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation</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 a 19-year twin experiment for the Red-Arkansas river basin we assimilate synthetic surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals into the NASA Catchment land surface model. We demonstrate how poorly specified model and observation error parameters <span class="hlt">affect</span> the quality of the assimilation products. In particul...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMNG22A..07C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMNG22A..07C"><span>Inference of <span class="hlt">Soil</span> Hydrologic Parameters from <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring 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, D. G.; Seyfried, M. S.; McNamara, J. P.; Hwang, K.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important control on hydrologic function, as it governs flux through the <span class="hlt">soil</span> and responds to and determines vertical fluxes from and to the atmosphere, groundwater recharge and lateral fluxes through the <span class="hlt">soil</span>. Most physically based hydrologic models require parameters to represent <span class="hlt">soil</span> physical properties governing flow and retention of vadose water. The presented analysis compares four methods of objective analysis to determine field capacity, plant extraction limit (or permanent wilting point) and field saturated <span class="hlt">soil</span> <span class="hlt">moisture</span> content from decadal records of volumetric water content. These values are found as either data attractors or limits in the VWC records and may vary with interannual <span class="hlt">moisture</span> availability. Results are compared to values from pedotransfer functions and discussed in terms of historic methods of measurement in <span class="hlt">soil</span> physics.</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://adsabs.harvard.edu/abs/2009AGUFM.H33A0863M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33A0863M"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Time Stability in Two Hydro-climatic Regions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mohanty, B. P.; Joshi, C.; Jacobs, J. M.</p> <p>2009-12-01</p> <p>In this study we present time stability analyses of <span class="hlt">soil</span> <span class="hlt">moisture</span> at different spatial measurement support scales (point-scale and airborne remote sensing footprint-scale 800 m X 800 m) in two different hydro-climatic regions. The data used in the analyses consist of in-situ and passive microwave remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data from Southern Great Plains hydrology experiments 1997 and 1999 (SGP97 and SGP99) conducted in Little Washita (LW) watershed, Oklahoma, and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiments 2002 and 2005 (SMEX02 and SMEX05) in Walnut Creek (WC) watershed, Iowa. Results show that in both the regions <span class="hlt">soil</span> properties (i.e., percentage clay, percentage sand, and <span class="hlt">soil</span> texture), and topography (elevation and slope) are significant physical controls jointly <span class="hlt">affecting</span> the spatio-temporal evolution and time stability of <span class="hlt">soil</span> <span class="hlt">moisture</span> at both point- and footprint-scale. In Iowa, using point scale <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements, WC11 field having higher %clay and lower %sand content was found to be more time stable than the WC12 field. The common time stable points using data across the 3-year period (2002-2005) were mostly located at moderate to high elevations in both the fields. Drainage features and cropping practices also <span class="hlt">affected</span> the field-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> variability in the WC fields. At the remote sensing footprint-scale, the ANOVA tests show that the percentage clay and percentage sand are better able to discern the time stable features of the footprints compared to the <span class="hlt">soil</span> texture in Iowa. Further, the footprints with steep slopes exhibited the best time stable characteristics in Iowa. On the other hand, in Oklahoma, ANOVA results show that the footprints with sandy clay and loam <span class="hlt">soil</span> texture are better indicators of the time stability phenomena. In terms of the hill slope position, depressions (0-0.93%) followed by mild slopes (0.93-1.85%) are the best indicators of time stable footprints. Also, at both point- and footprint-scale in both the regions, land use</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('http://adsabs.harvard.edu/abs/2015JHyd..524..576W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..524..576W"><span>Investigating <span class="hlt">soil</span> controls on <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial variability: Numerical simulations and field observations</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; Franz, Trenton E.; Zlotnik, Vitaly A.; You, Jinsheng; Shulski, Martha D.</p> <p>2015-05-01</p> <p>Due to its complex interactions with various processes and factors, <span class="hlt">soil</span> <span class="hlt">moisture</span> exhibits significant spatial variability across different spatial scales. In this study, a modeling approach and field observations were used to examine the <span class="hlt">soil</span> control on the relationship between mean (θ bar) and standard deviation (σθ) of <span class="hlt">soil</span> <span class="hlt">moisture</span> content. For the numerical experiments, a 1-D vadose zone model along with van Genuchten parameters generated by pedotransfer functions was used for simulating <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics under different climate and surface conditions. To force the model, hydrometeorological and physiological data that spanned over three years from five research sites within the continental US were used. The modeling results showed that under bare surface conditions, different forms of the θ bar -σθ relationship as observed in experimental studies were produced. For finer <span class="hlt">soils</span>, a positive θ bar -σθ relationship gradually changed to an upward convex and a negative one from arid to humid conditions; whereas, a positive relationship existed for coarser <span class="hlt">soils</span>, regardless of climatic conditions. The maximum σθ for finer <span class="hlt">soils</span> was larger under semiarid conditions than under arid and humid conditions, while the maximum σθ for coarser <span class="hlt">soils</span> increased with increasing precipitation. Moreover, vegetation tended to reduce θ bar and σθ, and thus <span class="hlt">affected</span> the θ bar -σθ relationship. A sensitivity analysis was also conducted to examine the controls of different van Genuchten parameters on the θ bar -σθ relationship under bare surface conditions. It was found that the residual <span class="hlt">soil</span> <span class="hlt">moisture</span> content mainly <span class="hlt">affected</span> σθ under dry conditions, while the saturated <span class="hlt">soil</span> <span class="hlt">moisture</span> content and the saturated hydraulic conductivity largely controlled σθ under wet conditions. Importantly, the upward convex θ bar -σθ relationship was mostly caused by the shape factor n that accounts for pore size distribution. Finally, measured <span class="hlt">soil</span> <span class="hlt">moisture</span> data from a</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('http://adsabs.harvard.edu/abs/2012HESS...16.3199Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012HESS...16.3199Y"><span>Spatial variations of shallow and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> 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, L.; Wei, W.; Chen, L.; Jia, F.; Mo, B.</p> <p>2012-09-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> in deep <span class="hlt">soil</span> layers is an important relatively stable water resource for vegetation growth in the semi-arid Loess Plateau of China. Characterizing the spatial variations of deep <span class="hlt">soil</span> <span class="hlt">moisture</span> with respect to the topographic conditions has significant importance for vegetation restoration. In this study, we focused on analyzing the spatial variations and factors influencing <span class="hlt">soil</span> <span class="hlt">moisture</span> content (SMC) in shallow (0-2 m) and deep (2-8 m) <span class="hlt">soil</span> layers, based on <span class="hlt">soil</span> <span class="hlt">moisture</span> observations in the Longtan watershed, Dingxi, Gansu province. The vegetation type of each sampling site for each comparison is same and varies by different positions, gradients, or aspects. The following discoveries were captured: (1) in comparison with shallow SMC, slope position and slope aspect may <span class="hlt">affect</span> shallow <span class="hlt">soil</span> <span class="hlt">moisture</span> more than deep layers, while slope gradient <span class="hlt">affects</span> both shallow and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> significantly. This indicates that a great difference in deep <span class="hlt">soil</span> hydrological processes between shallow and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> remains that can be attributed to the introduced vegetation and topography. (2) A clear negative relationship exists between vegetation growth condition and deep <span class="hlt">soil</span> <span class="hlt">moisture</span>, which indicates that plants under different growing conditions may differ in consuming <span class="hlt">soil</span> <span class="hlt">moisture</span>, thus causing higher spatial variations in deep <span class="hlt">soil</span> <span class="hlt">moisture</span>. (3) The dynamic role of slope position and slope aspect on deep <span class="hlt">soil</span> <span class="hlt">moisture</span> has been changed due to large-scale plantation in semi-arid environment. Consequently, vegetation growth conditions and slope gradients may become the key factors dominating the spatial variations in deep <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H13H1477W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H13H1477W"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variability Beneath a Melting Snowpack</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Webb, R.; Fassnacht, S. R.</p> <p>2013-12-01</p> <p>The melting of the winter snowpack often enters the <span class="hlt">soil</span> surface prior to flowing to a stream. Spatio-temporal variability in snowmelt infiltration can impact lateral and vertical hydraulic gradients. Previous snow hydrology modeling efforts often model the snowmelt as a uniform precipitation (or input to the <span class="hlt">soil</span>) event, which this is known to not be the manner which snowmelt actually occurs. To model the hydrologic processes occurring at the site, variable surface boundary conditions are necessary and were investigated. The Dry Lake campground near Steamboat Springs, CO was selected to study the variability in which melting snowpack infiltrates the <span class="hlt">soil</span>. The Dry Lake study site contains a small watershed of approximately 0.2 km2, and ranges in elevation from 2510 m to 2690 m containing deciduous and evergreen forests, and open grasslands. Both a Remote Automated Weather Station and Snow Telemetry site lie within the Dry Lake study site and provide meteorological, snow, and <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature data. During the spring of 2013, the variability in the snowpack was surveyed along with <span class="hlt">soil</span> <span class="hlt">moisture</span> beneath the snowpack. A time domain reflectometer was used at the bottom of snowpits and gravimetric samples were collected for calibration at the freezing temperatures. The results of the survey show the variability in the <span class="hlt">soil</span> <span class="hlt">moisture</span> and implicated infiltration variability which occurs. Such results may be used to improve modeling efforts through the inclusion of variable surface boundary conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060041588&hterms=moghaddam&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D10%26Ntt%3Dmoghaddam','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060041588&hterms=moghaddam&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D10%26Ntt%3Dmoghaddam"><span>Estimating Subcanopy <span class="hlt">Soil</span> <span class="hlt">Moisture</span> with RADAR</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moghaddam, M.; Saatchi, S.; Cuenca, R. H.</p> <p>1998-01-01</p> <p>The subcanopy <span class="hlt">soil</span> <span class="hlt">moisture</span> of a boreal old jack pine forest is estimated using polarimetric L- and P-band AIRSAR data. Model simulations have shown that for this stand, the principal scattering mechanism responsible for radar backscatter is the double-bounce mechanism between the tree trunks and the ground.</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://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('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> </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('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://adsabs.harvard.edu/abs/2012PhDT.......103T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhDT.......103T"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span>-Atmosphere Feedbacks on Atmospheric Tracers: The Effects of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on Precipitation and Near-Surface Chemistry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tawfik, Ahmed B.</p> <p></p> <p>The atmospheric component is described by rapid fluctuations in typical state variables, such as temperature and water vapor, on timescales of hours to days and the land component evolves on daily to yearly timescales. This dissertation examines the connection between <span class="hlt">soil</span> <span class="hlt">moisture</span> and atmospheric tracers under varying degrees of <span class="hlt">soil</span> <span class="hlt">moisture</span>-atmosphere coupling. Land-atmosphere coupling is defined over the United States using a regional climate model. A newly examined <span class="hlt">soil</span> <span class="hlt">moisture</span>-precipitation feedback is identified for winter months extending the previous summer feedback to colder temperature climates. This feedback is driven by the freezing and thawing of <span class="hlt">soil</span> <span class="hlt">moisture</span>, leading to coupled land-atmosphere conditions near the freezing line. <span class="hlt">Soil</span> <span class="hlt">moisture</span> can also <span class="hlt">affect</span> the composition of the troposphere through modifying biogenic emissions of isoprene (C5H8). A novel first-order Taylor series decomposition indicates that isoprene emissions are jointly driven by temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> in models. These compounds are important precursors for ozone formation, an air pollutant and a short-lived forcing agent for climate. A mechanistic description of commonly observed relationships between ground-level ozone and meteorology is presented using the concept of <span class="hlt">soil</span> <span class="hlt">moisture</span>-temperature coupling regimes. The extent of surface drying was found to be a better predictor of ozone concentrations than temperature or humidity for the Eastern U.S. This relationship is evaluated in a coupled regional chemistry-climate model under several land-atmosphere coupling and isoprene emissions cases. The coupled chemistry-climate model can reproduce the observed <span class="hlt">soil</span> <span class="hlt">moisture</span>-temperature coupling pattern, yet modeled ozone is insensitive to changes in meteorology due to the balance between isoprene and the primary atmospheric oxidant, the hydroxyl radical (OH). Overall, this work highlights the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span>-atmosphere coupling for previously neglected cold climate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9108N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9108N"><span>Drivers of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the global scale</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</p> <p>2015-04-01</p> <p>With this research we aim to identify the large-scale drivers of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the global scale. In order to determine these drivers, one needs access to a global <span class="hlt">soil</span> <span class="hlt">moisture</span> data set. The satellite derived ESA CCI ECV-SM <span class="hlt">soil</span> <span class="hlt">moisture</span> data set provides global <span class="hlt">soil</span> <span class="hlt">moisture</span> data dating back to 1978, allowing for insight into the long-term temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span>. As the ECV-SM data set represents the surface layer, additionally land-surface model <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates from ERA Interim/Land are used to determine possible differences in drivers of root zone and surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. The main drivers of <span class="hlt">soil</span> <span class="hlt">moisture</span> variability for a region are determined by identifying the amount of variability attributable to these drivers.</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://adsabs.harvard.edu/abs/2017EGUGA..19.3690Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.3690Z"><span>Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimates Across China Based on Multi-satellite Observations and A <span class="hlt">Soil</span> <span class="hlt">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>Zhang, Ke; Yang, Tao; Ye, Jinyin; Li, Zhijia; Yu, Zhongbo</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable that regulates exchanges of water and energy between land surface and atmosphere. <span class="hlt">Soil</span> <span class="hlt">moisture</span> retrievals based on microwave satellite remote sensing have made it possible to estimate global surface (up to about 10 cm in depth) <span class="hlt">soil</span> <span class="hlt">moisture</span> routinely. Although there are many satellites operating, including NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Acitive Passive mission (SMAP), ESA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity mission (SMOS), JAXA's Advanced Microwave Scanning Radiometer 2 mission (AMSR2), and China's Fengyun (FY) missions, key differences exist between different satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> products. In this study, we applied a single-channel <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval model forced by multiple sources of satellite brightness temperature observations to estimate consistent daily surface <span class="hlt">soil</span> <span class="hlt">moisture</span> across China at a spatial resolution of 25 km. By utilizing observations from multiple satellites, we are able to estimate daily <span class="hlt">soil</span> <span class="hlt">moisture</span> across the whole domain of China. We further developed a daily <span class="hlt">soil</span> <span class="hlt">moisture</span> accounting model and applied it to downscale the 25-km satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> to 5 km. By comparing our estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> with observations from a dense observation network implemented in Anhui Province, China, our estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> results show a reasonably good agreement with the observations (RMSE < 0.1 and r > 0.8).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.5456B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.5456B"><span>SMOS CATDS level 3 <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>Berthon, L.; Mialon, A.; Bitar, A. Al; Cabot, F.; Kerr, Y. H.</p> <p>2012-04-01</p> <p>The ESA's (European Space Agency) SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity) mission, operating since november 2009, is the first satellite dedicated to measuring surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and ocean salinity. The CNES (Centre National d'Etudes Spatiales) has developed a ground segment for the SMOS data, known as the CATDS (Centre Aval de Traitement des Données SMOS). Operational since June 2011, it provides data referred to as level 3 products at different time resolutions: daily products, 3 days global products insuring a complete coverage of the Earth surface, 10-days composite products, and monthly averages products. These products are presented in the NetCDF format on the EASE grid (Equal Area Scalable Earth grid) with a spatial resolution of ~ 25*25 km2. Having global maps at different time resolutions is of interest for different applications such as agriculture, water management, climatic events (especially droughts and floods) or climatology. The <span class="hlt">soil</span> <span class="hlt">moisture</span> level 3 algorithm is based on ESA's (European Space Agency) level 2 retrieval scheme with the improvement of using several overpasses (3 at most) over a 7-days window. The benefit of using many revisits is expected to improve the retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span>. Along with the surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, other geophysical parameters are retrieved such as the vegetation optical depth or the dielectric constant of the surface. The aim of this communication is to present the first results from the CATDS dataset and all the different data available. Comparisons with in situ data at different sites will be presented to assess the quality of these data. A comparison with the ESA level 2 SMOS products will also be shown to better understand the difference between these dataset, in terms of quality, coverage, applications and use. We will also present how the CATDS data can capture some special events. For instance, the dataset will be compared with meteorological events (rain events), or extreme events such as droughts or</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H51N..03B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H51N..03B"><span><span class="hlt">Soil</span>Net - A Zigbee based <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogena, H. R.; Weuthen, A.; Rosenbaum, U.; Huisman, J. A.; Vereecken, H.</p> <p>2007-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays a key role in partitioning water and energy fluxes, in providing <span class="hlt">moisture</span> to the atmosphere for precipitation, and controlling the pattern of groundwater recharge. Large-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> variability is driven by variation of precipitation and radiation in space and time. At local scales, land cover, <span class="hlt">soil</span> conditions, and topography act to redistribute <span class="hlt">soil</span> <span class="hlt">moisture</span>. Despite the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span>, it is not yet measured in an operational way, e.g. for a better prediction of hydrological and surface energy fluxes (e.g. runoff, latent heat) at larger scales and in the framework of the development of early warning systems (e.g. flood forecasting) and the management of irrigation systems. The <span class="hlt">Soil</span>Net project aims to develop a sensor network for the near real-time monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> changes at high spatial and temporal resolution on the basis of the new low-cost ZigBee radio network that operates on top of the IEEE 802.15.4 standard. The sensor network consists of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors attached to end devices by cables, router devices and a coordinator device. The end devices are buried in the <span class="hlt">soil</span> and linked wirelessly with nearby aboveground router devices. This ZigBee wireless sensor network design considers channel errors, delays, packet losses, and power and topology constraints. In order to conserve battery power, a reactive routing protocol is used that determines a new route only when it is required. The sensor network is also able to react to external influences, e.g. such as rainfall occurrences. The <span class="hlt">Soil</span>Net communicator, routing and end devices have been developed by the Forschungszentrum Juelich and will be marketed through external companies. We will present first results of experiments to verify network stability and the accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors. Simultaneously, we have developed a data management and visualisation system. We tested the wireless network on a 100 by 100 meter forest plot equipped with 25</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H42D..07Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H42D..07Y"><span>Distributed <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation in a Mountainous Semiarid Basin: Constraining <span class="hlt">Soil</span> Parameter Uncertainty through Field Studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yatheendradas, S.; Vivoni, E.</p> <p>2007-12-01</p> <p>A common practice in distributed hydrological modeling is to assign <span class="hlt">soil</span> hydraulic properties based on coarse textural datasets. For semiarid regions with poor <span class="hlt">soil</span> information, the performance of a model can be severely constrained due to the high model sensitivity to near-surface <span class="hlt">soil</span> characteristics. Neglecting the uncertainty in <span class="hlt">soil</span> hydraulic properties, their spatial variation and their naturally-occurring horizonation can potentially <span class="hlt">affect</span> the modeled hydrological response. In this study, we investigate such effects using the TIN-based Real-time Integrated Basin Simulator (tRIBS) applied to the mid-sized (100 km2) Sierra Los Locos watershed in northern Sonora, Mexico. The Sierra Los Locos basin is characterized by complex mountainous terrain leading to topographic organization of <span class="hlt">soil</span> characteristics and ecosystem distributions. We focus on simulations during the 2004 North American Monsoon Experiment (NAME) when intensive <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements and aircraft- based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals are available in the basin. Our experiments focus on <span class="hlt">soil</span> <span class="hlt">moisture</span> comparisons at the point, topographic transect and basin scales using a range of different <span class="hlt">soil</span> characterizations. We compare the distributed <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates obtained using (1) a deterministic simulation based on <span class="hlt">soil</span> texture from coarse <span class="hlt">soil</span> maps, (2) a set of ensemble simulations that capture <span class="hlt">soil</span> parameter uncertainty and their spatial distribution, and (3) a set of simulations that conditions the ensemble on recent <span class="hlt">soil</span> profile measurements. Uncertainties considered in near-surface <span class="hlt">soil</span> characterization provide insights into their influence on the modeled uncertainty, into the value of <span class="hlt">soil</span> profile observations, and into effective use of on-going field observations for constraining the <span class="hlt">soil</span> <span class="hlt">moisture</span> response uncertainty.</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://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://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://adsabs.harvard.edu/abs/2010EGUGA..1213326G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213326G"><span>First <span class="hlt">soil</span> <span class="hlt">moisture</span> values from SMOS over a Sahelian region.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gruhier, Claire; Kerr, Yann; de Rosnay, Patricia; Pellarin, Thierry; Grippa, Manuela</p> <p>2010-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a crucial variable which influences the land surface processes. Numerous studies shown microwaves at low frequency are particularly performed to access to <span class="hlt">soil</span> <span class="hlt">moisture</span> values. SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity), launched the November 2th 2009, is the first space mission dedicated to <span class="hlt">soil</span> <span class="hlt">moisture</span> observations. Before SMOS, several <span class="hlt">soil</span> <span class="hlt">moisture</span> products were provided, based on active or passive microwaves measurements. Gruhier et al. (2010) analyse five of them over a Sahelian area. The results show that the range of volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> values obtained over Sahel is drastically different depending on the remote sensing approach used to produce <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates. Although microwave bands currently available are not optimal, some products are in very good agreement with ground data. The main goal of this study is to introduce the first <span class="hlt">soil</span> <span class="hlt">moisture</span> maps from SMOS over West Africa. A first analyse of values over a Sahelian region is investigated. The study area is located in Gourma region in Mali. This site has been instrumented in the context of the AMMA project (African Monsoon Multidisciplinary Analysis) and was specifically designed to address the validation of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span>. SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> values was analysed with ground knowledge and placed in the context of previous <span class="hlt">soil</span> <span class="hlt">moisture</span> products. The high sensitivity of the L-band used by SMOS should provide very accurate <span class="hlt">soil</span> <span class="hlt">moisture</span> values.</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://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('https://www.ncbi.nlm.nih.gov/pubmed/25757307','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25757307"><span>[Characteristics of <span class="hlt">soil</span> <span class="hlt">moisture</span> in artificial impermeable layers].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Suo, Gai-Di; Xie, Yong-Sheng; Tian, Fei; Chuai, Jun-Feng; Jing, Min-Xiao</p> <p>2014-09-01</p> <p>For the problem of low water and fertilizer use efficiency caused by nitrate nitrogen lea- ching into deep <span class="hlt">soil</span> layer and <span class="hlt">soil</span> desiccation in dryland apple orchard, characteristics of <span class="hlt">soil</span> <span class="hlt">moisture</span> were investigated by means of hand tamping in order to find a new approach in improving the water and fertilizer use efficiency in the apple orchard. Two artificial impermeable layers of red clay and dark loessial <span class="hlt">soil</span> were built in <span class="hlt">soil</span>, with a thickness of 3 or 5 cm. Results showed that artificial impermeable layers with the two different thicknesses were effective in reducing or blocking water infiltration into <span class="hlt">soil</span> and had higher seepage controlling efficiency. Seepage controlling efficiency for the red clay impermeable layer was better than that for the dark loessial <span class="hlt">soil</span> impermeable layer. Among all the treatments, the red clay impermeable layer of 5 cm thickness had the highest bulk density, the lowest initial infiltration rate (0.033 mm · min(-1)) and stable infiltration rate (0.018 mm · min(-1)) among all treatments. After dry-wet alternation in summer and freezing-thawing cycle in winter, its physiochemical properties changed little. Increase in years did not <span class="hlt">affect</span> stable infiltration rate of <span class="hlt">soil</span> water. The red clay impermeable layer of 5 cm thickness could effectively increase <span class="hlt">soil</span> <span class="hlt">moisture</span> content in upper <span class="hlt">soil</span> layer which was conducive to raise the water and nutrient use efficiency. The approach could be applied to the apple production of dryland orchard.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFM.H21C..06C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFM.H21C..06C"><span>Use of TRMM Microwave Imager (TMI) to characterize <span class="hlt">soil</span> <span class="hlt">moisture</span> for the Little River Watershed</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cashion, J. E.; Lakshmi, V.; Bosch, D.</p> <p>2003-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays a critical role in many hydrological processes including infiltration, evaporation, and runoff. Additionally, <span class="hlt">soil</span> <span class="hlt">moisture</span> has a direct effect on weather patterns. Satellite based passive microwave sensors offer an effective way to observe <span class="hlt">soil</span> <span class="hlt">moisture</span> data over vast areas, and there are currently several satellite systems that detect <span class="hlt">soil</span> <span class="hlt">moisture</span>. Long-term in situ (field) measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> are collected in the Little River Watershed (LRWS) located in Tifton, Georgia and compared with the remotely sensed data collected over the watershed. The LRWS has been selected by the United States Department of Agriculture (USDA) to represent the south eastern costal plains region of North America. The LRWS is composed primarily of sandy <span class="hlt">soils</span> and has a flat topography with meandering streams. The in-situ measurements were collected by stationary <span class="hlt">soil</span> <span class="hlt">moisture</span> probes attached to rain gage stations throughout the LRWS for the period 2000-2002. The remotely sensed data was acquired by two satellites viz. - the Tropical Rainfall Measurement Mission Microwave Imager (TMI) for <span class="hlt">soil</span> <span class="hlt">moisture</span> and the Moderate Resolution Imaging Spectroradiometer (MODIS) for vegetation. The TMI is equipped with a passive vertically and horizontally polarized 10.65GHz sensor that is capable of detecting <span class="hlt">soil</span> <span class="hlt">moisture</span>. <span class="hlt">Soil</span> <span class="hlt">moisture</span> collected in the field is related to the TMI brightness temperatures. However, vegetation has a strong <span class="hlt">affect</span> on the 10.65GHz brightness temperature. The Normalized Difference Vegetation Index (NDVI) data, provided by the (MODIS), are used to evaluate the effect of vegetation on <span class="hlt">soil</span> microwave emission.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001AGUFM.H21C0323M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001AGUFM.H21C0323M"><span>Temporal Dynamics of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variability at the Landscape Scale: Implications for Land Surface Models.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Montaldo, N.; Albertson, J. D.</p> <p>2001-12-01</p> <p>Meteorological and hydrological forecasting models share <span class="hlt">soil</span> <span class="hlt">moisture</span> as a critical boundary condition. Partitioning of received energy at the land surface depends directly on this variable, as does the partitioning of rainfall into its possible routes over and through the <span class="hlt">soil</span>. In Land Surface Models (LSMs) the temporal dynamic of <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial variability is a fundamental issue in large-scale flux predictions. From remote sensing observations <span class="hlt">soil</span> <span class="hlt">moisture</span> values are averaged in the horizontal over rather large regions (pixels). The averaging areas will be getting even larger as we move from aircraft mounted sensors to satellite mounting. These data are to be used ultimately to estimate spatial averages of other processes that depend on <span class="hlt">soil</span> <span class="hlt">moisture</span>, such as, runoff generation, drainage, evaporation, sensible heat fluxes, crop yield, microbial activity, etc. Consequently, the LSMs have to predict spatial averaged flux over large region from average values of the <span class="hlt">soil</span> <span class="hlt">moisture</span>. But <span class="hlt">soil</span> <span class="hlt">moisture</span> variances <span class="hlt">affect</span> flux predictions, which depend nonlinearly on <span class="hlt">soil</span> <span class="hlt">moisture</span>, because many of the other processes possess distinct threshold aspects to their nonlinear dependence on <span class="hlt">soil</span> <span class="hlt">moisture</span>. Through application of well-developed Reynolds averaging rules from fluid mechanics to the equation of Richards and Darcy-Buckingham, we write a conservation equation for the horizontal variance of <span class="hlt">soil</span> <span class="hlt">moisture</span>. And, through closure arguments, we are able to describe the individual terms that produce and destroy spatial variance through time in terms of the mean <span class="hlt">soil</span> <span class="hlt">moisture</span> state and other observable system properties such as vegetation and <span class="hlt">soil</span> properties variability. Finally, we calculate land surface fluxes from second order Taylor expansion, using our <span class="hlt">soil</span> <span class="hlt">moisture</span> variance closure model, and the other observable system properties. In this work, we demonstrate significant improvements in land surface large-scale flux predictions using the proposed <span class="hlt">soil</span> <span class="hlt">moisture</span></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('https://ntrs.nasa.gov/search.jsp?R=19990064542&hterms=Amsterdam&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DAmsterdam','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064542&hterms=Amsterdam&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DAmsterdam"><span>Estimating <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from Satellite Microwave Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.