Sample records for absolute soil moisture

  1. [Simulation of cropland soil moisture based on an ensemble Kalman filter].

    PubMed

    Liu, Zhao; Zhou, Yan-Lian; Ju, Wei-Min; Gao, Ping

    2011-11-01

    By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.

  2. Evaluation of Assimilated SMOS Soil Moisture Data for US Cropland Soil Moisture Monitoring

    NASA Technical Reports Server (NTRS)

    Yang, Zhengwei; Sherstha, Ranjay; Crow, Wade; Bolten, John; Mladenova, Iva; Yu, Genong; Di, Liping

    2016-01-01

    Remotely sensed soil moisture data can provide timely, objective and quantitative crop soil moisture 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 Soil Moisture Ocean Salinity (SMOS)Mission L-band passive microwave data for operational US cropland soil surface moisture monitoring. The assimilated SMOS soil moisture data are first categorized to match with the United States Department of Agriculture (USDA)National Agricultural Statistics Service (NASS) survey based weekly soil moisture observation data, which are ordinal. The categorized assimilated SMOS soil moisture data are compared with NASSs survey-based weekly soil moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS soil moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland soil moisture monitoring.

  3. Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors

    NASA Astrophysics Data System (ADS)

    McJannet, David; Hawdon, Aaron; Baker, Brett; Renzullo, Luigi; Searle, Ross

    2017-12-01

    Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.

  4. Validation of soil moisture ocean salinity (SMOS) satellite soil moisture products

    USDA-ARS?s Scientific Manuscript database

    The surface soil moisture state controls the partitioning of precipitation into infiltration and runoff. High-resolution observations of soil moisture will lead to improved flood forecasts, especially for intermediate to large watersheds where most flood damage occurs. Soil moisture is also key in d...

  5. Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture

    NASA Astrophysics Data System (ADS)

    Chew, C. C.; Small, E. E.

    2018-05-01

    This paper quantifies the relationship between forward scattered L-band Global Navigation Satellite System (GNSS) signals, recorded by the Cyclone Global Navigation Satellite System (CYGNSS) constellation and Soil Moisture Active Passive (SMAP) soil moisture (SM). Although designed for tropical ocean surface wind sensing, the CYGNSS receivers also record GNSS reflections over land. The CYGNSS observations of reflection power are compared to SMAP SM between March 2017 and February 2018. A strong, positive linear relationship exists between changes in CYGNSS reflectivity and changes in SMAP SM, but not between the absolute magnitudes of the two observations. The sensitivity of CYGNSS reflectivity to SM varies spatially and can be used to convert reflectivity to estimates of SM. The unbiased root-mean-square difference between daily averaged CYGNSS-derived SM and SMAP SM is 0.045 cm3/cm3 and is similarly low between CYGNSS and in situ SM. These results show that CYGNSS, and future GNSS reflection missions, could provide global SM observations.

  6. Comparing soil moisture memory in satellite observations and models

    NASA Astrophysics Data System (ADS)

    Stacke, Tobias; Hagemann, Stefan; Loew, Alexander

    2013-04-01

    A major obstacle to a correct parametrization of soil processes in large scale global land surface models is the lack of long term soil moisture observations for large parts of the globe. Currently, a compilation of soil moisture data derived from a range of satellites is released by the ESA Climate Change Initiative (ECV_SM). Comprising the period from 1978 until 2010, it provides the opportunity to compute climatological relevant statistics on a quasi-global scale and to compare these to the output of climate models. Our study is focused on the investigation of soil moisture memory in satellite observations and models. As a proxy for memory we compute the autocorrelation length (ACL) of the available satellite data and the uppermost soil layer of the models. Additional to the ECV_SM data, AMSR-E soil moisture is used as observational estimate. Simulated soil moisture fields are taken from ERA-Interim reanalysis and generated with the land surface model JSBACH, which was driven with quasi-observational meteorological forcing data. The satellite data show ACLs between one week and one month for the greater part of the land surface while the models simulate a longer memory of up to two months. Some pattern are similar in models and observations, e.g. a longer memory in the Sahel Zone and the Arabian Peninsula, but the models are not able to reproduce regions with a very short ACL of just a few days. If the long term seasonality is subtracted from the data the memory is strongly shortened, indicating the importance of seasonal variations for the memory in most regions. Furthermore, we analyze the change of soil moisture memory in the different soil layers of the models to investigate to which extent the surface soil moisture includes information about the whole soil column. A first analysis reveals that the ACL is increasing for deeper layers. However, its increase is stronger in the soil moisture anomaly than in its absolute values and the first even exceeds the

  7. 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...

  8. Surface Soil Moisture Estimates Across China Based on Multi-satellite Observations and A Soil Moisture Model

    NASA Astrophysics Data System (ADS)

    Zhang, Ke; Yang, Tao; Ye, Jinyin; Li, Zhijia; Yu, Zhongbo

    2017-04-01

    Soil moisture is a key variable that regulates exchanges of water and energy between land surface and atmosphere. Soil moisture retrievals based on microwave satellite remote sensing have made it possible to estimate global surface (up to about 10 cm in depth) soil moisture routinely. Although there are many satellites operating, including NASA's Soil Moisture Acitive Passive mission (SMAP), ESA's Soil Moisture 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 soil moisture products. In this study, we applied a single-channel soil moisture retrieval model forced by multiple sources of satellite brightness temperature observations to estimate consistent daily surface soil moisture across China at a spatial resolution of 25 km. By utilizing observations from multiple satellites, we are able to estimate daily soil moisture across the whole domain of China. We further developed a daily soil moisture accounting model and applied it to downscale the 25-km satellite-based soil moisture to 5 km. By comparing our estimated soil moisture with observations from a dense observation network implemented in Anhui Province, China, our estimated soil moisture results show a reasonably good agreement with the observations (RMSE < 0.1 and r > 0.8).

  9. A Round Robin evaluation of AMSR-E soil moisture retrievals

    NASA Astrophysics Data System (ADS)

    Mittelbach, Heidi; Hirschi, Martin; Nicolai-Shaw, Nadine; Gruber, Alexander; Dorigo, Wouter; de Jeu, Richard; Parinussa, Robert; Jones, Lucas A.; Wagner, Wolfgang; Seneviratne, Sonia I.

    2014-05-01

    Large-scale and long-term soil moisture observations based on remote sensing are promising data sets to investigate and understand various processes of the climate system including the water and biochemical cycles. Currently, the ESA Climate Change Initiative for soil moisture develops and evaluates a consistent global long-term soil moisture data set, which is based on merging passive and active remotely sensed soil moisture. Within this project an inter-comparison of algorithms for AMSR-E and ASCAT Level 2 products was conducted separately to assess the performance of different retrieval algorithms. Here we present the inter-comparison of AMSR-E Level 2 soil moisture products. These include the public data sets from University of Montana (UMT), Japan Aerospace and Space Exploration Agency (JAXA), VU University of Amsterdam (VUA; two algorithms) and National Aeronautics and Space Administration (NASA). All participating algorithms are applied to the same AMSR-E Level 1 data set. Ascending and descending paths of scaled surface soil moisture are considered and evaluated separately in daily and monthly resolution over the 2007-2011 time period. Absolute values of soil moisture as well as their long-term anomalies (i.e. removing the mean seasonal cycle) and short-term anomalies (i.e. removing a five weeks moving average) are evaluated. The evaluation is based on conventional measures like correlation and unbiased root-mean-square differences as well as on the application of the triple collocation method. As reference data set, surface soil moisture of 75 quality controlled soil moisture sites from the International Soil Moisture Network (ISMN) are used, which cover a wide range of vegetation density and climate conditions. For the application of the triple collocation method, surface soil moisture estimates from the Global Land Data Assimilation System are used as third independent data set. We find that the participating algorithms generally display a better

  10. The effect of soil moisture anomalies on maize yield in Germany

    NASA Astrophysics Data System (ADS)

    Peichl, Michael; Thober, Stephan; Meyer, Volker; Samaniego, Luis

    2018-03-01

    Crop models routinely use meteorological variations to estimate crop yield. Soil moisture, however, is the primary source of water for plant growth. The aim of this study is to investigate the intraseasonal predictability of soil moisture to estimate silage maize yield in Germany. We also evaluate how approaches considering soil moisture perform compare to those using only meteorological variables. Silage maize is one of the most widely cultivated crops in Germany because it is used as a main biomass supplier for energy production in the course of the German Energiewende (energy transition). Reduced form fixed effect panel models are employed to investigate the relationships in this study. These models are estimated for each month of the growing season to gain insights into the time-varying effects of soil moisture and meteorological variables. Temperature, precipitation, and potential evapotranspiration are used as meteorological variables. Soil moisture is transformed into anomalies which provide a measure for the interannual variation within each month. The main result of this study is that soil moisture anomalies have predictive skills which vary in magnitude and direction depending on the month. For instance, dry soil moisture anomalies in August and September reduce silage maize yield more than 10 %, other factors being equal. In contrast, dry anomalies in May increase crop yield up to 7 % because absolute soil water content is higher in May compared to August due to its seasonality. With respect to the meteorological terms, models using both temperature and precipitation have higher predictability than models using only one meteorological variable. Also, models employing only temperature exhibit elevated effects.

  11. 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.

  12. 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.

  13. 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.

  14. Soil moisture variability across different scales in an Indian watershed for satellite soil moisture product validation

    NASA Astrophysics Data System (ADS)

    Singh, Gurjeet; Panda, Rabindra K.; Mohanty, Binayak P.; Jana, Raghavendra B.

    2016-05-01

    Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA's Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.

  15. Utilization of point soil moisture measurements for field scale soil moisture averages and variances in agricultural landscapes

    USDA-ARS?s Scientific Manuscript database

    Soil moisture is a key variable in understanding the hydrologic processes and energy fluxes at the land surface. In spite of new technologies for in-situ soil moisture measurements and increased availability of remotely sensed soil moisture data, scaling issues between soil moisture observations and...

  16. Dynamics and characteristics of soil temperature and moisture of active layer in central Tibetan Plateau

    NASA Astrophysics Data System (ADS)

    Zhao, L.; Hu, G.; Wu, X.; Tian, L.

    2017-12-01

    Research on the hydrothermal properties of active layer during the thawing and freezing processes was considered as a key question to revealing the heat and moisture exchanges between permafrost and atmosphere. The characteristics of freezing and thawing processes at Tanggula (TGL) site in permafrost regions on the Tibetan Plateau, the results revealed that the depth of daily soil temperature transmission was about 40 cm shallower during thawing period than that during the freezing period. Soil warming process at the depth above 140 cm was slower than the cooling process, whereas they were close below 140 cm depth. Moreover, the hydro-thermal properties differed significantly among different stages. Precipitation caused an obviously increase in soil moisture at 0-20 cm depth. The vertical distribution of soil moisture could be divided into two main zones: less than 12% in the freeze state and greater than 12% in the thaw state. In addition, coupling of moisture and heat during the freezing and thawing processes also showed that soil temperature decreased faster than soil moisture during the freezing process. At the freezing stage, soil moisture exhibited an exponential relationship with the absolute soil temperature. Energy consumed for water-ice conversion during the freezing process was 149.83 MJ/m2 and 141.22 MJ/m2 in 2011 and 2012, respectively, which was estimated by the soil moisture variation.

  17. The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

    NASA Astrophysics Data System (ADS)

    Dorigo, W. A.; Wagner, W.; Hohensinn, R.; Hahn, S.; Paulik, C.; Drusch, M.; Mecklenburg, S.; van Oevelen, P.; Robock, A.; Jackson, T.

    2011-02-01

    In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status January 2011), the ISMN contains data of 16 networks and more than 500 stations located in the North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that

  18. The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

    NASA Astrophysics Data System (ADS)

    Dorigo, W. A.; Wagner, W.; Hohensinn, R.; Hahn, S.; Paulik, C.; Xaver, A.; Gruber, A.; Drusch, M.; Mecklenburg, S.; van Oevelen, P.; Robock, A.; Jackson, T.

    2011-05-01

    In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the

  19. Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

    NASA Technical Reports Server (NTRS)

    Girotto, Manuela

    2018-01-01

    Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages).

  20. 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.

  1. Soil Moisture Memory in Climate Models

    NASA Technical Reports Server (NTRS)

    Koster, Randal D.; Suarez, Max J.; Zukor, Dorothy J. (Technical Monitor)

    2000-01-01

    Water balance considerations at the soil surface lead to an equation that relates the autocorrelation of soil moisture in climate models to (1) seasonality in the statistics of the atmospheric forcing, (2) the variation of evaporation with soil moisture, (3) the variation of runoff with soil moisture, 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 soil moisture memory and thus, in effect, to geographical variations in seasonal precipitation predictability associated with soil moisture. The use of the equation to characterize controls on soil moisture memory is demonstrated with data from the modeling system of the NASA Seasonal-to-Interannual Prediction Project.

  2. Canadian Experiment for Soil Moisture in 2010 (CanEX-SM10): Overview and Preliminary Results

    NASA Technical Reports Server (NTRS)

    Magagi, Ramata; Berg, Aaron; Goita, Kalifa; Belair, Stephane; Jackson, Tom; Toth, B.; Walker, A.; McNairn, H.; O'Neill, P.; Moghdam. M; hide

    2011-01-01

    The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean salinity (SMOS) mission validation and the pre-launch assessment of Soil Moisture and Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (Leaf Area Index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Besides, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km x 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data. The Radio frequency interference (RFI) observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of SMOS soil moisture product matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates are more closely correlated with measured soil moisture.

  3. Converting Soil Moisture Observations to Effective Values for Improved Validation of Remotely Sensed Soil Moisture

    NASA Technical Reports Server (NTRS)

    Laymon, Charles A.; Crosson, William L.; Limaye, Ashutosh; Manu, Andrew; Archer, Frank

    2005-01-01

    We compare soil moisture retrieved with an inverse algorithm with observations of mean moisture in the 0-6 cm soil layer. A significant discrepancy is noted between the retrieved and observed moisture. Using emitting depth functions as weighting functions to convert the observed mean moisture to observed effective moisture removes nearly one-half of the discrepancy noted. This result has important implications in remote sensing validation studies.

  4. The international soil moisture network: A data hosting facility for global in situ soil moisture measurements

    USDA-ARS?s Scientific Manuscript database

    In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land co...

  5. Modeling soil moisture memory in savanna ecosystems

    NASA Astrophysics Data System (ADS)

    Gou, S.; Miller, G. R.