</p> <p>1998-01-01</p> <p>Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of <span class="hlt">soil</span> <span class="hlt">moisture</span> from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data</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('https://ntrs.nasa.gov/search.jsp?R=19990064542&hterms=Desertification&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DDesertification','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064542&hterms=Desertification&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DDesertification"><span>Estimating <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from Satellite Microwave Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.</p> <p>1998-01-01</p> <p>Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of <span class="hlt">soil</span> <span class="hlt">moisture</span> from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data</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://www.ars.usda.gov/research/publications/publication/?seqNo115=315194','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=315194"><span>Application of triple collocation for the ground-based validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> active/passive (SMAP) <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 in horizontal spatial support between ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> observations and satellite-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates represents a long-standing challenge for the validation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data products [Crow et al., 2014]. This challenge can be alleviated by limiting ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080004233','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080004233"><span>Method for evaluating <span class="hlt">moisture</span> tensions of <span class="hlt">soils</span> using spectral data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peterson, John B. (Inventor)</p> <p>1982-01-01</p> <p>A method is disclosed which permits evaluation of <span class="hlt">soil</span> <span class="hlt">moisture</span> utilizing remote sensing. Spectral measurements at a plurality of different wavelengths are taken with respect to sample <span class="hlt">soils</span> and the bidirectional reflectance factor (BRF) measurements produced are submitted to regression analysis for development therefrom of predictable equations calculated for orderly relationships. <span class="hlt">Soil</span> of unknown reflective and unknown <span class="hlt">soil</span> <span class="hlt">moisture</span> tension is thereafter analyzed for bidirectional reflectance and the resulting data utilized to determine the <span class="hlt">soil</span> <span class="hlt">moisture</span> tension of the <span class="hlt">soil</span> as well as providing a prediction as to the bidirectional reflectance of the <span class="hlt">soil</span> at other <span class="hlt">moisture</span> tensions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.7361Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.7361Z"><span>COsmic-ray <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observing System (COSMOS): <span class="hlt">soil</span> <span class="hlt">moisture</span> and beyond</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zreda, Marek; Shuttleworth, William J.; Zeng, Xubin; Zweck, Chris; Franz, Trenton; Rosolem, Rafael</p> <p>2013-04-01</p> <p>COSMOS, a project funded by the US National Science Foundation, was designed to measure average <span class="hlt">soil</span> <span class="hlt">moisture</span> in the top 10-70 cm of <span class="hlt">soil</span> over the horizontal footprint of approximately 700 m by measuring cosmic-ray neutrons in air above the ground surface. It is in its fourth, final, year of the feasibility phase in which 60 neutron probes have been installed in the USA to provide continental-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The cosmic-ray neutron probe responds to all sources of hydrogen present within the footprint. Therefore, in addition to <span class="hlt">soil</span> <span class="hlt">moisture</span>, other pools of hydrogen can be measured; these include atmospheric water vapor, organic matter in <span class="hlt">soil</span>, water in <span class="hlt">soil</span> minerals, biomass water (including hydrogen bound in cellulose), and snow on the ground and on the canopy. All these pools of hydrogen form the "total surface <span class="hlt">moisture</span>" that is measured by COSMOS probes. The first four pools are measured independently (water vapor) or are implicitly included in the probe calibration (water in minerals and organic matter, biomass water). The other two can be separated from one another to produce time series of <span class="hlt">soil</span> <span class="hlt">moisture</span> and snow water equivalent. Work is in progress to assimilate neutron data into land-surface models, to produce <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles, to validate satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> products (the current SMOS mission and the future SMAP mission), to measure temporal variations in biomass, and to measure area-average unsaturated hydraulic properties of <span class="hlt">soils</span>. Separately, mobile COSMOS probe, called COSMOS rover, is being developed. COSMOS rover can be used to map <span class="hlt">soil</span> <span class="hlt">moisture</span> over large areas or along long transects. Cosmic-ray sensing of <span class="hlt">moisture</span> at the land surface has gained popularity outside of the USA. Approximately 60 probes have been purchased in addition to the 60 probes in the COSMOS project. Funds for additional 80 probes, most of them in Germany, have been secured, and large new proposals will be submitted in the USA and Australia in 2013. These</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H33D1388R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H33D1388R"><span>Quantifying Shrink Swell Capacity of <span class="hlt">Soil</span> Using <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Isotherms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rivera, L. D.; Cobos, D. R.; Campbell, C. S.; Morgan, C.</p> <p>2013-12-01</p> <p>Vertisols, <span class="hlt">soils</span> instinctively known for their expansive clays that cause them to have a high shrink swell potential, cover 2.4% of the earths ice-free land. In the United States these expansive <span class="hlt">soils</span> can cause upwards of 6 billion in damages to pavements, foundations, and utility lines annually (Brady & Weil, 2010). Because of this, it is especially important that a <span class="hlt">soils</span> ability to shrink and swell is well characterized when making engineering decisions. One traditional method for measuring a <span class="hlt">soil</span>'s expansive potential, the Coefficient of Linear Extensibility (COLE), can take weeks to months to complete (Grossman et al., 1968; Schafer and Singer, 1976b). Use of <span class="hlt">soil</span> <span class="hlt">moisture</span> isotherms, or the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Characteristic Curve (SMCC), in recent research has shown that the slope of the SMCC is related to a <span class="hlt">soils</span> swelling potential (McKeen, 1992). The goal of this research is to evaluate the robustness of the relationship between the SMCC and COLE for a set of well-characterized test <span class="hlt">soils</span> with COLE ranging from 0 to 0.176. If expansive potential can be reliably predicted from the SMCC, then data from recently developed automatic <span class="hlt">soil</span> <span class="hlt">moisture</span> isotherm generators could be used to characterize expansive potential with a fraction of the time and effort necessary for traditional techniques.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhDT........60C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT........60C"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Remote Sensing using GPS-Interferometric Reflectometry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chew, Clara</p> <p></p> <p>Ground-reflected Global Positioning System (GPS) signals can be used opportunistically to infer changes in land-surface characteristics surrounding a GPS monument. GPS satellites transmit at L-band, and at microwave frequencies the permittivity of the ground surface changes primarily due to its <span class="hlt">moisture</span> content. Temporal changes in ground-reflected GPS signals are thus indicative of temporal changes in the <span class="hlt">moisture</span> content surrounding a GPS antenna. The interference pattern of the direct and reflected GPS signal for a single satellite track is recorded in signal-to-noise ratio (SNR) data. Alternating constructive and destructive interference as the satellite passes over the antenna results in a noisy oscillating wave at low satellite elevation angles, from which the phase, amplitude, and frequency (or reflector height) can be calculated. Here, an electrodynamic model that simulates SNR data is validated against field observations. The model is then used to show that temporal changes in these SNR metrics may be used to estimate changes in surface <span class="hlt">soil</span> <span class="hlt">moisture</span> in the top 5 cm of the <span class="hlt">soil</span> column. Results show that changes in SNR phase are best correlated with changes in <span class="hlt">soil</span> <span class="hlt">moisture</span>, with an approximately linear slope. Surface roughness decreases the sensitivity of SNR phase to <span class="hlt">soil</span> <span class="hlt">moisture</span>, though the effect is not significant for small roughness values (<5 cm). Modeling experiments show that all three SNR metrics are <span class="hlt">affected</span> by changes in the permittivity and height of a vegetation canopy. SNR amplitude is the best indicator of changes in vegetation. An increase in either canopy permittivity or height will cause a corresponding decrease in SNR phase. Seasonal changes in vegetation must be removed if <span class="hlt">soil</span> <span class="hlt">moisture</span> is to be estimated using phase data. An algorithm is presented that uses modeled relationships between canopy parameters and SNR metrics to remove seasonal vegetation effects from the phase time series, from which <span class="hlt">soil</span> <span class="hlt">moisture</span> time series may be</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012HESSD...9.4553Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012HESSD...9.4553Y"><span>Spatial variation of shallow and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> in the semi-arid 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>Yang, L.; Wei, W.; Chen, L.; Jia, F.; Mo, B.</p> <p>2012-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> in deep <span class="hlt">soil</span> layers is the only relatively stable water resource for introduced vegetation in the semi-arid Loess Plateau of China. Characterizing the spatial variation of deep <span class="hlt">soil</span> <span class="hlt">moisture</span> is significant for vegetation restoration with respect to the topographic conditions. In this study, we focused on analyzing the spatial variations and influencing factors of <span class="hlt">soil</span> <span class="hlt">moisture</span> content (SMC) in shallow (0-2 m) and deep (2-8 m) <span class="hlt">soil</span> layers based on <span class="hlt">soil</span> <span class="hlt">moisture</span> observation in the Longtan watershed. The vegetation type of each sampling site for each comparison is same, while varies with slope position, slope gradient, or slope aspect. The following results are found: (1) compared with shallow SMC, slope position and slope aspect may <span class="hlt">affect</span> shallow <span class="hlt">soil</span> <span class="hlt">moisture</span> more, rather than deep layers. Slope gradient however, <span class="hlt">affect</span> both shallow and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> significantly. It indicates that high difference of deep <span class="hlt">soil</span> hydrological processes between shallow and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> remains, which can be attributed to the introduced vegetation and topography. (2) The vegetation growth condition has significant negative relation with deep <span class="hlt">soil</span> <span class="hlt">moisture</span>. This result indicates that plants under different growth conditions may consume <span class="hlt">soil</span> <span class="hlt">moisture</span> differently, thus causing higher spatial variation of deep <span class="hlt">soil</span> <span class="hlt">moisture</span>. (3) The dynamic role of slope position and slope aspect on deep SMC has been changed by introduced vegetation in semi-arid environment. Consequently, vegetation growth condition and slope gradient may be the major factor contributing to the spatial variation of deep <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080001HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080001HQ.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/2060/19850014927','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19850014927"><span>A method for estimating <span class="hlt">soil</span> <span class="hlt">moisture</span> availability</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>1985-01-01</p> <p>A method for estimating values of <span class="hlt">soil</span> <span class="hlt">moisture</span> based on measurements of infrared surface temperature is discussed. A central element in the method is a boundary layer model. Although it has been shown that <span class="hlt">soil</span> <span class="hlt">moistures</span> determined by this method using satellite measurements do correspond in a coarse fashion to the antecedent precipitation, the accuracy and exact physical interpretation (with respect to ground water amounts) are not well known. This area of ignorance, which currently impedes the practical application of the method to problems in hydrology, meteorology and agriculture, is largely due to the absence of corresponding surface measurements. Preliminary field measurements made over France have led to the development of a promising vegetation formulation (Taconet et al., 1985), which has been incorporated in the model. It is necessary, however, to test the vegetation component, and the entire method, over a wide variety of surface conditions and crop canopies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19930036630&hterms=soil+surveys&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dsoil%2Bsurveys','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930036630&hterms=soil+surveys&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dsoil%2Bsurveys"><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> <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('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('https://pubs.usgs.gov/wsp/1619u/report.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/wsp/1619u/report.pdf"><span>Methods of measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> in the field</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Johnson, A.I.</p> <p>1962-01-01</p> <p>For centuries, the amount of <span class="hlt">moisture</span> in the <span class="hlt">soil</span> has been of interest in agriculture. The subject of <span class="hlt">soil</span> <span class="hlt">moisture</span> is also of great importance to the hydrologist, forester, and <span class="hlt">soils</span> engineer. Much equipment and many methods have been developed to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> under field conditions. This report discusses and evaluates the various methods for measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> and describes the equipment needed for each method. The advantages and disadvantages of each method are discussed and an extensive list of references is provided for those desiring to study the subject in more detail. The gravimetric method is concluded to be the most satisfactory method for most problems requiring onetime <span class="hlt">moisture</span>-content data. The radioactive method is normally best for obtaining repeated measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> in place. It is concluded that all methods have some limitations and that the ideal method for measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> under field conditions has yet to be perfected.</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('http://adsabs.harvard.edu/abs/2013aero.confE..98K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013aero.confE..98K"><span>NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) observatory</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kellogg, K.; Thurman, S.; Edelstein, W.; Spencer, M.; Chen, Gun-Shing; Underwood, M.; Njoku, E.; Goodman, S.; Jai, Benhan</p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, one of the first-tier missions recommended by the 2007 U.S. National Research Council Committee on Earth Science and Applications from Space, was confirmed in May 2012 by NASA to proceed into Implementation Phase (Phase C) with a planned launch in October 2014. SMAP will produce high-resolution and accurate global maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its freeze/thaw state using data from a non-imaging synthetic aperture radar and a radiometer, both operating at L-band. Major challenges addressed by the observatory design include: (1) achieving global coverage every 2-3 days with a single observatory; (2) producing both high resolution and high accuracy <span class="hlt">soil</span> <span class="hlt">moisture</span> data, including through moderate vegetation; (3) using a mesh reflector antenna for L-band radiometry; (4) minimizing science data loss from terrestrial L-band radio frequency interference; (5) designing fault protection that also minimizes science data loss; (6) adapting planetary heritage avionics to meet SMAP's unique application and data volume needs; (7) ensuring observatory electromagnetic compatibility to avoid degrading science; (8) controlling a large spinning instrument with a small spacecraft; and (9) accommodating launch vehicle selection late in the observatory's development lifecycle.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010WRR....46.3509V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010WRR....46.3509V"><span>Traditional and microirrigation with stochastic <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>Vico, Giulia; Porporato, Amilcare</p> <p>2010-03-01</p> <p>Achieving a sustainable use of water resources, in view of the increased food and biofuel demand and possible climate change, will require optimizing irrigation, a highly nontrivial task given the unpredictability of rainfall and the numerous <span class="hlt">soil</span>-plant-atmosphere interactions. Here we theoretically analyze two different irrigation schemes, a traditional scheme, consisting of the application of fixed water volumes that bring <span class="hlt">soil</span> <span class="hlt">moisture</span> to field capacity, and a microirrigation scheme supplying water continuously in order to avoid plant water stress. These two idealized irrigation schemes are optimal in the sense that they avoid crop water stress while minimizing water losses by percolation and runoff. Furthermore, they cover the two extremes cases of continuous and fully concentrated irrigation. For both irrigation schemes, we obtain exact solutions of the steady state <span class="hlt">soil</span> <span class="hlt">moisture</span> probability density function with random timing and amounts of rainfall. We also give analytical expressions for irrigation frequency and volumes under different rainfall regimes, evaporative demands, and <span class="hlt">soil</span> types. We quantify the excess volumes required by traditional irrigation, mostly lost in runoff and deep infiltration, as a function of climate, <span class="hlt">soil</span>, and vegetation parameters.</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('https://ntrs.nasa.gov/search.jsp?R=19870008712&hterms=soil+temperature&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsoil%2Btemperature','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870008712&hterms=soil+temperature&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsoil%2Btemperature"><span>Estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> from diurnal surface temperature observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Vandegriend, A. A.; Camillo, P. J.</p> <p>1986-01-01</p> <p>A coupled heat and <span class="hlt">moisture</span> balance model was used to determine the thermal inertia of a grass covered top <span class="hlt">soil</span> under different meteorological conditions. Relations between thermal inertia and <span class="hlt">soil</span> <span class="hlt">moisture</span> were established using the De Vries models for thermal conductivity and heat capacity to relate <span class="hlt">soil</span> <span class="hlt">moisture</span> and thermal inertia as a function of <span class="hlt">soil</span> type. A sensitivity study of the surface roughness length and thermal inertia on diurnal surface temperature shows the necessity of focusing on the night time surface temperature rather than on the day time surface temperature, in order to estimate the <span class="hlt">soil</span> <span class="hlt">moisture</span> content of the top <span class="hlt">soil</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_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://ntrs.nasa.gov/search.jsp?R=19870008712&hterms=different+types+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Ddifferent%2Btypes%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870008712&hterms=different+types+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Ddifferent%2Btypes%2Bsoil"><span>Estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> from diurnal surface temperature observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Vandegriend, A. A.; Camillo, P. J.</p> <p>1986-01-01</p> <p>A coupled heat and <span class="hlt">moisture</span> balance model was used to determine the thermal inertia of a grass covered top <span class="hlt">soil</span> under different meteorological conditions. Relations between thermal inertia and <span class="hlt">soil</span> <span class="hlt">moisture</span> were established using the De Vries models for thermal conductivity and heat capacity to relate <span class="hlt">soil</span> <span class="hlt">moisture</span> and thermal inertia as a function of <span class="hlt">soil</span> type. A sensitivity study of the surface roughness length and thermal inertia on diurnal surface temperature shows the necessity of focusing on the night time surface temperature rather than on the day time surface temperature, in order to estimate the <span class="hlt">soil</span> <span class="hlt">moisture</span> content of the top <span class="hlt">soil</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H31A1390Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H31A1390Y"><span>Evaluation of <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations in the GLACE-CMIP5 experiment using satellite and in situ observations over CONUS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yuan, S.; Quiring, S. M.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is one of important components of land surface process. It plays critical role in the earth system through <span class="hlt">affecting</span> both surface energy and water balances at boundary layer. Because of spatial and temporal discontinuities in observed <span class="hlt">soil</span> <span class="hlt">moisture</span>, model-simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> is widely used instead. The accuracy of the model-simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> has a significant impact on <span class="hlt">soil</span> <span class="hlt">moisture</span>-related research, such as investigations of <span class="hlt">soil</span> <span class="hlt">moisture</span>-climate interactions. This project aims will evaluate the performance of <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations in 6 earth system models from the Global Land Atmosphere Coupling Experiment - Coupled Model Intercomparison Project 5 (GLACE-CMIP5) by using both in situ and satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> observations over CONUS. In situ <span class="hlt">soil</span> <span class="hlt">moisture</span> (top 5 cm and total <span class="hlt">soil</span> column) will be derived from the North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database. Satellite-based observations, specifically the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Essential Climate Variable (ECV), will also be used to evaluate the models. This project will help to assess the performance of these models and to identify areas where further improvement in the accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations are needed.</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('http://adsabs.harvard.edu/abs/2010JHyd..389..289M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JHyd..389..289M"><span>Effects of gullies on space-time patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> in a semiarid grassland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Melliger, Joshua J.; Niemann, Jeffrey D.</p> <p>2010-08-01</p> <p>SummaryGullies are pervasive topographic features in many regions, and they can represent an important form of land degradation. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is expected to play a significant role in the gullying process because it <span class="hlt">affects</span> the health of the vegetation cover and the shear strength of the <span class="hlt">soil</span>. However, the interaction of <span class="hlt">soil</span> <span class="hlt">moisture</span> with the gully topography remains poorly understood. The primary objectives of this study are to determine whether the development of a gully <span class="hlt">affects</span> the spatial pattern of near-surface (0-10 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> and to determine whether the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics differ between the gully bottoms, sidewalls, and uplands. Three study sites in southeastern Colorado were intensively monitored. One site is ungullied, while the other two sites each contain a gully. Hourly <span class="hlt">soil</span> <span class="hlt">moisture</span> observations were collected using time domain reflectometry probes installed along four transects at each site. Each transect contains 6-8 probes that are positioned at the mid-points between breakpoints in topographic slope. Overall, the occurrence of a gully was observed to have little impact on the spatial average <span class="hlt">soil</span> <span class="hlt">moisture</span> within the study sites, but it does promote spatial variability in <span class="hlt">soil</span> <span class="hlt">moisture</span>. Gully bottoms have lower wind speeds and thus lower reference evapotranspiration rates, and they tend to be wetter than the uplands. Gully sidewalls tend to be drier due to rapid drainage during and after precipitation events. Differences in the insolation of gully sidewalls are also associated with differences in their <span class="hlt">soil</span> <span class="hlt">moisture</span>.</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('http://adsabs.harvard.edu/abs/2007AGUSM.H51C..02G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUSM.H51C..02G"><span>Large Scale Evaluation of AMSR-E <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products Based on Ground <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network Measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gruhier, C.; de Rosnay, P.; Richaume, P.; Kerr, Y.; Rudiger, C.; Boulet, G.; Walker, J. P.; Mougin, E.; Ceschia, E.; Calvet, J.</p> <p>2007-05-01</p> <p>This paper presents an evaluation of AMSR-E (Advanced Microwave Scanning Radiometer for EOS) <span class="hlt">soil</span> <span class="hlt">moisture</span> products, based on a comparison with three ground <span class="hlt">soil</span> <span class="hlt">moisture</span> networks. The selected ground sites are representative of various climatic, hydrologic and environmental conditions in temperate and semi-arid areas. They are located in the south-west of France, south-east of Australia and the Gourma region of the Sahel. These sites were respectively implemented in the framework of the projects SMOSREX (Surface Monitoring Of <span class="hlt">Soil</span> Reservoir Experiment), SASMAS/GoREx (Scaling and Assimilation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Streamflow in the Goulburn River Experimental catchment) and AMMA (African Monsoon Multidisciplinary Analysis). In all cases, the arrangement of the <span class="hlt">soil</span> <span class="hlt">moisture</span> measuring sites was specifically designed to address the validation of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> in the context of the preparation of the SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity) project. For the purpose of this study, 25km AMSR-E products were used, including brightness temperatures at 6.9 and 10.7 GHz, and derived <span class="hlt">soil</span> <span class="hlt">moisture</span>. The study is focused on the year 2005. It is based on ground <span class="hlt">soil</span> <span class="hlt">moisture</span> network measurements from 4 stations for SMOSREX extended to the SUDOUEST project of CESBIO, 12 stations for GoRex, and 4 stations for AMMA. Temporal and spatial features of <span class="hlt">soil</span> <span class="hlt">moisture</span> variability and stability is a critical issue to be addressed for remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> validation. While ground measurements provide information on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics at local scale and high temporal resolution (hourly), satellite measurements are sparser in time (up to several days), but cover a larger region (25km x 25km for AMSR-E). First, a statistical analysis, including mean relative difference and Spearman rank, is conducted for the three <span class="hlt">soil</span> <span class="hlt">moisture</span> networks. This method is mainly based on the approach proposed by Cosh et al. (2004) for the purpose of the use of ground networks for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=245135','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=245135"><span>Uncertainty in SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurements Caused by Dew</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 reservoir of the hydrologic cycle that regulates the exchange of <span class="hlt">moisture</span> and energy between the land surface the atmosphere. Two satellite missions will soon make the first global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the optimal microwave wavelength within L-band: ESA's So...</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('https://ntrs.nasa.gov/search.jsp?R=19740041948&hterms=gas+chromatography&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dgas%2Bchromatography','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19740041948&hterms=gas+chromatography&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dgas%2Bchromatography"><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('http://www.dtic.mil/docs/citations/ADA254702','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA254702"><span>Vegetation Response to Rainfall and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variability in Botswana</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1991-01-01</p> <p>34 4 Results and Discussion 35 4.1 NDVI Spatial and Temporal Characteristics ....................... 36 4.2 A Comparison of Mean...Patterns of NDVI , Rainfall, and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> . . . 45 4.3 Relationships Between NDVI , Rainfall, and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> By Vegetation Type 48 4.4...Correlations/Regression Analyses by Vegetation Type ................ 56 4.5 Effects of Varying <span class="hlt">Soil</span> Type on the NDVI /Rainfall and NDVI /<span class="hlt">Soil</span> <span class="hlt">Moisture</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.P51D0945K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.P51D0945K"><span>Microbiology and <span class="hlt">Moisture</span> Uptake of Desert <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>Kress, M. E.; Bryant, E. P.; Morgan, S. W.; Rech, S.; McKay, C. P.</p> <p>2005-12-01</p> <p>We have initiated an interdisciplinary study of the microbiology and water content of desert <span class="hlt">soils</span> to better understand microbial activity in extreme arid environments. Water is the one constituent that no organism can live without; nevertheless, there are places on Earth with an annual rainfall near zero that do support microbial ecosystems. These hyperarid deserts (e.g. Atacama and the Antarctic Dry Valleys) are the closest terrestrial analogs to Mars, which is the subject of future exploration motivated by the search for life beyond Earth. We are modeling the <span class="hlt">moisture</span> uptake by <span class="hlt">soils</span> in hyperarid environments to quantify the environmental constraints that regulate the survival and growth of micro-organisms. Together with the studies of <span class="hlt">moisture</span> uptake, we are also characterizing the microbial population in these <span class="hlt">soils</span> using molecular and culturing methods. We are in the process of extracting DNA from these <span class="hlt">soils</span> using MoBio extraction kits. This DNA will be used as a template to amplify bacterial and eukaryotic ribosomal DNA to determine the diversity of the microbial population. We also have been attempting to determine the density of organisms by culturing on one-half strength R2A agar. The long-range goal of this research is to identify special adaptations of terrestrial life that allow them to inhabit extreme arid environments, while simultaneously quantifying the environmental parameters that enforce limits on these organisms' growth and survival.</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/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/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/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/2016EGUGA..18.2463Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2463Z"><span>Integrating Real-time and Manual Monitored <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data to Predict Hillslope <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variations with High Temporal Resolutions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhu, Qing; Lv, Ligang; Zhou, Zhiwen; Liao, Kaihua</p> <p>2016-04-01</p> <p>Spatial-temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> 15 has been remaining an challenge to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring methods. This restricted the comprehensive and intensive examination of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. In this study, we aimed to integrate the manual and real-time monitored <span class="hlt">soil</span> <span class="hlt">moisture</span> to depict the hillslope dynamics of <span class="hlt">soil</span> <span class="hlt">moisture</span> with good spatial coverage and temporal resolution. Linear (stepwise multiple linear regression-SMLR) and non-linear models (support vector machines-SVM) were used to predict <span class="hlt">soil</span> <span class="hlt">moisture</span> at 38 manual sites (collected 1-2 times per month) with <span class="hlt">soil</span> <span class="hlt">moisture</span> automatically collected at three real-time monitoring sites (collected every 5 mins). By comparing the accuracies of SMLR and SVM for each manual site, optimal <span class="hlt">soil</span> <span class="hlt">moisture</span> prediction model of this site was then determined. Results show that <span class="hlt">soil</span> <span class="hlt">moisture</span> at these 38 manual sites can be reliably predicted (root mean square errors<0.035 m3 m-3) using this approach. Absence or occurrence of subsurface flow can probably influence the choosing of SMLR or SVM in the prediction, respectively. Depth to bedrock, elevation, topographic wetness index, profile curvature, and relative difference of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its standard deviation influenced the selection of prediction model since they related to the dynamics of <span class="hlt">soil</span> water distribution and movement. By using this approach, hillslope <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial distributions at un-sampled times and dates were predicted after a typical rainfall event. Missing information of hillslope <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics was then acquired successfully. This can be benefit for determining the hot spots and moments of <span class="hlt">soil</span> water movement, as well as designing the proper <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring plan at the field scale.