    2011-12-01

    Antecedent soil conditions create an ecosystem's "memory" of past rainfall events. Such soil moisture 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 soil moisture 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 soil moisture and evapotranspiration data collected at three study sites. The model was then used to simulate scenarios with various initial soil moisture conditions and antecedent precipitation regimes, in order to study the soil moisture memory effects on the evapotranspiration of understory and overstory species. Based on the model results, soil texture and antecedent precipitation regime impact the redistribution of water within soil layers, potentially causing deeper soil layers to influence the ecosystem for a longer time. Of all the study areas modeled, soil moisture memory of California savanna ecosystem site is replenished and dries out most rapidly. Thus soil moisture memory could not maintain the high rate evapotranspiration for more than a few days without incoming rainfall event. On the contrary, soil moisture 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 soil moisture memory in the shallow soil. The growing season of grass is largely depended on the soil moisture memory of the top 25cm soil 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 soil moisture memory with longer time duration Soil moisture memory does not have obvious impacts on the phenology of woody plants

  6. Validation of the Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval in an Arctic tundra environment

    NASA Astrophysics Data System (ADS)

    Wrona, Elizabeth; Rowlandson, Tracy L.; Nambiar, Manoj; Berg, Aaron A.; Colliander, Andreas; Marsh, Philip

    2017-05-01

    This study examines the Soil Moisture Active Passive soil moisture product on the Equal Area Scalable Earth-2 (EASE-2) 36 km Global cylindrical and North Polar azimuthal grids relative to two in situ soil moisture monitoring networks that were installed in 2015 and 2016. Results indicate that there is no relationship between the Soil Moisture Active Passive (SMAP) Level-2 passive soil moisture 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 soil moisture product could be improved with interpolation on the North Polar grid.

  7. Using Data Assimilation Diagnostics to Assess the SMAP Level-4 Soil Moisture Product

    NASA Technical Reports Server (NTRS)

    Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

    2018-01-01

    The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx.2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

  8. Global Assessment of the SMAP Level-4 Soil Moisture Product Using Assimilation Diagnostics

    NASA Technical Reports Server (NTRS)

    Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

    2018-01-01

    The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx. 2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

  9. Soil moisture monitoring for crop management

    NASA Astrophysics Data System (ADS)

    Boyd, Dale

    2015-07-01

    The 'Risk management through soil moisture monitoring' project has demonstrated the capability of current technology to remotely monitor and communicate real time soil moisture 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 soil moisture compared to prior to the project, with the most used indicator of soil moisture still being rain fall records; and iii) 100% have indicated they will continue to use some form of the technology to monitor soil moisture 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 soil moisture base and maximise yield potential in more favourable conditions based on soil moisture and positive seasonal forecasts

  10. Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

    NASA Technical Reports Server (NTRS)

    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.

    2011-01-01

    The contributions of precipitation and soil moisture observations to the skill of soil moisture 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 soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil 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 soil moisture 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 soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

  11. A comparative study of the SMAP passive soil moisture product with existing satellite-based soil moisture products

    USDA-ARS?s Scientific Manuscript database

    NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The radiometer-only soil moisture product (L2...

  12. Stochastic Analysis and Probabilistic Downscaling of Soil Moisture

    NASA Astrophysics Data System (ADS)

    Deshon, J. P.; Niemann, J. D.; Green, T. R.; Jones, A. S.

    2017-12-01

    Soil moisture is a key variable for rainfall-runoff response estimation, ecological and biogeochemical flux estimation, and biodiversity characterization, each of which is useful for watershed condition assessment. These applications require not only accurate, fine-resolution soil-moisture estimates but also confidence limits on those estimates and soil-moisture patterns that exhibit realistic statistical properties (e.g., variance and spatial correlation structure). The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution (9-40 km) soil moisture from satellite remote sensing or land-surface models to produce fine-resolution (10-30 m) estimates. The model was designed to produce accurate deterministic soil-moisture estimates at multiple points, but the resulting patterns do not reproduce the variance or spatial correlation of observed soil-moisture patterns. The primary objective of this research is to generalize the EMT+VS model to produce a probability density function (pdf) for soil moisture at each fine-resolution location and time. Each pdf has a mean that is equal to the deterministic soil-moisture estimate, and the pdf can be used to quantify the uncertainty in the soil-moisture estimates and to simulate soil-moisture patterns. Different versions of the generalized model are hypothesized based on how uncertainty enters the model, whether the uncertainty is additive or multiplicative, and which distributions describe the uncertainty. These versions are then tested by application to four catchments with detailed soil-moisture observations (Tarrawarra, Satellite Station, Cache la Poudre, and Nerrigundah). The performance of the generalized models is evaluated by comparing the statistical properties of the simulated soil-moisture patterns to those of the observations and the deterministic EMT+VS model. The versions of the generalized EMT+VS model with normally distributed stochastic components produce soil-moisture

  13. SoilNet - A Zigbee based soil moisture sensor network

    NASA Astrophysics Data System (ADS)

    Bogena, H. R.; Weuthen, A.; Rosenbaum, U.; Huisman, J. A.; Vereecken, H.

    2007-12-01

    Soil moisture plays a key role in partitioning water and energy fluxes, in providing moisture to the atmosphere for precipitation, and controlling the pattern of groundwater recharge. Large-scale soil moisture variability is driven by variation of precipitation and radiation in space and time. At local scales, land cover, soil conditions, and topography act to redistribute soil moisture. Despite the importance of soil moisture, 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 SoilNet project aims to develop a sensor network for the near real-time monitoring of soil moisture 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 soil moisture sensors attached to end devices by cables, router devices and a coordinator device. The end devices are buried in the soil 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 SoilNet 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 soil moisture 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

  14. Soil-moisture constants and their variation

    Treesearch

    Walter M. Broadfoot; Hubert D. Burke

    1958-01-01

    "Constants" like field capacity, liquid limit, moisture equivalent, and wilting point are used by most students and workers in soil moisture. These constants may be equilibrium points or other values that describe soil moisture. Their values under specific soil and cover conditions have been discussed at length in the literature, but few general analyses and...

  15. On-irrigator pasture soil moisture sensor

    NASA Astrophysics Data System (ADS)

    Eng-Choon Tan, Adrian; Richards, Sean; Platt, Ian; Woodhead, Ian

    2017-02-01

    In this paper, we presented the development of a proximal soil moisture sensor that measured the soil moisture content of dairy pasture directly from the boom of an irrigator. The proposed sensor was capable of soil moisture measurements at an accuracy of  ±5% volumetric moisture 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 soil moisture measurements.

  16. Australian Soil Moisture Field Experiments in Support of Soil Moisture Satellite Observations

    NASA Technical Reports Server (NTRS)

    Kim, Edward; Walker, Jeff; Rudiger, Christopher; Panciera, Rocco

    2010-01-01

    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 soil moisture 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 soil moisture 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 soil moisture 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 soil moisture remote sensing at large scales over heterogeneous areas. These field data have been used to test and refine retrieval algorithms for soil moisture satellite missions, and most recently with the launch of the European Space Agency's Soil Moisture 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

  17. The Integration of SMOS Soil Moisture in a Consistent Soil Moisture Climate Record

    NASA Astrophysics Data System (ADS)

    de Jeu, Richard; Kerr, Yann; Wigneron, Jean Pierre; Rodriguez-Fernandez, Nemesio; Al-Yaari, Amen; van der Schalie, Robin; Dolman, Han; Drusch, Matthias; Mecklenburg, Susanne

    2015-04-01

    Recently, a study funded by the European Space Agency (ESA) was set up to provide guidelines for the development of a global soil moisture 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 Soil Moisture 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 soil moisture 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 soil moisture 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 soil moisture climate record. References Wigneron J.-P., J.-C. Calvet, P. de Rosnay, Y. Kerr, P. Waldteufel, K. Saleh, M. J. Escorihuela, A. Kruszewski, 'Soil Moisture Retrievals from Bi-Angular L-band Passive Microwave Observations', IEEE Trans. Geosc. Remote Sens. Let., vol 1, no. 4, 277-281, 2004.

  18. The soil moisture active passive experiments (SMAPEx): Towards soil moisture retrieval from the SMAP mission

    USDA-ARS?s Scientific Manuscript database

    NASA’s Soil Moisture 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 soil moisture and freeze/thaw state globally at near-daily time step (2-3 days). SMAP will provide three soil ...

  19. Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

    NASA Astrophysics Data System (ADS)

    Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.

    2017-03-01

    Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture 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 soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture 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 soil moisture over broad extents and has

  20. 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...

  1. Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

    NASA Technical Reports Server (NTRS)

    Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika

    2014-01-01

    Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture 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.

  2. Space-time modeling of soil moisture

    NASA Astrophysics Data System (ADS)

    Chen, Zijuan; Mohanty, Binayak P.; Rodriguez-Iturbe, Ignacio

    2017-11-01

    A physically derived space-time mathematical representation of the soil moisture field is carried out via the soil moisture balance equation driven by stochastic rainfall forcing. The model incorporates spatial diffusion and in its original version, it is shown to be unable to reproduce the relative fast decay in the spatial correlation functions observed in empirical data. This decay resulting from variations in local topography as well as in local soil and vegetation conditions is well reproduced via a jitter process acting multiplicatively over the space-time soil moisture field. The jitter is a multiplicative noise acting on the soil moisture dynamics with the objective to deflate its correlation structure at small spatial scales which are not embedded in the probabilistic structure of the rainfall process that drives the dynamics. These scales of order of several meters to several hundred meters are of great importance in ecohydrologic dynamics. Properties of space-time correlation functions and spectral densities of the model with jitter are explored analytically, and the influence of the jitter parameters, reflecting variabilities of soil moisture at different spatial and temporal scales, is investigated. A case study fitting the derived model to a soil moisture dataset is presented in detail.

  3. Assimilation of Passive and Active Microwave Soil Moisture Retrievals

    NASA Technical Reports Server (NTRS)

    Draper, C. S.; Reichle, R. H.; DeLannoy, G. J. M.; Liu, Q.

    2012-01-01

    Root-zone soil moisture is an important control over the partition of land surface energy and moisture, and the assimilation of remotely sensed near-surface soil moisture has been shown to improve model profile soil moisture [1]. To date, efforts to assimilate remotely sensed near-surface soil moisture at large scales have focused on soil moisture 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 soil moisture observations has not yet been directly compared, and so this study compares the impact of assimilating ASCAT and AMSR-E soil moisture data, both separately and together. Since the soil moisture 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 soil moisture skill is assessed according to land cover type, by comparison to in situ soil moisture observations.

  4. The potential of remotely sensed soil moisture for operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S.; Bierkens, M. F.

    2013-12-01

    Nowadays, remotely sensed soil moisture is readily available from multiple space born sensors. The high temporal resolution and global coverage make these products very suitable for large-scale land-surface applications. The potential to use these products in operational flood forecasting has thus far not been extensively studied. In this study, we evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the timing and height of the flood peak and low flows. EFAS is used for operational flood forecasting in Europe and uses a distributed hydrological model for flood predictions for lead times up to 10 days. Satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of only discharge observations. Discharge observations are available at the outlet and at six additional locations throughout the catchment. To assimilate soil moisture data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, derived from a detailed model-satellite soil moisture comparison study, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are used in that the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 10-15% on average

  5. Plan of research for integrated soil moisture studies. Recommendations of the Soil Moisture Working Group

    NASA Technical Reports Server (NTRS)

    1980-01-01

    Soil moisture 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 soil moisture and to utilize soil moisture information in support of agricultural, water resources, and climate applications. The soil moisture 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.

  6. Using satellite image data to estimate soil moisture

    NASA Astrophysics Data System (ADS)

    Chuang, Chi-Hung; Yu, Hwa-Lung

    2017-04-01

    Soil moisture is considered as an important parameter in various study fields, such as hydrology, phenology, and agriculture. In hydrology, soil moisture is an significant parameter to decide how much rainfall that will infiltrate into permeable layer and become groundwater resource. Although soil moisture is a critical role in many environmental studies, so far the measurement of soil moisture is using ground instrument such as electromagnetic soil moisture 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 soil moisture 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 soil moisture pattern estimation, i.e., crop water stress index (cwsi), over the year of 2015. The estimations are compared with the observations at the soil moisture stations from Taiwan Bureau of soil and water conservation. Results show that the satellite remote sensing data can be helpful to the soil moisture estimation. Further analysis can be required to obtain the optimal parameters for soil moisture estimation in Taiwan.

  7. High-resolution soil moisture mapping in Afghanistan

    NASA Astrophysics Data System (ADS)

    Hendrickx, Jan M. H.; Harrison, J. Bruce J.; Borchers, Brian; Kelley, Julie R.; Howington, Stacy; Ballard, Jerry

    2011-06-01

    Soil moisture conditions have an impact upon virtually all aspects of Army activities and are increasingly affecting its systems and operations. Soil moisture conditions affect operational mobility, detection of landmines and unexploded ordinance, natural material penetration/excavation, military engineering activities, blowing dust and sand, watershed responses, and flooding. This study further explores a method for high-resolution (2.7 m) soil moisture mapping using remote satellite optical imagery that is readily available from Landsat and QuickBird. The soil moisture estimations are needed for the evaluation of IED sensors using the Countermine Simulation Testbed in regions where access is difficult or impossible. The method has been tested in Helmand Province, Afghanistan, using a Landsat7 image and a QuickBird image of April 23 and 24, 2009, respectively. In previous work it was found that Landsat soil moisture can be predicted from the visual and near infra-red Landsat bands1-4. Since QuickBird bands 1-4 are almost identical to Landsat bands 1- 4, a Landsat soil moisture map can be downscaled using QuickBird bands 1-4. However, using this global approach for downscaling from Landsat to QuickBird scale yielded a small number of pixels with erroneous soil moisture values. Therefore, the objective of this study is to examine how the quality of the downscaled soil moisture maps can be improved by using a data stratification approach for the development of downscaling regression equations for each landscape class. It was found that stratification results in a reliable downscaled soil moisture map with a spatial resolution of 2.7 m.

  8. Using the Spatial Persistence of Soil Moisture Patterns to Estimate Catchment Soil Moisture in Semi-arid Areas

    NASA Astrophysics Data System (ADS)

    Willgoose, G. R.

    2006-12-01

    In humid catchments the spatial distribution of soil water is dominated by subsurface lateral fluxes, which leads to a persistent spatial pattern of soil moisture principally described by the topographic index. In contrast, semi-arid, and dryer, catchments are dominated by vertical fluxes (infiltration and evapotranspiration) and persistent spatial patterns, if they exist, are subtler. In the first part of this presentation the results of a reanalysis of a number of catchment-scale long-term spatially-distributed soil moisture data sets are presented. We concentrate on Tarrawarra and SASMAS, both catchments in Australia that are water-limited for at least part of the year and which have been monitored using a variety of technologies. Using the data from permanently installed instruments (neutron probe and reflectometry) both catchments show persistent patterns at the 1-3 year timescale. This persistent pattern is not evident in the field campaign data where field portable instruments (reflectometry) instruments were used. We argue, based on high-resolution soil moisture semivariograms, that high short-distance variability (100mm scale) means that field portable instrument cannot be replaced at the same location with sufficient accuracy to ensure deterministic repeatability of soil moisture measurements from campaign to campaign. The observed temporal persistence of the spatial pattern can be caused by; (1) permanent features of the landscape (e.g. vegetation, soils), or (2) long term memory in the soil moisture store. We argue that it is permanent in which case it is possible to monitor the soil moisture status of a catchment using a single location measurement (continuous in time) of soil moisture using a permanently installed reflectometry instrument. This instrument will need to be calibrated to the catchment averaged soil moisture but the temporal persistence of the spatial pattern of soil moisture will mean that this calibration will be deterministically

  9. Irrigation scheduling using soil moisture sensors

    USDA-ARS?s Scientific Manuscript database

    Soil moisture sensors were evaluated and used for irrigation scheduling in humid region. Soil moisture sensors were installed in soil at depths of 15cm, 30cm, and 61cm belowground. Soil volumetric water content was automatically measured by the sensors in a time interval of an hour during the crop g...

  10. Soil-moisture sensors and irrigation management

    USDA-ARS?s Scientific Manuscript database

    This agricultural irrigation seminar will cover the major classes of soil-moisture sensors; their advantages and disadvantages; installing and reading soil-moisture sensors; and using their data for irrigation management. The soil water sensor classes include the resistance sensors (gypsum blocks, g...

  11. Use of midlatitude soil moisture and meteorological observations to validate soil moisture simulations with biosphere and bucket models

    NASA Technical Reports Server (NTRS)

    Robock, Alan; Vinnikov, Konstantin YA.; Schlosser, C. Adam; Speranskaya, Nina A.; Xue, Yongkang

    1995-01-01

    Soil moisture 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 soil moisture simulations with biosphere and bucket models. Some early and current general circulation models (GCMs) use bucket models for soil hydrology calculations. More recently, the Simple Biosphere Model (SiB) was developed to incorporate the effects of vegetation on fluxes of moisture, momentum, and energy at the earth's surface into soil hydrology models. Until now, the bucket and SiB have been verified by comparison with actual soil moisture data only on a limited basis. In this study, a Simplified SiB (SSiB) soil 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 soil moisture are compared to observations of soil moisture, 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 soil moisture. 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 soil moisture 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 soil moisture simulations, the models produce very different surface latent and sensible heat fluxes, which

  12. The moisture response of soil heterotrophic respiration: Interaction with soil properties.

    USDA-ARS?s Scientific Manuscript database

    Soil moisture-respiration functions are used to simulate the various mechanisms determining the relations between soil moisture content and carbon mineralization. Soil models used in the simulation of global carbon fluxes often apply simplified functions assumed to represent an average moisture-resp...

  13. Effect of soil moisture on the temperature sensitivity of Northern soils

    NASA Astrophysics Data System (ADS)

    Minions, C.; Natali, S.; Ludwig, S.; Risk, D.; Macintyre, C. M.

    2017-12-01

    Arctic and boreal ecosystems are vast reservoirs of carbon and are particularly sensitive to climate warming. Changes in the temperature and precipitation regimes of these regions could significantly alter soil respiration rates, impacting atmospheric concentrations and affecting climate change feedbacks. Many incubation studies have shown that both temperature and soil moisture are important environmental drivers of soil respiration; this relationship, however, has rarely been demonstrated with in situ data. Here we present the results of a study at six field sites in Alaska from 2016 to 2017. Low-power automated soil gas systems were used to measure soil surface CO2 flux from three forced diffusion chambers and soil profile concentrations from three soil depth chambers at hourly intervals at each site. HOBO Onset dataloggers were used to monitor soil moisture and temperature profiles. Temperature sensitivity (Q10) was determined at each site using inversion analysis applied over different time periods. With highly resolved data sets, we were able to observe the changes in soil respiration in response to changes in temperature and soil moisture. Through regression analysis we confirmed that temperature is the primary driver in soil respiration, but soil moisture becomes dominant beyond a certain threshold, suppressing CO2 flux in soils with high moisture content. This field study supports the conclusions made from previous soil incubation studies and provides valuable insights into the impact of both temperature and soil moisture changes on soil respiration.

  14. Historical climate controls soil respiration responses to current soil moisture.

    PubMed

    Hawkes, Christine V; Waring, Bonnie G; Rocca, Jennifer D; Kivlin, Stephanie N

    2017-06-13

    Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40-70 Pg carbon from soil 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 soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration-moisture 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.

  15. Historical climate controls soil respiration responses to current soil moisture

    PubMed Central

    Waring, Bonnie G.; Rocca, Jennifer D.; Kivlin, Stephanie N.

    2017-01-01

    Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40–70 Pg carbon from soil 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 soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration–moisture relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall. PMID:28559315

  16. Passive Microwave Remote Sensing of Soil Moisture

    NASA Technical Reports Server (NTRS)

    Njoku, Eni G.; Entekhabi, Dara

    1996-01-01

    Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture 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 soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture 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 soil moisture 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 soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.

  17. Evaluation of gravimetric ground truth soil moisture data collected for the agricultural soil moisture experiment, 1978 Colby, Kansas, aircraft mission

    NASA Technical Reports Server (NTRS)

    Arya, L. M.; Phinney, D. E. (Principal Investigator)

    1980-01-01

    Soil moisture data acquired to support the development of algorithms for estimating surface soil moisture from remotely sensed backscattering of microwaves from ground surfaces are presented. Aspects of field uniformity and variability of gravimetric soil moisture measurements are discussed. Moisture 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 moisture content an variability are indicated. For the various sets of measurements, soil moisture values that appear as outliers are flagged. The distribution and legal descriptions of the test fields are included along with examinations of soil types, agronomic features, and sampling plan. Bulk density data for experimental fields are appended, should analyses involving volumetric moisture content be of interest to the users of data in this report.

  18. New Physical Algorithms for Downscaling SMAP Soil Moisture

    NASA Astrophysics Data System (ADS)

    Sadeghi, M.; Ghafari, E.; Babaeian, E.; Davary, K.; Farid, A.; Jones, S. B.; Tuller, M.

    2017-12-01

    The NASA Soil Moisture Active Passive (SMAP) mission provides new means for estimation of surface soil moisture at the global scale. However, for many hydrological and agricultural applications the spatial SMAP resolution is too low. To address this scale issue we fused SMAP data with MODIS observations to generate soil moisture maps at 1-km spatial resolution. In course of this study we have improved several existing empirical algorithms and introduced a new physical approach for downscaling SMAP data. The universal triangle/trapezoid model was applied to relate soil moisture to optical/thermal observations such as NDVI, land surface temperature and surface reflectance. These algorithms were evaluated with in situ data measured at 5-cm depth. Our results demonstrate that downscaling SMAP soil moisture data based on physical indicators of soil moisture derived from the MODIS satellite leads to higher accuracy than that achievable with empirical downscaling algorithms. Keywords: Soil moisture, microwave data, downscaling, MODIS, triangle/trapezoid model.

  19. Methods of measuring soil moisture in the field

    USGS Publications Warehouse

    Johnson, A.I.

    1962-01-01

    For centuries, the amount of moisture in the soil has been of interest in agriculture. The subject of soil moisture is also of great importance to the hydrologist, forester, and soils engineer. Much equipment and many methods have been developed to measure soil moisture under field conditions. This report discusses and evaluates the various methods for measurement of soil moisture 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 moisture-content data. The radioactive method is normally best for obtaining repeated measurements of soil moisture in place. It is concluded that all methods have some limitations and that the ideal method for measurement of soil moisture under field conditions has yet to be perfected.

  20. Estimating Surface Soil Moisture in Simulated AVIRIS Spectra

    NASA Technical Reports Server (NTRS)

    Whiting, Michael L.; Li, Lin; Ustin, Susan L.

    2004-01-01

    Soil albedo is influenced by many physical and chemical constituents, with moisture being the most influential on the spectra general shape and albedo (Stoner and Baumgardner, 1981). Without moisture, the intrinsic or matrix reflectance of dissimilar soils varies widely due to differences in surface roughness, particle and aggregate sizes, mineral types, including salts, and organic matter contents. The influence of moisture on soil reflectance can be isolated by comparing similar soils in a study of the effects that small differences in moisture content have on reflectance. However, without prior knowledge of the soil physical and chemical constituents within every pixel, it is nearly impossible to accurately attribute the reflectance variability in an image to moisture or to differences in the physical and chemical constituents in the soil. The effect of moisture on the spectra must be eliminated to use hyperspectral imagery for determining minerals and organic matter abundances of bare agricultural soils. Accurate soil 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 soil moisture and reflectance will also improve the selection of soil endmembers for spectral mixture analysis.

  1. Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

    NASA Astrophysics Data System (ADS)

    Sure, A.; Dikshit, O.

    2017-12-01

    Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

  2. SMAP Radiometer Captures Views of Global Soil Moisture

    NASA Image and Video Library

    2015-05-06

    These maps of global soil moisture were created using data from the radiometer instrument on NASA Soil Moisture Active Passive SMAP observatory. Evident are regions of increased soil moisture and flooding during April, 2015.

  3. Improved Absolute Radiometric Calibration of a UHF Airborne Radar

    NASA Technical Reports Server (NTRS)

    Chapin, Elaine; Hawkins, Brian P.; Harcke, Leif; Hensley, Scott; Lou, Yunling; Michel, Thierry R.; Moreira, Laila; Muellerschoen, Ronald J.; Shimada, Joanne G.; Tham, Kean W.; hide

    2015-01-01

    The AirMOSS airborne SAR operates at UHF and produces fully polarimetric imagery. The AirMOSS radar data are used to produce Root Zone Soil Moisture (RZSM) depth profiles. The absolute radiometric accuracy of the imagery, ideally of better than 0.5 dB, is key to retrieving RZSM, especially in wet soils where the backscatter as a function of soil moisture function tends to flatten out. In this paper we assess the absolute radiometric uncertainty in previously delivered data, describe a method to utilize Built In Test (BIT) data to improve the radiometric calibration, and evaluate the improvement from applying the method.

  4. Soil moisture depletion patterns around scattered trees

    Treesearch

    Robert R. Ziemer

    1968-01-01

    Soil moisture 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 soil moisture profiles were developed. Estimated soil moisture depletion from the 61-foot radius plot...

  5. Upscaling sparse ground-based soil moisture observations for the validation of satellite surface soil moisture products

    USDA-ARS?s Scientific Manuscript database

    The contrast between the point-scale nature of current ground-based soil moisture instrumentation and the footprint resolution (typically >100 square kilometers) of satellites used to retrieve soil moisture poses a significant challenge for the validation of data products from satellite missions suc...

  6. Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

    NASA Astrophysics Data System (ADS)

    Crow, W. T.; Chen, F.; Reichle, R. H.; Xia, Y.; Liu, Q.

    2018-05-01

    Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective of land surface models tasked with characterizing the surface water and energy balance. Temporal variability in this partitioning is due, in part, to changes in prestorm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the National Aeronautics and Space Administration Soil Moisture Active Passive Level-4 soil moisture product in combination with streamflow and precipitation observations, we demonstrate that land surface models (LSMs) generally underestimate the strength of the positive rank correlation between prestorm soil moisture and event runoff coefficients (i.e., the fraction of rainfall accumulation volume converted into stormflow runoff during a storm event). Underestimation is largest for LSMs employing an infiltration-excess approach for stormflow runoff generation. More accurate coupling strength is found in LSMs that explicitly represent subsurface stormflow or saturation-excess runoff generation processes.

  7. Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought.

    PubMed

    Chen, Hui; Fan, Li; Wu, Wei; Liu, Hong-Bin

    2017-09-26

    Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010-2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R 2 , MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.

  8. Electrical methods of determining soil moisture content

    NASA Technical Reports Server (NTRS)

    Silva, L. F.; Schultz, F. V.; Zalusky, J. T.

    1975-01-01

    The electrical permittivity of soils is a useful indicator of soil moisture content. Two methods of determining the permittivity profile in soils 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 soil moisture profile (percent available soil moisture as a function of depth) from a surface measurement to an expected resolution of 10 to 20 cm.

  9. Automated Quality Control of in Situ Soil Moisture from the North American Soil Moisture Database Using NLDAS-2 Products

    NASA Astrophysics Data System (ADS)

    Ek, M. B.; Xia, Y.; Ford, T.; Wu, Y.; Quiring, S. M.

    2015-12-01

    The North American Soil Moisture Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models and validating satellite-derived soil moisture algorithms. The NASMD has collected data from over 30 soil moisture observation networks providing millions of in situ soil moisture observations in all 50 states as well as Canada and Mexico. It is recognized that the quality of measured soil moisture in NASMD is highly variable due to the diversity of climatological conditions, land cover, soil 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 soil porosity, soil temperature, and fraction of liquid and total soil moisture to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the soil is partially frozen. A validation example using NLDAS-2 multiple model soil moisture products at the 20 cm soil 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.

  10. Ultrasound Algorithm Derivation for Soil Moisture Content Estimation

    NASA Technical Reports Server (NTRS)

    Belisle, W.R.; Metzl, R.; Choi, J.; Aggarwal, M. D.; Coleman, T.

    1997-01-01

    Soil moisture 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 soil algorithms relating the moisture 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 soil property descriptions to derive algorithms appropriate for describing the effects of moisture content variation on the velocity of sound waves in soils with and without complete soil pore water volumes, An elementary algorithm was used to estimate soil moisture 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 soil moisture content from sound wave velocities through soils with pores that were filled predominantly with air or water.

  11. Soil Moisture-Atmosphere Feedbacks on Atmospheric Tracers: The Effects of Soil Moisture on Precipitation and Near-Surface Chemistry

    NASA Astrophysics Data System (ADS)

    Tawfik, Ahmed B.

    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 soil moisture and atmospheric tracers under varying degrees of soil moisture-atmosphere coupling. Land-atmosphere coupling is defined over the United States using a regional climate model. A newly examined soil moisture-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 soil moisture, leading to coupled land-atmosphere conditions near the freezing line. Soil moisture can also affect 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 soil moisture 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 soil moisture-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 soil moisture-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 soil moisture-atmosphere coupling for previously neglected cold climate

  12. Impacts of soil moisture content on visual soil evaluation

    NASA Astrophysics Data System (ADS)

    Emmet-Booth, Jeremy; Forristal, Dermot; Fenton, Owen; Bondi, Giulia; Creamer, Rachel; Holden, Nick

    2017-04-01

    Visual Soil Examination and Evaluation (VSE) techniques offer tools for soil quality assessment. They involve the visual and tactile assessment of soil properties such as aggregate size and shape, porosity, redox morphology, soil 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 soil moisture variation during sampling. As part of a national survey of grassland soil quality in Ireland, an evaluation of the impact of soil moisture on two widely used VSE techniques was conducted. The techniques were Visual Evaluation of Soil Structure (VESS) (Guimarães et al., 2011) and Visual Soil Assessment (VSA) (Shepherd, 2009). Both generate summarising numeric scores that indicate soil 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 soils. Additional samples were taken for soil 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 affected by soil moisture variation while VSA appear unaffected. The different scoring mechanisms, where the separate assessment and scoring of individual properties employed by VSA, may limit soil moisture effects. However, moisture content appears not to affect overall structural quality classification by either method. References

  13. Soil moisture in sessile oak forest gaps

    NASA Astrophysics Data System (ADS)

    Zagyvainé Kiss, Katalin Anita; Vastag, Viktor; Gribovszki, Zoltán; Kalicz, Péter

    2015-04-01

    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 soil. This research focuses on soil moisture patterns caused by gaps. The spatio-temporal variability of soil water content is measured in gaps and in surrounding sessile oak (Quercus petraea) forest stand. Soil moisture was determined with manual soil moisture 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 soil moisture between forest stand and gaps. Second, it was defined that how the gap size influences the soil moisture content. To explore the short term variability of soil moisture, 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 soil moisture were performed. The measured soil moisture 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.