</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/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> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870052970&hterms=leaf+stomata&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dleaf%2Bstomata','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870052970&hterms=leaf+stomata&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dleaf%2Bstomata"><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> </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('https://ntrs.nasa.gov/search.jsp?R=19870052970&hterms=evapotranspiration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Devapotranspiration','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870052970&hterms=evapotranspiration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Devapotranspiration"><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/2012JGRD..11718115K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JGRD..11718115K"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span>-vegetation-precipitation feedback over North America: Its sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span> climatology</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Yeonjoo; Wang, Guiling</p> <p>2012-09-01</p> <p>Our previous studies examined how vegetation feedback at the seasonal time scale influenced the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies (SMAs) on subsequent summer precipitation with a modified version of the coupled Community Atmosphere Model-Community Land Model 3 that includes a predictive phenology scheme. Here we investigate the climatology sensitivity of <span class="hlt">soil</span> <span class="hlt">moisture</span>-vegetation-precipitation feedback using the same model as the baseline model (BASE) and its derivative with modifications to the model runoff parameterization as the experiment model (EXP), in which we eliminate the subsurface lateral drainage to reduce the known dry biases of BASE. With vegetation feedback ignored, precipitation is more sensitive to wet SMAs than dry SMAs in BASE; opposite to BASE, the wetter mean <span class="hlt">soil</span> <span class="hlt">moisture</span> in EXP leads to higher sensitivity of precipitation to dry SMAs than to wet SMAs. However, in both BASE and EXP, the impact of dry SMAs on subsequent precipitation persists longer than the impact of wet SMAs. With vegetation feedback included, EXP shows a positive feedback between vegetation and precipitation following both dry and wet SMAs in summer, while BASE shows a positive feedback following wet SMAs only, with no clear signal following dry SMAs due to dry <span class="hlt">soil</span> biases. In BASE, the magnitude of precipitation changes due to vegetation feedback is comparable to that due to <span class="hlt">soil</span> <span class="hlt">moisture</span> feedback when more realistic SMAs are applied. In addition, a major difference is found in spring when the vegetation impact on subsequent precipitation is negative and significant in BASE, but not significant in EXP.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/764658','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/764658"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurements and their Applications at the Savannah River Site</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Buckley, R.</p> <p>2000-09-26</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a very important component of the land-atmosphere exchange. Practically, it is valuable in both the agricultural and meteorological industries. Farmers require <span class="hlt">soil</span> <span class="hlt">moisture</span> for crop yields, while the atmospheric numerical modeling community has found <span class="hlt">soil</span> <span class="hlt">moisture</span> to be extremely important in generating realistic forecasts. Physically, the <span class="hlt">soil</span> <span class="hlt">moisture</span> not only provides water vapor for precipitation through evapotranspiration and controls the splitting of net radiation into sensible and latent heat components, but it also provides thermal inertia to the climate through heat storage and release from large water reservoirs (Famiglietti et al. 1998). Quantification of <span class="hlt">soil</span> <span class="hlt">moisture</span> is challenging since the range of spatial scale varies from centimeters to thousands of kilometers, while temporal scales vary from minutes to months. In the short term, <span class="hlt">soil</span> <span class="hlt">moisture</span> is influenced by topography, <span class="hlt">soil</span> type, texture, and vegetation and <span class="hlt">affects</span> the infiltration of water into and through the <span class="hlt">soil</span>, as well as how much water will be held within the <span class="hlt">soil</span>. In the long term, <span class="hlt">soil</span> <span class="hlt">moisture</span> is impacted by atmospheric forcing and <span class="hlt">affects</span> the amount of water available to the <span class="hlt">soil</span> through rain (or snowmelt), as well as removal by evapo-transpiration (Entin et al. 2000). Due to the expense and difficulty of measuring <span class="hlt">soil</span> <span class="hlt">moisture</span>, few extensive data sets currently exist. Exceptions include those found in Russia (dating back to the 1930s), Mongolia (1973-1995), China (1981-1991), India (1987-1995), and the US (Illinois, Iowa and Oklahoma, from the early 1980s to the present), (Robock et al. 2000). Most of these data were taken several times per month and do not provide high-frequency variations in time. A real-time, operational monitoring network for <span class="hlt">soil</span> <span class="hlt">moisture</span> detection has very important ramifications in the satellite industry, where such measures could serve as a ground truth. This paper discusses a real-time <span class="hlt">soil</span> monitoring station that has been established at</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMED31F3477P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMED31F3477P"><span>Quantifying the Rate of Surface <span class="hlt">Soil</span> Drying Following Precipitation Events Using PBO H2o <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>Prue, A. M.</p> <p>2014-12-01</p> <p>Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> <span class="hlt">affects</span> latent and sensible heat fluxes, as well as setting the top boundary condition for water redistribution within the <span class="hlt">soil</span> column. The fluctuations in surface <span class="hlt">soil</span> <span class="hlt">moisture</span> have been described in numerous modeling studies, but characterization based on measurements is lacking. We use a new <span class="hlt">soil</span> <span class="hlt">moisture</span> dataset based on reflected GPS signals to provide some constraints on rates of surface <span class="hlt">soil</span> drying after a rain event. The <span class="hlt">soil</span> <span class="hlt">moisture</span> time series used in this study are derived from GPS data collected at NSF's EarthScope Plate Boundary Observatory (PBO) sites. The University of Colorado Boulder's PBO H2O project estimates daily near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (approximately 0-5 cm) from the interference pattern between the direct and ground-reflected GPS signals. The sensing footprint is ~1000 m2, and thus intermediate in scale between in situ and remotely sensed observations. Twelve sites from this network of more than 100 were used in this study. To characterize the rate of <span class="hlt">soil</span> drying, we fit exponential curves to daily <span class="hlt">soil</span> <span class="hlt">moisture</span> observations following ten isolated rainfall events at each site. Event sizes varied from 5 to 40 mm and were followed by 17 days without rain. The decay model fits the data quite well, with r2 values exceeding 0.85 in nearly all cases. For 95% of the events studied, the exponential decay constant (e-folding time) fell between 2 and 6 days. Precipitation amount is not correlated with drydown rates. Instead, the rate of <span class="hlt">soil</span> drying is well-correlated with air temperature: the exponential constant decreases by 0.1 days per degree Celsius. We are currently investigating how other factors, such as <span class="hlt">soil</span> type and vegetation, influence <span class="hlt">soil</span> drying. This study highlights the utility of the PBO H2O <span class="hlt">soil</span> <span class="hlt">moisture</span> product. Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> changes rapidly, and thus the dynamics of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> cannot be accurately characterized using datasets based on less than daily measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013HESSD..10.7127C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013HESSD..10.7127C"><span>Quantifying mesoscale <span class="hlt">soil</span> <span class="hlt">moisture</span> with the cosmic-ray rover</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chrisman, B.; Zreda, M.</p> <p>2013-06-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> governs the surface fluxes of mass and energy and is a major influence on floods and drought. Existing techniques measure <span class="hlt">soil</span> <span class="hlt">moisture</span> either at a point or over a large area many kilometers across. To bridge these two scales we used the cosmic-ray rover, an instrument similar to the recently developed COSMOS probe, but bigger and mobile. This paper explores the challenges and opportunities for mapping <span class="hlt">soil</span> <span class="hlt">moisture</span> over large areas using the cosmic-ray rover. In 2012, <span class="hlt">soil</span> <span class="hlt">moisture</span> was mapped 22 times in a 25 km × 40 km survey area of the Tucson Basin at 1 km2 resolution, i.e., a survey area extent comparable to that of a pixel for the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite mission. The <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution is dominated by climatic variations, notably by the North American monsoon, that results in a systematic increase in the standard deviation, observed up to 0.022 m3 m-3, as a function of the mean, between 0.06 and 0.14 m3 m-3. Two techniques are explored to use the cosmic-ray rover data for hydrologic applications: (1) interpolation of the 22 surveys into a daily <span class="hlt">soil</span> <span class="hlt">moisture</span> product by defining an approach to utilize and quantify the observed temporal stability producing an average correlation coefficient of 0.82 for the <span class="hlt">soil</span> <span class="hlt">moisture</span> distributions that were surveyed and (2) estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles by combining surface <span class="hlt">moisture</span> from satellite microwave sensors with deeper measurements from the cosmic-ray rover. The interpolated <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> <span class="hlt">moisture</span> profile estimates allow for basin-wide mass balance calculation of evapotranspiration, totaling 241 mm for the year 2012. Generating <span class="hlt">soil</span> <span class="hlt">moisture</span> maps with cosmic-ray rover at this intermediate scale may help in the calibration and validation of satellite campaigns and may also aid in various large scale hydrologic studies.</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/2016ClDy...46..467W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...46..467W"><span>Impact of <span class="hlt">moisture</span> flux convergence and <span class="hlt">soil</span> <span class="hlt">moisture</span> on precipitation: a case study for the southern United States with implications for the globe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wei, Jiangfeng; Su, Hua; Yang, Zong-Liang</p> <p>2016-01-01</p> <p>Interactions between <span class="hlt">soil</span> <span class="hlt">moisture</span>, evapotranspiration (ET), atmospheric <span class="hlt">moisture</span> fluxes and precipitation are complex. It is difficult to attribute the variations of one variable to another. In this study, we investigate the influence of atmospheric <span class="hlt">moisture</span> fluxes and land surface <span class="hlt">soil</span> <span class="hlt">moisture</span> on local precipitation, with a focus on the southern United States (U.S.), a region with a strong humidity gradient and intense <span class="hlt">moisture</span> fluxes. Experiments with the Weather Research and Forecasting model show that the variation of <span class="hlt">moisture</span> flux convergence (MFC) is more important than that of <span class="hlt">soil</span> <span class="hlt">moisture</span> for precipitation variation over the southern U.S. Further analyses decompose the precipitation change into several contributing factors and show that MFC <span class="hlt">affects</span> precipitation both directly through changing <span class="hlt">moisture</span> inflow (wet areas) and indirectly by changing the precipitation efficiency (transitional zones). <span class="hlt">Soil</span> <span class="hlt">moisture</span> <span class="hlt">affects</span> precipitation mainly by changing the precipitation efficiency, and secondly through direct surface ET contribution. The greatest <span class="hlt">soil</span> <span class="hlt">moisture</span> effects are over transitional zones. MFC is more important for the probability of heavier rainfall; <span class="hlt">soil</span> <span class="hlt">moisture</span> has much weaker impact on rainfall probability and its roles are similar for the probability of intermediate-to-heavy rainfall (>10 mm day-1). Although MFC is more important than <span class="hlt">soil</span> <span class="hlt">moisture</span> for precipitation over most regions, the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> could be large over certain transitional regions. At the submonthly time scale, the African Sahel appears to be the only major region where <span class="hlt">soil</span> <span class="hlt">moisture</span> has a greater impact than MFC on precipitation. This study provides guidance to understanding and further investigation of the roles of local land surface processes and large-scale circulations on precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830015380','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830015380"><span>Remote sensing of vegetation 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>Kong, J. A.; Shin, R. T. (Principal Investigator)</p> <p>1983-01-01</p> <p>Progress in the investigation of problems related to the remote sensing of vegetation and <span class="hlt">soil</span> <span class="hlt">moisture</span> is reported. Specific topics addressed include: (1) microwave scattering from periodic surfaces using a rigorous modal technique; (2) combined random rough surface and volume scattering effects; (3) the anisotropic effects of vegetation structures; (4) the application of the strong fluctuation theory to the the study of electromagnetic wave scattering from a layer of random discrete scatterers; and (5) the investigation of the scattering of a plane wave obliquely incident on a half space of densely distributed spherical dielectric scatterers using a quantum mechanical potential approach.</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('http://adsabs.harvard.edu/abs/2013WRR....49..408I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013WRR....49..408I"><span>An unmixing algorithm for remotely sensed <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>Ines, Amor V. M.; Mohanty, Binayak P.; Shin, Yongchul</p> <p>2013-01-01</p> <p>We present an unmixing method, based on genetic algorithm-<span class="hlt">soil</span>-vegetation-atmosphere-transfer modeling to extract subgrid information of <span class="hlt">soil</span> and vegetation from remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> (downscaled; e.g., <span class="hlt">soil</span> hydraulic properties, area fractions of <span class="hlt">soil</span>-vegetation combinations, and unmixed <span class="hlt">soil</span> <span class="hlt">moisture</span> time series) that most land surface models use. The unmixing method was evaluated using numerical experiments comprising mixed pixels with simple and complex <span class="hlt">soil</span>-vegetation combinations, in idealized case studies (with or without uncertainty) and under actual field conditions (Walnut Creek (WC11) field, <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment 2005, Iowa). Additional validation experiments were conducted at an airborne-remote sensing footprint (Little Washita (LW21) site, Southern Great Plains 1997 hydrology campaign, Oklahoma) using Electronically Scanning Thin Array Radiometer (ESTAR). Results of the idealized experiments suggest that the unmixing method can extract optimal or near-optimal solutions to the inverse problem under different hydrologic and climatic conditions. Errors in <span class="hlt">soil</span> <span class="hlt">moisture</span> data and initial and boundary conditions can compound uncertainty in the solution. The solutions generated under actual field conditions (WC11 field) were able to match <span class="hlt">soil</span> <span class="hlt">moisture</span> observations. Analysis showed that typical <span class="hlt">soil</span> <span class="hlt">moisture</span> retention curves of cataloged dominant <span class="hlt">soils</span> in WC11 field did not match well with the measurements, but those derived from actual field-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion matched better. The unmixing method performed well in replicating <span class="hlt">soil</span> hydraulic behavior at the ESTAR footprint. Unlike in WC11 field, the typical <span class="hlt">soil</span> <span class="hlt">moisture</span> retention curves of cataloged <span class="hlt">soils</span> in LW21 field matched better with the measurements. We envisaged that the unmixing method can provide quick and easy way of extracting subgrid <span class="hlt">soil</span> <span class="hlt">moisture</span> variability and <span class="hlt">soil</span>-vegetation information in a pixel.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000032232&hterms=ph+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dph%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000032232&hterms=ph+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dph%2Bsoil"><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('https://ntrs.nasa.gov/search.jsp?R=20000032232&hterms=effects+soil+pH&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Deffects%2Bsoil%2BpH','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000032232&hterms=effects+soil+pH&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Deffects%2Bsoil%2BpH"><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/2060/19740026660','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('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('https://ntrs.nasa.gov/search.jsp?R=20100031193&hterms=laying&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dlaying','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20100031193&hterms=laying&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dlaying"><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://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('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> </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://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/2015PhDT........90F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT........90F"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> impacts on convective precipitation in Oklahoma</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ford, Trenton W.</p> <p></p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is vital to the climate system, as root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> has a significant influence on evapotranspiration rates and latent and sensible heat exchange. Through the modification of <span class="hlt">moisture</span> flux from the land surface to the atmosphere, <span class="hlt">soil</span> <span class="hlt">moisture</span> can impact regional temperature and precipitation. Despite a wealth of studies examining land-atmosphere interactions, model and observation-driven studies show conflicting results with regard to the sign and strength of <span class="hlt">soil</span> <span class="hlt">moisture</span> feedback to precipitation, particularly in the Southern Great Plains of the United States. This research provides observational evidence for a preferential dry (or negative) <span class="hlt">soil</span> <span class="hlt">moisture</span> feedback to precipitation in Oklahoma. The ability of <span class="hlt">soil</span> <span class="hlt">moisture</span> to impact the location and occurrence of afternoon convective precipitation is constrained by synoptic-scale atmospheric circulation and resulting mid- and low-level wind patterns and sensible and latent heat flux. Overall, the preference for precipitation initiation over dry <span class="hlt">soils</span> is enhanced when regional <span class="hlt">soil</span> <span class="hlt">moisture</span> gradients exhibit a weakened east to west, wet to dry pattern. Based on these results, we conclude that <span class="hlt">soil</span> <span class="hlt">moisture</span> can modify atmospheric conditions potentially leading to convective initiation. However, the land surface feedback signal is weak at best, suggesting that regional-scale circulation is the dominant driver of warm season precipitation in the Southern Great Plains.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=115365&keyword=radium&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&CFID=78772845&CFTOKEN=60519519','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=115365&keyword=radium&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&CFID=78772845&CFTOKEN=60519519"><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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=115365&keyword=222&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="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=115365&keyword=222&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/2014AGUFM.H13C1128C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H13C1128C"><span>Recent Enhancements in the North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database (NASMD)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chavez, N.; Galvan, J., III; Quiring, S. M.; Ford, T.</p> <p>2014-12-01</p> <p>The North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database (soilmoisture.tamu.edu) is a high-quality observational <span class="hlt">soil</span> <span class="hlt">moisture</span> database that contains data from >1800 stations. In the last year we have enhanced the database by identifying sites in Mexico and expanding the database to also include <span class="hlt">soil</span> temperature data. Here we provide an overview of how the in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature observations are assembled, quality controlled and harmonized prior to being incorporated in the NASMD. The database is designed to facilitate observationally-driven investigations of land-atmosphere interactions, validation of the accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations in global land surface models, satellite calibration/validation for SMOS and SMAP, and an improved understanding of how <span class="hlt">soil</span> <span class="hlt">moisture</span> influences climate on seasonal to interannual timescales. This paper provides some examples of how the NASMD has been utilized to enhance understanding of land-atmosphere interactions in the U.S. Great Plains.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19840025826&hterms=Agricultural+soils&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DAgricultural%2Bsoils','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19840025826&hterms=Agricultural+soils&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DAgricultural%2Bsoils"><span>Correlation of microwave sensor returns with <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>Taube, D. W.; Theis, S. W.</p> <p>1984-01-01</p> <p>Microwave sensor <span class="hlt">soil</span> data were collected by aircraft over agricultural croplands. Multiple incident angle scatterometer data (13.3, 4.75, 1.6 and 0.4 GHz), passive radiometer data (L and C-band), and <span class="hlt">soil</span> <span class="hlt">moisture</span> ground truth measurements were collected coincidentally. Each sensor and angle of incidence was linearly analyzed against the measured <span class="hlt">soil</span> <span class="hlt">moisture</span>. For bare agricultural <span class="hlt">soils</span>, the optimal single sensor for <span class="hlt">soil</span> <span class="hlt">moisture</span> preduction is the L-band passive radiometer. The effects of vegetation and differing surface roughness prove significant. When both bare and vegetated surfaces were studied, the masking due to the vegetation renders the single sensor approach ineffective in <span class="hlt">soil</span> <span class="hlt">moisture</span> prediction. Multisensor techniques are necessary to remotely measure <span class="hlt">soil</span> <span class="hlt">moisture</span> when a priori knowledge of vegetation is not available.</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://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/2006AGUSM.H43B..04C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSM.H43B..04C"><span>Scaling surface <span class="hlt">soil</span> <span class="hlt">moisture</span> in the Walnut Gulch Experimental Watershed</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cosh, M. H.; Jackson, T. J.; Moran, S.; Bindlish, R.; Mladenova, I.</p> <p>2006-05-01</p> <p>The estimation of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> in semi-arid and arid regions is complicated by the inherent heterogeneous precipitation patterns and ephemeral surface water characteristics. Large-scale sampling is inefficient for long term monitoring of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, especially with the goal of calibration of a satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> product such as that generated by the AMSR-E instrument for example. Statistical methods of accurately monitoring and scaling point and watershed estimates to satellite scale <span class="hlt">moisture</span> values are explored. The location for this study is the Walnut Gulch Experimental Watershed in Tombstone, Arizona, which is also the location of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment in 2004 (SMEX04). The variability of radiometric temperature brightness data is also examined for its relationships to land surface parameters, climate variables, and insitu <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements. Variability assessment is also evaluated for consistency and persistence over a three-year period.</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/2017WRR....53.4022W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017WRR....53.4022W"><span>Evaluating climate and <span class="hlt">soil</span> effects on regional <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial variability using EOFs</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; Franz, Trenton E.; Li, Ruopu; You, Jinsheng; Shulski, Martha D.; Ray, Chittaranjan</p> <p>2017-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important state variable in terrestrial water cycles; however, only few studies are available on regional <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial variability (SMSV), which yielded inconsistent findings about regional controls on SMSV. Here, long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> data were obtained from the Automated Weather Data Network and <span class="hlt">Soil</span> Climate Analysis Network in three regions with different climate regimes across the continental U.S. Comprehensive data sets were compiled to examine regional controls on SMSV using the method of Empirical Orthogonal Function. One dominant spatial structure (EOF1) of <span class="hlt">soil</span> <span class="hlt">moisture</span> was found in the study regions, which explained over 75%, 67%, and 86% of the spatial variance in <span class="hlt">soil</span> <span class="hlt">moisture</span> in Nebraska, Utah, and the Southeast U.S., respectively. Despite the significant spatial variability in precipitation and potential evapotranspiration in all the study regions, the results showed that meteorological forcings had limited effects on regional SMSV in those regions with different climatic conditions, which differed from the traditional notion that SMSV is mainly controlled by meteorological forcings at the scale from 50 to 400 km. Instead, local factors related to <span class="hlt">soil</span> (e.g., sand and clay fractions) were found to have significant correlations with EOF1, although the effects of other local factors (e.g., topography and vegetation) were generally negligible. This study provides strong field evidence that <span class="hlt">soil</span> can exert much stronger impacts on regional SMSV than previously thought, which can override the effects of meteorological forcings. Future studies are still needed to elaborate on the relative roles of climate and <span class="hlt">soil</span> in <span class="hlt">affecting</span> regional SMSV.</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/2009AGUSM.H24A..01C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUSM.H24A..01C"><span>Assessment of Errors in AMSR-E Derived <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>Champagne, C.; McNairn, H.; Berg, A.; de Jeu, R. A.</p> <p>2009-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> derived from passive microwave satellites provides information at a coarse spatial scale, but with temporally frequent, global coverage that can be used for monitoring applications over agricultural regions. Passive microwave satellites measure surface brightness temperature, which is largely a function of vegetation water content (which is directly related to the vegetation optical depth), surface temperature and surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at low frequencies. Retrieval algorithms for global <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets by necessity require limited site-specific information to derive these parameters, and as such may show variations in local accuracy. The objective of this study is to examine the errors in passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> data over agricultural sites in Canada to provide guidelines on data quality assessment for using these data sets in monitoring applications. Global gridded <span class="hlt">soil</span> <span class="hlt">moisture</span> was acquired from the AMSR-E satellite using the Land Parameter Retrieval Model, LPRM (Owe et al., 2008). The LPRM model derives surface <span class="hlt">soil</span> <span class="hlt">moisture</span> through an iterative optimization procedure using a polarization difference index to estimate vegetation optical depth and surface dielectric constant using frequencies at 6.9 and 10.7 GHz. The LPRM model requires no a-priori information on surface conditions, but retrieval errors are expected to increase as the amount of open water and dense vegetation within each pixel increases (Owe et al., 2008) Satellite-derived LPRM <span class="hlt">soil</span> <span class="hlt">moisture</span> values were used to assess changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval accuracy over the 2007 growing season for a largely agricultural site near Guelph (Ontario), Canada. Accuracy was determined by validating LPRM <span class="hlt">soil</span> <span class="hlt">moisture</span> against a network of 16 in-situ monitoring sites distributed at the pixel scale for AMSR-E. Changes in squared error, and pairwise correlation coefficient between satellite and in-situ surface <span class="hlt">soil</span> <span class="hlt">moisture</span> were assessed against changes in satellite orbit and</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://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('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('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('http://adsabs.harvard.edu/abs/2016cosp...41E.938K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016cosp...41E.938K"><span>Validation of SMOS Satellite <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products over Tropical Region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kanniah, Kasturi; Siang, Kang Chuen</p> <p>2016-07-01</p> <p>Calibration and validation (cal/val) activities on <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite derived <span class="hlt">soil</span> <span class="hlt">moisture</span> products has been conducted worldwide since the data has become available but not over the tropical region . This study focuses on the installation of a <span class="hlt">soil</span> <span class="hlt">moisture</span> data collection network over an agricultural site in a tropical region in Peninsular Malaysia, and the validation of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products. The in-situ data over one year period was analysed and validation of SMOS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> products with these in-situ data was conducted.Bias and root mean square errors (RMSE) were computed between SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products and the in-situ surface <span class="hlt">soil</span> <span class="hlt">moisture</span> collected at the satellite passing time (6 am and 6 pm local time). Due to the known limitations of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval over vegetated areas with vegetation water content higher than 5 kgm-2, overestimation of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products to in-situ data was noticed in this study. The bias is ranging from 0.064 to 0.119 m3m-3 and the RMSE is from 0.090 to 0.158 m3m-3, when both ascending and descending data were validated. This RMSE was found to be similar to a number of studies conducted previously at different regions. However a wet bias was found during the validation, while previous validation activities at other regions showed dry biases. The result of this study is useful to support the continuous development and improvement of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval model, aims to produce <span class="hlt">soil</span> <span class="hlt">moisture</span> products with higher accuracy, especially in the tropical region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUSM.H21B..07R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUSM.H21B..07R"><span>Factors <span class="hlt">Affecting</span> <span class="hlt">Moisture</span> Exchange in the Organic Layer of the Forest Floor</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Raaflaub, L.; Valeo, C.