  14. Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network

    NASA Astrophysics Data System (ADS)

    Wu, Qiusheng; Liu, Hongxing; Wang, Lei; Deng, Chengbin

    2016-03-01

    High quality soil moisture datasets are required for various environmental applications. The launch of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1-Water (GCOM-W1) in May 2012 has provided global near-surface soil moisture data, with an average revisit frequency of two days. Since AMSR2 is a new passive microwave system in operation, it is very important to evaluate the quality of AMSR2 products before widespread utilization of the data for scientific research. In this paper, we provide a comprehensive evaluation of the AMSR2 soil moisture products retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm. The evaluation was performed for a three-year period (July 2012-June 2015) over the contiguous United States. The AMSR2 soil moisture products were evaluated by comparing ascending and descending overpass products to each other as well as comparing them to in situ soil moisture observations of 598 monitoring stations obtained from the International Soil Moisture Network (ISMN). The accuracy of AMSR2 soil moisture product was evaluated against several types of monitoring networks, and for different land cover types and ecoregions. Three performance metrics, including mean difference (MD), root mean squared difference (RMSD), and correlation coefficient (R), were used in our accuracy assessment. Our evaluation results revealed that AMSR2 soil moisture retrievals are generally lower than in situ measurements. The AMSR2 soil moisture retrievals showed the best agreement with in situ measurements over the Great Plains and the worst agreement over forested areas. This study offers insights into the suitability and reliability of AMSR2 soil moisture products for different ecoregions. Although AMSR2 soil moisture retrievals represent useful and effective measurements for some regions, further studies are required to improve the data accuracy.

  15. Assimilating soil moisture into an Earth System Model

    NASA Astrophysics Data System (ADS)

    Stacke, Tobias; Hagemann, Stefan

    2017-04-01

    Several modelling studies reported potential impacts of soil moisture anomalies on regional climate. In particular for short prediction periods, perturbations of the soil moisture state may result in significant alteration of surface temperature in the following season. However, it is not clear yet whether or not soil moisture anomalies affect climate also on larger temporal and spatial scales. In an earlier study, we showed that soil moisture anomalies can persist for several seasons in the deeper soil layers of a land surface model. Additionally, those anomalies can influence root zone moisture, in particular during explicitly dry or wet periods. Thus, one prerequisite for predictability, namely the existence of long term memory, is evident for simulated soil moisture and might be exploited to improve climate predictions. The second prerequisite is the sensitivity of the climate system to soil moisture. In order to investigate this sensitivity for decadal simulations, we implemented a soil moisture 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 soil moisture 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 moisture 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

  16. Drive by Soil Moisture Measurement: A Citizen Science Project

    NASA Astrophysics Data System (ADS)

    Senanayake, I. P.; Willgoose, G. R.; Yeo, I. Y.; Hancock, G. R.

    2017-12-01

    Two of the common attributes of soil moisture are that at any given time it varies quite markedly from point to point, and that there is a significant deterministic pattern that underlies this spatial variation and which is typically 50% of the spatial variability. The spatial variation makes it difficult to determine the time varying catchment average soil moisture using field measurements because any individual measurement is unlikely to be equal to the average for the catchment. The traditional solution to this is to make many measurements (e.g. with soil moisture probes) spread over the catchment, which is very costly and manpower intensive, particularly if we need a time series of soil moisture variation across a catchment. An alternative approach, explored in this poster is to use the deterministic spatial pattern of soil moisture to calibrate one site (e.g. a permanent soil moisture probe at a weather station) to the spatial pattern of soil moisture over the study area. The challenge is then to determine the spatial pattern of soil moisture. This poster will present results from a proof of concept project, where data was collected by a number of undergraduate engineering students, to estimate the spatial pattern. The approach was to drive along a series of roads in a catchment and collect soil moisture measurements at the roadside using field portable soil moisture probes. This drive was repeated a number of times over the semester, and the time variation and spatial persistence of the soil moisture pattern were examined. Provided that the students could return to exactly the same location on each collection day there was a strong persistent pattern in the soil moisture, even while the average soil moisture varied temporally as a result of preceding rainfall. The poster will present results and analysis of the student data, and compare these results with several field sites where we have spatially distributed permanently installed soil moisture probes. The

  17. State of the Art in Large-Scale Soil Moisture Monitoring

    NASA Technical Reports Server (NTRS)

    Ochsner, Tyson E.; Cosh, Michael Harold; Cuenca, Richard H.; Dorigo, Wouter; Draper, Clara S.; Hagimoto, Yutaka; Kerr, Yan H.; Larson, Kristine M.; Njoku, Eni Gerald; Small, Eric E.; hide

    2013-01-01

    Soil moisture is an essential climate variable influencing land atmosphere interactions, an essential hydrologic variable impacting rainfall runoff processes, an essential ecological variable regulating net ecosystem exchange, and an essential agricultural variable constraining food security. Large-scale soil moisture monitoring has advanced in recent years creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication. For some applications, the science required to utilize large-scale soil moisture data is poorly developed. In this review, we describe the state of the art in large-scale soil moisture monitoring and identify some critical needs for research to optimize the use of increasingly available soil moisture data. We review representative examples of 1) emerging in situ and proximal sensing techniques, 2) dedicated soil moisture remote sensing missions, 3) soil moisture monitoring networks, and 4) applications of large-scale soil moisture measurements. Significant near-term progress seems possible in the use of large-scale soil moisture data for drought monitoring. Assimilation of soil moisture data for meteorological or hydrologic forecasting also shows promise, but significant challenges related to model structures and model errors remain. Little progress has been made yet in the use of large-scale soil moisture observations within the context of ecological or agricultural modeling. Opportunities abound to advance the science and practice of large-scale soil moisture monitoring for the sake of improved Earth system monitoring, modeling, and forecasting.

  18. Soil moisture sensors for continuous monitoring

    USGS Publications Warehouse

    Amer, Saud A.; Keefer, T. O.; Weltz, M.A.; Goodrich, David C.; Bach, Leslie

    1995-01-01

    Certain physical and chemical properties of soil vary with soil water content. The relationship between these properties and water content is complex and involves both the pore structure and constituents of the soil solution. One of the most economical techniques to quantify soil water content involves the measurement of electrical resistance of a dielectric medium that is in equilibrium with the soil water content. The objective of this research was to test the reliability and accuracy of fiberglass soil-moisture 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 soil rock content (>45 percent) at seven locations resulted in consistent overestimation of soil water content by the ERS method. Where rock content was less than 10 percent, estimation of soil water was within 5 percent of the gravimetric soil water content. New methodology to calibrate the ERS sensors for rocky soils will need to be developed before soil water content values can be determined with these sensors. (KEY TERMS: soil moisture; soil water; infiltration; instrumentation; soil moisture sensors.)

  19. Evaluating ESA CCI Soil Moisture in East Africa

    NASA Technical Reports Server (NTRS)

    McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.

    2016-01-01

    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 soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASAs Soil Moisture 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 soil moisture 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 soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture 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 soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

  20. Evaluating ESA CCI soil moisture in East Africa.

    PubMed

    McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R; Wang, Shugong; Peters-Lidard, Christa D; Verdin, James P

    2016-06-01

    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 soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture 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 soil moisture 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 soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

  1. AMSR2 Soil Moisture Product Validation

    NASA Technical Reports Server (NTRS)

    Bindlish, R.; Jackson, T.; Cosh, M.; Koike, T.; Fuiji, X.; de Jeu, R.; Chan, S.; Asanuma, J.; Berg, A.; Bosch, D.; hide

    2017-01-01

    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 soil moisture. 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 soil moisture products. This study focuses on validation of the AMSR2 soil moisture 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 Soil Moisture 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.

  2. Soil Moisture or Groundwater?

    NASA Astrophysics Data System (ADS)

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

    2017-12-01

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

  3. 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.

  4. Survey of methods for soil moisture determination

    NASA Technical Reports Server (NTRS)

    Schmugge, T. J.; Jackson, T. J.; Mckim, H. L.

    1979-01-01

    Existing and proposed methods for soil moisture 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) soil physics models that track the behavior of water in the soil in response to meteorological inputs (precipitation) and demands (evapotranspiration). The capacities of these approaches to satisfy various user needs for soil moisture information vary from application to application, but a conceptual scheme for merging these approaches into integrated systems to provide soil moisture information is proposed that has the potential for meeting various application requirements.

  5. Concerning the relationship between evapotranspiration and soil moisture

    NASA Technical Reports Server (NTRS)

    Wetzel, Peter J.; Chang, Jy-Tai

    1987-01-01

    The relationship between the evapotranspiration and soil moisture 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 moisture release from bare soil pores. A simple model for evapotranspiration is proposed. The effects of natural soil heterogeneities on evapotranspiration computed from the model are investigated. It is observed that the natural variability in soil moisture, caused by the heterogeneities, alters the relationship between regional evapotranspiration and the area average soil moisture.

  6. Summary: Remote sensing soil moisture research

    NASA Technical Reports Server (NTRS)

    Schmer, F. A.; Werner, H. D.; Waltz, F. A.

    1970-01-01

    During the 1969 and 1970 growing seasons research was conducted to investigate the relationship between remote sensing imagery and soil moisture. 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 moisture supply and water use. Results show that remote sensing is a feasible method for monitoring soil moisture.

  7. [Bare Soil Moisture Inversion Model Based on Visible-Shortwave Infrared Reflectance].

    PubMed

    Zheng, Xiao-po; Sun, Yue-jun; Qin, Qi-ming; Ren, Hua-zhong; Gao, Zhong-ling; Wu, Ling; Meng, Qing-ye; Wang, Jin-liang; Wang, Jian-hua

    2015-08-01

    Soil is the loose solum of land surface that can support plants. It consists of minerals, organics, atmosphere, moisture, microbes, et al. Among its complex compositions, soil moisture varies greatly. Therefore, the fast and accurate inversion of soil moisture by using remote sensing is very crucial. In order to reduce the influence of soil type on the retrieval of soil moisture, this paper proposed a normalized spectral slope and absorption index named NSSAI to estimate soil moisture. The modeling of the new index contains several key steps: Firstly, soil samples with different moisture level were artificially prepared, and soil reflectance spectra was consequently measured using spectroradiometer produced by ASD Company. Secondly, the moisture absorption spectral feature located at shortwave wavelengths and the spectral slope of visible wavelengths were calculated after analyzing the regular spectral feature change patterns of different soil at different moisture conditions. Then advantages of the two features at reducing soil types' effects was synthesized to build the NSSAI. Thirdly, a linear relationship between NSSAI and soil moisture was established. The result showed that NSSAI worked better (correlation coefficient is 0.93) than most of other traditional methods in soil moisture extraction. It can weaken the influences caused by soil types at different moisture levels and improve the bare soil moisture inversion accuracy.

  8. NASA Soil Moisture Data Products and Their Incorporation in DREAM

    NASA Technical Reports Server (NTRS)

    Blonski, Slawomir; Holland, Donald; Henderson, Vaneshette

    2005-01-01

    NASA provides soil moisture data products that include observations from the Advanced Microwave Scanning Radiometer on the Earth Observing System Aqua satellite, field measurements from the Soil Moisture Experiment campaigns, and model predictions from the Land Information System and the Goddard Earth Observing System Data Assimilation System. Incorporation of the NASA soil moisture products in the Dust Regional Atmospheric Model is possible through use of the satellite observations of soil moisture to set initial conditions for the dust simulations. An additional comparison of satellite soil moisture observations with mesoscale atmospheric dynamics modeling is recommended. Such a comparison would validate the use of NASA soil moisture data in applications and support acceptance of satellite soil moisture data assimilation in weather and climate modeling.

  9. Soil moisture and vegetation patterns in northern California forests

    Treesearch

    James R. Griffin

    1967-01-01

    Twenty-nine soil-vegetation plots were studied in a broad transect across the southern Cascade Range. Variations in soil moisture patterns during the growing season and in soil moisture tension values are discussed. Plot soil moisture values for 40- and 80-cm. depths in August and September are integrated into a soil drought index. Vegetation patterns are described in...

  10. Drought monitoring with soil moisture active passive (SMAP) measurements

    NASA Astrophysics Data System (ADS)

    Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara

    2017-09-01

    Recent launch of space-borne systems to estimate surface soil moisture may expand the capability to map soil moisture deficit and drought with global coverage. In this study, we use Soil Moisture Active Passive (SMAP) soil moisture geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the Soil Water Deficit Index (SWDI) based on SMAP and soil properties (field capacity and available water content) information. The soil properties are computed using pedo-transfer function with soil characteristics derived from Harmonized World Soil Database. The SMAP soil moisture product needs to be rescaled to be compatible with the soil parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ soil moisture well and shows the expected lag between accumulations of precipitation and delayed increased in surface soil moisture. However, the SMAP soil moisture 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 soil moisture 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

  11. Measuring Soil Moisture in Skeletal Soils Using a COSMOS Rover

    NASA Astrophysics Data System (ADS)

    Medina, C.; Neely, H.; Desilets, D.; Mohanty, B.; Moore, G. W.

    2017-12-01

    The presence of coarse fragments directly influences the volumetric water content of the soil. Current surface soil moisture sensors often do not account for the presence of coarse fragments, and little research has been done to calibrate these sensors under such conditions. The cosmic-ray soil moisture observation system (COSMOS) rover is a passive, non-invasive surface soil moisture sensor with a footprint greater than 100 m. Despite its potential, the COSMOS rover has yet to be validated in skeletal soils. The goal of this study was to validate measurements of surface soil moisture as taken by a COSMOS rover on a Texas skeletal soil. Data was collected for two soils, a Marfla clay loam and Chinati-Boracho-Berrend association, in West Texas. Three levels of data were collected: 1) COSMOS surveys at three different soil moistures, 2) electrical conductivity surveys within those COSMOS surveys, and 3) ground-truth measurements. Surveys with the COSMOS rover covered an 8000-h area and were taken both after large rain events (>2") and a long dry period. Within the COSMOS surveys, the EM38-MK2 was used to estimate the spatial distribution of coarse fragments in the soil around two COSMOS points. Ground truth measurements included coarse fragment mass and volume, bulk density, and water content at 3 locations within each EM38 survey. Ground-truth measurements were weighted using EM38 data, and COSMOS measurements were validated by their distance from the samples. There was a decrease in water content as the percent volume of coarse fragment increased. COSMOS estimations responded to both changes in coarse fragment percent volume and the ground-truth volumetric water content. Further research will focus on creating digital soil maps using landform data and water content estimations from the COSMOS rover.

  12. Gravity changes, soil moisture and data assimilation

    NASA Astrophysics Data System (ADS)

    Walker, J.; Grayson, R.; Rodell, M.; Ellet, K.

    2003-04-01

    Remote sensing holds promise for near-surface soil moisture and snow mapping, but current techniques do not directly resolve the deeper soil moisture or groundwater. The benefits that would arise from improved monitoring of variations in terrestrial water storage are numerous. The year 2002 saw the launch of NASA's Gravity Recovery And Climate Experiment (GRACE) satellites, which are mapping the Earth's gravity field at such a high level of precision that we expect to be able to infer changes in terrestrial water storage (soil moisture, groundwater, snow, ice, lake, river and vegetation). The project described here has three distinct yet inter-linked components that all leverage off the same ground-based monitoring and land surface modelling framework. These components are: (i) field validation of a relationship between soil moisture and changes in the Earth's gravity field, from ground- and satellite-based measurements of changes in gravity; (ii) development of a modelling framework for the assimilation of gravity data to constrain land surface model predictions of soil moisture content (such a framework enables the downscaling and disaggregation of low spatial (500 km) and temporal (monthly) resolution measurements of gravity change to finer spatial and temporal resolutions); and (iii) further refining the downscaling and disaggregation of space-borne gravity measurements by making use of other remotely sensed information, such as the higher spatial (25 km) and temporal (daily) resolution remotely sensed near-surface soil moisture measurements from the Advanced Microwave Scanning Radiometer (AMSR) instruments on Aqua and ADEOS II. The important field work required by this project will be in the Murrumbidgee Catchment, Australia, where an extensive soil moisture monitoring program by the University of Melbourne is already in place. We will further enhance the current monitoring network by the addition of groundwater wells and additional soil moisture sites. Ground

  13. Soil Moisture and the Persistence of North American Drought.

    NASA Astrophysics Data System (ADS)

    Oglesby, Robert J.; Erickson, David J., III

    1989-11-01

    We describe numerical sensitivity experiments exploring the effects of soil moisture on North American summertime climate using the NCAR CCMI, a 12-layer global atmospheric general circulation model. In particular. the hypothesis that reduced soil moisture may help induce and amplify warm, dry summers over midlatitude continental interiors is examined. Equilibrium climate statistics are computed for the perpetual July model response to imposed soil moisture anomalies over North America between 36° and 49°N. In addition, the persistence of imposed soil moisture anomalies is examined through use of the seasonal cycle mode of operation with use of various initial atmospheric states both equilibrated and nonequilibrated to the initial soil moisture anomaly.The climate statistics generated by thew model simulations resemble in a general way those of the summer of 1988, when extensive heat and drought occurred over much of North America. A reduction in soil moisture in the model leads to an increase in surface temperature, lower surface pressure, increased ridging aloft, and a northward shift of the jet stream. Low-level moisture advection from the Gulf of Mexico is important in determining where persistent soil moisture deficits can be maintained. In seasonal cycle simulations, it lock longer for an initially unequilibrated atmosphere to respond to the imposed soil moisture anomaly, via moisture transport from the Gulf of Mexico, than when initially the atmosphere was in equilibrium with the imposed anomaly., i.e., the initial state was obtained from the appropriate perpetual July simulation. The results demonstrate the important role of soil moisture in prolonging and/or amplifying North American summertime drought.

  14. Downscaling SMAP Soil Moisture Using Geoinformation Data and Geostatistics

    NASA Astrophysics Data System (ADS)

    Xu, Y.; Wang, L.

    2017-12-01

    Soil moisture is important for agricultural and hydrological studies. However, ground truth soil moisture data for wide area is difficult to achieve. Microwave remote sensing such as Soil Moisture Active Passive (SMAP) can offer a solution for wide coverage. However, existing global soil moisture products only provide observations at coarse spatial resolutions, which often limit their applications in regional agricultural and hydrological studies. This paper therefore aims to generate fine scale soil moisture information and extend soil moisture spatial availability. A statistical downscaling scheme is presented that incorporates multiple fine scale geoinformation data into the downscaling of coarse scale SMAP data in the absence of ground measurement data. Geoinformation data related to soil moisture patterns including digital elevation model (DEM), land surface temperature (LST), land use and normalized difference vegetation index (NDVI) at a fine scale are used as auxiliary environmental variables for downscaling SMAP data. Generalized additive model (GAM) and regression tree are first conducted to derive statistical relationships between SMAP data and auxiliary geoinformation data at an original coarse scale, and residuals are then downscaled to a finer scale via area-to-point kriging (ATPK) by accounting for the spatial correlation information of the input residuals. The results from standard validation scores as well as the triple collocation (TC) method against soil moisture in-situ measurements show that the downscaling method can significantly improve the spatial details of SMAP soil moisture while maintain the accuracy.

  15. The global distribution and dynamics of surface soil moisture

    NASA Astrophysics Data System (ADS)

    McColl, Kaighin A.; Alemohammad, Seyed Hamed; Akbar, Ruzbeh; Konings, Alexandra G.; Yueh, Simon; Entekhabi, Dara

    2017-01-01

    Surface soil moisture 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 soil moisture. Here we introduce a metric of soil moisture memory and use a full year of global observations from NASA's Soil Moisture Active Passive mission to show that surface soil moisture--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 soil moisture retains a median 14% of precipitation falling on land after three days. Furthermore, the retained fraction of the surface soil moisture 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 soil moisture storage layer at the land surface.

  16. The suitability of remotely sensed soil moisture for improving operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

    2013-11-01

    We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations

  17. The suitability of remotely sensed soil moisture for improving operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

    2014-06-01

    We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into

  18. Crop yield monitoring in the Sahel using root zone soil moisture anomalies derived from SMOS soil moisture data assimilation

    NASA Astrophysics Data System (ADS)

    Gibon, François; Pellarin, Thierry; Alhassane, Agali; Traoré, Seydou; Baron, Christian

    2017-04-01

    West Africa is greatly vulnerable, especially in terms of food sustainability. Mainly based on rainfed agriculture, the high variability of the rainy season strongly impacts the crop production driven by the soil water availability in the soil. To monitor this water availability, classical methods are based on daily precipitation measurements. However, the raingauge network suffers from the poor network density in Africa (1/10000km2). Alternatively, real-time satellite-derived precipitations can be used, but they are known to suffer from large uncertainties which produce significant error on crop yield estimations. The present study proposes to use root soil moisture rather than precipitation to evaluate crop yield variations. First, a local analysis of the spatiotemporal impact of water deficit on millet crop production in Niger was done, from in-situ soil moisture measurements (AMMA-CATCH/OZCAR (French Critical Zone exploration network)) and in-situ millet yield survey. Crop yield measurements were obtained for 10 villages located in the Niamey region from 2005 to 2012. The mean production (over 8 years) is 690 kg/ha, and ranges from 381 to 872 kg/ha during this period. Various statistical relationships based on soil moisture estimates were tested, and the most promising one (R>0.9) linked the 30-cm soil moisture anomalies from mid-August to mid-September (grain filling period) to the crop yield anomalies. Based on this local study, it was proposed to derive regional statistical relationships using 30-cm soil moisture maps over West Africa. The selected approach was to use a simple hydrological model, the Antecedent Precipitation Index (API), forced by real-time satellite-based precipitation (CMORPH, PERSIANN, TRMM3B42). To reduce uncertainties related to the quality of real-time rainfall satellite products, SMOS soil moisture measurements were assimilated into the API model through a Particular Filter algorithm. Then, obtained soil moisture anomalies were

  19. What is the philosophy of modelling soil moisture movement?

    NASA Astrophysics Data System (ADS)

    Chen, J.; Wu, Y.

    2009-12-01

    In laboratory, the soil moisture movement in the different soil textures has been analysed. From field investigation, at a spot, the soil moisture movement in the root zone, vadose zone and shallow aquifer has been explored. In addition, on ground slopes, the interflow in the near surface soil layers has been studied. Along the regions near river reaches, the expansion and shrink of the saturated area due to rainfall occurrences have been observed. From those previous explorations regarding soil moisture movement, numerical models to represent this hydrologic process have been developed. However, generally, due to high heterogeneity and stratification of soil in a basin, modelling soil moisture movement is rather challenging. Normally, some empirical equations or artificial manipulation are employed to adjust the soil moisture movement in various numerical models. In this study, we inspect the soil moisture movement equations used in a watershed model, SWAT (Soil and Water Assessment Tool) (Neitsch et al., 2005), to examine the limitations of our knowledge in such a hydrologic process. Then, we adopt the features of a topographic-information based on a hydrologic model, TOPMODEL (Beven and Kirkby, 1979), to enhance the representation of soil moisture movement in SWAT. Basically, the results of the study reveal, to some extent, the philosophy of modelling soil moisture movement in numerical models, which will be presented in the conference. Beven, K.J. and Kirkby, M.J., 1979. A physically based variable contributing area model of basin hydrology. Hydrol. Science Bulletin, 24: 43-69. Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Williams, J.R. and King, K.W., 2005. Soil and Water Assessment Tool Theoretical Documentation, Grassland, soil and research service, Temple, TX.

  20. An overview of the measurements of soil moisture and modeling of moisture flux in FIFE

    NASA Technical Reports Server (NTRS)

    Wang, J. R.

    1992-01-01

    Measurements of soil moisture and calculations of moisture transfer in the soil medium and at the air-soil interface were performed over a 15-km by 15-km test site during FIFE in 1987 and 1989. The measurements included intensive soil moisture sampling at the ground level and surveys at aircraft altitudes by several passive and active microwave sensors as well as a gamma radiation device.

  1. A microwave systems approach to measuring root zone soil moisture

    NASA Technical Reports Server (NTRS)

    Newton, R. W.; Paris, J. F.; Clark, B. V.

    1983-01-01

    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 soil moisture over large areas, and to evaluate the effect of heterogeneous ground covers with the resolution cell on the accuracy of the soil moisture estimate. The use of realistic scenes containing only 10% to 15% bare soil and significant vegetation made it possible to observe a 60% K decrease in brightness temperature from a 5% soil moisture to a 35% soil moisture at a 21 cm microwave wavelength, providing a 1.5 K to 2 K per percent soil moisture sensitivity to soil moisture. It was shown that resolution does not affect the basic ability to measure soil moisture with a microwave radiometer system. Experimental microwave and ground field data were acquired for developing and testing a root zone soil moisture prediction algorithm. The experimental measurements demonstrated that the depth of penetration at a 21 cm microwave wavelength is not greater than 5 cm.

  2. 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

  3. Soil moisture at local scale: Measurements and simulations

    NASA Astrophysics Data System (ADS)

    Romano, Nunzio

    2014-08-01

    Soil moisture refers to the water present in the uppermost part of a field soil and is a state variable controlling a wide array of ecological, hydrological, geotechnical, and meteorological processes. The literature on soil moisture 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 soil moisture interwoven with applications of modeling tools that exploit the observed datasets. This paper restricts its analysis to the evolution of soil moisture at the local (spatial) scale. Though a somewhat loosely defined term, it is linked here to a characteristic length of the soil volume investigated by the soil moisture sensing probe. After presenting the most common concepts and definitions about the amount of water stored in a certain volume of soil close to the land surface, this paper proceeds to review ground-based methods for monitoring soil moisture and evaluates modeling tools for the analysis of the gathered information in various applications. Concluding remarks address questions of monitoring and modeling of soil moisture 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.

  4. Soil moisture needs in earth sciences

    NASA Technical Reports Server (NTRS)

    Engman, Edwin T.

    1992-01-01

    The author reviews the development of passive and active microwave techniques for measuring soil moisture 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 soil moisture either as a storage in water balance computations or as a state variable in-process modeling. The author discusses future soil moisture 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.

  5. Soil Moisture Estimation Using Hyperspectral SWIR Imagery

    NASA Astrophysics Data System (ADS)

    Lewis, D.

    2007-12-01

    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 moisture indicators for soil. 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 soil moisture. Data taken from the ITD SWIR sensor and the USDA/ARS soil moisture meters were analyzed to study the informatics relationships between SWIR data and measured soil moisture. The geographic locations of 29 soil moisture 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 soil moisture 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

  6. Evaluation of Ku-Band Sensitivity To Soil Moisture: Soil Moisture Change Detection Over the NAFE06 Study Area

    USDA-ARS?s Scientific Manuscript database

    A very promising technique for spatial disaggregation of soil moisture is on the combination of radiometer and radar observations. Despite their demonstrated potential for long term large scale monitoring of soil moisture, passive and active have their disadvantages in terms of temporal and spatial ...

  7. Soil moisture profile variability in land-vegetation- atmosphere continuum

    NASA Astrophysics Data System (ADS)

    Wu, Wanru

    Soil moisture is of critical importance to the physical processes governing energy and water exchanges at the land-air boundary. With respect to the exchange of water mass, soil moisture controls the response of the land surface to atmospheric forcing and determines the partitioning of precipitation into infiltration and runoff. Meanwhile, the soil acts as a reservoir for the storage of liquid water and slow release of water vapor into the atmosphere. The major motivation of the study is that the soil moisture profile is thought to make a substantial contribution to the climate variability through two-way interactions between the land-surface and the atmosphere in the coupled ocean-atmosphere-land climate system. The characteristics of soil moisture variability with soil depth may be important in affecting the atmosphere. The natural variability of soil moisture profile is demonstrated using observations. The 16-year field observational data of soil moisture with 11-layer (top 2.0 meters) measured soil depths over Illinois are analyzed and used to identify and quantify the soil moisture profile variability, where the atmospheric forcing (precipitation) anomaly propagates down through the land-branch of the hydrological cycle with amplitude damping, phase shift, and increasing persistence. Detailed statistical data analyses, which include application of the periodogram method, the wavelet method and the band-pass filter, are made of the variations of soil moisture profile and concurrently measured precipitation for comparison. Cross-spectral analysis is performed to obtain the coherence pattern and phase correlation of two time series for phase shift and amplitude damping calculation. A composite of the drought events during this time period is analyzed and compared with the normal (non-drought) case. A multi-layer land surface model is applied for modeling the soil moisture profile variability characteristics and investigating the underlying mechanisms. Numerical

  8. Measuring soil moisture with imaging radars

    NASA Technical Reports Server (NTRS)

    Dubois, Pascale C.; Vanzyl, Jakob; Engman, Ted

    1995-01-01

    An empirical model was developed to infer soil moisture and surface roughness from radar data. The accuracy of the inversion technique is assessed by comparing soil moisture 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.

  9. Logging effects on soil moisture losses

    Treesearch

    Robert R. Ziemer

    1978-01-01

    Abstract - The depletion of soil moisture 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. Soil moisture recharge was measured periodically during the intervening winters. Groundwater fluctuations within the surface 50...

  10. Soil Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns

    NASA Astrophysics Data System (ADS)

    Dong, Jingnuo; Ochsner, Tyson E.

    2018-03-01

    Soil moisture patterns are commonly thought to be dominated by land surface characteristics, such as soil texture, at small scales and by atmospheric processes, such as precipitation, at larger scales. However, a growing body of evidence challenges this conceptual model. We investigated the structural similarity and spatial correlations between mesoscale (˜1-100 km) soil moisture patterns and land surface and atmospheric factors along a 150 km transect using 4 km multisensor precipitation data and a cosmic-ray neutron rover, with a 400 m diameter footprint. The rover was used to measure soil moisture along the transect 18 times over 13 months. Spatial structures of soil moisture, soil texture (sand content), and antecedent precipitation index (API) were characterized using autocorrelation functions and fitted with exponential models. Relative importance of land surface characteristics and atmospheric processes were compared using correlation coefficients (r) between soil moisture and sand content or API. The correlation lengths of soil moisture, sand content, and API ranged from 12-32 km, 13-20 km, and 14-45 km, respectively. Soil moisture was more strongly correlated with sand content (r = -0.536 to -0.704) than with API for all but one date. Thus, land surface characteristics exhibit coherent spatial patterns at scales up to 20 km, and those patterns often exert a stronger influence than do precipitation patterns on mesoscale spatial patterns of soil moisture.