</p> <p>2004-05-01</p> <p>Virtually all of the mineral <span class="hlt">soil</span> in the Canadian boreal forest is covered by a layer of decomposing organic matter known as duff. Due to its highly porous nature, only a small portion of incoming precipitation is absorbed by the duff layer, even when it is not completely saturated. The duff layer fosters high percolation rates to the mineral <span class="hlt">soil</span> below and dries rather quickly; thus, it has a relatively low <span class="hlt">moisture</span> content. This level of <span class="hlt">moisture</span> content is not conducive to either seed germination or seedling survival. Duff consumption during forest fires provides areas of exposed mineral <span class="hlt">soil</span> that are suitable for seed germination. Tree regrowth is much more likely to occur in areas where the mineral <span class="hlt">soil</span> has been exposed. The location and amount of duff consumed during a fire is a function of several factors including duff density, depth and <span class="hlt">moisture</span> content. Factors <span class="hlt">affecting</span> duff <span class="hlt">moisture</span> content include its physical characteristics, overlying canopy type, hillslope position, <span class="hlt">soil</span> type and climate. In this study, an investigation is made into the relative significance of capillary suction and other processes influencing the <span class="hlt">moisture</span> exchange between duff and mineral <span class="hlt">soil</span>. These processes are analysed in a controlled laboratory experiment using duff collected from different types of canopies. The duff samples, extracted from the boreal forest in central Saskatchewan, were collected from canopies of black spruce, jack pine and trembling aspen. Comparisons are made between the overlying canopy types, as well as between samples from similar canopies at different locations. Physical properties of the duff, such as the porosity and hydraulic conductivity, are analysed in combination with <span class="hlt">soil</span>-duff interactions. The influence between mineral <span class="hlt">soil</span> and duff <span class="hlt">moisture</span> is determined through the use of a <span class="hlt">soil</span> column that allows for both variable <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation inputs. Results from this investigation give insight into the relationship between duff and <span class="hlt">soil</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://pubs.er.usgs.gov/publication/70185708','USGSPUBS'); return false;" href="http://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('https://ntrs.nasa.gov/search.jsp?R=19790008160&hterms=soil+erosion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%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%3D30%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> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014SPIE.9299E..0JW','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014SPIE.9299E..0JW"><span>An integrated GIS application system for <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>Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang</p> <p>2014-11-01</p> <p>The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive <span class="hlt">soil</span> <span class="hlt">moisture</span> data visually and mine the data for further research.This article introduces a comprehensive <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and <span class="hlt">soil</span> <span class="hlt">moisture</span> products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of <span class="hlt">soil</span> moiture will be plotted. By analyzing the <span class="hlt">soil</span> <span class="hlt">moisture</span> impact factors, it is possible to acquire the correlation coefficients between <span class="hlt">soil</span> <span class="hlt">moisture</span> value and its every single impact factor. Daily and monthly comparative analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, <span class="hlt">soil</span> <span class="hlt">moisture</span> map production function is realized for business application.</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://www.ncbi.nlm.nih.gov/pubmed/24052568','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24052568"><span>Acclimation and <span class="hlt">soil</span> <span class="hlt">moisture</span> constrain sugar maple root respiration in experimentally warmed <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>Jarvi, Mickey P; Burton, Andrew J</p> <p>2013-09-01</p> <p>The response of root respiration to warmer <span class="hlt">soil</span> can <span class="hlt">affect</span> ecosystem carbon (C) allocation and the strength of positive feedbacks between climatic warming and <span class="hlt">soil</span> CO2 efflux. This study sought to determine whether fine-root (<1 mm) respiration in a sugar maple (Acer saccharum Marsh.)-dominated northern hardwood forest would adjust to experimentally warmed <span class="hlt">soil</span>, reducing C return to the atmosphere at the ecosystem scale to levels lower than that would be expected using an exponential temperature response function. Infrared heating lamps were used to warm the <span class="hlt">soil</span> (+4 to +5 °C) in a mature sugar maple forest in a fully factorial design, including water additions used to offset the effects of warming-induced dry <span class="hlt">soil</span>. Fine-root-specific respiration rates, root biomass, root nitrogen (N) concentration, <span class="hlt">soil</span> temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> were measured from 2009 to 2011, with experimental treatments conducted from late 2010 to 2011. Partial acclimation of fine-root respiration to <span class="hlt">soil</span> warming occurred, with <span class="hlt">soil</span> <span class="hlt">moisture</span> deficit further constraining specific respiration rates in heated plots. Fine-root biomass and N concentration remained unchanged. Over the 2011 growing season, ecosystem root respiration was not significantly greater in warmed <span class="hlt">soil</span>. This result would not be predicted by models that allow respiration to increase exponentially with temperature and do not directly reduce root respiration in drier <span class="hlt">soil</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JHyd..542..938P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..542..938P"><span>Estimating daily root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> in snow-dominated regions using an empirical <span class="hlt">soil</span> <span class="hlt">moisture</span> diagnostic equation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pan, Feifei; Nieswiadomy, Michael</p> <p>2016-11-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> in snow-dominated regions has many important applications including evapotranspiration estimation, flood forecasting, water resource and ecosystem services management, weather prediction and climate modeling, and quantification of denudation processes. A simple and robust empirical approach to estimate root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> in snow-dominated regions using a <span class="hlt">soil</span> <span class="hlt">moisture</span> diagnostic equation that incorporates snowfall and snowmelt processes is suggested and tested. A five-water-year dataset (10/1/2010-9/30/2015) of daily precipitation, air temperature, snow water equivalent and <span class="hlt">soil</span> <span class="hlt">moistures</span> at three depths (i.e., 5 cm, 20 cm, and 50 cm) at each of 12 Snow Telemetry (SNOTEL) sites across Utah (37.583°N-41.883°N, 110.183°W-112.9°W), is applied to test the proposed method. The first three water years are designated as the parameter-estimation period (PEP) and the last two water years are chosen as the model-testing period (MTP). Applying the estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> loss function parameters and other empirical parameters in the <span class="hlt">soil</span> <span class="hlt">moisture</span> diagnostic equation in the PEP, <span class="hlt">soil</span> <span class="hlt">moistures</span> in three <span class="hlt">soil</span> columns (0-5 cm, 0-20 cm, and 0-50 cm) are estimated in the MTP. The relatively accurate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimations compared to the observations at 12 SNOTEL sites (RMSE ⩽ 6.23 (%V/V), average RMSE = 4.28 (%V/V), correlation coefficient ⩾0.75, average correlation coefficient =0.89, the Nash-Sutcliffe efficient coefficient Ec ⩾ 0.24, average Ec = 0.72) indicate that the <span class="hlt">soil</span> <span class="hlt">moisture</span> diagnostic equation is capable of accurately estimating <span class="hlt">soil</span> <span class="hlt">moisture</span> in snow-dominated regions after the snowfall and snowmelt processes are included in the <span class="hlt">soil</span> <span class="hlt">moisture</span> diagnostic equation.</p> </li> <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/2016Sci...352..825T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016Sci...352..825T"><span>Empirical evidence of contrasting <span class="hlt">soil</span> <span class="hlt">moisture</span>-precipitation feedbacks across 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>Tuttle, Samuel; Salvucci, Guido</p> <p>2016-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> influences fluxes of heat and <span class="hlt">moisture</span> originating at the land surface, thus altering atmospheric humidity and temperature profiles. However, empirical and modeling studies disagree on how this <span class="hlt">affects</span> the propensity for precipitation, mainly owing to the difficulty in establishing causality. We use Granger causality to estimate the relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and occurrence of subsequent precipitation over the contiguous United States using remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> and gauge-based precipitation observations. After removing potential confounding effects of daily persistence, and seasonal and interannual variability in precipitation, we find that <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies significantly influence rainfall probabilities over 38% of the area with a median factor of 13%. The feedback is generally positive in the west and negative in the east, suggesting dependence on regional aridity.</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('http://adsabs.harvard.edu/abs/1990PhDT.......215S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1990PhDT.......215S"><span>The Temperature Dependence of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Characteristics of Agricultural <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>Salehzadeh, Amir</p> <p>1990-01-01</p> <p>The temperature dependence of static and dynamic characteristics of four <span class="hlt">soils</span>: glass beads, Plainfield sand, Plano silt loam, and Elkmound sandy loam were explored. Gain -factor model was employed for quantifying the temperature dependences. The study required novel methods and technologies which were developed and employed for the rapid, and transient measurement of <span class="hlt">soil-moisture</span> characteristics of these <span class="hlt">soils</span>. A pressurized 2 cm-high column of <span class="hlt">soil</span> is sandwiched between two air blocking membranes interfacing outside pressurized water system. Water content (Theta ) is measured with a 2 Curie gamma-ray source combined with a fast detection system giving a statistical accuracy of +/-0.2%. <span class="hlt">Moisture</span> potential ( Psi) down to -2000 cm was measured with a newly developed "stripper" tensionmeter. While a slowly varying <span class="hlt">soil</span>-water pressure was imposed on the thin sample through the membranes, firmly held in contact with the <span class="hlt">soil</span>, water content and <span class="hlt">moisture</span> -potentials were being monitored in the sample. A plot of water content versus water pressure gave the static characteristics (Theta,Psi ) of <span class="hlt">soils</span>. An array of tensiometers (between the membranes) allowed measurement of the potential profile; in conjunction with the time-varying water content this permitted measurement of dynamic characteristics, conductivity versus water content (K,Theta). For the (Theta, Psi) characteristics, the measurements indicated that, wholly for glass beads, and largely for sand, the surface tension of pure water governs the temperature response. The temperature dependence of Plano silt loam was largely independent of water content and was roughly five times the temperature dependence of the surface tension of pure water. For Elkmound sandy loam the dependence was complex and not easily explained. Two factors appear to limit further system improvement. (1) A sample thinner than 2 cm faces difficulties of fitting three tensionmeters into the thickness. This limit on the thickness, in turn</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('https://www.ncbi.nlm.nih.gov/pubmed/26672301','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26672301"><span>[Modeling <span class="hlt">Soil</span> Spectral Reflectance with Different Mass <span class="hlt">Moisture</span> Content].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sun, Yue-jun; Zheng, Xiao-po; Qin, Qi-ming; Meng, Qing-ye; Gao, Zhong-ling; Ren, Hua-zhong; Wu, Ling; Wang, Jun; Wang, Jian-hua</p> <p>2015-08-01</p> <p>The spatio-temporal distribution and variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> content have a significant impact on <span class="hlt">soil</span> temperature, heat balance between land and atmosphere and atmospheric circulation. Hence, it is of great significance to monitor the <span class="hlt">soil</span> <span class="hlt">moisture</span> content dynamically at a large scale and to acquire its continuous change during a certain period of time. The object of this paper is to explore the relationship between the mass <span class="hlt">moisture</span> content of <span class="hlt">soil</span> and <span class="hlt">soil</span> spectrum. This was accomplished by building a spectral simulation model of <span class="hlt">soil</span> with different mass <span class="hlt">moisture</span> content using hyperspectral remote sensing data. The spectra of <span class="hlt">soil</span> samples of 8 sampling sites in Beijing were obtained using ASD Field Spectrometer. Their mass <span class="hlt">moisture</span> contents were measured using oven drying method. Spectra of two <span class="hlt">soil</span> samples under different mass <span class="hlt">moisture</span> content were used to construct <span class="hlt">soil</span> spectral simulation model, and the model was validated using spectra of the other six <span class="hlt">soil</span> samples. The results show that the accuracy of the model is higher when the mass water content of <span class="hlt">soil</span> is below field capacity. At last, we used the spectra of three sampling points on campus of Peking University to test the model, and the minimum value of root mean square error between simulated and measured spectral reflectance was 0.0058. Therefore the model is expected to perform well in simulating the spectrum reflectance of different types of <span class="hlt">soil</span> when mass water content below field capacity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1992JGR....9718955W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1992JGR....9718955W"><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://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, J. R.</p> <p>1992-11-01</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 conducted by a group of investigators over a 15-km by 15-km test site south of Manhattan, Kansas, during the First ISLSCP Field Experiment (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 active and passive microwave sensors as well as a gamma radiation device. The calculations were based on a catchment-scale water balance model that is driven by spatially interpolated rainfalls and estimated potential evaporation. The results of this group effort are presented in the five papers in this section. They include discussions on the statistics of <span class="hlt">soil</span> <span class="hlt">moisture</span> variability within a pixel of a remote sensor, <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements by impedance probes, the comparison of active and passive microwave sensing of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, the statistics of <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation by a gamma-radiation technique, and the comparison of the calculated and measured latent heat fluxes at the catchment scale (4 km by 4 km).</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('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://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('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=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=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> <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> </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=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://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/2017EGUGA..19.8087G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8087G"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> retrival from Sentinel-1 and Modis synergy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Qi; Zribi, Mehrez; Escorihuela, Maria Jose; Baghdadi, Nicolas</p> <p>2017-04-01</p> <p>This study presents two methodologies retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span> from SAR remote sensing data. The study is based on Sentinel-1 data in the VV polarization, over a site in Urgell, Catalunya (Spain). In the two methodologies using change detection techniques, preprocessed radar data are combined with normalized difference vegetation index (NDVI) auxiliary data to estimate the mean <span class="hlt">soil</span> <span class="hlt">moisture</span> with a resolution of 1km. By modeling the relationship between the backscatter difference and NDVI, the <span class="hlt">soil</span> <span class="hlt">moisture</span> at a specific NDVI value is retrieved. The first algorithm is already developed on West Africa(Zribi et al., 2014) from ERS scatterometer data to estimate <span class="hlt">soil</span> water status. In this study, it is adapted to Sentinel-1 data and take into account the high repetitiveness of data in optimizing the inversion approach. Another new method is developed based on the backscatter difference between two adjacent days of Sentinel-1 data w.r.t. NDVI, with smaller vegetation change, the backscatter difference is more sensitive to <span class="hlt">soil</span> <span class="hlt">moisture</span>. The proposed methodologies have been validated with the ground measurement in two demonstrative fields with RMS error about 0.05 (in volumetric <span class="hlt">moisture</span>), and the coherence between <span class="hlt">soil</span> <span class="hlt">moisture</span> variations and rainfall events is observed. <span class="hlt">Soil</span> <span class="hlt">moisture</span> maps at 1km resolution are generated for the study area. The results demonstrate the potential of Sentinel-1 data for the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> at 1km or even better resolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJAEO..48...96M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJAEO..48...96M"><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://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</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 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) over East Africa. Compared to the Normalized Difference Vegetation index (NDVI) and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> products, we find 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 are well correlated (R > 0.5) with modeled <span class="hlt">soil</span> <span class="hlt">moisture</span>, and in some regions, NDVI. We use pixel-wise correlation analysis and qualitative comparisons of seasonal maps and time series to show that remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> can inform remote drought monitoring that has traditionally relied 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=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=252623','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=252623"><span>SMOS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Validation with Dense and Sparse Networks</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>Validation is an important but particularly challenging task for passive microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> from Earth orbit. The key issue is spatial scale; conventional measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> are made at a point whereas satellite sensors provide an integrated area/volume value for a ...</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://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=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('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://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://hdl.handle.net/2060/19810012915','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810012915"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> determination study. [Guymon, Oklahoma</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>1979-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> data collected in conjunction with aircraft sensor and SEASAT SAR data taken near Guymon, Oklahoma are summarized. In order to minimize the effects of vegetation and roughness three bare and uniformly smooth fields were sampled 6 times at three day intervals on the flight days from August 2 through 17. Two fields remained unirrigated and dry. A similar pair of fields was irrigated at different times during the sample period. In addition, eighteen other fields were sampled on the nonflight days with no field being sampled more than 24 hours from a flight time. The aircraft sensors used included either black and white or color infrared photography, L and C band passive microwave radiometers, the 13.3, 4.75, 1.6 and .4 GHz scatterometers, the 11 channel modular microwave scanner, and the PRT5.</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('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/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/2009SPIE.7494E..0AF','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009SPIE.7494E..0AF"><span>Spectrum properties analysis of different <span class="hlt">soil</span> <span class="hlt">moisture</span> content</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fang, Shenghui; Hu, Bo; Lin, Fan</p> <p>2009-10-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content is one of the most important factors in <span class="hlt">soil</span> business. The basic of detecting <span class="hlt">soil</span> <span class="hlt">moisture</span> content using remote sensing technology is to analyze the relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> content and emissivity. In this paper, based on the analysis of spectrum collection and processing by a portable spectrometer, a set of measure schemes were first established which can accurately measure the reflectivity and emissivity of <span class="hlt">soil</span> spectrum with different <span class="hlt">moisture</span> content in near-infrared and thermal infrared bands. Then we selected different bare <span class="hlt">soil</span> areas as the areas for survey, and studied the relationship of different <span class="hlt">moisture</span> content and the spectrum curve in the <span class="hlt">soil</span> both of the same kind and of different kind (like the <span class="hlt">soil</span> whose structure has been modified caused by the change of organic matter contents or <span class="hlt">soil</span> particle size). Finally, we emphasized on the quantitative relationship between <span class="hlt">soil</span> reflectivity & emissivity and <span class="hlt">soil</span> <span class="hlt">moisture</span> content using the test data, and establish a model depicting the quantitative relationship above in near-infrared and thermal infrared bands.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013HESS...17.5097C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013HESS...17.5097C"><span>Quantifying mesoscale <span class="hlt">soil</span> <span class="hlt">moisture</span> with the cosmic-ray rover</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chrisman, B.; Zreda, M.</p> <p>2013-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> governs the surface fluxes of mass and energy and is a major influence on floods and drought. Existing techniques measure <span class="hlt">soil</span> <span class="hlt">moisture</span> either at a point or over a large area many kilometers across. To bridge these two scales we used the cosmic-ray rover, an instrument similar to the recently developed COSMOS probe, but bigger and mobile. This paper explores the challenges and opportunities for mapping <span class="hlt">soil</span> <span class="hlt">moisture</span> over large areas using the cosmic-ray rover. In 2012, <span class="hlt">soil</span> <span class="hlt">moisture</span> was mapped 22 times in a 25 km × 40 km survey area of the Tucson Basin at an average of 1.7 km2 resolution, i.e., a survey area extent comparable to that of a pixel for the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite mission. The <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution is dominated by climatic variations, notably by the North American monsoon, that results in a systematic increase in the standard deviation, observed up to 0.022 m3 m-3, as a function of the mean, between 0.06 m3 m-3 and 0.14 m3 m-3. Two techniques are explored to use the cosmic-ray rover data for hydrologic applications: (1) interpolation of the 22 surveys into a daily <span class="hlt">soil</span> <span class="hlt">moisture</span> product by defining an approach to utilize and quantify the observed temporal stability producing an average correlation coefficient of 0.82 for the <span class="hlt">soil</span> <span class="hlt">moisture</span> distributions that were surveyed, and (2) estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles by combining surface <span class="hlt">moisture</span> from satellite microwave sensors (SMOS) with deeper measurements from the cosmic-ray rover. The interpolated <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles allow for basin-wide mass balance calculation of evapotranspiration, which amounted to 241 mm in 2012. Generating <span class="hlt">soil</span> <span class="hlt">moisture</span> maps with a cosmic-ray rover at this intermediate scale may help in the calibration and validation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data products and may also aid in various large-scale hydrologic studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFM.H22D0966W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFM.H22D0966W"><span>The GLOBE <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Campaign's Light Bulb Oven</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Whitaker, M. P.; Tietema, D.; Ferre, T. P.; Nijssen, B.; Washburne, J.</p> <p>2003-12-01</p> <p>The GLOBE <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Campaign (SMC) (www.hwr.arizona.edu/globe/sci/SM/SMC) has developed a light bulb oven to provide a low budget, low-technology method for drying <span class="hlt">soil</span> samples. Three different <span class="hlt">soils</span> were used to compare the ability of the light bulb oven to dry <span class="hlt">soils</span> against a standard laboratory convection oven. The <span class="hlt">soils</span> were: 1) a very fine sandy loam (the "Gila" <span class="hlt">soil</span>); 2) a silty clay (the "Pima" <span class="hlt">soil</span>); and 3) a sandy <span class="hlt">soil</span> (the "Sonoran" <span class="hlt">soil</span>). A large batch of each <span class="hlt">soil</span> was wetted uniformly in the laboratory. Twelve samples of each <span class="hlt">soil</span> were placed in the light bulb oven and twelve samples were placed in the standard oven. The average gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> of the Gila <span class="hlt">soil</span> was 0.214 g/cm3 for both ovens; the average Pima <span class="hlt">soil</span> <span class="hlt">moisture</span> was 0.332 g/cm3 and 0.331 g/cm3 for the traditional and light bulb ovens, respectively; and the Sonoran <span class="hlt">soil</span> <span class="hlt">moisture</span> was 0.077 g/cm3 for both ovens. These results demonstrate that the low technology light-bulb oven was able to dry the <span class="hlt">soil</span> samples as well as a standard laboratory oven, offering the ability to make gravimetric water content measurements when a relatively expensive drying oven is not available.</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/2010EGUGA..1212739I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1212739I"><span>Geophysical mapping of variations 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>Ioane, Dumitru; Scradeanu, Daniel; Chitea, Florina; Garbacea, George</p> <p>2010-05-01</p> <p>The geophysical investigation of <span class="hlt">soil</span> characteristics is a matter of great actuality for agricultural, hydrogeological, geotechnical or archaeological purposes. The geophysical mapping of <span class="hlt">soil</span> quality is subject of a recently started scientific project in Romania: "<span class="hlt">Soil</span> investigation and monitoring techniques - modern tools for implementing the precision agriculture in Romania - CNCSIS 998/2009". One of the first studied <span class="hlt">soil</span> parameter is <span class="hlt">moisture</span> content, in irrigated or non-irrigated agricultural areas. The geophysical techniques employed in two areas located within the Romanian Plain, Prahova and Buzau counties, are the following: - electromagnetic (EM), using the EM38B (Geonics) conductivity meter for getting areal distribution of electric conductivity and magnetic susceptibility; - electric resistivity tomography (ERT), using the SuperSting (AGI) multi-electrode instrument for getting in-depth distribution of electric resistivity. The electric conductivity mapping was carried out on irrigated cultivated land in a vegetable farm in the Buzau county, the distribution of conductivity being closely related to the <span class="hlt">soil</span> water content due to irrigation works. The <span class="hlt">soil</span> profile is represented by a chernozem with the following structure: Am (0 - 40 cm), Bt (40-150 cm), Bt/C (150-170 cm), C (starting at 170 cm). The electromagnetic measurements showed large variations of this geophysical parameter within different cultivated sectors, ranging from 40 mS/m to 85 mS/m. The close association between conductivity and water content in this area is illustrated by such geophysical measurements on profiles situated at ca 50 m on non-irrigated land, displaying a mean value of 15 mS/m. This low conductivity is due to quite long time interval, of about three weeks, without precipitations. The ERT measurements using multi-electrode acquisition systems for 2D and 3D results, showed by means of electric resistivity variations, the penetration of water along the cultivated rows from 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_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('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/2010EGUGA..12.3999B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.3999B"><span>Spatio-temporal <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution in a Maize field</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beff, Laure; Couvreur, Valentin; Javaux, Mathieu</p> <p>2010-05-01</p> <p>The spatio-temporal distribution of water content is important for predicting water flow and solute transport in the unsaturated zone. In a cropped field, this distribution is <span class="hlt">affected</span> by the interception and redistribution of water by the plants, by surface runoff, by root water uptake, and by the distribution of <span class="hlt">soil</span> hydraulic properties and boundary conditions of the system. This study was conducted to investigate the relationship between plant root water uptake, <span class="hlt">soil</span> structure and flow field variability. An experimental plot in a Maize field was installed in July 2009 and measurements were performed between the 23 of July and the 21 of September 2009. Upper boundary conditions were followed with a weather station, while drainage was estimated with deep tensiometers (1.4 m). Four TDR profiles (14 TDR probes at 10, 30, 70 and 125 cm depth) perpendicular to two maize rows were monitored every hour. In addition, a square grid of 76 surface electrodes and 8 boreholes each with 7 electrodes (5, 15, 30, 50, 75, 105 and 140 cm deep) were inserted in the maize field to evaluate the 3-D distribution of the electrical conductivity by ERT. Weekly ERT measurements were performed. Subsequently, the ERT and TDR data were used to estimate the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics based on petrophysical relationships. We performed root profiles at four times during the experiment to quantify the root distribution. This allowed us to investigate the relationship between plant root water uptake and <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. After the growing season, a dye tracer experiment was conducted on a 1.4 m-side square, to assess the influence of roots and macropores in water infiltration. The results show the importance of using depth electrodes to estimate the vertical distribution of water content accurately. A high measurement resolution allowed us to observe the 3-D <span class="hlt">soil</span> water content variability. During the growing season, we observed an increase in the coefficient of variation of <span class="hlt">soil</span> water content</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/12848506','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/12848506"><span><span class="hlt">Soil</span> photolysis of herbicides in a <span class="hlt">moisture</span>- and temperature-controlled environment.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Graebing, Phillip; Frank, Michael P; Chib, J S</p> <p>2003-07-16</p> <p>The problem of maintaining the <span class="hlt">moisture</span> content of samples throughout the course of a <span class="hlt">soil</span> photolysis study is addressed. The photolytic degradations of asulam, triclopyr, acifluorfen, and atrazine were independently compared in air-dried <span class="hlt">soils</span> and in moist (75% field <span class="hlt">moisture</span> capacity at 0.33 bar) <span class="hlt">soils</span> maintained at initial conditions through the use of a specially designed <span class="hlt">soil</span> photolysis apparatus. Each pesticide was applied at 5 microg/g. The exposure phase extended from 144 to 360 h, depending on the half-life of the compound. A dark control study, also using moist and air-dried <span class="hlt">soils</span>, was performed concurrently at 25 degrees C. The results showed significant differences in half-life. The dissipations generally demonstrated a strong dependence on <span class="hlt">moisture</span>. In most cases, photolytic degradation on air-dried <span class="hlt">soil</span> was longer than in the moist dark control <span class="hlt">soils</span>. Half-lives in dry <span class="hlt">soil</span> were 2-7 times longer, and in the case of atrazine, the absence of <span class="hlt">moisture</span> precluded significant degradation. Moist <span class="hlt">soil</span> experiments also tended to correlate more strongly with linear first-order degradations. The dark control experiments also demonstrated shorter half-lives in moist <span class="hlt">soil</span>. <span class="hlt">Moisture</span> was also observed to <span class="hlt">affect</span> the amount of degradate formed in the <span class="hlt">soils</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.6707S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.6707S"><span>Uncertainties of seasonal surface climate predictions induced by <span class="hlt">soil</span> <span class="hlt">moisture</span> biases in the La Plata Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sorensson, Anna; Berbery, E. Hugo</p> <p>2015-04-01</p> <p>This work examines the evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization biases and their effects on seasonal forecasts depending on the season and vegetation type for a regional model over the La Plata Basin in South America. WRF/Noah model simulations covering multiple cases during a two-year period are designed to emphasize the conceptual nature of the simulations at the expense of statistical significance of the results. Analysis of the surface climate shows that the seasonal predictive skill is higher when the model is initialized during the wet season and the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> differences are small. Large <span class="hlt">soil</span> <span class="hlt">moisture</span> biases introduce large surface temperature biases, particularly for Savanna, Grassland and Cropland vegetation covers at any time of the year, thus introducing uncertainty in the surface climate. Regions with Evergreen Broadleaf Forest have roots that extend to the deep layer whose <span class="hlt">moisture</span> content <span class="hlt">affects</span> the surface temperature through changes in the partitioning of the surface fluxes. The uncertainties of monthly maximum temperature can reach several degrees during the dry season in cases when: (a) the <span class="hlt">soil</span> is much wetter in the reanalysis than in the WRF/Noah equilibrium <span class="hlt">soil</span> <span class="hlt">moisture</span>, and (b) the memory of the initial value is long due to scarce rainfall and low temperatures. This study suggests that responses of the atmosphere to <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization depend on how the initial wet and dry conditions are defined, stressing the need to take into account the characteristics of a particular region and season when defining <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/1342334','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1342334"><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.; Liu, Yuanyuan; Bond-Lamberty, Ben; Bailey, Vanessa L.</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<sub>2</sub> 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>. 