  11. Evaluation of soil moisture barrier.

    DOT National Transportation Integrated Search

    2000-06-01

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

  12. Value of Available Global Soil Moisture Products for Agricultural Monitoring

    NASA Astrophysics Data System (ADS)

    Mladenova, Iliana; Bolten, John; Crow, Wade; de Jeu, Richard

    2016-04-01

    The first operationally derived and publicly distributed global soil moil moisture 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 soil moisture retrieval. Theoretical research and small-/field-scale airborne campaigns, however, have demonstrated that soil moisture 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 Soil Moisture Ocean Salinity (SMOS) mission (2009). In early 2015 NASA launched the second L-band-based mission, the Soil Moisture Active Passive (SMAP). These satellite-based soil moisture 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 soil moisture products for improving their decision making activities, determining global crop production and crop prices, identifying food restricted areas, etc. The basic premise of applying soil moisture observations for vegetation monitoring is that the change in soil moisture conditions will precede the change in vegetation status, suggesting that soil moisture 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 soil moisture 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 soil moisture products derived using L-band (SMOS

  13. Remote sensing of soil moisture using airborne hyperspectral data

    USGS Publications Warehouse

    Finn, M.; Lewis, M.; Bosch, D.; Giraldo, Mario; Yamamoto, K.; Sullivan, D.; Kincaid, R.; Luna, R.; Allam, G.; Kvien, Craig; Williams, M.

    2011-01-01

    Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture 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 soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture 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 soil moisture values. A significant statistical correlation (R2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture 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 soil moisture to the same degree.

  14. Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

    NASA Astrophysics Data System (ADS)

    Drusch, M.

    2007-02-01

    Satellite-derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analyzed from the modeled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. For this study, three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been performed for the 2-month period of June and July 2002: a control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating TMI (TRMM Microwave Imager) derived soil moisture over the southern United States. In this experimental run the satellite-derived soil moisture product is introduced through a nudging scheme using 6-hourly increments. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analyzed in the nudging experiment is the most accurate estimate when compared against in situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage.

  15. Impact of SMOS soil moisture data assimilation on NCEP-GFS forecasts

    NASA Astrophysics Data System (ADS)

    Zhan, X.; Zheng, W.; Meng, J.; Dong, J.; Ek, M.

    2012-04-01

    Soil moisture is one of the few critical land surface state variables that have long memory to impact the exchanges of water, energy and carbon between the land surface and atmosphere. Accurate information about soil moisture status is thus required for numerical weather, seasonal climate and hydrological forecast as well as for agricultural production forecasts, water management and many other water related economic or social activities. Since the successful launch of ESA's soil moisture ocean salinity (SMOS) mission in November 2009, about 2 years of soil moisture retrievals has been collected. SMOS is believed to be the currently best satellite sensors for soil moisture remote sensing. Therefore, it becomes interesting to examine how the collected SMOS soil moisture data are compared with other satellite-sensed soil moisture retrievals (such as NASA's Advanced Microwave Scanning Radiometer -AMSR-E and EUMETSAT's Advanced Scatterometer - ASCAT)), in situ soil moisture measurements, and how these data sets impact numerical weather prediction models such as the Global Forecast System of NOAA-NCEP. This study implements the Ensemble Kalman filter in GFS to assimilate the AMSR-E, ASCAT and SMOS soil moisture observations after a quantitative assessment of their error rate based on in situ measurements from ground networks around contiguous United States. in situ soil moisture measurements from ground networks (such as USDA Soil Climate Analysis network - SCAN and NOAA's U.S. Climate Reference Network -USCRN) are used to evaluate the GFS soil moisture simulations (analysis). The benefits and uncertainties of assimilating the satellite data products in GFS are examined by comparing the GFS forecasts of surface temperature and rainfall with and without the assimilations. From these examinations, the advantages of SMOS soil moisture data products over other satellite soil moisture data sets will be evaluated. The next step toward operationally assimilating soil moisture

  16. Soil Moisture under Different Vegetation cover in response to Precipitation

    NASA Astrophysics Data System (ADS)

    Liang, Z.; Zhang, J.; Guo, B.; Ma, J.; Wu, Y.

    2016-12-01

    The response study of soil moisture to different precipitation and landcover is significant in the field of Hydropedology. The influence of precipitation to soil moisture is obvious in addition to individual stable aquifer. With data of Hillsborough County, Florida, USA, the alluvial wetland forest and ungrazed Bahia grass that under wet and dry periods were chosen as the research objects, respectively. HYDRUS-3D numerical simulation method was used to simulate soil moisture dynamics in the root zone (10-50 cm) of those vegetation. The soil moisture response to precipitation was analyzed. The results showed that the simulation results of alluvial wetland forest by HYDRUS-3D were better than that of the Bahia grass, and for the same vegetation, the simulation results of soil moisture under dry period were better. Precipitation was more in June, 2003, the soil moisture change of alluvial wetland forest in 10-30 cm soil layer and Bahia grass in 10 cm soil layer were consistent with the precipitation change conspicuously. The alluvial wetland forest soil moisture declined faster than Bahia grass under dry period, which demonstrated that Bahia grass had strong ability to hold water. Key words: alluvial wetland forest; Bahia grass; soil moisture; HYDRUS-3D; precipitation

  17. Soil Moisture Remote Sensing with GNSS-R at the Valencia Anchor Station. The SOMOSTA (Soil Moisture Station) Experiment

    NASA Astrophysics Data System (ADS)

    Lopez-Baeza, Ernesto

    2016-07-01

    In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on soil moisture monitoring byGlobal Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced. L-band microwaves have very good advantages in soil moisture remote sensing, for being unaffected by clouds and the atmosphere, and for the ability to penetrate vegetation. During this experimental campaign, the ESA GNSS-R Oceanpal antenna was installed on the same tower as the ESA ELBARA-II passive microwave radiometer, both measuring instruments having similar field of view. This experiment is fruitfully framed within the ESA - China Programme of Collaboration on GNSS-R. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and two down-looking antennas for receiving LHCP (left-hand circular polarisation) and RHCP (right-hand circular polarisation) reflected signals from the soil surface. We could collect data from the three different antennas through the two channels of Oceanpal and, in addition, calibration could be performed to reduce the impact from the differing channels. Reflectivity was thus measured and soil moisture could be retrieved by the L- MEB (L-band Microwave Emission of the Biosphere) model considering the effect of vegetation optical thickness and soil roughness. By contrasting GNSS-R and ELBARA-II radiometer data, a negative correlation existed between reflectivity measured by GNSS-R and brightness temperature measured by the radiometer. The two parameters represent reflection and absorption of the soil. Soil moisture retrieved by both L-band remote sensing methods shows good agreement. In addition, correspondence with in-situ measurements and rainfall is also good.

  18. Temporal transferability of soil moisture calibration equations

    USDA-ARS?s Scientific Manuscript database

    Several large-scale field campaigns have been conducted over the last 20 years that require accurate estimates of soil moisture conditions. These measurements are manually conducted using soil moisture probes which require calibration. The calibration process involves the collection of hundreds of...

  19. Retrieving pace in vegetation growth using precipitation and soil moisture

    NASA Astrophysics Data System (ADS)

    Sohoulande Djebou, D. C.; Singh, V. P.

    2013-12-01

    The complexity of interactions between the biophysical components of the watershed increases the challenge of understanding water budget. Hence, the perspicacity of the continuum soil-vegetation-atmosphere's functionality still remains crucial for science. This study targeted the Texas Gulf watershed and evaluated the behavior of vegetation covers by coupling precipitation and soil moisture patterns. Growing season's Normalized Differential Vegetation Index NDVI for deciduous forest and grassland were used over a 23 year period as well as precipitation and soil moisture data. The role of time scales on vegetation dynamics analysis was appraised using both entropy rescaling and correlation analysis. This resulted in that soil moisture at 5 cm and 25cm are potentially more efficient to use for vegetation dynamics monitoring at finer time scale compared to precipitation. Albeit soil moisture at 5 cm and 25 cm series are highly correlated (R2>0.64), it appeared that 5 cm soil moisture series can better explain the variability of vegetation growth. A logarithmic transformation of soil moisture and precipitation data increased correlation with NDVI for the different time scales considered. Based on a monthly time scale we came out with a relationship between vegetation index and the couple soil moisture and precipitation [NDVI=a*Log(% soil moisture)+b*Log(Precipitation)+c] with R2>0.25 for each vegetation type. Further, we proposed to assess vegetation green-up using logistic regression model and transinformation entropy using the couple soil moisture and precipitation as independent variables and vegetation growth metrics (NDVI, NDVI ratio, NDVI slope) as the dependent variable. The study is still ongoing and the results will surely contribute to the knowledge in large scale vegetation monitoring. Keywords: Precipitation, soil moisture, vegetation growth, entropy Time scale, Logarithmic transformation and correlation between soil moisture and NDVI, precipitation and

  20. Microwave remote sensing and its application to soil moisture detection

    NASA Technical Reports Server (NTRS)

    Newton, R. W. (Principal Investigator)

    1977-01-01

    The author has identified the following significant results. Experimental measurements were utilized to demonstrate a procedure for estimating soil moisture, using a passive microwave sensor. The investigation showed that 1.4 GHz and 10.6 GHz can be used to estimate the average soil moisture within two depths; however, it appeared that a frequency less than 10.6 GHz would be preferable for the surface measurement. Average soil moisture within two depths would provide information on the slope of the soil moisture gradient near the surface. Measurements showed that a uniform surface roughness similar to flat tilled fields reduced the sensitivity of the microwave emission to soil moisture changes. Assuming that the surface roughness was known, the approximate soil moisture estimation accuracy at 1.4 GHz calculated for a 25% average soil moisture 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.

  1. Soil moisture retrival from Sentinel-1 and Modis synergy

    NASA Astrophysics Data System (ADS)

    Gao, Qi; Zribi, Mehrez; Escorihuela, Maria Jose; Baghdadi, Nicolas

    2017-04-01

    This study presents two methodologies retrieving soil moisture 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 soil moisture with a resolution of 1km. By modeling the relationship between the backscatter difference and NDVI, the soil moisture 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 soil 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 soil moisture. The proposed methodologies have been validated with the ground measurement in two demonstrative fields with RMS error about 0.05 (in volumetric moisture), and the coherence between soil moisture variations and rainfall events is observed. Soil moisture maps at 1km resolution are generated for the study area. The results demonstrate the potential of Sentinel-1 data for the retrieval of soil moisture at 1km or even better resolution.

  2. Soil moisture and soil temperature variability among three plant communities in a High Arctic Lake Basin

    NASA Astrophysics Data System (ADS)

    Davis, M. L.; Konkel, J.; Welker, J. M.; Schaeffer, S. M.

    2017-12-01

    Soil moisture and soil temperature are critical to plant community distribution and soil carbon cycle processes in High Arctic tundra. As environmental drivers of soil biochemical processes, the predictability of soil moisture and soil temperature by vegetation zone in High Arctic landscapes has significant implications for the use of satellite imagery and vegetation distribution maps to estimate of soil gas flux rates. During the 2017 growing season, we monitored soil moisture and soil temperature weekly at 48 sites in dry tundra, moist tundra, and wet grassland vegetation zones in a High Arctic lake basin. Soil temperature in all three communities reflected fluctuations in air temperature throughout the season. Mean soil temperature was highest in the dry tundra community at 10.5±0.6ºC, however, did not differ between moist tundra and wet grassland communities (2.7±0.6 and 3.1±0.5ºC, respectively). Mean volumetric soil moisture differed significantly among all three plant communities with the lowest and highest soil moisture measured in the dry tundra and wet grassland (30±1.2 and 65±2.7%), respectively. For all three communities, soil moisture was highest during the early season snow melt. Soil moisture in wet grassland remained high with no significant change throughout the season, while significant drying occurred in dry tundra. The most significant change in soil moisture was measured in moist tundra, ranging from 61 to 35%. Our results show different gradients in soil moisture variability within each plant community where: 1) soil moisture was lowest in dry tundra with little change, 2) highest in wet grassland with negligible change, and 3) variable in moist tundra which slowly dried but remained moist. Consistently high soil moisture in wet grassland restricts this plant community to areas with no significant drying during summer. The moist tundra occupies the intermediary areas between wet grassland and dry tundra and experiences the widest range

  3. The Soil Moisture Active/Passive Mission (SMAP)

    USDA-ARS?s Scientific Manuscript database

    The Soil Moisture Active/Passive (SMAP) mission will deliver global views of soil moisture 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...

  4. Remote sensing of soil moisture using airborne hyperspectral data

    USGS Publications Warehouse

    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.

    2011-01-01

    Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture 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 soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture 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 soil moisture values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture 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 soil moisture to the same degree.

  5. Evaluation of SMAP Level 2 Soil Moisture Algorithms Using SMOS Data

    NASA Technical Reports Server (NTRS)

    Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann; Shi, J. C.

    2011-01-01

    The objectives of the SMAP (Soil Moisture Active Passive) mission are global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolution, respectively. SMAP will provide soil moisture with a spatial resolution of 10 km with a 3-day revisit time at an accuracy of 0.04 m3/m3 [1]. In this paper we contribute to the development of the Level 2 soil moisture algorithm that is based on passive microwave observations by exploiting Soil Moisture Ocean Salinity (SMOS) satellite observations and products. SMOS brightness temperatures provide a global real-world, rather than simulated, test input for the SMAP radiometer-only soil moisture algorithm. Output of the potential SMAP algorithms will be compared to both in situ measurements and SMOS soil moisture products. The investigation will result in enhanced SMAP pre-launch algorithms for soil moisture.

  6. The Soil Moisture Active and Passive (SMAP) Mission

    NASA Technical Reports Server (NTRS)

    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; hide

    2009-01-01

    The Soil Moisture 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 moisture present at Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy and carbon transfers between land and atmosphere. Soil moisture 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 soil moisture 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 soil moisture 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 soil moisture and estimates of land surface-atmosphere exchanges of water, energy and carbon. SMAP is scheduled for a 2014 launch date

  7. Soil moisture retrieval from Sentinel-1 satellite data

    NASA Astrophysics Data System (ADS)

    Benninga, Harm-Jan; van der Velde, Rogier; Su, Zhongbo

    2016-04-01

    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 soil moisture) from space on an operational basis at unprecedented fine spatial and temporal resolutions. However, the influences of soil roughness and vegetation cover complicate the retrieval of soil moisture states from radar data. In this contribution, we investigate the sensitivity of Sentinel-1 radar backscatter to soil moisture 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 soil moisture 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 soil moisture states and they will facilitate the further development of operational retrieval methods. An operationally applicable soil moisture 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 soil moisture retrievals from Sentinel-1 data, multiple soil moisture retrieval methods will be studied in which the fine spatiotemporal

  8. Method for evaluating moisture tensions of soils using spectral data

    NASA Technical Reports Server (NTRS)

    Peterson, John B. (Inventor)

    1982-01-01

    A method is disclosed which permits evaluation of soil moisture utilizing remote sensing. Spectral measurements at a plurality of different wavelengths are taken with respect to sample soils and the bidirectional reflectance factor (BRF) measurements produced are submitted to regression analysis for development therefrom of predictable equations calculated for orderly relationships. Soil of unknown reflective and unknown soil moisture tension is thereafter analyzed for bidirectional reflectance and the resulting data utilized to determine the soil moisture tension of the soil as well as providing a prediction as to the bidirectional reflectance of the soil at other moisture tensions.