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<sub>2</sub> produced from microbial respiration can be accumulated inside <span class="hlt">soil</span> cores under higher saturation conditions, implying that CO<sub>2</sub> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817667M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817667M"><span>GNSSProbe, penetrating GNSS signals for measuring <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>Martin, Francisco; Navarro, Victor; Reppucci, Antonio; Mollfulleda, Antonio; Balzter, Heiko; Nicolas-Perea, Virginia; Kissick, Lucy</p> <p>2016-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content (SMC) is an essential parameter from both a scientific and economical point of view. On one hand, it is key for the understanding of hydrological. Secondly, it is a most relevant parameter for agricultural activities and water management. Wide research has been done in this field using different sensors, spanning different parts of the measured electromagnetic spectrum, leading thus several methodologies to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> content. However complying with requirements in terms of accuracy and spatial resolution is still a major challenge. A novel approach based on the measurement of GNSS signals penetrating a <span class="hlt">soil</span> volume is proposed here. This model relates <span class="hlt">soil</span> <span class="hlt">moisture</span> content to the measured <span class="hlt">soil</span> transmissivity, and attenuation coefficient, which are a function of the <span class="hlt">soil</span> characteristics (i.e <span class="hlt">soil</span> <span class="hlt">moisture</span> content, soit type, <span class="hlt">soil</span> temperature, etc). A preliminary experiment has been performed to demonstrate the validity of this technique, where the signal received by a GNSS-R L1/E1 RHCP antenna buried at 5, 10, and 15 cm below the surface, was compared to the one received by a GNSS-R L1/E1 RHCP antenna with clear sky visibility. Preliminary results show agreement with theoretical results based on transmissivity and with previous campaigns performed where the <span class="hlt">soil</span> <span class="hlt">moisture</span> were collected at two different depths (5 and 15 cm). Details related to the GNSS <span class="hlt">soil</span> <span class="hlt">moisture</span> modeling, instrument preparation, measurement campaign, data processing and main results will be presented at the conference.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19910031252&hterms=different+types+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddifferent%2Btypes%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910031252&hterms=different+types+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddifferent%2Btypes%2Bsoil"><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/2011AGUFM.H21F1207I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H21F1207I"><span>Remote Sensing of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and the Effects of Biomass as it Pertains to COSMOS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Irvin, S.; Hornbuckle, B. K.; Patton, J.; Wang, C.; Logsdon, S. D.; Kaleita, A.; Van Arkel, Z.</p> <p>2011-12-01</p> <p>In November 2009, the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite was launched by the European Space Agency (ESA). This satellite orbits the earth every 2 or 3 days while taking measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and ocean salinity. It has a spatial view of ~ 40 km, which is impressive considering the resolution of current weather and climate models, and measures <span class="hlt">soil</span> <span class="hlt">moisture</span> to a depth of a few centimeters. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is important because of its <span class="hlt">affect</span> on weather and climate in a manner similar to sea surface temperature. However, future weather and climate models will operate at smaller spatial scales and a deeper <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement is more desirable. The Cosmic-ray <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observing System (COSMOS) is beneficial in this regard because these sensors have a footprint of ~700 meters and are sensitive to a depth of 12-70 cm. COSMOS sensors also produce hourly data with a precision as good as or better than SMOS. There is a COSMOS sensor located at the Iowa Validation Site, maintained by Iowa State University, south of Ames, Iowa. This site was a field of maize during the 2011 growing season. A COSMOS sensor counts fast neutrons that are scattered by hydrogen contained in <span class="hlt">soil</span> in order to determine <span class="hlt">soil</span> <span class="hlt">moisture</span>. There is a potential problem when significant vegetation is present -since COSMOS is sensitive to the hydrogen contained in the plants as well. The question becomes how to distinguish between the two pools of hydrogen in order to obtain an accurate reading of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Not only is the presence of the biomass problematic in finding the <span class="hlt">soil</span> <span class="hlt">moisture</span>, but the rate at which the vegetation is growing needs to be taken into account. We will compare the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimated by the COSMOS sensor with in-situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements made with TDR, gravimetric samples, and a neutron probe over the course of the growing season. To characterize the amount of vegetation, a correlation was found between the stem diameter and canopy height of</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> <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://www.ncbi.nlm.nih.gov/pubmed/14982383','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/14982383"><span>Influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> on linear alkylbenzene sulfonate-induced toxicity in ammonia-oxidizing bacteria.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Nielsen, Klaus B; Brandt, Kristian K; Jacobsen, Anne-Marie; Mortensen, Gerda K; Sørensen, Jan</p> <p>2004-02-01</p> <p><span class="hlt">Moisture</span> <span class="hlt">affects</span> bioavailability and fate of pollutants in <span class="hlt">soil</span>, but very little is known about <span class="hlt">moisture</span>-induced effects on pollutant toxicity. We here report on a modifying effect of <span class="hlt">moisture</span> on degradation of linear alkylbenzene sulfonates (LASs) and on their toxicity towards ammonia-oxidizing bacteria (AOB) in agricultural <span class="hlt">soil</span>. In <span class="hlt">soil</span> spiked with two LAS levels (250 or 1,000 mg/kg) and incubated at four different <span class="hlt">moisture</span> levels (9-100% of water-holding capacity), degradation was strongly <span class="hlt">affected</span> by both <span class="hlt">soil</span> <span class="hlt">moisture</span> and initial LAS concentration, resulting in degradation half-lives ranging from 13 to more than 160 d. Toxicity towards AOB assessed by a novel Nitrosomonas europaea luxAB-reporter assay was correlated to total LAS concentration, indicating that LAS remained bioavailable over time without accumulation of toxic intermediates. Toxicity towards indigenous AOB increased with increasing <span class="hlt">soil</span> <span class="hlt">moisture</span>. The results indicate that dry <span class="hlt">soil</span> conditions inhibit LAS degradation and provide protection against toxicity within the indigenous AOB, thus allowing for a rapid recovery of this population when LAS degradation is resumed and completed after rewetting. We propose that the protection of microbial populations against toxicity in dry <span class="hlt">soil</span> may be a general phenomenon caused primarily by limited diffusion and thus a low bioavailability of the toxicant.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900028789&hterms=drought&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Ddrought','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900028789&hterms=drought&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Ddrought"><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://www.ncbi.nlm.nih.gov/pubmed/26601394','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26601394"><span>[Transferability of Hyperspectral Model for Estimating <span class="hlt">Soil</span> Organic Matter Concerned with <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>Chen, Yi-yun; Qi, Kun; Liu, Yao-lin; He, Jian-hua; Jiang, Qing-hu</p> <p>2015-06-01</p> <p>Hyperspectral remote sensing, known as the state-of-the-art technology in the field of remote sensing, can be used to retrieve physical and chemical properties of surface objects based on the interactions between electromagnetic waves and the objects. <span class="hlt">Soil</span> organic matter (SOM) is one of the most important parameters used in the assessment of <span class="hlt">soil</span> fertility. Quick estimation of SOM with hyperspectral remote sensing technique can provide essential <span class="hlt">soil</span> data to support the development of precision agriculture. The presence of external parameters, however, may <span class="hlt">affect</span> the modeling precision, and further handicap the transfer ability of existing model. With the aim to study the effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the Vis/NIR estimation of <span class="hlt">soil</span> organic matter, and the capacity of direct standardization(DS)algorithm in the calibration transfer, 95 <span class="hlt">soil</span> samples collected in the Jianghan plain were rewetted and air-dried. Reflectance of these samples at 13 <span class="hlt">moisture</span> levels was measured. Results show that the model calibrated using air-dried samples has the highest prediction accuracy. This model, however, was not suitable for SOM prediction of the rewetted samples. Prediction bias and RPD improved from -8.34-3.32 g x kg(-1) and 0.64-2.04 to 0 and 7.01, when DS algorithm was applied to the spectra of the rewetted samples. DS algorithm has been proven to be effective in removing the effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the Vis/NIR estimation of SOM, ensuring a transferrable model for SOM prediction with <span class="hlt">soil</span> samples at different <span class="hlt">moisture</span> levels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JARS...10a5014P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JARS...10a5014P"><span>Elimination of the <span class="hlt">soil</span> <span class="hlt">moisture</span> effect on the spectra for reflectance prediction of <span class="hlt">soil</span> salinity using external parameter orthogonalization method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, Xiang; Xu, Chi; Zeng, Wenzhi; Wu, JingWei; Huang, JieSheng</p> <p>2016-01-01</p> <p><span class="hlt">Soil</span> salinization is a common desertification process, especially in arid lands. Hyperspectral remote sensing of salinized <span class="hlt">soil</span> is favored for its advantages of being efficient and inexpensive. However, <span class="hlt">soil</span> <span class="hlt">moisture</span> often jointly has a great influence on the <span class="hlt">soil</span> reflectance spectra under field conditions. It is a challenge to establish a model to eliminate the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> and quantitatively estimate the salinity contents of slightly and moderately salt-<span class="hlt">affected</span> <span class="hlt">soil</span>. A controlled laboratory experiment was conducted by way of continuously monitoring changes of <span class="hlt">soil</span> <span class="hlt">moisture</span> and salt content, which was mainly focused on the slightly and moderately salt-<span class="hlt">affected</span> <span class="hlt">soil</span>. We investigated the external parameter orthogonalization (EPO) method to remove the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> (4 to 36% in weight base) by preprocessing <span class="hlt">soil</span> spectral reflectance and establishing the partial least squares regression after EPO preprocessing model (EPO-PLS) to predict <span class="hlt">soil</span> salt content. Through comparing PLS with EPO-PLS model, R2 and ratio of prediction to deviation rose from 0.604 and 1.063, respectively, to 0.874 and 2.865 for validation data. Root mean square error and bias were, respectively, reduced from 1.163 and 0.141 g/100 g to 0.718 and 0.044 g/100 g. The performance of the model after EPO algorithm preprocessing was improved significantly.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001PhDT.......133H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001PhDT.......133H"><span>Estimating root zone <span class="hlt">soil</span> water content using limited <span class="hlt">soils</span> information and surface <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>Heathman, Gary Claude</p> <p>2001-10-01</p> <p>The various hydrologic processes of infiltration, redistribution, drainage, evaporation, and water uptake by plants are strongly interdependent, as they occur sequentially or simultaneously. An important state variable that strongly influences the magnitude to which these rate processes occur is the amount of water present within the root zone, and in particular, the top few centimeters near the <span class="hlt">soil</span> surface. Traditionally, measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> have been limited to point measurements made in the field. In general, averages of point measurements are used to characterize the <span class="hlt">soil</span> <span class="hlt">moisture</span> of an area, but these averages seldom yield information that is adequate to characterize large scale hydrologic processes. Recent advancements in remote sensing now make it possible to obtain areal estimates of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. The use of remotely sensed data to estimate surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, combined with <span class="hlt">soil</span> water and hydrologic modeling, provides a unique opportunity to advance our understanding of hydrologic processes at a much larger scale. Standard techniques for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> have been well documented, with commercial instrumentation being widely available. Various computer models have been developed to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> in the root and vadose zone, although their application over large scales is limited due to varying spatial and temporal field conditions. It is the combination of ground-based data (in-situ measurements), near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> data, and modeling that form the basis for this research. The interactive use of field research, remote sensing ground truth data, and integrated systems modeling is used to describe surface and profile <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions at several locations within a large watershed. Successful application of this approach should improve our capabilities for estimating <span class="hlt">soil</span> hydraulic properties and to better estimate water and chemical transport in the root zone, thus enhancing water use efficiency and plant</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GI......5...95I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GI......5...95I"><span>The Sodankylä in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observation network: an example application of ESA CCI <span class="hlt">soil</span> <span class="hlt">moisture</span> product evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ikonen, Jaakko; Vehviläinen, Juho; Rautiainen, Kimmo; Smolander, Tuomo; Lemmetyinen, Juha; Bircher, Simone; Pulliainen, Jouni</p> <p>2016-04-01</p> <p>During the last decade there has been considerable development in remote sensing techniques relating to <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new <span class="hlt">soil</span> <span class="hlt">moisture</span> product has been generated, merging different satellite-based surface <span class="hlt">soil</span> <span class="hlt">moisture</span> based products. Such remotely sensed data need to be validated by means of in situ observations in different climatic regions. In that context, a comprehensive, distributed network of in situ measurement stations gathering information on <span class="hlt">soil</span> <span class="hlt">moisture</span>, as well as <span class="hlt">soil</span> temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a calibration and validation (CAL-VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of <span class="hlt">soil</span> properties. In this paper we present the Sodankylä CAL-VAL reference site <span class="hlt">soil</span> <span class="hlt">moisture</span> observation network, its instrumentation as well as its areal representativeness over the study area and the region in general as a whole. As an example of data utilization, comparisons of spatially weighted average top-layer <span class="hlt">soil</span> <span class="hlt">moisture</span> observations between the years 2012 and 2014 against ESA CCI <span class="hlt">soil</span> <span class="hlt">moisture</span> data product estimates are presented and discussed. The comparisons were made against a single ESA CCI data product pixel encapsulating most of the Sodankylä CAL-VAL network sites. Comparisons are made with daily averaged and running weekly averaged <span class="hlt">soil</span> <span class="hlt">moisture</span> data as well as through application of an exponential <span class="hlt">soil</span> <span class="hlt">moisture</span> filter. The overall achieved correlation between the ESA CCI data product and in situ observations varies considerably (from 0.479 to 0.637) depending on the applied comparison perspective. Similarly, depending on the comparison perspective used, inter-annual correlation comparison results exhibit even more pronounced variation, ranging from 0.166 to 0.840.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H31G1472W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H31G1472W"><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="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wakefield, R. A.; Basara, J. B.; Fang, B.; Lakshmi, V.; Starks, P. J.; Cosh, M. H.; Steiner, J. L.; Xiao, X.; Illston, B.</p> <p>2016-12-01</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 platform. This study applies the downscaling algorithm to coarse resolution observations collected by the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite platform during the bulk of the growing season period spanning May through September for 2015 and 2016 over the Southern Great Plains (SGP) of the United States. The resultant downscaled <span class="hlt">soil</span> <span class="hlt">moisture</span> values at a spatial resolution of 1 km were compared with high density in situ observations at (1) the Marena Oklahoma In Situ <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Testbed (MOISST) near Marena, OK and (2) three Integrated Grassland/Cropland Observing System (IGOS/ICOS) sites deployed at the United States Department of Agriculture (USDA) Grazinglands Research Laboratory (GRL) near El Reno, Oklahoma. Each in situ location (i.e., MOISST, two IGOS sites, and one ICOS site) includes a Cosmic Ray <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observing System (COSMOS) and point scale observations from various in situ sensors (e.g., Stevens Water Hydra Probe, Campbell Scientific 616 and 229, Decagon EC-TM, Delta-T Theta Probe, etc.) deployed in arrays within the COSMOS footprint. Each primary evaluation location (MOISST and GRL) also includes an Oklahoma Mesonet site.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4656P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4656P"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> downscaling using a simple thermal based proxy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, Jian; Loew, Alexander; Niesel, Jonathan</p> <p>2016-04-01</p> <p>Microwave remote sensing has been largely applied to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) from active and passive sensors. The obvious advantage of microwave sensor is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products only provide observations at coarse spatial resolutions, which often hamper their applications in regional hydrological studies. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> products. The aim of this study is to investigate the validity and robustness of a simple Vegetation Temperature Condition Index (VTCI) downscaling scheme over different climates and regions. Both polar orbiting (MODIS) and geostationary (MSG SEVIRI) satellite data are used to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) <span class="hlt">soil</span> <span class="hlt">moisture</span>, which is a merged product based on both active and passive microwave observations. The results from direct validation against <span class="hlt">soil</span> <span class="hlt">moisture</span> in-situ measurements, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI <span class="hlt">soil</span> <span class="hlt">moisture</span> while maintain the accuracy of CCI <span class="hlt">soil</span> <span class="hlt">moisture</span>. The application of the scheme with different satellite platforms and over different regions further demonstrate the robustness and effectiveness of the proposed method. Therefore, the VTCI downscaling method has the potential to facilitate relevant hydrological applications that require high spatial and temporal resolution <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ccta.conf..285Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ccta.conf..285Z"><span>Research on the Spatial Variability 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>Zhang, Changli; Liu, Shuqiang; Zhang, Xianyue; Tan, Kezhu</p> <p></p> <p>China is a country seriously suffering from the lack of water resource, especially the north of China (a dense area) where there are more agricultural production than other places in China. Therefore, some have become most important problems which should be settled down right now for precision agriculture: saving the water of agriculture, optimizing the water for cropland as well as making use of <span class="hlt">soil</span> <span class="hlt">moisture</span> effectively. To realise the potential of <span class="hlt">soil-moisture</span>, protect the water source , strengthen the management of the <span class="hlt">soil</span> <span class="hlt">moisture</span> of farm, design the irrigation and drainage, monitor the <span class="hlt">soil-moisture</span>, etc. ,the data collection of <span class="hlt">soil</span> <span class="hlt">moisture</span> and the study on how to could provide the far-reaching and academic significance of guidance together with higher regional and practical use value. The IDW, Spline and Kriging in the Spatial Analyst of ArcGIS 9.0 are applied on drawing the distributing map of <span class="hlt">soil</span> <span class="hlt">moisture</span> and it also offers the theoretical foundation for the connection between studying <span class="hlt">soil</span> <span class="hlt">moisture</span> and enhancing the yield.</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/2016ERL....11f4003H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ERL....11f4003H"><span>The influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> deficits on Australian heatwaves</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herold, N.; Kala, J.; Alexander, L. V.</p> <p>2016-06-01</p> <p>Several regions of Australia are projected to experience an increase in the frequency, intensity and duration of heatwaves (HWs) under future climate change. The large-scale dynamics of HWs are well understood, however, the influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> deficits—due for example to drought—remains largely unexplored in the region. Using the standardised precipitation evapotranspiration index, we show that the statistical responses of HW intensity and frequency to <span class="hlt">soil</span> <span class="hlt">moisture</span> deficits at the peak of the summer season are asymmetric and occur mostly in the lower and upper tails of the probability distribution, respectively. For aspects of HWs related to intensity, substantially greater increases are experienced at the 10th percentile when antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> is low (mild HWs get hotter). Conversely, HW aspects related to longevity increase much more strongly at the 90th percentile in response to low antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> (long HWs get longer). A corollary to this is that in the eastern and northern parts of the country where HW-<span class="hlt">soil</span> <span class="hlt">moisture</span> coupling is evident, high antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> effectively ensures few HW days and low HW temperatures, while low antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> ensures high HW temperatures but not necessarily more HW days.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060050765&hterms=contamination+soils&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dcontamination%2Bsoils','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060050765&hterms=contamination+soils&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dcontamination%2Bsoils"><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('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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4216705','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4216705"><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="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</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 (Qp model and discrete model), including the effect of row structure, and flat rough surface assumption (Qp 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 cm3/cm3 better than the flat rough surface assumption for vegetated <span class="hlt">soil</span>, as well as 0.015 cm3/cm3 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. PMID:25386626</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H54D..05P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H54D..05P"><span>Relating <span class="hlt">Soil</span> <span class="hlt">Moisture</span> to TRMMPR Backscatter in Southern 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>Puri, S.; Stephen, H.; Ahmad, S.</p> <p>2009-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> is an important variable in hydrological cycle. It plays a vital role in agronomy, meteorology, and hydrology. In spite of being an important variable, <span class="hlt">soil</span> <span class="hlt">moisture</span> measuring stations are sparse. This is due to high cost involved in the installation of dense network of measuring stations required to map a comprehensive spatio-temporal behavior of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Hence, there is a need to develop an alternate method to measure <span class="hlt">soil</span> <span class="hlt">moisture</span>. This research relates <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) to backscatter (σ°) obtained from Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) and Normalized Difference Vegetation Index (NDVI) obtained from Advanced Very High Resolution Radiometer. SM data is obtained from <span class="hlt">Soil</span> Climate Analysis Network (SCAN). σ° measurements are normalized at an incidence angle of 10° at which it has the highest sensitivity to SM. An empirical model that relates SM to normalized σ° and NDVI is developed. NDVI takes into account the different vegetation densities. The relationship between model variables is approximated to be linear. The model is applied to data from 1998 to 2008 where 75% of the data is used for calibration and the remaining 25% for validation. Figure 1 shows the comparison of observed and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> for a site with low vegetation. Even though the model underestimates the <span class="hlt">soil</span> <span class="hlt">moisture</span> content, it captures the signal well and produces peaks similar to the observed <span class="hlt">soil</span> <span class="hlt">moisture</span>. The model performs well with a correlation of 0.71 and root mean square error of 4.0%. The accuracy of the model depends on vegetation density. Table 1 summarizes the model performance for different vegetation densities. The model performance decreases with the increase in vegetation as the leaves in the vegetation canopy attenuate the incident microwaves which reduces the penetration depth and subsequently the sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span>. This research provides a new insight into the microwave remote sensing of <span class="hlt">soil</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..546...71O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..546...71O"><span>Effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> content on upland nitrogen loss</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ouyang, Wei; Xu, Xueting; Hao, Zengchao; Gao, Xiang</p> <p>2017-03-01</p> <p>In recent years, nitrogen (N) loss from upland fields has become one of the most important sources for agricultural nonpoint source (NPS) pollution. Understanding the relationships between <span class="hlt">soil</span> hydrological processes and N loss in NPS pollution is vital for controlling the agricultural NPS pollution in upland fields. The objective of this study was to analyze the interaction of N loss with different <span class="hlt">moisture</span> conditions in the freeze-thaw zone. The semi-distributed hydrologic model <span class="hlt">Soil</span> and Water Assessment Tool (SWAT) was used in this study to simulate runoff and different forms of N loss, which provided a basis for analyzing characteristics of N loss in the study region. Results showed that the <span class="hlt">soil</span> <span class="hlt">moisture</span> content was an important factor <span class="hlt">affecting</span> N loss in the study region. Different forms of N loss were also analyzed and it was found that N loss occurred primarily in the form of organic-N, which is likely due to the dominant role of erosion-induced pollution. This study provides useful information for preventing NPS pollution within the study region.</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('http://adsabs.harvard.edu/abs/2014ThApC.118..675L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ThApC.118..675L"><span>A statistical retrieval algorithm for root zone <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>Lindau, Ralf; Simmer, Clemens</p> <p>2014-11-01</p> <p>An algorithm for the estimation of root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> is presented. Global fields of the <span class="hlt">soil</span> <span class="hlt">moisture</span> within the uppermost metre of <span class="hlt">soil</span> are derived with a temporal resolution of 10 days. For calibration, long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from the former Soviet Union are used. The variance of the measurements is largely dominated by the spatial variability of the long-term mean <span class="hlt">soil</span> <span class="hlt">moisture</span>, while the temporal variability gives comparatively small contribution. Consequently, the algorithm is organised into two steps. The first step concentrates on the retrieval of the spatial variance of the long-term means, which comprises more than 85% of the total <span class="hlt">soil</span> <span class="hlt">moisture</span> variability. A major part of the spatial variance can be explained by four easily available fields: the climatological precipitation, land use, <span class="hlt">soil</span> texture, and terrain slope. The second step of the algorithm is dedicated to the local temporal variability. This part of variability is recovered by using passive microwave data from scanning multichannel microwave radiometre (SMMR) supported by monthly averaged fields of air temperature and precipitation. The 6-GHz channel of SMMR is shown to be severely disturbed by radio frequency interference, so that information from the 10-GHz channel is used instead. The algorithm provides reasonable <span class="hlt">soil</span> <span class="hlt">moisture</span> fields which is confirmed by a comparison with independent measurements from Illinois.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GID.....5..447B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GID.....5..447B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> sensor calibration for organic <span class="hlt">soil</span> surface layers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bircher, S.; Andreasen, M.; Vuollet, J.; Vehviläinen, J.; Rautiainen, K.; Jonard, F.; Weihermüller, L.; Zakharova, E.; Wigneron, J.-P.; Kerr, Y. H.</p> <p>2015-12-01</p> <p>This paper's objective is to present generic calibration functions for organic surface layers derived for the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology HOBE. For the Decagon 5TE sensor such a function is currently not reported in literature. Data were compared with measurements from underlying mineral <span class="hlt">soils</span> including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low <span class="hlt">soil</span> organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified: for the Decagon 5TE apparent relative permittivities at a given <span class="hlt">moisture</span> content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large surface areas compared to the studied mineral <span class="hlt">soils</span>. ThetaProbe measurements from organic horizons showed stronger non-linearity in the sensor response and signal saturation in the high level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and HOBE sites are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GI......5..109B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GI......5..109B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> sensor calibration for organic <span class="hlt">soil</span> surface layers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bircher, Simone; Andreasen, Mie; Vuollet, Johanna; Vehviläinen, Juho; Rautiainen, Kimmo; Jonard, François; Weihermüller, Lutz; Zakharova, Elena; Wigneron, Jean-Pierre; Kerr, Yann H.</p> <p>2016-04-01</p> <p>This paper's objective is to present generic calibration functions for organic surface layers derived for the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finnish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology (HOBE). For the Decagon 5TE sensor such a function is currently not reported in the literature. Data were compared with measurements from underlying mineral <span class="hlt">soils</span> including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low <span class="hlt">soil</span> organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified. For the Decagon 5TE, apparent relative permittivities at a given <span class="hlt">moisture</span> content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large specific surface areas compared to the studied mineral <span class="hlt">soils</span>. ThetaProbe measurements from organic horizons showed stronger nonlinearity in the sensor response and signal saturation in the high-level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here-proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and</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://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; Kimball, John; Piepmeier, Jeffrey R.; Koster, Randal D.; McDonald, Kyle C.; Moghaddam, Mahta; Moran, Susan; Reichle, Rolf; Shi, J. C.; Spencer, Michael W.; Thurman, Samuel W.; Tsang, Leung; VanZyl, Jakob</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('http://adsabs.harvard.edu/abs/2006JHyd..323..120B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006JHyd..323..120B"><span>Large scale measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> for validation of remotely sensed data: Georgia <span class="hlt">soil</span> <span class="hlt">moisture</span> experiment of 2003</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bosch, D. D.; Lakshmi, V.; Jackson, T. J.; Choi, M.; Jacobs, J. M.