  9. Soil moisture and evapotranspiration predictions using Skylab data

    NASA Technical Reports Server (NTRS)

    Myers, V. I. (Principal Investigator); Moore, D. G.; Horton, M. L.; Russell, M. J.

    1975-01-01

    The author has identified the following significant results. Multispectral reflectance and emittance data from the Skylab workshop were evaluated for prediction of evapotranspiration and soil moisture 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 soil moisture than did data from the reflective bands. Thermal data were dependent on soil moisture 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 soil moisture can be accomplished with space altitude thermal data. Thermal data will provide a reliable input into irrigation scheduling.

  10. Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

    NASA Astrophysics Data System (ADS)

    Tobin, Kenneth J.; Torres, Roberto; Crow, Wade T.; Bennett, Marvin E.

    2017-09-01

    . The best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash-Sutcliffe coefficients (NSs) for ARM ranged from -0. 1 to 0.3 and were similar to the results obtained from the USCRN network (0.2-0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1-0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02-0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05-0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

  11. Soil moisture and the persistence of North American drought

    NASA Technical Reports Server (NTRS)

    Oglesby, Robert J.; Erickson, David J., III

    1989-01-01

    Numerical sensitivity experiments on the effects of soil moisture on North American summertime climate are performed using a 12-layer global atmospheric general circulation model. Consideration is given to the hypothesis that reduced soil moisture 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 soil moisture 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 moisture advection from the Gulf of Mexico is important in the maintenance of persistent soil moisture deficits.

  12. Use of Ultrasonic Technology for Soil Moisture Measurement

    NASA Technical Reports Server (NTRS)

    Choi, J.; Metzl, R.; Aggarwal, M. D.; Belisle, W.; Coleman, T.

    1997-01-01

    In an effort to improve existing soil moisture 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 moisture content changes. Preliminary values of velocities are 676.1 m/s in dry soil and 356.8 m/s in 100% moist soils. Intermediate values can be calibrated to give exact values for the moisture content in an unknown sample.

  13. Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers

    PubMed Central

    Liu, Cheng; Qian, Hongzhou; Cao, Weixing; Ni, Jun

    2018-01-01

    To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and some accessory components. Low-resistivity copper rings were used as components of the sensing probes. Composable simulation of the sensor’s sensing probes was carried out using a high-frequency structure simulator. According to the effective radiation range of electric field intensity, width and spacing of copper ring were set to 30 mm and 40 mm, respectively. A parallel resonance circuit of voltage-controlled oscillator and high-frequency inductance-capacitance (LC) was designed for signal frequency division and conditioning. A data processor was used to process moisture-related frequency signals for soil profile moisture sensing. The sensor was able to detect real-time soil moisture at the depths of 20, 30, and 50 cm and conduct online inversion of moisture in the soil layer between 0–100 cm. According to the calibration results, the degree of fitting (R2) between the sensor’s measuring frequency and the volumetric moisture content of soil sample was 0.99 and the relative error of the sensor consistency test was 0–1.17%. Field tests in different loam soils showed that measured soil moisture from our sensor reproduced the observed soil moisture dynamic well, with an R2 of 0.96 and a root mean square error of 0.04. In a sensor accuracy test, the R2 between the measured value of the proposed sensor and that of the Diviner2000 portable soil moisture monitoring system was higher than 0.85, with a relative error smaller than 5%. The R2 between measured values and inversed soil moisture values for other soil layers were consistently higher than 0.8. According to calibration test and field test, this sensor, which features low cost, good operability, and high integration, is qualified for

  14. Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers.

    PubMed

    Gao, Zhenran; Zhu, Yan; Liu, Cheng; Qian, Hongzhou; Cao, Weixing; Ni, Jun

    2018-05-21

    To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and some accessory components. Low-resistivity copper rings were used as components of the sensing probes. Composable simulation of the sensor’s sensing probes was carried out using a high-frequency structure simulator. According to the effective radiation range of electric field intensity, width and spacing of copper ring were set to 30 mm and 40 mm, respectively. A parallel resonance circuit of voltage-controlled oscillator and high-frequency inductance-capacitance (LC) was designed for signal frequency division and conditioning. A data processor was used to process moisture-related frequency signals for soil profile moisture sensing. The sensor was able to detect real-time soil moisture at the depths of 20, 30, and 50 cm and conduct online inversion of moisture in the soil layer between 0⁻100 cm. According to the calibration results, the degree of fitting ( R ²) between the sensor’s measuring frequency and the volumetric moisture content of soil sample was 0.99 and the relative error of the sensor consistency test was 0⁻1.17%. Field tests in different loam soils showed that measured soil moisture from our sensor reproduced the observed soil moisture dynamic well, with an R ² of 0.96 and a root mean square error of 0.04. In a sensor accuracy test, the R ² between the measured value of the proposed sensor and that of the Diviner2000 portable soil moisture monitoring system was higher than 0.85, with a relative error smaller than 5%. The R ² between measured values and inversed soil moisture values for other soil layers were consistently higher than 0.8. According to calibration test and field test, this sensor, which features low cost, good operability, and high integration

  15. Remotely sensed soil moisture input to a hydrologic model

    NASA Technical Reports Server (NTRS)

    Engman, E. T.; Kustas, W. P.; Wang, J. R.

    1989-01-01

    The possibility of using detailed spatial soil moisture 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 soil moisture were used to construct two-dimensional maps of the spatial distribution of the soil moisture. Data from overflights on different dates provided the temporal changes resulting from soil drainage and evapotranspiration. The study site and data collection are described, and the soil measurement data are given. The model selection is discussed, and the simulation results are summarized. It is concluded that a time series of soil moisture is a valuable new type of data for verifying model performance and for updating and correcting simulated streamflow.

  16. Evaluation of a Soil Moisture Data Assimilation System Over West Africa

    NASA Astrophysics Data System (ADS)

    Bolten, J. D.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.

    2009-05-01

    A crucial requirement of global crop yield forecasts by the U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and resulting soil moisture, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. IPAD estimates global soil moisture using daily estimates of minimum and maximum temperature and precipitation applied to a modified Palmer two-layer soil moisture model which calculates the daily amount of soil moisture withdrawn by evapotranspiration and replenished by precipitation. We attempt to improve upon the existing system by applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA soil moisture model. This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9°N- 20°N; 20°W-20°E). This region contains many key agricultural areas and has a high agro- meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model

  17. Microstrip Ring Resonator for Soil Moisture Measurements

    NASA Technical Reports Server (NTRS)

    Sarabandi, Kamal; Li, Eric S.

    1993-01-01

    Accurate determination of spatial soil moisture 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 soil moisture relies on the accurate knowledge of the soil dielectric constant (epsilon(sub soil)) to its moisture content. Two existing methods for measurement of dielectric constant of soil 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 soil). In this paper a microstrip ring resonator is proposed for the accurate measurement of soil dielectric constant. In this technique the microstrip ring resonator is placed in contact with soil medium and the real and imaginary parts of epsilon(sub soil) 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 soil) = 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 soil with different moisture contents are presented and compared with other approaches.

  18. Evaluation of the validated soil moisture product from the SMAP radiometer

    USDA-ARS?s Scientific Manuscript database

    In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA’s Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite retrieved sur...

  19. Soil moisture - precipitation feedbacks in observations and models (Invited)

    NASA Astrophysics Data System (ADS)

    Taylor, C.

    2013-12-01

    There is considerable uncertainty about the strength, geographical extent, and even the sign of feedbacks between soil moisture and precipitation. Whilst precipitation trivially increases soil moisture, the impact of soil moisture, via surface fluxes, on convective rainfall is far from straight-forward, and likely depends on space and time scale, soil 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 soil moisture 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 soil moisture datasets, a methodology has been developed to compare the statistics of antecedent soil moisture 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 soil moisture 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 soil. 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 soil moisture

  20. Statistical analysis of simulated global soil moisture and its memory in an ensemble of CMIP5 general circulation models

    NASA Astrophysics Data System (ADS)

    Wiß, Felix; Stacke, Tobias; Hagemann, Stefan

    2014-05-01

    Soil moisture and its memory can have a strong impact on near surface temperature and precipitation and have the potential to promote severe heat waves, dry spells and floods. To analyze how soil moisture is simulated in recent general circulation models (GCMs), soil moisture data from a 23 model ensemble of Atmospheric Model Intercomparison Project (AMIP) type simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are examined for the period 1979 to 2008 with regard to parameterization and statistical characteristics. With respect to soil moisture processes, the models vary in their maximum soil and root depth, the number of soil layers, the water-holding capacity, and the ability to simulate freezing which all together leads to very different soil moisture characteristics. Differences in the water-holding capacity are resulting in deviations in the global median soil moisture of more than one order of magnitude between the models. In contrast, the variance shows similar absolute values when comparing the models to each other. Thus, the input and output rates by precipitation and evapotranspiration, which are computed by the atmospheric component of the models, have to be in the same range. Most models simulate great variances in the monsoon areas of the tropics and north western U.S., intermediate variances in Europe and eastern U.S., and low variances in the Sahara, continental Asia, and central and western Australia. In general, the variance decreases with latitude over the high northern latitudes. As soil moisture trends in the models were found to be negligible, the soil moisture anomalies were calculated by subtracting the 30 year monthly climatology from the data. The length of the memory is determined from the soil moisture anomalies by calculating the first insignificant autocorrelation for ascending monthly lags (insignificant autocorrelation folding time). The models show a great spread of autocorrelation length from a few months in

  1. Soil moisture dynamics and smoldering combustion limits of pocosin soils in North Carolina, USA

    Treesearch

    James Reardon; Gary Curcio; Roberta Bartlette

    2009-01-01

    Smoldering combustion of wetland organic soils in the south-eastern USA is a serious management concern. Previous studies have reported smoldering was sensitive to a wide range of moisture contents, but studies of soil moisture dynamics and changing smoldering combustion potential in wetland communities are limited. Linking soil moisture measurements with estimates of...

  2. Downscaled soil moisture from SMAP evaluated using high density observations

    USDA-ARS?s Scientific Manuscript database

    Recently, a soil moisture downscaling algorithm based on a regression relationship between daily temperature changes and daily average soil moisture was developed to produce an enhanced spatial resolution on soil moisture product for the Advanced Microwave Scanning Radiometer–EOS (AMSR-E) satellite ...

  3. Data assimilation to extract soil moisture information from SMAP observations

    USDA-ARS?s Scientific Manuscript database

    This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network(NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United Sta...

  4. Moisture-strength-constructability guidelines for subgrade foundation soils found in Indiana.

    DOT National Transportation Integrated Search

    2016-09-01

    Soil moisture is an important indicator of constructability in the field. Construction activities become difficult when the soil moisture content is excessive, especially in fine-grained soils. Change orders caused by excessive soil moisture during c...

  5. Spatial variability of soil moisture retrieved by SMOS satellite

    NASA Astrophysics Data System (ADS)

    Lukowski, Mateusz; Marczewski, Wojciech; Usowicz, Boguslaw; Rojek, Edyta; Slominski, Jan; Lipiec, Jerzy

    2015-04-01

    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 soil moisture. Using in situ methods it is difficult to estimate soil moisture 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 Soil Moisture and Ocean Salinity mission launch in 2009, the estimation of soil moisture 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 moisture distributions of this area were studied for selected natural soil phenomena for 2010-2014 including: freezing, thawing, rainfalls (wetting), drying and drought. Those soil 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 soil moisture, 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 soil moisture in the adopted scale satisfies

  6. Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active Passive satellite and evaluation at core validation sites

    USDA-ARS?s Scientific Manuscript database

    This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture ...

  7. Application of Multitemporal Remotely Sensed Soil Moisture for the Estimation of Soil Physical Properties

    NASA Technical Reports Server (NTRS)

    Mattikalli, N. M.; Engman, E. T.; Jackson, T. J.; Ahuja, L. R.

    1997-01-01

    This paper demonstrates the use of multitemporal soil moisture derived from microwave remote sensing to estimate soil physical properties. The passive microwave ESTAR instrument was employed during June 10-18, 1992, to obtain brightness temperature (TB) and surface soil moisture data in the Little Washita watershed, Oklahoma. Analyses of spatial and temporal variations of TB and soil moisture during the dry-down period revealed a direct relationship between changes in T and soil moisture and soil physical (viz. texture) and hydraulic (viz. saturated hydraulic conductivity, K(sat)) properties. Statistically significant regression relationships were developed for the ratio of percent sand to percent clay (RSC) and K(sat), in terms of change components of TB and surface soil moisture. Validation of results using field measured values and soil texture map indicated that both RSC and K(sat) can be estimated with reasonable accuracy. These findings have potential applications of microwave remote sensing to obtain quick estimates of the spatial distributions of K(sat), over large areas for input parameterization of hydrologic models.

  8. Soil moisture observations using L-, C-, and X-band microwave radiometers

    NASA Astrophysics Data System (ADS)

    Bolten, John Dennis

    The purpose of this thesis is to further the current understanding of soil moisture remote sensing under varying conditions using L-, C-, and X-band. Aircraft and satellite instruments are used to investigate the effects of frequency and spatial resolution on soil moisture sensitivity. The specific objectives of the research are to examine multi-scale observed and modeled microwave radiobrightness, evaluate new EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperature and soil moisture retrievals, and examine future satellite-based technologies for soil moisture sensing. The cycling of Earth's water, energy and carbon is vital to understanding global climate. Over land, these processes are largely dependent on the amount of moisture within the top few centimeters of the soil. However, there are currently no methods available that can accurately characterize Earth's soil moisture layer at the spatial scales or temporal resolutions appropriate for climate modeling. The current work uses ground truth, satellite and aircraft remote sensing data from three large-scale field experiments having different land surface, topographic and climate conditions. A physically-based radiative transfer model is used to simulate the observed aircraft and satellite measurements using spatially and temporally co-located surface parameters. A robust analysis of surface heterogeneity and scaling is possible due to the combination of multiple datasets from a range of microwave frequencies and field conditions. Accurate characterization of spatial and temporal variability of soil moisture during the three field experiments is achieved through sensor calibration and algorithm validation. Comparisons of satellite observations and resampled aircraft observations are made using soil moisture from a Numerical Weather Prediction (NWP) model in order to further demonstrate a soil moisture correlation where point data was unavailable. The influence of vegetation, spatial

  9. Evaluation of soil and vegetation response to drought using SMOS soil moisture satellite observations

    NASA Astrophysics Data System (ADS)

    Piles, Maria; Sánchez, Nilda; Vall-llossera, Mercè; Ballabrera, Joaquim; Martínez, Justino; Martínez-Fernández, José; Camps, Adriano; Font, Jordi

    2014-05-01

    Soil moisture plays an important role in determining the likelihood of droughts and floods that may affect an area. Knowledge of soil moisture 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 soil moisture 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 soil moisture estimates. The ESA's Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to measuring the Earth's surface soil moisture 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 soil moisture 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 soil moisture 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) soil moisture estimates, which permits extending the applicability of the data to regional and local studies. Fine-scale soil moisture maps are currently limited to the Iberian Peninsula but the algorithm is dynamic and can be transported to any region. Soil moisture 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 soil 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

  10. Misrepresentation and amendment of soil moisture in conceptual hydrological modelling

    NASA Astrophysics Data System (ADS)

    Zhuo, Lu; Han, Dawei

    2016-04-01

    Although many conceptual models are very effective in simulating river runoff, their soil moisture 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 soil moisture 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 soil 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 soil moisture 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 soil moisture. The correlation between the XAJ and the observed soil moisture is enhanced significantly from 0.64 to 0.70. In addition, a new soil moisture term called SMDS (Soil Moisture Deficit to Saturation) is proposed to complement the conventional SMD (Soil Moisture Deficit).