</p> <p>2006-05-01</p> <p>A series of <span class="hlt">soil</span> <span class="hlt">moisture</span> experiments were conducted in 2003 (SMEX03) to develop enhanced datasets necessary to improve spatiotemporal characterization of <span class="hlt">soil</span> <span class="hlt">moisture</span> and to enhance satellite-based retrievals. One component of this research was conducted in South Central Georgia of the US, from June 17th to July 21st (SMEX03 GA). This study analyzes measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature collected during SMEX03 GA. A network of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement devices, established to provide validation data for the satellite collections and for long-term estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions throughout the region, provided continuous measurements at 19 sites. Additional <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature validation data were collected daily from 49 field sites. These sites represented a diversity of land covers including forest, cotton, peanut, and pasture. Precipitation that occurred prior to June 22nd and from June 29th through July 2nd produced drying conditions from June 23rd to June 28th and gradual wetting from June 29th through July 2nd. <span class="hlt">Soil</span> <span class="hlt">moisture</span> in the top 0-1 cm of the <span class="hlt">soil</span> was found to be more responsive to precipitation and to have greater variability than <span class="hlt">soil</span> <span class="hlt">moisture</span> at the 0-3 or 3-6 cm layers. Within different land covers, <span class="hlt">soil</span> <span class="hlt">moisture</span> followed the same trends, but varied with land use. Pasture sites were consistently the wettest while row-crop sites were normally the driest. Good agreement was observed between <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements collected with the in situ network and the 49 SMEX sites. For the study period, <span class="hlt">soil</span> <span class="hlt">moisture</span> across the entire 50 km by 75 km region and five of the six 25 km by 25 km EASE-Grids demonstrated time stable characteristics. Time stability analysis and statistical tests demonstrated the in situ stations had a small dry bias as compared to the SMEX03 GA measurements. These results indicate that the in situ network will be a good resource for long-term calibration of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> and provide</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/2015AGUFM.H33M..01C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H33M..01C"><span>Feedbacks between vegetation and <span class="hlt">soil</span> <span class="hlt">moisture</span> in mountain grasslands</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Castelli, M.; Bertoldi, G.; Notarnicola, C.; Brenner, J.; Greifeneder, F.; Niedrist, G.; Tappeiner, U.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content (SMC) is a key variable for water budget and controls both physical processes, as runoff generation, and biological processes, as vegetation development. On the other hand, vegetation and land management influence <span class="hlt">soil</span> evolution and therefore SMC dynamic. Moreover, in mountain areas complex topography adds an additional control on water fluxes and climate. For those reasons, understanding the controls on the spatio-temporal variability of SMC is essential to predict how perturbations in vegetation and climate <span class="hlt">affects</span> mountain hydrology. In this contribution we want to analyze the impact of different land management (meadows versus pastures) on the spatial and temporal dynamic of surface and root-zone SMC, and its relationships with climate and topography. We focus on water-limited alpine grasslands in the LTER area Mazia Valley in the European Alps. The infrastructure includes a dense network of more than 20 stations measuring <span class="hlt">soil</span> <span class="hlt">moisture</span>, biomass production observations and two eddy-covariance stations over meadow and pasture. Moreover, more than ten high-resolution SAR (Sentinel1 and RADARSAT2) images were acquired, in combination with ground surveys to monitor SMC spatial distribution. In order to understand the different physical controls, SMC has been also modelled using the GEOtop hydrological model, coupled with a dynamic vegetation model. Results show that meadows and pastures have different behaviors. Meadows are in general wetter and in flatter locations. This leads to higher vegetation productivity, development of <span class="hlt">soils</span> with higher water holding capacity and to a positive feedback on SMC. In contrast, pastures are drier, in steeper locations with lower vegetation density and more compact <span class="hlt">soils</span> due animal trampling, with a negative feedback on SMC. This co-evolution of land cover and SMC leads to persistent spatial patterns controlled by both topography and management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3472810','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3472810"><span>Validation of SMOS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products over the Maqu and Twente Regions</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Dente, Laura; Su, Zhongbo; Wen, Jun</p> <p>2012-01-01</p> <p>The validation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) <span class="hlt">soil</span> <span class="hlt">moisture</span> products is a crucial step in the investigation of their inaccuracies and limitations, before planning further refinements of the retrieval algorithm. Therefore, this study intended to contribute to the validation of the SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products, by comparing them with the data collected in situ in the Maqu (China) and Twente (The Netherlands) regions in 2010. The seasonal behavior of the SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products is generally in agreement with the in situ measurements for both regions. However, the validation analysis resulted in determination coefficients of 0.55 and 0.51 over the Maqu and Twente region, respectively, for the ascending pass data, and of 0.24 and 0.41, respectively, for the descending pass data. Moreover, a systematic dry bias of the SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> was found of approximately 0.13 m3/m3 for the Maqu region and 0.17 m3/m3 for the Twente region for ascending pass data. Several factors might have <span class="hlt">affected</span> the retrieval accuracy, such as the presence of Radio Frequency Interference (RFI), the use of inaccurate land cover information and the presence of frozen <span class="hlt">soils</span> not correctly detected in winter. Improving the RFI filtering method and the quality of the retrieval algorithm inputs, such as land surface temperature and land cover, would certainly improve the accuracy of the retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span>. PMID:23112582</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/1996PhDT........16F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1996PhDT........16F"><span><span class="hlt">Soil</span> Albedo in Relation to <span class="hlt">Soil</span> Color, <span class="hlt">Moisture</span> and Roughness</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fontes, Adan Fimbres</p> <p></p> <p>Land surface albedo is the ratio of reflected to incident solar radiation. It is a function of several surface parameters including <span class="hlt">soil</span> color, <span class="hlt">moisture</span>, roughness and vegetation cover. A better understanding of albedo and how it changes in relation to variations in these parameters is important in order to help improve our ability to model the effects of land surface modifications on climate. The objectives of this study were (1) To determine empirical relationships between smooth bare <span class="hlt">soil</span> albedo and <span class="hlt">soil</span> color, (2) To develop statistical relationships between albedo and ground-based thematic mapper (TM) measurements of spectral reflectances, (3) To determine how increased surface roughness caused by tillage reduces bare <span class="hlt">soil</span> albedo and (4) To empirically relate albedo with TM data and other physical characteristics of mixed grass/shrubland sites at Walnut Gulch Watershed. Albedos, colors and spectral reflectances were measured by Eppley pyranometer, Chroma Meter CR-200 and a Spectron SE-590, respectively. Measurements were made on two field <span class="hlt">soils</span> (Gila and Pima) at the Campus Agricultural Center (CAC), Tucson, AZ. <span class="hlt">Soil</span> surface roughness was measured by a profile meter developed by the USDA/ARS. Additional measurements were made at the Maricopa Agricultural Center (MAC) for statistical model testing. Albedos of the 15 smooth, bare <span class="hlt">soils</span> (plus silica sand) were determined by linear regression to be highly correlated (r^2 = 0.93, p > 0.01) with color values for both wet and dry <span class="hlt">soil</span> conditions. Albedos of the same smooth bare <span class="hlt">soils</span> were also highly correlated (r^2>=q 0.86, p > 0.01) with spectral reflectances. Testing of the linear regression equations relating albedo to <span class="hlt">soil</span> color and spectral reflectances using the data from MAC showed a high correlation. A general nonlinear relationship given by y = 8.366ln(x) + 37.802 r^2 = 0.71 was determined between percent reduction in albedo (y) and surface roughness index (x) for wet and dry Pima and Gila field <span class="hlt">soils</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.4168M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.4168M"><span>The Capability of Microwave Radiometers In Retrieving <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Profiles Using A Neural Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Macelloni, G.; Paloscia, S.; Santi, E.; Tedesco, M.</p> <p></p> <p>Hydrological models require the knowledge of land surface parameters like <span class="hlt">soil</span> mois- ture and snow properties with a large spatial distribution and high temporal frequency. Whilst conventional methods are unable to satisfy the constraints of space and time estimation of these parameters, the use of remote sensing data represents a real im- provement. In particular the potential of data collected by microwave radiometers at low frequencies to extract <span class="hlt">soil</span> <span class="hlt">moisture</span> has been clearly demonstrated in several pa- pers. However, the penetration power into the <span class="hlt">soil</span> depends on frequency and, whereas L-band is able to estimate the <span class="hlt">moisture</span> of a relatively thick <span class="hlt">soil</span> layer, higher frequen- cies are only sensitive to the <span class="hlt">moisture</span> of <span class="hlt">soil</span> layer closer to the surface. This remark leads to the hypothesis that multifrequency observations could be able to retrieve a <span class="hlt">soil</span> <span class="hlt">moisture</span> profile. In several experiments carried out both on agricultural fields and on samples of <span class="hlt">soil</span> in a tank, by using the IROE multifrequency microwave radiometers, the effect of <span class="hlt">moisture</span> and surface roughness on different frequencies was studied. From this experiments the capability of L-band in measuring the <span class="hlt">moisture</span> of a <span class="hlt">soil</span> layer of several centimeters, in the order of the wavelength, was confirmed, as well the sensitivity to the <span class="hlt">moisture</span> of the first centimeters layer at C- and X-bands, and the one of the very first layer of smooth <span class="hlt">soil</span> at Ka-band. Using an electromagnetic model (Integral Equation Model, IEM) the brightness temperatures as a function of the in- cidence angle were computed at 1.4, 6, 10, and 37 GHz for different <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles and different surface roughness. A particular consideration was dedicated to the latter parameter, since, especially at Ka band, surface roughness strongly <span class="hlt">affects</span> the emission and masks the effect of <span class="hlt">moisture</span>. Different <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles have been tested: increasing and decreasing with depth and also constant for sandy and sandy-loam <span class="hlt">soils</span>. After this</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016SPIE10004E..1ZA','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016SPIE10004E..1ZA"><span>Downscaling <span class="hlt">soil</span> <span class="hlt">moisture</span> using multisource data in China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>An, Ru; Wang, Hui-Lin; You, Jia-jun; Wang, Ying; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballardd, Jonathan Arthur; Chen, Yuehong</p> <p>2016-10-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays an important role in the water cycle within the surface ecosystem and it is the basic condition for the growth and development of plants. Currently, the spatial resolution of most <span class="hlt">soil</span> <span class="hlt">moisture</span> data from remote sensing ranges from ten to several tens of kilometres whilst those observed in situ and simulated for watershed hydrology, ecology, agriculture, weather and drought research are generally less than 1 kilometre. Therefore, the existing coarse resolution remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data needs to be down-scaled. In this paper, a universal <span class="hlt">soil</span> <span class="hlt">moisture</span> downscaling model through stepwise regression with moving window suitable for large areas and multi temporal has been established. Datasets comprise land surface, brightness temperature, precipitation, <span class="hlt">soil</span> and topographic parameters from high resolution data, and active/passive microwave remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data from Essential Climate Variables (ECV_SM) with 25 km spatial resolution were used. With this model, a total of 288 <span class="hlt">soil</span> <span class="hlt">moisture</span> maps of 1 km resolution from the first ten-day of January 2003 to the last tenth-day of December 2010 were derived. The in situ observations were used to validate the down-scaled ECV_SM for different land cover and land use types and seasons. In addition, various errors comparative analysis was also carried out for the down-scaled ECV_SM and original one. In general, the down-scaled <span class="hlt">soil</span> <span class="hlt">moisture</span> for different land cover and land use types is consistent with the in situ observations. The accuracy is relatively high in autumn and winter. The validation results show that downscaled <span class="hlt">soil</span> <span class="hlt">moisture</span> can be improved not only on spatial resolution, but also on estimation accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JHyd..422...63B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JHyd..422...63B"><span>Catchment scale <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial-temporal variability</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brocca, L.; Tullo, T.; Melone, F.; Moramarco, T.; Morbidelli, R.</p> <p>2012-02-01</p> <p>SummaryThe characterization of the spatial-temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> is of paramount importance in many scientific fields and operational applications. However, due to the high variability of <span class="hlt">soil</span> <span class="hlt">moisture</span>, its monitoring over large areas and for extended periods through in situ point measurements is not straightforward. Usually, in the scientific literature, <span class="hlt">soil</span> <span class="hlt">moisture</span> variability has been investigated over short periods and in large areas or over long periods but in small areas. In this study, an effort to understanding <span class="hlt">soil</span> <span class="hlt">moisture</span> variability at catchment scale (>100 km2), which is the size needed for some hydrological applications and for remote sensing validation analysis, is done. Specifically, measurements were carried out in two adjacent areas located in central Italy with extension of 178 and 242 km2 and over a period of 1 year (35 sampling days) with almost weekly frequency except for the summer period because of <span class="hlt">soil</span> hardness. For each area, 46 sites were monitored and, for each site, 3 measurements were performed to obtain reliable <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was measured with a portable Time Domain Reflectometer for a layer depth of 0-15 cm. A statistical and temporal stability analysis is employed to assess the space-time variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> at local and catchment scale. Moreover, by comparing the results with those obtained in previous studies conducted in the same study area, a synthesis of <span class="hlt">soil</span> <span class="hlt">moisture</span> variability for a range of spatial scales, from few square meters to several square kilometers, is attempted. For the investigated area, the two main findings inferred are: (1) the spatial variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> increases with the area up to ˜10 km2 and then remains quite constant with an average coefficient of variation equal to ˜0.20; (2) regardless of the areal extension, the <span class="hlt">soil</span> <span class="hlt">moisture</span> exhibits temporal stability features and, hence, few measurements can be used to infer areal mean values with a</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('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/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://www.ars.usda.gov/research/publications/publication/?seqNo115=247682','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=247682"><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="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 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 f...</p> </li> <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.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4934312','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4934312"><span>Evaluating <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Status Using an e-Nose</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Bieganowski, Andrzej; Jaromin-Glen, Katarzyna; Guz, Łukasz; Łagód, Grzegorz; Jozefaciuk, Grzegorz; Franus, Wojciech; Suchorab, Zbigniew; Sobczuk, Henryk</p> <p>2016-01-01</p> <p>The possibility of distinguishing different <span class="hlt">soil</span> <span class="hlt">moisture</span> levels by electronic nose (e-nose) was studied. Ten arable <span class="hlt">soils</span> of various types were investigated. The measurements were performed for air-dry (AD) <span class="hlt">soils</span> stored for one year, then moistened to field water capacity and finally dried within a period of 180 days. The volatile fingerprints changed during the course of drying. At the end of the drying cycle, the fingerprints were similar to those of the initial AD <span class="hlt">soils</span>. Principal component analysis (PCA) and artificial neural network (ANN) analysis showed that e-nose results can be used to distinguish <span class="hlt">soil</span> <span class="hlt">moisture</span>. It was also shown that different <span class="hlt">soils</span> can give different e-nose signals at the same <span class="hlt">moistures</span>. PMID:27338404</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19840022405&hterms=soil+recovery&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsoil%2Brecovery','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19840022405&hterms=soil+recovery&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsoil%2Brecovery"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> from temperature measurements at the Earth's surface, update</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Welker, J. E.</p> <p>1984-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> budgets at the Earth's surface were investigated based on <span class="hlt">soil</span> and atmospheric temperature variations. A number of data sets were plotted and statistically analyzed in order to accentuate the existence and the characteristics of mesoscale <span class="hlt">soil</span> temperature extrema variations and their relations to other parameters. The correlations between diurnal temperature extrema for air and <span class="hlt">soil</span> in drought and non-drought periods appear to follow different characteristic patterns, allowing an inference of <span class="hlt">soil</span> <span class="hlt">moisture</span> content from temperature data. The recovery of temperature extrema after a precipitation event also follows a characteristic power curve rise between two limiting values which is an indicator of evaporation rates. If these indicators are applied universally to regional temperature data, <span class="hlt">soil</span> <span class="hlt">moisture</span> content or drought conditions can be inferred directly from temperature measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19840022405&hterms=follow+surface&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dfollow%2Bsurface','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19840022405&hterms=follow+surface&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dfollow%2Bsurface"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> from temperature measurements at the Earth's surface, update</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Welker, J. E.</p> <p>1984-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> budgets at the Earth's surface were investigated based on <span class="hlt">soil</span> and atmospheric temperature variations. A number of data sets were plotted and statistically analyzed in order to accentuate the existence and the characteristics of mesoscale <span class="hlt">soil</span> temperature extrema variations and their relations to other parameters. The correlations between diurnal temperature extrema for air and <span class="hlt">soil</span> in drought and non-drought periods appear to follow different characteristic patterns, allowing an inference of <span class="hlt">soil</span> <span class="hlt">moisture</span> content from temperature data. The recovery of temperature extrema after a precipitation event also follows a characteristic power curve rise between two limiting values which is an indicator of evaporation rates. If these indicators are applied universally to regional temperature data, <span class="hlt">soil</span> <span class="hlt">moisture</span> content or drought conditions can be inferred directly from temperature measurements.</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/2060/20110015242','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('https://ntrs.nasa.gov/search.jsp?R=19910035161&hterms=soil+surveys&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsoil%2Bsurveys','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910035161&hterms=soil+surveys&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsoil%2Bsurveys"><span>Airborne gamma radiation <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements over short flight lines</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peck, Eugene L.; Carrol, Thomas R.; Lipinski, Daniel M.</p> <p>1990-01-01</p> <p>Results are presented on airborne gamma radiation measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> condition, carried out along short flight lines as part of the First International Satellite Land Surface Climatology Project Field Experiment (FIFE). Data were collected over an area in Kansas during the summers of 1987 and 1989. The airborne surveys, together with ground measurements, provide the most comprehensive set of airborne and ground truth data available in the U.S. for calibrating and evaluating airborne gamma flight lines. Analysis showed that, using standard National Weather Service weights for the K, Tl, and Gc radiation windows, the airborne <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates for the FIFE lines had a root mean square error of no greater than 3.0 percent <span class="hlt">soil</span> <span class="hlt">moisture</span>. The <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates for sections having acquisition time of at least 15 sec were found to be reliable.</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/2060/19780021589','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://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=20060040337&hterms=inversion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dinversion','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060040337&hterms=inversion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dinversion"><span>A quantitative comparison of <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion algorithms</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zyl, J. J. van; Kim, Y.</p> <p>2001-01-01</p> <p>This paper compares the performance of four bare surface radar <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion algorithms in the presence of measurement errors. The particular errors considered include calibration errors, system thermal noise, local topography and vegetation cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060040337&hterms=inversion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dinversion','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060040337&hterms=inversion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dinversion"><span>A quantitative comparison of <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion algorithms</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zyl, J. J. van; Kim, Y.</p> <p>2001-01-01</p> <p>This paper compares the performance of four bare surface radar <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion algorithms in the presence of measurement errors. The particular errors considered include calibration errors, system thermal noise, local topography and vegetation cover.</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/2004AGUSM.H23B..02W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUSM.H23B..02W"><span>Solute movement in the rhizosphere with the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> and plant uptake</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, P.; Linker, L. C.</p> <p>2004-05-01</p> <p>Nutrient loads from land to a water body effects the eutrophication processes. The assessment of leakage of dissolve inorganic nitrogen (DIN) solute from land provides useful information for nutrient management. Solute transport in the rhizosphere is a complicate process, which depends on solute property, water supply and movement, <span class="hlt">soil</span> physiologic property, plant transpiration and uptake, etc. This presentation focuses on the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> and plant uptake on DIN leakage from <span class="hlt">soil</span>. Plant nitrogen uptake is not a monotonic function with <span class="hlt">soil</span> <span class="hlt">moisture</span>. When <span class="hlt">moisture</span> is deficient, increasing <span class="hlt">moisture</span> will increase water uptake and increase nitrogen uptake; when <span class="hlt">moisture</span> is sufficient (e.g., at the field capacity), increasing it may dilute the solution, decrease nitrogen uptake, and increase nitrogen export. Under <span class="hlt">moisture</span> over-sufficient conditions, anaerobiosis may harm plant and cause even lower nitrogen uptake. In nutrient management, it is important to know how flow <span class="hlt">affects</span> nutrient leakage from the land. A proper mathematic function describing such processes is desirable to establish a correct mathematical model for nutrient management. A mechanistic computer model procedure for DIN uptake as a function of <span class="hlt">soil</span> <span class="hlt">moisture</span> is also presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014cosp...40E1475K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014cosp...40E1475K"><span>Sensitivity of seasonal weather prediction and extreme precipitation events to <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization uncertainty using 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>Khodayar-Pardo, Samiro; Lopez-Baeza, Ernesto; Coll Pajaron, M. Amparo</p> <p></p> <p>Sensitivity of seasonal weather prediction and extreme precipitation events to <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization uncertainty using SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products (1) S. Khodayar, (2) A. Coll, (2) E. Lopez-Baeza (1) Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe Germany (2) University of Valencia. Earth Physics and Thermodynamics Department. Climatology from Satellites Group <span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important variable in agriculture, hydrology, meteorology and related disciplines. Despite its importance, it is complicated to obtain an appropriate representation of this variable, mainly because of its high temporal and spatial variability. SVAT (<span class="hlt">Soil</span>-Vegetation-Atmosphere-Transfer) models can be used to simulate the temporal behaviour and spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> in a given area and/or state of the art products such as the <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements from the SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity) space mission may be also convenient. The potential role of <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization and associated uncertainty in numerical weather prediction is illustrated in this study through sensitivity numerical experiments using the SVAT SURFEX model and the non-hydrostatic COSMO model. The aim of this investigation is twofold, (a) to demonstrate the sensitivity of model simulations of convective precipitation to <span class="hlt">soil</span> <span class="hlt">moisture</span> initial uncertainty, as well as the impact on the representation of extreme precipitation events, and (b) to assess the usefulness of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products to improve the simulation of water cycle components and heavy precipitation events. Simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation fields are compared with observations and with level-1(~1km), level-2(~15 km) and level-3(~35 km) <span class="hlt">soil</span> <span class="hlt">moisture</span> maps generated from SMOS over the Iberian Peninsula, the SMOS validation area (50 km x 50 km, eastern Spain) and selected stations, where in situ measurements are available covering different vegetation cover</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/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/2007AdWR...30..883C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AdWR...30..883C"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> variability of root zone profiles within SMEX02 remote sensing footprints</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Choi, Minha; Jacobs, Jennifer M.</p> <p>2007-04-01</p> <p>Remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> effectively provides <span class="hlt">soil</span> <span class="hlt">moisture</span> at a large scale, but does not explain highly heterogeneous <span class="hlt">soil</span> <span class="hlt">moisture</span> characteristics within remote sensing footprints. In this study, field scale spatio-temporal variability of root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> was analyzed. During the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment 2002 (SMEX02), daily <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles (i.e., 0-6, 5-11, 15-21, and 25-31 cm) were measured in two fields in Walnut Creek watershed, Ames, Iowa, USA. Theta probe measurements of the volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> profile data were used to analyze statistical moments and time stability and to validate <span class="hlt">soil</span> <span class="hlt">moisture</span> predicted by a simple physical model simulation. For all depths, the coefficient of variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> is well explained by the mean <span class="hlt">soil</span> <span class="hlt">moisture</span> using an exponential relationship. The simple model simulated very similar variability patterns as those observed. As <span class="hlt">soil</span> depth increases, <span class="hlt">soil</span> <span class="hlt">moisture</span> distributions shift from skewed to normal patterns. At the surface depth, the <span class="hlt">soil</span> <span class="hlt">moisture</span> during dry down is log-normally distributed, while the <span class="hlt">soil</span> <span class="hlt">moisture</span> is normally distributed after rainfall. At all depths below the surface, the normal distribution captures the <span class="hlt">soil</span> <span class="hlt">moisture</span> variability for all conditions. Time stability analyses show that spatial patterns of sampling points are preserved for all depths and that time stability of surface measurements is a good indicator of subsurface time stability. The most time stable sampling sites estimate the field average root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> value within ±2.1% volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span>.</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('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('http://adsabs.harvard.edu/abs/2012AGUFM.H13F1429B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H13F1429B"><span>SMOS CATDS Level 3 products, <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Brightness Temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berthon, L.; Mialon, A.; Al Bitar, A.; Cabot, F.; Kerr, Y. H.</p> <p>2012-12-01</p> <p>The ESA's (European Space Agency) SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity) mission, operating since november 2009, is the first satellite dedicated to measuring the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and the ocean salinity. The CNES (Centre National d'Etudes Spatiales) has developed the CATDS (Centre Aval de Traitement des Données SMOS) ground segment. The CATDS provides temporal synthesis products (referred to as level 3) of <span class="hlt">soil</span> <span class="hlt">moisture</span>, which are now covering the whole SMOS period, i.e. since January 2010. These products have different time resolutions: daily products, 3-day global products (insuring a complete coverage of the Earth surface), 10-day composite products, and monthly averaged products. Moreover, a new product provides brightness temperatures at H and V polarizations which are computed at fixed incidence angles every 5 degrees. As the instrument measures L-band brightness temperatures at the antenna frame (X/Y polarizations), a rotation is applied to transform the observations to V/H polarizations. All the CATDS products are presented in the NetCDF format and on the EASE grid (Equal Area Scalable Earth grid) with a spatial resolution of ~ 25*25 km2 The <span class="hlt">soil</span> <span class="hlt">moisture</span> level 3 algorithm is based on ESA's (European Space Agency) level 2 retrieval scheme with the improvement of using several overpasses (3 at most) over a 7-day window. The use of many revisits is expected to improve the retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span>. This communication aims at presenting the <span class="hlt">soil</span> <span class="hlt">moisture</span> and brightness temperature products from the CATDS as well as the other geophysical parameters retrieved, such as the vegetation optical depth or the dielectric constant of the surface. SMOS Level 3 <span class="hlt">soil</span> <span class="hlt">moisture</span>. 3-days aggregation product, the best estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> is chosen.</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('http://adsabs.harvard.edu/abs/2011WRR....47.1508J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011WRR....47.1508J"><span>Spatiotemporal analyses of <span class="hlt">soil</span> <span class="hlt">moisture</span> from point to footprint scale in two different hydroclimatic regions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Joshi, Champa; Mohanty, Binayak P.; Jacobs, Jennifer M.; Ines, Amor V. M.</p> <p>2011-01-01</p> <p>This paper presents time stability analyses of <span class="hlt">soil</span> <span class="hlt">moisture</span> at different spatial measurement support scales (point scale and airborne remote sensing (RS) footprint scale 800 m × 800 m) in two different hydroclimatic regions. The data used in the analyses consist of in situ and passive microwave remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data from the Southern Great Plains Hydrology Experiments 1997 and 1999 (SGP97 and SGP99) conducted in the Little Washita (LW) watershed, Oklahoma, and the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiments 2002 and 2005 (SMEX02 and SMEX05) in the Walnut Creek (WC) watershed, Iowa. Results show that in both the regions <span class="hlt">soil</span> properties (i.e., percent silt, percent sand, and <span class="hlt">soil</span> texture) and topography (elevation and slope) are significant physical controls jointly <span class="hlt">affecting</span> the spatiotemporal evolution and time stability of <span class="hlt">soil</span> <span class="hlt">moisture</span> at both point and footprint scales. In Iowa, using point-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements, the WC11 field was found to be more time stable (TS) than the WC12 field. The common TS points using data across the 3 year period (2002-2005) were mostly located at moderate to high elevations in both the fields. Furthermore, the <span class="hlt">soil</span> texture at these locations consists of either loam or clay loam <span class="hlt">soil</span>. Drainage features and cropping practices also <span class="hlt">affected</span> the field-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> variability in the WC fields. In Oklahoma, the field having a flat topography (LW21) showed the worst TS features compared to the fields having gently rolling topography (LW03 and LW13). The LW13 field (silt loam) exhibited better time stability than the LW03 field (sandy loam) and the LW21 field (silt loam). At the RS footprint scale, in Iowa, the analysis of variance (ANOVA) tests show that the percent clay and percent sand are better able to discern the TS features of the footprints compared to the <span class="hlt">soil</span> texture. The best <span class="hlt">soil</span> indicator of <span class="hlt">soil</span> <span class="hlt">moisture</span> time stability is the loam <span class="hlt">soil</span> texture. Furthermore, the hilltops (slope ˜0%-0.45%) exhibited the best TS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H13K1738P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H13K1738P"><span>A Methodology for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval from Land Surface Temperature, Vegetation Index, Topography and <span class="hlt">Soil</span> Type</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pradhan, N. R.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> conditions have an impact upon hydrological processes, biological and biogeochemical processes, eco-hydrology, floods and droughts due to changing climate, near-surface atmospheric conditions and the partition of incoming solar and long-wave radiation between sensible and latent heat fluxes. Hence, <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions virtually effect on all aspects of engineering / military engineering activities such as operational mobility, detection of landmines and unexploded ordinance, natural material penetration/excavation, peaking factor analysis in dam design etc. Like other natural systems, <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern can vary from completely disorganized (disordered, random) to highly organized. To understand this varying <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern, this research utilized topographic wetness index from digital elevation models (DEM) along with vegetation index from remotely sensed measurements in red and near-infrared bands, as well as land surface temperature (LST) in the thermal infrared bands. This research developed a methodology to relate a combined index from DEM, LST and vegetation index with the physical <span class="hlt">soil</span> <span class="hlt">moisture</span> properties of <span class="hlt">soil</span> types and the degree of saturation. The advantage in using this relationship is twofold: first it retrieves <span class="hlt">soil</span> <span class="hlt">moisture</span> content at the scale of <span class="hlt">soil</span> data resolution even though the derived indexes are in a coarse resolution, and secondly the derived <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution represents both organized and disorganized patterns of actual <span class="hlt">soil</span> <span class="hlt">moisture</span>. The derived <span class="hlt">soil</span> <span class="hlt">moisture</span> is used in driving the hydrological model simulations of runoff, sediment and nutrients.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H31A0770Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H31A0770Z"><span>Root Zone <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Forecasting Using Multivariate Relevance Vector Machines</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zaman, B.; McKee, M.</p> <p>2009-12-01</p> <p>Root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> at depths of 1 and 2 meters are forecasted four days into the future. Prediction of <span class="hlt">soil</span> <span class="hlt">moisture</span> can be of paramount importance owing to its applicability in <span class="hlt">soil</span> water balance calculations, modeling of various hydrometeorological, ecological, and biogeochemical factors, and initialization of various land-atmosphere models. In this study, we propose a new multivariate output prediction approach for forecasting root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> using learning machine models. These models are known for their robustness, efficiency, and sparseness, and provide a statistically sound approach to solving the inverse problems and thus to building statistical models. The multivariate relevance vector machine (MVRVM) is used to build a model that predicts future <span class="hlt">soil</span> state based upon current <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature conditions. The predicting function learns the input-output response pattern from the training dataset. <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurements acquired by the <span class="hlt">Soil</span> Climate Analysis Network (SCAN) site at Rees Center, Texas are used for this study. The methodology combines the data at different depths from 5 cm to 50 cm, the largest of which corresponds to the depth at which the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors are generally operational, to produce <span class="hlt">soil</span> <span class="hlt">moisture</span> predictions at larger depths. The MVRVM model demonstrates superior performance. The results for <span class="hlt">soil</span> <span class="hlt">moisture</span> predictions at 1 m and 2 m depth for the fourth day are excellent, with RMSE = 0.0125 m3water/m3<span class="hlt">soil</span>; IoA = 0.96; CoE = 0.88 at 1 m depth, and RMSE = 0.0021 m3/m3; IoA = 0.98; CoE = 0.93 for 2 m depth. The statistics indicate good model generalization capability and computations show good agreement with the actual <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements with R2 = 0.89 and R2 = 0.94 for 1 m and 2 m depths on fourth day, respectively. The MVRVM produces good results for all four days with a reduced computational complexity and more suitable real-time implementation. Bootstrapping is used to check over</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.8055M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.8055M"><span>Predicting root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> using surface data</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.; Brocca, L.; Moramarco, T.; Melone, F.; Sheffield, J.; Fiorentino, M.</p> <p>2012-04-01</p> <p>In recent years, much effort has been given to monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> from satellite remote sensing. These tools represent an extraordinary source of information for hydrological applications, but they only provide information on near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. In the present work, we developed a new formulation for the estimation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> in the root zone based on the measured value of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the surface. The method derives from a simplified form of the <span class="hlt">soil</span> water balance equation and for this reason all parameters adopted are physically consistent. The formulation provides a closed form of the relationship between the root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> and the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> with a limited number of parameters, such as: the ratio between the depth of the surface layer and the deeper layer, the water loss coefficient, and the field capacity. The method has been tested using modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> obtained from the North American Land Data Assimilation System (NLDAS). The NLDAS is a multi-institution partnership aimed at developing a retrospective data set, using available atmospheric and land surface meteorological observations to compute the land surface hydrological budget. The NLDAS database was extremely useful for the scope of the present research since it provides simulated data over an extended area with different climatic and physical condition and moreover it provides <span class="hlt">soil</span> <span class="hlt">moisture</span> data averaged over different depths. In particular, we used values in the top 10 cm and 100 cm layers. One year of simulation was used to test the ability of the developed method to describe <span class="hlt">soil</span> <span class="hlt">moisture</span> fluctuation in the 100cm layer over the entire NLDAS domain. The method was adopted by calibrating one of its three parameters and defining the remaining two based on physical characteristics of the site (using the potential evapotranspiration and ratio between the first and the second <span class="hlt">soil</span> layer depth). In general, the method performed better than</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1211291C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1211291C"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> - resistivity relation at the plot and catchment scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Calamita, Giuseppe; Perrone, Angela; Satriani, Antonio; Brocca, Luca; Moramarco, Tommaso</p> <p>2010-05-01</p> <p>The key role played by <span class="hlt">soil</span> <span class="hlt">moisture</span> in both Global Hydrological Cycle and Earth Radiation Budget has been claimed by numerous authors during past decades. The importance of this environmental variable is evident in several natural processes operating in a wide range of spatial and temporal scales. At continental and regional scales <span class="hlt">soil</span> <span class="hlt">moisture</span> influences the evapotranspiration process and so acts indirectly on the climate processes; at middle scale is one of the major controls of the infiltration-runoff <span class="hlt">soil</span> response during rainfall events; at small scales the knowledge of <span class="hlt">soil</span> <span class="hlt">moisture</span> evolution is crucial for precision agriculture and the associated site-specific management practices. However, <span class="hlt">soil</span> <span class="hlt">moisture</span> exhibits an high temporal and spatial variability and this is even more evident in the vadose zone. Thus, in order to better understand the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics it is desirable to capture its behavior at different temporal and/or spatial scales. Traditional in situ methods to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> like TDR can be very precise and allows an high temporal resolution. Recently, the application in field of geophysical methods for capturing <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial and temporal variations has demonstrated to be a promising tool for hydro-geological studies. One of the major advantages relies on the capability to capture the <span class="hlt">soil</span> <span class="hlt">moisture</span> variability at larger scales, that is decametric or hectometric scale. In particular, this study is based on the simultaneous application of the electrical resistivity and the TDR methods. We present two study cases that differ from each other by both spatial and temporal resolution. For the first one, simultaneous measurements obtained during four different period of the year and carried out within a test catchment (~60 km2) in Umbria region (central Italy) were analyzed. The second case concerns almost three months of simultaneous measurements carried out in a small test site ( <200 m2), located in the garden of IMAA</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1997SPIE.3222...98T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1997SPIE.3222...98T"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> measurement techniques for remote sensing ground truth: evaluation and performance test of <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>Tsegaye, Teferi D.; Laymon, Charles A.; Crosson, William L.; Coleman, Tommy L.; Rajbhandari, Narayan B.</p> <p>1997-12-01</p> <p>Remote sensing technology requires fast and sufficiently accurate devices to take repetitive and less destructive <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement techniques for validation of remotely sensed data. This study was conducted at Winfred Thomas Agricultural Research Station (WTARS) in Hazel Green, Alabama. The objectives of this study were to compare volumetric water content values measured with the time domain reflectometry (TDR) and water content reflectometry (WCR) instruments to the values obtained by the standard gravimetric technique for the upper <span class="hlt">soil</span> depth and to examine the performance of the different types of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors and the effect of the probe length on the accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> determination. From Huntsville '96 field research, we found that the emitting depth is 5 cm or less, possibly as low as 1 cm. This suggests that, in order to validate remotely sensed data, it is necessary to have fast and sufficiently accurate instruments to take repetitive and non-destructive <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement to measure <span class="hlt">soil</span> <span class="hlt">moisture</span>. Our results indicated no significance difference between the Delta-T 6 cm probe output with GSM, MESA 10 cm probe output with GSM, and WCR30 and 20 cm probe output with GSM measurements. Even though the standard gravimetric technique is very reliable to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> content, it is relatively time consuming and very destructive. Therefore, it may not be used for repetitive measurement at exactly the same location. The different types of TDR and WCR probes we tested can be used for measuring the <span class="hlt">moisture</span> content. Except the WCR 5 and 10 cm probes, all probes tested in this experiment provided similar results. Therefore, this probe can replace the traditional gravimetric technique as long as the proper calibration is performed for a range of <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> types.</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://adsabs.harvard.edu/abs/2014EGUGA..16.1825G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.1825G"><span>Roughness characterizations in active and passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Ying; Walker, Jeffrey; Panciera, Rocco; Monerris, Alessandra; Ryu, Dongryeol</p> <p>2014-05-01</p> <p>Passive microwave remote sensing at L-band is widely recognized as the preferred technique to measure surface <span class="hlt">soil</span> <span class="hlt">moisture</span> globally, with resolution ranging from 40-100km. However, passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval is highly dependent on ancillary data such as surface roughness, which is difficult to characterize at such a large footprint by ground measurement. NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, scheduled for launch in Nov 2014, will deploy both active and passive microwave instruments to enhance <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval capabilities. This provides an opportunity to characterize the roughness parameter in passive observations with active measurements. However, the roughness parameters derived from active measurements cannot be directly used in the passive <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval models. While roughness is usually characterized by surface Root Mean Square (RMS) height in active microwave, roughness in passive microwave is described using a parameter HR. This paper compares the two roughness parameters, retrieved from active and passive scattering and emission models respectively, using data sets from the third <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Experiment (SMAPEx-3) conducted in south-eastern Australia in 2011. Analysis was done over a series of grassland surfaces located within the experiment focus areas. The retrieved surface RMS heights from active measurements were validated against ground samples, after which the relationship between HR and RMS was examined.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.5540H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.5540H"><span>Improving the vegetation parametrization in the ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hahn, Sebastian; Wagner, Wolfgang</p> <p>2016-04-01</p> <p>The TU Wien <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm is based upon a backscatter model designed to exploit the multi-angle viewing capabilities of space-borne fan-beam scatterometers. In the beginning the backscatter model has been developed for the scatterometers on-board ERS-1 and ERS-2 and later successfully applied on the successor instrument ASCAT (Advanced Scatterometer) on-board the series of Metop satellites. The <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm represents a physically motivated change detection method, which requires model parameters derived along the way to the final <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates. The computation of the model parameters needs to be done in the time domain and is computationally expensive. However, not all model parameters are computationally estimated from the backscatter measurements, but rather defined by empirical observations. The cross-over angles belong to this group of model parameters, which unlike other model parameters, remain spatially and temporally constant on a global scale. This study investigates the possibility to optimize the cross-over angles, which are important parameters for the vegetation correction in the TU Wien <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm. The optimization is carried out with various cost functions and compared against <span class="hlt">soil</span> <span class="hlt">moisture</span> values from land surface models. First results indicate that spatially varying cross-over angles help to improve the mean annual cycle of <span class="hlt">soil</span> <span class="hlt">moisture</span>.</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> </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('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> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012PhPro..25.1523L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhPro..25.1523L"><span>FDR <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensor for Environmental Testing and Evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Linmao, Ye; longqin, Xue; guangzhou, Zhang; haibo, Chen; likuai, Shi; zhigang, Wu; gouhe, Yu; yanbin, Wang; sujun, Niu; Jin, Ye; Qi, Jin</p> <p></p> <p>To test the <span class="hlt">affect</span> of environmental stresses on a adaptability of <span class="hlt">soil</span> <span class="hlt">moisture</span> capacitance sensor(FDR) a number of stresses were induced including vibrational shock as well as temperature and humidity through the use of a CH-I constant humidity chamber with variable temperature. A Vibrational platform was used to exam the resistance and structural integrity of the sensor after vibrations simulating the process of using, transporting and handling the sensor. A Impactive trial platform was used to test the resistance and structural integrity of the sensor after enduring repeated mechanical shocks. An CH-I constant humidity chamber with high-low temperature was used to test the adaptability of sensor in different environments with high temperature, low temperature and constant humidity. Otherwise, scope of magnetic force line of sensor was also tested in this paper. Test show:the capacitance type <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor spread a feeling machine to bear heat, high wet and low temperature, at bear impact and vibration experiment in pass an examination, is a kind of environment to adapt to ability very strong instrument;Spread a feeling machine moreover electric field strength function radius scope 7 cms.</p> </li> <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://www.ars.usda.gov/research/publications/publication/?seqNo115=302334','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=302334"><span>Evaluation of SMAP radiometer level 2 <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms using four years of SMOS 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 objectives of the SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive) mission include global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> at three different spatial resolutions. SMAP will provide <span class="hlt">soil</span> <span class="hlt">moisture</span> with a 3-day revisit time at an accuracy of 0.04 m3/m3 The 36 km gridded <span class="hlt">soil</span> <span class="hlt">moisture</span> product (L2_SM_P) is primar...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=267960','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=267960"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> sensor intercomparisons at the SMAP marena in situ testbed</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 May 2010, a <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor intercomparison study was begun in Marena, Oklahoma. This effort is designed to serve as a foundation for incorporating diverse <span class="hlt">soil</span> <span class="hlt">moisture</span> networks into the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Calibration and Validation program. Various <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors, w...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=278224','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=278224"><span>Application of observation operators for field scale <span class="hlt">soil</span> <span class="hlt">moisture</span> averages and variances in agricultural landscapes</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 a key variable in understanding hydrologic processes and energy fluxes at the land surface. In spite of developing technologies for in-situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements and increased availability of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data, scaling issues between <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/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.osti.gov/scitech/biblio/1157092','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1157092"><span>Potential <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products from the Aquarius Radiometer and Scatterometer Using an Observing System Simulation Experiment</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Luo, Yan; Houser, Paul; Anantharaj, Valentine G; Fan, Xingang; De Lannoy, Gabrielle; Zhan, Xiwu</p> <p>2013-01-01</p> <p>Using an observing system simulation experiment (OSSE), we investigate the potential <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval capability of the National Aeronautics and Space Administration (NASA) Aquarius radiometer (L-band 1.413 GHz) and scatterometer (L-band, 1.260 GHz). We estimate potential errors in <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals and identify the sources that could cause those errors. The OSSE system includes (i) a land surface model in the NASA Land Information System, (ii) a radiative transfer and backscatter model, (iii) a realistic orbital sampling model, and (iv) an inverse <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval model. We execute the OSSE over a 1000 2200 km2 region in the central United States, including the Red and Arkansas river basins. Spatial distributions of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieved from the radiometer and scatterometer are close to the synthetic truth. High root mean square errors (RMSEs) of radiometer retrievals are found over the heavily vegetated regions, while large RMSEs of scatterometer retrievals are scattered over the entire domain. The temporal variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> are realistically captured over a sparely vegetated region with correlations 0.98 and 0.63, and RMSEs 1.28% and 8.23% vol/vol for radiometer and scatterometer, respectively. Over the densely vegetated region, <span class="hlt">soil</span> <span class="hlt">moisture</span> exhibits larger temporal variation than the truth, leading to correlation 0.70 and 0.67, respectively, and RMSEs 9.49% and 6.09% vol/vol respectively. The domain-averaged correlations and RMSEs suggest that radiometer is more accurate than scatterometer in retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span>. The analysis also demonstrates that the accuracy of the retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span> is <span class="hlt">affected</span> by vegetation coverage and spatial aggregation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H21H1491N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H21H1491N"><span>Satellite Based <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product Validation Using NOAA-CREST Ground and L-Band Observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Norouzi, H.; Campo, C.; Temimi, M.; Lakhankar, T.; Khanbilvardi, R.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content is among most important physical parameters in hydrology, climate, and environmental studies. Many microwave-based satellite observations have been utilized to estimate this parameter. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is one of many remotely sensors that collects daily information of land surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. However, many factors such as ancillary data and vegetation scattering can <span class="hlt">affect</span> the signal and the estimation. Therefore, this information needs to be validated against some "ground-truth" observations. NOAA - Cooperative Remote Sensing and Technology (CREST) center at the City University of New York has a site located at Millbrook, NY with several insitu <span class="hlt">soil</span> <span class="hlt">moisture</span> probes and an L-Band radiometer similar to <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Passive and Active (SMAP) one. This site is among SMAP Cal/Val sites. <span class="hlt">Soil</span> <span class="hlt">moisture</span> information was measured at seven different locations from 2012 to 2015. Hydra probes are used to measure six of these locations. This study utilizes the observations from insitu data and the L-Band radiometer close to ground (at 3 meters height) to validate and to compare <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates from AMSR2. Analysis of the measurements and AMSR2 indicated a weak correlation with the hydra probes and a moderate correlation with Cosmic-ray <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observing System (COSMOS probes). Several differences including the differences between pixel size and point measurements can cause these discrepancies. Some interpolation techniques are used to expand point measurements from 6 locations to AMSR2 footprint. Finally, the effect of penetration depth in microwave signal and inconsistencies with other ancillary data such as skin temperature is investigated to provide a better understanding in the analysis. The results show that the retrieval algorithm of AMSR2 is appropriate under certain circumstances. This validation algorithm and similar study will be conducted for SMAP mission. Keywords: Remote Sensing, <span class="hlt">Soil</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9995H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9995H"><span>Catchment controls on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics: from site-specific hysteresis in event responses to temporal stability of patterns</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hassler, Sibylle K.; Weiler, Markus; Blume, Theresa</p> <p>2015-04-01</p> <p>Understanding <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics is a prerequisite for predicting hydrological response at the hillslope and catchment scale. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is not only determined by its input characteristics such as rainfall, its redistribution by vegetation and evapotranspiration. Catchment characteristics resulting from the interplay of geology, topography, land cover and associated <span class="hlt">soil</span> hydraulic properties also <span class="hlt">affect</span> the distribution, storage and transport of water in the vadose zone. Successful process predictions and appropriate hydrological model structures thus rely on a good representation of <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns and dynamics and benefit from insights into their dependence on catchment characteristics. In a unique measurement setup at the CAOS hydrological observatory in Luxemburg (http://www.caos-project.de) we record hydro-meteorological variables at 45 sensor cluster sites. These sites are distributed across the mesoscale Attert catchment and cover three different geological units (schist, marls and sandstone), two types of land use (forest and grassland), different topographical positions (up- and downslope with north- and south-facing aspects as well as plateau and floodplain locations). At each sensor cluster, each covering approximately an area of 30 m², <span class="hlt">soil</span> <span class="hlt">moisture</span> is measured in three profiles at three different depths, in piezometers groundwater levels are recorded, and rain gauges collect throughfall or gross precipitation. At near-stream locations we also measure stream water levels. This extensive sensor network enables us to study the influence of geology, land use and topography on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. In this study we focus on short-term hysteretic responses related to individual rainfall events and on longer-term temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns. Similarities in the hysteresis loops of rainfall/<span class="hlt">soil</span> <span class="hlt">moisture</span>, <span class="hlt">soil</span> <span class="hlt">moisture</span>/groundwater levels and <span class="hlt">soil</span> <span class="hlt">moisture</span>/stream water levels can give some indication of the dominant catchment</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.B34B..05S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.B34B..05S"><span>Modeling in situ <span class="hlt">soil</span> enzyme activity using continuous field <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Steinweg, J. M.; Wallenstein, M. D.</p> <p>2010-12-01</p> <p><span class="hlt">Moisture</span> and temperature are key drivers of <span class="hlt">soil</span> organic matter decomposition, but there is little consensus on how climate change will <span class="hlt">affect</span> the degradation of specific <span class="hlt">soil</span> compounds under field conditions. <span class="hlt">Soil</span> enzyme activities are a useful metric of <span class="hlt">soil</span> community microbial function because they are they are the direct agents of decomposition for specific substrates in <span class="hlt">soil</span>. However, current standard enzyme assays are conducted under optimized conditions in the laboratory and do not accurately reflect in situ enzyme activity, where diffusion and substrate availability may limit reaction rates. The Arrhenius equation, k= A*e(-Ea/RT), can be used to predict enzyme activity (k), collision frequency (A) or activation energy (Ea), but is difficult to parameterize when activities are measured under artificial conditions without diffusion or substrate limitation. We developed a modifed equation to estimate collision frequency and activation energy based on <span class="hlt">soil</span> <span class="hlt">moisture</span> to model in-situ enzyme activites. Our model was parameterized using data we collected from the Boston Area Climate Experiment (BACE) in Massachusetts; a multi-factor climate change experiment that provides an opportunity to assess how changes in <span class="hlt">moisture</span> availability and temperature may impact enzyme activity. <span class="hlt">Soils</span> were collected from three precipitation treatments and four temperature treatments arranged in a full-factorial design at the BACE site in June 2008, August 2008, January 2009 and June 2009. Enzyme assays were performed at four temperatures (4, 15, 25 and 35°C) to calculate temperature sensitivity and activation energy over the different treatments and seasons. Enzymes activities were measured for six common enzymes involved in carbon (β-glucosidase, cellobiohydrolase, xylosidase), phosphorus (phosphatase) and nitrogen cycling (N-acetyl glucosaminidase, and leucine amino peptidase). Potential enzyme activity was not significantly <span class="hlt">affected</span> by precipitation, warming or the interaction of</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.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5492425','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5492425"><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="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Leone, Marco; Principe, Sofia; Consales, Marco; Parente, Roberto; Laudati, Armando; Caliro, Stefano; Cutolo, Antonello; Cusano, Andrea</p> <p>2017-01-01</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>. PMID:28632172</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.7987L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.7987L"><span>Mapping <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface heat fluxes by assimilating GOES land surface temperature and SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, Yang; Steele-Dunne, Susan C.; van de Giesen, Nick</p> <p>2017-04-01</p> <p>This study is focused on estimating <span class="hlt">soil</span> <span class="hlt">moisture</span> and sensible/latent heat fluxes by assimilating remotely-sensed land surface temperature (LST) and <span class="hlt">soil</span> <span class="hlt">moisture</span> data. Surface heat fluxes interact with the overlying atmosphere, and play a crucial role in the water and energy cycles. However, they cannot be directly measured using remote sensing. It has been demonstrated that LST time series contain information about the surface energy balance, and that assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> further improves the estimation by putting more constraints on the energy partitioning. In previous studies, two controlling factors were estimated: (1) a monthly constant bulk heat transfer coefficient (CHN) that scales the sum of surface heat fluxes, and (2) an evaporative fraction (EF) which governs the energy partitioning and stays quasi-constant during the near-peak hours. Considering the fact that CHN is not constant especially in the growing season, here CHN is assumed a function of leaf area index (LAI). LST data from GOES (Geostationary Operational Environmental Satellites) and <span class="hlt">soil</span> <span class="hlt">moisture</span> data from SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive) are both assimilated into a simply heat and water transfer model to update LST, <span class="hlt">soil</span> <span class="hlt">moisture</span>, CHN and EF , and to map surface heat fluxes over a study area in central US. A hybrid data assimilation strategy is necessary because SMAP data are available every 2-3 days, while GOES LST data are provided every hour. In this study, LST data are assimilated using an adaptive particle batch smoother (APBS) and <span class="hlt">soil</span> <span class="hlt">moisture</span> is periodically updated using a particle filter (PF). Results show that <span class="hlt">soil</span> <span class="hlt">moisture</span> is greatly improved, and that EF estimates are restored very well after assimilation. As forcing data are provided by remote sensing or reanalysis products to minimize the dependence on ground measurements, this methodology can be easily applied in other regions with limited data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H31D0637L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H31D0637L"><span>Indirect Measurement of Evapotranspiration from <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Depletion</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, M.; Chen, Y.</p> <p>2007-12-01</p> <p>Direct and in situ measurement of evapotranspiration (ET), such as the eddy covariance (EC) method, is often expensive and complicated, especially over tall canopy. In view of <span class="hlt">soil</span> water balance, depletion of <span class="hlt">soil</span> <span class="hlt">moisture</span> can be attributed to canopy ET when horizontal <span class="hlt">soil</span> <span class="hlt">moisture</span> movement is negligible and percolation ceases. This study computed the daily <span class="hlt">soil</span> <span class="hlt">moisture</span> depletion at the Lien-Hua-Chih (LHC) station (23°55'52"N, 120°53'39"E, 773 m elevation) from July, 2004 to June, 2007 to estimate daily ET. The station is inside an experimental watershed of a natural evergreen forest and the canopy height is about 17 m. Rainfall days are assumed to be no ET. For those days with high <span class="hlt">soil</span> <span class="hlt">moisture</span> content, normally 2 to 3 days after significant rainfall input, ET is estimated by potential ET. <span class="hlt">Soil</span> <span class="hlt">moistures</span> were measured by capacitance probes at -10 cm, - 30 cm, -50 cm, -70 cm, and -90 cm. A <span class="hlt">soil</span> heat flux plate was placed at -5 cm. In the summer of 2006, a 22 m tall observation tower was constructed. Temperature and relative humidity sensors were placed every 5 m from ground surface to 20 m for inner and above canopy measurements. Net radiation and wind speed/directions were also installed. A drainage gauge was installed at -50 cm to collect infiltrated water. Continuous measurements of low response instruments were recorded every 30-minute averaged from 10-minute samplings. A nearby weather station provides daily pan evaporation and precipitation data. Since the response of <span class="hlt">soil</span> water variations is relatively slow to the fluctuations of atmospheric forcing, only daily ET is estimated from daily <span class="hlt">soil</span> <span class="hlt">moisture</span> depletion. The annual average precipitation is 2902 mm and the annual average ET is 700 mm. The seasonal ET patterns of the first two water years are similar. The third year has a higher ET because <span class="hlt">soil</span> <span class="hlt">moisture</span> was recharged frequently by rainfall In order to examine the applicability of this approach, an EC system, including a 3-D sonic anemometer (Young</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011HESS...15..561W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011HESS...15..561W"><span>Desert shrub stemflow and its significance in <span class="hlt">soil</span> <span class="hlt">moisture</span> replenishment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, X.-P.; Wang, Z.-N.; Berndtsson, R.; Zhang, Y.-F.; Pan, Y.-X.</p> <p>2011-02-01</p> <p>Stemflow of xerophytic shrubs represents a significant component of water replenishment to the <span class="hlt">soil</span>-root system influencing water utilization of plant roots at the stand scale, especially in water scarce desert ecosystems. In this study, stemflow of Caragana korshinskii was quantified by an aluminum foil collar collection method on re-vegetated sand dunes of the Shapotou restored desert ecosystem in northwestern China. Time domain reflectometry probes were inserted horizontally at 20 different <span class="hlt">soil</span> profile depths under the C. korshinskii shrub to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span> variation at hourly intervals. Results indicated that 2.2 mm precipitation was necessary for the generation of stemflow for C. korshinskii. Stemflow averaged 8% of the gross precipitation and the average funnelling ratio was as high as 90. The <span class="hlt">soil</span> <span class="hlt">moisture</span> in the uppermost <span class="hlt">soil</span> profile was strongly correlated with individual rainfall and the stemflow strengthened this relationship. Therefore, it is favourable for the infiltrated water redistribution in the deeper <span class="hlt">soil</span> profile of the root zone. Consequently, stemflow contributes significantly to a positive <span class="hlt">soil</span> <span class="hlt">moisture</span> balance in the root zone and the replenishment of <span class="hlt">soil</span> <span class="hlt">moisture</span> at deeper <span class="hlt">soil</span> layers. This plays an important role in plant survival and the general ecology of arid desert environments.</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('https://ntrs.nasa.gov/search.jsp?R=19840050551&hterms=watershed&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dwatershed','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19840050551&hterms=watershed&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dwatershed"><span>Aircraft scatterometer observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> on rangeland watersheds</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jackson, T. J.; Oneill, P. E.</p> <p>1983-01-01</p> <p>Extensive studies conducted by several researchers using truck-mounted active microwave sensors have shown the sensitivity of these sensors to <span class="hlt">soil</span> <span class="hlt">moisture</span> variations. The logical extension of these results is the evaluation of similar systems at lower resolutions typical of operational systems. Data collected during a series of aircraft flights in 1978 and 1980 over four rangeland watersheds located near Chickasha, Oklahoma, were analyzed in this study. These data included scatterometer measurements made at 1.6 and 4.75 GHz using a NASA aircraft and ground observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> for a wide range of <span class="hlt">moisture</span> conditions. Data were analyzed for consistency and compared to previous truck and aircraft results. Results indicate that the sensor system is capable of providing consistent estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span> under the conditions tested.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020023144&hterms=different+types+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Ddifferent%2Btypes%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020023144&hterms=different+types+soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Ddifferent%2Btypes%2Bsoil"><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> </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('https://ntrs.nasa.gov/search.jsp?R=20020023144&hterms=zones+moisture&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dzones%2Bmoisture','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020023144&hterms=zones+moisture&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dzones%2Bmoisture"><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('https://www.ncbi.nlm.nih.gov/pubmed/18246293','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/18246293"><span><span class="hlt">Soil</span> microbial responses to temporal variations of <span class="hlt">moisture</span> and temperature in a chihuahuan desert grassland.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bell, Colin; McIntyre, Nancy; Cox, Stephen; Tissue, David; Zak, John</p> <p>2008-07-01</p> <p>Global climate change models indicate that storm magnitudes will increase in many areas throughout southwest North America, which could result in up to a 25% increase in seasonal precipitation in the Big Bend region of the Chihuahuan Desert over the next 50 years. Seasonal precipitation is a key limiting factor regulating primary productivity, <span class="hlt">soil</span> microbial activity, and ecosystem dynamics in arid and semiarid regions. As decomposers, <span class="hlt">soil</span> microbial communities mediate critical ecosystem processes that ultimately <span class="hlt">affect</span> the success of all trophic levels, and the activity of these microbial communities is primarily regulated by <span class="hlt">moisture</span> availability. This research is focused on elucidating <span class="hlt">soil</span> microbial responses to seasonal and yearly changes in <span class="hlt">soil</span> <span class="hlt">moisture</span>, temperature, and selected <span class="hlt">soil</span> nutrient and edaphic properties in a Sotol Grassland in the Chihuahuan Desert at Big Bend National Park. <span class="hlt">Soil</span> samples were collected over a 3-year period in March and September (2004-2006) at 0-15 cm <span class="hlt">soil</span> depth from 12 3 x 3 m community plots. Bacterial and fungal carbon usage (quantified using Biolog 96-well micro-plates) was related to <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns (ranging between 3.0 and 14%). In addition to <span class="hlt">soil</span> <span class="hlt">moisture</span>, the seasonal and yearly variability of <span class="hlt">soil</span> bacterial activity was most closely associated with levels of <span class="hlt">soil</span> organic matter, extractable NH(4)-N, and <span class="hlt">soil</span> pH. Variability in fungal activity was related to <span class="hlt">soil</span> temperatures ranging between 13 and 26 degrees C. These findings indicate that changes in <span class="hlt">soil</span> <span class="hlt">moisture</span>, coupled with <span class="hlt">soil</span> temperatures and resource availability, drive the functioning of <span class="hlt">soil</span>-microbial dynamics in these desert grasslands. Temporal patterns in microbial activity may reflect the differences in the ability of bacteria and fungi to respond to seasonal patterns of <span class="hlt">moisture</span> and temperature. Bacteria were more able to respond to <span class="hlt">moisture</span> pulses regardless of temperature, while fungi only responded to <span class="hlt">moisture</span> pulses during cooler seasons with</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('http://adsabs.harvard.edu/abs/2015AGUFM.B53A0538W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.B53A0538W"><span>Upscaling sparse, irregularly spaced in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements for calibration and validation of SMAP <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>Whitcomb, J.; Clewley, D.; Moghaddam, M.; Akbar, R.; Silva, A. R. D.</p> <p>2015-12-01</p> <p>There is a large difference in the footprints over which remote sensing instruments, such as the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> and that of in situ networks. Therefore a method for upscaling in situ measurements is required before they can be used to validate remote sensing instruments. The upscaling problem is made more difficult when measurements are sparse and irregularly spaced within the footprint. To address these needs, we have developed a method for producing upscaled estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span> based on a network of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements and airborne P-band SAR data, and utilizing a Random Forests-based regression algorithm. Sites within the <span class="hlt">Soil</span>SCAPE network, for which the technique was developed, typically contains sensors at ~30 locations, with each location sampled at multiple depths. Measurements are taken at 20 minute intervals and averaged over a selectable time interval, thereby supporting near-real time generation of <span class="hlt">soil</span> <span class="hlt">moisture</span> maps. The collected measurements are automatically uploaded to a central database from which they can be accessed for use in the regression algorithm. Our regression-based approach works well with irregularly-spaced sensors by incorporating a set of data layers that correlate well with <span class="hlt">soil</span> <span class="hlt">moisture</span>. The layers include thematic land cover, elevation, slope, aspect, flow accumulation, clay fraction, air temperature, precipitation, and P-Band HH, VV, and HV backscatter. Values from these data layers are extracted for each sensor location and applied to train the Random Forests algorithm. The decision trees generated are then applied to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> at a 100 m spacing throughout the network region, after which the evenly-spaced values are averaged to accord with the 3-, 9-, and 36-km SMAP measurement grids. The resulting set of near-real time <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates suitable for SMAP calibration and validation is placed online for use by the SMAP Cal/Val team</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> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24170970','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24170970"><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="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Shen, Rui; Pennell, Kelly G; Suuberg, Eric M</p> <p>2013-10-01</p> <p>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.</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('http://pubs.er.usgs.gov/publication/70030715','USGSPUBS'); return false;" href="http://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('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://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('http://adsabs.harvard.edu/abs/2017JGRD..122.5533Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRD..122.5533Z"><span>Development and evaluation of the MTVDI for <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhu, Wenbin; Lv, Aifeng; Jia, Shaofeng; Sun, Liang</p> <p>2017-06-01</p> <p>Several parameterization schemes have been developed to retrieve the <span class="hlt">soil</span> <span class="hlt">moisture</span> information involved in the remotely sensed surface temperature-vegetation index (Ts - VI) space. However, most of them are performed with the constraint of the dry edge of the Ts - VI space to define the maximum water stressed conditions. In view of the subjectivity and uncertainty involved in the determination of the dry edge, a new index termed as the Modified Temperature-Vegetation Dryness Index (MTVDI) was developed in this paper to reduce the reliance of the parameterization scheme on the dry edge. In the parameterization scheme of MTVDI, isopleth lines of <span class="hlt">soil</span> <span class="hlt">moisture</span> involved in the feature space were retrieved by the temperature-vegetation index method, and only the maximum surface temperature of bare <span class="hlt">soil</span> (Tsmax) was indispensable in the definition of maximum water stressed conditions. For evaluation purpose, the MTVDI was demonstrated in the Southern Great Plains region of the U.S. and was compared with two other traditional <span class="hlt">soil</span> <span class="hlt">moisture</span> indexes developed under the constraint of dry edge. The comparison confirmed the effectivity of the MTVDI in monitoring the spatial pattern and seasonal variation of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Our analyses also suggest that Tsmax, the only parameter needed in the definition of maximum water stressed conditions, can be retrieved directly from the parameterization scheme itself. Therefore, the retrieval of MTVDI can be performed independent of the dry edge, which is a significant improvement to the traditional parameterization schemes of <span class="hlt">soil</span> <span class="hlt">moisture</span> from the Ts - VI feature space.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=156896&keyword=photosynthesis&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&CFID=91080822&CFTOKEN=32715263','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=156896&keyword=photosynthesis&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&CFID=91080822&CFTOKEN=32715263"><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('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=156896&keyword=seedlings&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="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=156896&keyword=seedlings&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('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('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('https://ntrs.nasa.gov/search.jsp?R=20170002025&hterms=David+Scott&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DDavid%2BH.%2BT.%2BScott','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170002025&hterms=David+Scott&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DDavid%2BH.%2BT.%2BScott"><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> </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('https://ntrs.nasa.gov/search.jsp?R=20170002025&hterms=Martinez&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DMartinez','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170002025&hterms=Martinez&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DMartinez"><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; Yueh, Simon; Entekhabi, Dara; Cosh, Michael H.; Caldwell, Todd; Walker, Jeffrey; Wu, Xiaoling; Berg, Aaron; Rowlandson, Tracy; Pacheco, Anna; McNairn, Heather; Thibeault, Marc; Martinez-Fernandez, Jose; Gonzalez-Zamora, Angel; Seyfried, Mark; Bosch, David; Starks, Patrick; Goodrich, David; Prueger, John; Palecki, Michael; Small, Eric E.; Zreda, Marek; Calvet, Jean-Christophe; Crow, Wade T.; Kerr, Yann</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/2006HyPr...20.2477B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006HyPr...20.2477B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> dynamics in an eastern Amazonian tropical forest</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bruno, Rogério D.; da Rocha, Humberto R.; de Freitas, Helber C.; Goulden, Michael L.; Miller, Scott D.</p> <p>2006-08-01</p> <p>We used frequency-domain reflectometry to make continuous, high-resolution measurements for 22 months of the <span class="hlt">soil</span> <span class="hlt">moisture</span> to a depth of 10 m in an Amazonian rain forest. We then used these data to determine how <span class="hlt">soil</span> <span class="hlt">moisture</span> varies on diel, seasonal and multi-year timescales, and to better understand the quantitative and mechanistic relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> and forest evapotranspiration. The mean annual precipitation at the site was over 1900 mm. The field capacity was approximately 0.53 m3 m-3 and was nearly uniform with <span class="hlt">soil</span> depth. <span class="hlt">Soil</span> <span class="hlt">moisture</span> decreased at all levels during the dry season, with the minimum of 0.38 m3 m-3 at 3 m beneath the surface. The <span class="hlt">moisture</span> in the upper 1 m showed a strong diel cycle with daytime depletion due to evapotranspiration. The <span class="hlt">moisture</span> beneath 1 m declined during both day and night due to the combined effects of evapotranspiration, drainage and a nighttime upward movement of water. The depth of active water withdrawal changed markedly over the year. The upper 2 m of <span class="hlt">soil</span> supplied 56% of the water used for evapotranspiration in the wet season and 28% of the water used in the dry season. The zone of active water withdrawal extended to a depth of at least 10 m. The day-to-day rates of <span class="hlt">moisture</span> withdrawal from the upper 10 m of <span class="hlt">soil</span> during rain-free periods agreed well with simultaneous measurements of whole-forest evapotranspiration made by the eddy covariance technique. The forest at the site was well adapted to the normal cycle of wet and dry seasons, and the dry season had only a small effect on the rates of land-atmosphere water vapour exchange.</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('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://ntrs.nasa.gov/search.jsp?R=20000038011&hterms=Runoff+water&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DRunoff%2Bwater','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038011&hterms=Runoff+water&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DRunoff%2Bwater"><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('https://ntrs.nasa.gov/search.jsp?R=20000038011&hterms=SPECIFIC+SURFACE+AREA&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DSPECIFIC%2BSURFACE%2BAREA','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038011&hterms=SPECIFIC+SURFACE+AREA&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DSPECIFIC%2BSURFACE%2BAREA"><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://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/2013AGUFM.H11L..06T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H11L..06T"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> - precipitation feedbacks in observations and models (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Taylor, C.</p> <p>2013-12-01</p> <p>There is considerable uncertainty about the strength, geographical extent, and even the sign of feedbacks between <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation. Whilst precipitation trivially increases <span class="hlt">soil</span> <span class="hlt">moisture</span>, the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span>, via surface fluxes, on convective rainfall is far from straight-forward, and likely depends on space and time scale, <span class="hlt">soil</span> and synoptic conditions, and the nature of the convection itself. In considering how daytime convection responds to surface fluxes, large-scale models based on convective parameterisations may not necessarily provide reliable depictions, particularly given their long-standing inability to reproduce a realistic diurnal cycle of convection. On the other hand, long-term satellite data provide the potential to establish robust relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation across the world, notwithstanding some fundamental weaknesses and uncertainties in the datasets. Here, results from regional and global satellite-based analyses are presented. Globally, using 3-hourly precipitation and daily <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets, a methodology has been developed to compare the statistics of antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> in the region of localised afternoon rain events (Taylor et al 2012). Specifically the analysis tests whether there are any significant differences in pre-event <span class="hlt">soil</span> <span class="hlt">moisture</span> between rainfall maxima and nearby (50-100km) minima. The results reveal a clear signal across a number of semi-arid regions, most notably North Africa, indicating a preference for afternoon rain over drier <span class="hlt">soil</span>. Analysis by continent and by climatic zone reveals that this signal (locally a negative feedback) is evident in other continents and climatic zones, but is somewhat weaker. This may be linked to the inherent geographical differences across the world, as detection of a feedback requires water-stressed surfaces coincident with frequent active convective initiations. The differences also reflect the quality and utility of the <span class="hlt">soil</span> <span class="hlt">moisture</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESASP.740E.137M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESASP.740E.137M"><span>Integrating Microwave and Optical Data for Monitoring <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>Morgan, R. S.; Abd El-Hady, M.; Rahim, I. S.; Silva, J.; Berg, A.</p> <p>2016-08-01</p> <p>In arid regions, such as Egypt, irrigation is the main source of water consumption and freshwater resources are getting scarcer. Therefore, the development of an appropriate irrigation water practices becomes a necessity. <span class="hlt">Soil</span> <span class="hlt">moisture</span>, in particular, plays a key role in any efficient water use strategy for agriculture. This study aims at suggesting a protocol for processing microwave data (Sentinel-1) supported by optical data (Landsat 8) with and without ancillary data and utilizing Artificial Neural Network (ANN) to provide repeatable, reliable and accurate estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> content and at a practical interval. The results of this study suggested two approaches for <span class="hlt">soil</span> <span class="hlt">moisture</span> predictions using Sentienl-1 data. The first approach depended totally on remote sensing data with a correlation of 0.76. The second approach is more suitable when accurate detailed field survey of <span class="hlt">soil</span> field capacity is available and reached a correlation of about 0.98.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.8067A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.8067A"><span>Sensitivity of Severe Convective Storms to <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Lower Atmospheric Water Vapor</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ancell, Brian; Nauert, Christian</p> <p>2014-05-01</p> <p>Numerous studies have examined the sensitivity of the atmospheric state to <span class="hlt">soil</span> <span class="hlt">moisture</span> on time scales of up to a day. Dry line intensity, lower tropospheric water vapor content, and precipitation have all been shown through modeling studies to be <span class="hlt">affected</span> by modest perturbations to upstream <span class="hlt">soil</span> <span class="hlt">moisture</span> content and subsequent lower atmospheric water vapor. Since all of these aspects could be associated with convection, a high-impact forecast event that exhibits rapid nonlinear error growth, it is reasonable to expect that irrigation practices might influence severe convective storms. Understanding the link between <span class="hlt">soil</span> <span class="hlt">moisture</span> and specific convective elements could have broad implications for severe weather forecasting, and could reveal the degree to which irrigation-induced storm-scale inadvertent weather modification exists. This work examines the sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span> and lower atmospheric water vapor content of a severe convective storm that struck Moore, Oklahoma, USA on May 20th, 2013, killing 24 people. While adjoint sensitivity analysis that employs the tangent linear version of a numerical weather prediction model might be used to examine convective sensitivities to <span class="hlt">soil</span> <span class="hlt">moisture</span>, the strong nonlinearity associated with these events likely renders this technique inaccurate. Alternatively, the approach here utilizes backward trajectory analysis to identify the regions up to a day prior to which the storm might be sensitive. Once the regions are identified, an ensemble of model forecasts is created by varying initial <span class="hlt">soil</span> <span class="hlt">moisture</span> to reveal the degree to which perturbations must be made to influence the downstream storm. Subsequent comparisons are made between the required <span class="hlt">soil</span> <span class="hlt">moisture</span> perturbations and realistic <span class="hlt">soil</span> water values added through irrigation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H33H1659H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H33H1659H"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and total water storage changes assimilation at global scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Han, X.; Hendricks Franssen, H. J.; Montzka, C.; Vereecken, H.</p> <p>2016-12-01</p> <p>The global water cycle estimation with land surface models is <span class="hlt">affected</span> by uncertainties in the model input data, model parameters and model physics. Data assimilation has the potential to improve model estimation by incorporating multivariate measurements. In this study, we investigated the assimilation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and total water storage changes in the community land model (CLM), at the global scale. As the first step, the global land surface states were estimated by the Community Land Mode version 4.5 (CLM) for the period 2009-2010. These simulations were driven by CRU_NCEP reanalysis data at 0.5o spatial resolution. In a second step, the different combinations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and total water storage changes data were assimilated into CLM using the local ensemble transform Kalman filter. The daily remote sensing <span class="hlt">soil</span> <span class="hlt">moisture</span> product of the climate changes initiative from the European Space Agency and monthly total water storage changes of GRACE from the National Aeronautics and Space Administration were used as the observation data to be assimilated. The CLM uncertainties were represented by 20 ensemble members on the basis of perturbed atmospheric forcing data and <span class="hlt">soil</span> properties. The results show that the latent heat flux was improved compared with FLUXNET data in the tropical region by assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> or total water storage changes. Assimilation of <span class="hlt">soil</span> <span class="hlt">moisture</span> alone could not improve the latent heat flux estimation on average, probably related to the microwave band at which the <span class="hlt">soil</span> <span class="hlt">moisture</span> content was measured. The best results were obtained for the univariate assimilation of total water storage changes. The bivariate assimilation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and total water storage changes did not improve the results further.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=330382','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=330382"><span>Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval using the L-band synthetic aperture radar onboard the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive satellite and evaluation at 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>This paper evaluates the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814925B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814925B"><span>The role of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the coevolution of <span class="hlt">soil</span> and vegetation in mountain grasslands</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bertoldi, Giacomo; Claudia, Notarnicola; Brenner, Johannes; Castelli, Mariapina; Greifeneder, Felix; Niedrist, Georg; Seeber, Julia; Tappeiner, Ulrike</p> <p>2016-04-01</p> <p>One of the key variables controlling the organization of vegetation and the coevolution of <span class="hlt">soils</span> and landforms is <span class="hlt">soil</span> <span class="hlt">moisture</span> content (SMC). For this reason, understanding the controls on the spatial and temporal patterns of SMC is essential to predict how perturbations in vegetation and climate will <span class="hlt">affect</span> mountain ecosystem functioning. In this contribution, we focus on the dynamic of surface SMC of water-limited alpine grasslands in the Long Term Ecological Research area Mazia Valley in the European Alps. We analyze the impacts of different land managements (meadows versus pastures) and its relationships with climate and topography. The area has been equipped since 2009 with a network of more than 20 stations, measuring SMC and climatic variables and with two eddy-covariance stations, measuring surface fluxes over meadows and pastures. Monthly biomass production data have been collected and detailed <span class="hlt">soil</span> and spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> surveys are available. Moreover, high spatial resolution SMC maps have been derived from satellites Synthetic Aperture Radar Radar (SAR) images (Sentinel 1 and RADARSAT2 images). Both ground surveys and remote sensing observations show persistent landscape-level patterns. Meadows, in general located in flatter areas, tend to be wetter. This leads to higher vegetation productivity and to the development of <span class="hlt">soils</span> with higher water holding capacity, thus to a positive feedback on SMC. In contrast, pastures, located on steeper slopes with lower vegetation density and higher <span class="hlt">soil</span> erosion, tend to be drier, leading to a negative feedback on SMC and <span class="hlt">soil</span> development. This co-evolution of land cover and SMC leads therefore to persistent spatial patterns. In order to understand quantitatively such linked interactions, a sensitivity analysis has been performed with the GEOtop hydrological model. Results show how both abiotic (mainly slope and elevation) and anthropogenic (irrigation and <span class="hlt">soil</span> management) factors exert a significant control on</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.2702L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.2702L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensing Using Reflected GPS Signals: Description of the GPS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Larson, Kristine; Small, Eric; Chew, Clara</p> <p>2015-04-01</p> <p>As first demonstrated by the GPS reflections group in 2008, data from GPS networks can be used to monitor multiple parameters of the terrestrial water cycle. The GPS L-band signals take two paths: (1) the "direct" signal travels from the satellite to the antenna, which is typically located 2-3 meters above the ground; (2) the reflected signal interacts with the Earth's surface before traveling to the antenna. The direct signal is used by geophysicists and surveyors to measure the position of the antenna, while the effects of reflected signals are a source of error. If one focuses on the reflected signal rather than the positioning observables, one has a method that is sensitive to surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (top 5 cm), vegetation water content, and snow depth. This method - known as GPS Interferometric Reflectometry (GPS-IR) - has a footprint of ~1000 m2 for most GPS sites. This is intermediate in scale to most in situ and satellite observations. A significant advantage of GPS-IR is that data from existing GPS networks can be used without any changes to the instrumentation. This means that there is a new source of cost-effective instrumentation for satellite validation and climate studies. This presentation will provide an overview of the GPS-IR methodology with an emphasis on the <span class="hlt">soil</span> <span class="hlt">moisture</span> product. GPS water cycle products are currently produced on a daily basis for a network of ~500 sites in the western United States; results are freely available at http://xenon.colorado.edu/portal. Plans to expand the GPS-IR method to the network of international GPS sites will also be discussed.</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=meteorological+calculation+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dmeteorological%2Bcalculation%2Bmodel','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950048080&hterms=meteorological+calculation+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dmeteorological%2Bcalculation%2Bmodel"><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('https://ntrs.nasa.gov/search.jsp?R=19950048080&hterms=soil+differences&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsoil%2Bdifferences','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950048080&hterms=soil+differences&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsoil%2Bdifferences"><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('https://ntrs.nasa.gov/search.jsp?R=19930036632&hterms=gravimetric+determination&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dgravimetric%2Bdetermination','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930036632&hterms=gravimetric+determination&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dgravimetric%2Bdetermination"><span>Determination of <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution from impedance and gravimetric measurements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ungar, Stephen G.; Layman, Robert; Campbell, Jeffrey E.; Walsh, John; Mckim, Harlan J.</p> <p>1992-01-01</p> <p>Daily measurements of the <span class="hlt">soil</span> dielectric properties at 5 and 10 cm were obtained at five locations throughout the First ISLSCP Field Experiment (FIFE) test site during the 1987 intensive field campaigns (IFCs). An automated vector voltmeter was used to monitor the complex electrical impedance, at 10 MHz, of cylindrical volumes of <span class="hlt">soil</span> delineated by specially designed <span class="hlt">soil</span> <span class="hlt">moisture</span> probes buried at these locations. The objective of this exercise was to test the hypothesis that the <span class="hlt">soil</span> impedance is sensitive to the <span class="hlt">moisture</span> content of the <span class="hlt">soil</span> and that the imaginary part (that is, capacitive reactance) can be used to calculate the volumetric water content of the <span class="hlt">soil</span>. These measurements were compared with gravimetric samples collected at these locations by the FIFE staff science team.</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://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('https://www.ncbi.nlm.nih.gov/pubmed/15765677','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15765677"><span>Effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature on reproduction and development of twospotted spider mite (Acari: Tetranychidae) in strawberries.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>White, Jeffrey C; Liburd, Oscar E</p> <p>2005-02-01</p> <p>The effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature on the reproduction of twospotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae), were examined in laboratory and field tests in strawberries, Fragaria x ananassa Duchesne, in Florida. Different <span class="hlt">soil</span> <span class="hlt">moisture</span> levels (low, moderate, and high) were compared to determine how <span class="hlt">soil</span> <span class="hlt">moisture</span> <span class="hlt">affects</span> the reproduction and development of twospotted spider mite. In addition to <span class="hlt">soil</span> <span class="hlt">moisture</span>, different irrigation techniques (drip versus drip/overhead) were compared to determine their effects on twospotted spider mite reproduction as well as the incidence of angular leaf spot, Xanthomonas fragaria Kennedy & King disease. Similar studies were conducted to determine how different temperatures (18, 27, and 35 degrees C) <span class="hlt">affect</span> the reproduction and development of twospotted spider mites. In the laboratory, low <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as temperatures >27 degrees C promoted twospotted spider mite development. A similar trend was observed in a field study with low <span class="hlt">soil</span> <span class="hlt">moisture</span> promoting twospotted spider mite reproduction during the early season (11 November--8 December). Irrespective of <span class="hlt">moisture</span> levels, a significantly higher incidence of X. fragaria was recorded in treatments with drip/overhead irrigation systems compared with drip irrigation. Implications for management of <span class="hlt">soil</span> <span class="hlt">moisture</span> levels are discussed with respect to the abundance of twospotted spider mite and X. fragaria in strawberries.</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 variabili