  11. An integrated GIS application system for soil moisture data assimilation

    NASA Astrophysics Data System (ADS)

    Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang

    2014-11-01

    The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture 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 soil moisture 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 soil moisture 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 soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.

  12. Soil microbial community responses to antibiotic-contaminated manure under different soil moisture regimes.

    PubMed

    Reichel, Rüdiger; Radl, Viviane; Rosendahl, Ingrid; Albert, Andreas; Amelung, Wulf; Schloter, Michael; Thiele-Bruhn, Sören

    2014-01-01

    Sulfadiazine (SDZ) is an antibiotic frequently administered to livestock, and it alters microbial communities when entering soils with animal manure, but understanding the interactions of these effects to the prevailing climatic regime has eluded researchers. A climatic factor that strongly controls microbial activity is soil moisture. Here, we hypothesized that the effects of SDZ on soil microbial communities will be modulated depending on the soil moisture conditions. To test this hypothesis, we performed a 49-day fully controlled climate chamber pot experiments with soil grown with Dactylis glomerata (L.). Manure-amended pots without or with SDZ contamination were incubated under a dynamic moisture regime (DMR) with repeated drying and rewetting changes of >20 % maximum water holding capacity (WHCmax) in comparison to a control moisture regime (CMR) at an average soil moisture of 38 % WHCmax. We then monitored changes in SDZ concentration as well as in the phenotypic phospholipid fatty acid and genotypic 16S rRNA gene fragment patterns of the microbial community after 7, 20, 27, 34, and 49 days of incubation. The results showed that strongly changing water supply made SDZ accessible to mild extraction in the short term. As a result, and despite rather small SDZ effects on community structures, the PLFA-derived microbial biomass was suppressed in the SDZ-contaminated DMR soils relative to the CMR ones, indicating that dynamic moisture changes accelerate the susceptibility of the soil microbial community to antibiotics.

  13. Assessment of the SMAP Passive Soil Moisture Product

    NASA Technical Reports Server (NTRS)

    Chan, Steven K.; Bindlish, Rajat; O'Neill, Peggy E.; Njoku, Eni; Jackson, Tom; Colliander, Andreas; Chen, Fan; Burgin, Mariko; Dunbar, Scott; Piepmeier, Jeffrey; hide

    2016-01-01

    The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture 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 soil moisture product became the only operational Level 2 soil moisture product for SMAP. The product provides soil moisture 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 Soil Moisture 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 soil moisture 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.

  14. A Methodology for Soil Moisture Retrieval from Land Surface Temperature, Vegetation Index, Topography and Soil Type

    NASA Astrophysics Data System (ADS)

    Pradhan, N. R.

    2015-12-01

    Soil moisture 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, soil moisture 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, soil moisture pattern can vary from completely disorganized (disordered, random) to highly organized. To understand this varying soil moisture 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 soil moisture properties of soil types and the degree of saturation. The advantage in using this relationship is twofold: first it retrieves soil moisture content at the scale of soil data resolution even though the derived indexes are in a coarse resolution, and secondly the derived soil moisture distribution represents both organized and disorganized patterns of actual soil moisture. The derived soil moisture is used in driving the hydrological model simulations of runoff, sediment and nutrients.

  15. Inter-Comparison of SMAP, SMOS and GCOM-W Soil Moisture Products

    NASA Astrophysics Data System (ADS)

    Bindlish, R.; Jackson, T. J.; Chan, S.; Burgin, M. S.; Colliander, A.; Cosh, M. H.

    2016-12-01

    The Soil Moisture Active Passive (SMAP) mission was launched on Jan 31, 2015. The goal of the SMAP mission is to produce soil moisture with accuracy better than 0.04 m3/m3 with a revisit frequency of 2-3 days. The validated standard SMAP passive soil moisture product (L2SMP) with a spatial resolution of 36 km was released in May 2016. Soil moisture observations from in situ sensors are typically used to validate the satellite estimates. But, in situ observations provide ground truth for limited amount of landcover and climatic conditions. Although each mission will have its own issues, observations by other satellite instruments can be play a role in the calibration and validation of SMAP. SMAP, SMOS and GCOM-W missions share some commonnalities because they are currently providing operational brightness temperature and soil moisture products. SMAP and SMOS operate at L-band but GCOM-W uses X-band observations for soil moisture estimation. All these missions use different ancillary data sources, parameterization and algorithm to retrieve soil moisture. Therefore, it is important to validate and to compare the consistency of these products. Soil moisture products from the different missions will be compared with the in situ observations. SMAP soil moisture products will be inter-compared at global scales with SMOS and GCOM-W soil moisture products. The major contribution of satellite product inter-comparison is that it allows the assessment of the quality of the products over wider geographical and climate domains. Rigorous assessment will lead to a more reliable and accurate soil moisture product from all the missions.

  16. Remote Sensing Soil Moisture Analysis by Unmanned Aerial Vehicles Digital Imaging

    NASA Astrophysics Data System (ADS)

    Yeh, C. Y.; Lin, H. R.; Chen, Y. L.; Huang, S. Y.; Wen, J. C.

    2017-12-01

    In recent years, remote sensing analysis has been able to apply to the research of climate change, environment monitoring, geology, hydro-meteorological, and so on. However, the traditional methods for analyzing wide ranges of surface soil moisture of spatial distribution surveys may require plenty resources besides the high cost. In the past, remote sensing analysis performed soil moisture estimates through shortwave, thermal infrared ray, or infrared satellite, which requires lots of resources, labor, and money. Therefore, the digital image color was used to establish the multiple linear regression model. Finally, we can find out the relationship between surface soil color and soil moisture. In this study, we use the Unmanned Aerial Vehicle (UAV) to take an aerial photo of the fallow farmland. Simultaneously, we take the surface soil sample from 0-5 cm of the surface. The soil will be baking by 110° C and 24 hr. And the software ImageJ 1.48 is applied for the analysis of the digital images and the hue analysis into Red, Green, and Blue (R, G, B) hue values. The correlation analysis is the result from the data obtained from the image hue and the surface soil moisture at each sampling point. After image and soil moisture analysis, we use the R, G, B and soil moisture to establish the multiple regression to estimate the spatial distributions of surface soil moisture. In the result, we compare the real soil moisture and the estimated soil moisture. The coefficient of determination (R2) can achieve 0.5-0.7. The uncertainties in the field test, such as the sun illumination, the sun exposure angle, even the shadow, will affect the result; therefore, R2 can achieve 0.5-0.7 reflects good effect for the in-suit test by using the digital image to estimate the soil moisture. Based on the outcomes of the research, using digital images from UAV to estimate the surface soil moisture is acceptable. However, further investigations need to be collected more than ten days (four

  17. Soil moisture downscaling using a simple thermal based proxy

    NASA Astrophysics Data System (ADS)

    Peng, Jian; Loew, Alexander; Niesel, Jonathan

    2016-04-01

    Microwave remote sensing has been largely applied to retrieve soil moisture (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 soil moisture 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) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture 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 soil moisture while maintain the accuracy of CCI soil moisture. 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 soil moisture.

  18. Microwave Soil Moisture Retrieval Under Trees

    NASA Technical Reports Server (NTRS)

    O'Neill, P.; Lang, R.; Kurum, M.; Joseph, A.; Jackson, T.; Cosh, M.

    2008-01-01

    Soil moisture 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 soil moisture 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 soil moisture retrieval plans due to the large expected impact of trees on masking the microwave response to the underlying soil moisture. Our understanding of the microwave properties of trees of various sizes and their effect on soil moisture 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 soil moisture 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

  19. Soil moisture under contrasted atmospheric conditions in Eastern Spain

    NASA Astrophysics Data System (ADS)

    Azorin-Molina, César; Cerdà, Artemi; Vicente-Serrano, Sergio M.

    2014-05-01

    Soil moisture plays a key role on the recently abandoned agriculture land where determine the recovery and the erosion rates (Cerdà, 1995), on the soil water repellency degree (Bodí et al., 2011) and on the hydrological cycle (Cerdà, 1999), the plant development (García Fayos et al., 2000) and the seasonality of the geomorphological processes (Cerdà, 2002). Moreover, Soil moisture is a key factor on the semiarid land (Ziadat and Taimeh, 2013), on the productivity of the land (Qadir et al., 2013) and soils treated with amendments (Johnston et al., 2013) and on soil reclamation on drained saline-sodic soils (Ghafoor et al., 2012). In previous study (Azorin-Molina et al., 2013) we investigated the intraannual evolution of soil moisture in soils under different land managements in the Valencia region, Eastern Spain, and concluded that soil moisture recharges are much controlled by few heavy precipitation events; 23 recharge episodes during 2012. Most of the soil moisture recharge events occurred during the autumn season under Back-Door cold front situations. Additionally, sea breeze front episodes brought isolated precipitation and moisture to mountainous areas within summer (Azorin-Molina et al., 2009). We also evidenced that the intraanual evolution of soil moisture changes are positively and significatively correlated (at p<0.01) with the amount of measured precipitation. In this study we analyze the role of other crucial atmospheric parameters (i.e., temperature, relative humidity, global solar radiation, and wind speed and wind direction) in the intraanual evolution of soil moisture; focussing our analyses on the soil moisture discharge episodes. Here we present 1-year of soil moisture measurements at two experimental sites in the Valencia region, one representing rainfed orchard typical from the Mediterranean mountains (El Teularet-Sierra de Enguera), and a second site corresponding to an irrigated orange crop (Alcoleja). Key Words: Soil Moisture Discharges

  20. Observing and modeling links between soil moisture, microbes and CH4 fluxes from forest soils

    NASA Astrophysics Data System (ADS)

    Christiansen, Jesper; Levy-Booth, David; Barker, Jason; Prescott, Cindy; Grayston, Sue

    2017-04-01

    Soil moisture is a key driver of methane (CH4) fluxes in forest soils, both of the net uptake of atmospheric CH4 and emission from the soil. Climate and land use change will alter spatial patterns of soil moisture as well as temporal variability impacting the net CH4 exchange. The impact on the resultant net CH4 exchange however is linked to the underlying spatial and temporal distribution of the soil microbial communities involved in CH4 cycling as well as the response of the soil microbial community to environmental changes. Significant progress has been made to target specific CH4 consuming and producing soil organisms, which is invaluable in order to understand the microbial regulation of the CH4 cycle in forest soils. However, it is not clear as to which extent soil moisture shapes the structure, function and abundance of CH4 specific microorganisms and how this is linked to observed net CH4 exchange under contrasting soil moisture regimes. Here we report on the results from a research project aiming to understand how the CH4 net exchange is shaped by the interactive effects soil moisture and the spatial distribution CH4 consuming (methanotrophs) and producing (methanogens). We studied the growing season variations of in situ CH4 fluxes, microbial gene abundances of methanotrophs and methanogens, soil hydrology, and nutrient availability in three typical forest types across a soil moisture gradient in a temperate rainforest on the Canadian Pacific coast. Furthermore, we conducted laboratory experiments to determine whether the net CH4 exchange from hydrologically contrasting forest soils responded differently to changes in soil moisture. Lastly, we modelled the microbial mediation of net CH4 exchange along the soil moisture gradient using structural equation modeling. Our study shows that it is possible to link spatial patterns of in situ net exchange of CH4 to microbial abundance of CH4 consuming and producing organisms. We also show that the microbial

  1. SMOS soil moisture validation with U.S. in situ newworks

    USDA-ARS?s Scientific Manuscript database

    Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors using a variety of retrieval methods. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. Since it is a new sensor u...

  2. Soil moisture dynamics modeling considering multi-layer root zone.

    PubMed

    Kumar, R; Shankar, V; Jat, M K

    2013-01-01

    The moisture uptake by plant from soil is a key process for plant growth and movement of water in the soil-plant system. A non-linear root water uptake (RWU) model was developed for a multi-layer crop root zone. The model comprised two parts: (1) model formulation and (2) moisture flow prediction. The developed model was tested for its efficiency in predicting moisture depletion in a non-uniform root zone. A field experiment on wheat (Triticum aestivum) was conducted in the sub-temperate sub-humid agro-climate of Solan, Himachal Pradesh, India. Model-predicted soil moisture parameters, i.e., moisture status at various depths, moisture depletion and soil moisture profile in the root zone, are in good agreement with experiment results. The results of simulation emphasize the utility of the RWU model across different agro-climatic regions. The model can be used for sound irrigation management especially in water-scarce humid, temperate, arid and semi-arid regions and can also be integrated with a water transport equation to predict the solute uptake by plant biomass.

  3. Spatial-temporal variability of soil moisture and its estimation across scales

    NASA Astrophysics Data System (ADS)

    Brocca, L.; Melone, F.; Moramarco, T.; Morbidelli, R.

    2010-02-01

    The soil moisture is a quantity of paramount importance in the study of hydrologic phenomena and soil-atmosphere interaction. Because of its high spatial and temporal variability, the soil moisture monitoring scheme was investigated here both for soil moisture retrieval by remote sensing and in view of the use of soil moisture data in rainfall-runoff modeling. To this end, by using a portable Time Domain Reflectometer, a sequence of 35 measurement days were carried out within a single year in seven fields located inside the Vallaccia catchment, central Italy, with area of 60 km2. Every sampling day, soil moisture measurements were collected at each field over a regular grid with an extension of 2000 m2. The optimization of the monitoring scheme, with the aim of an accurate mean soil moisture estimation at the field and catchment scale, was addressed by the statistical and the temporal stability. At the field scale, the number of required samples (NRS) to estimate the field-mean soil moisture within an accuracy of 2%, necessary for the validation of remotely sensed soil moisture, ranged between 4 and 15 for almost dry conditions (the worst case); at the catchment scale, this number increased to nearly 40 and it refers to almost wet conditions. On the other hand, to estimate the mean soil moisture temporal pattern, useful for rainfall-runoff modeling, the NRS was found to be lower. In fact, at the catchment scale only 10 measurements collected in the most "representative" field, previously determined through the temporal stability analysis, can reproduce the catchment-mean soil moisture with a determination coefficient, R2, higher than 0.96 and a root-mean-square error, RMSE, equal to 2.38%. For the "nonrepresentative" fields the accuracy in terms of RMSE decreased, but similar R2 coefficients were found. This insight can be exploited for the sampling in a generic field when it is sufficient to know an index of soil moisture temporal pattern to be incorporated in

  4. Inter-comparison of soil moisture sensors from the soil moisture active passive marena Oklahoma in situ sensor testbed (SMAP-MOISST)

    USDA-ARS?s Scientific Manuscript database

    The diversity of in situ soil moisture network protocols and instrumentation led to the development of a testbed for comparing in situ soil moisture sensors. Located in Marena, Oklahoma on the Oklahoma State University Range Research Station, the testbed consists of four base stations. Each station ...

  5. Multifrequency remote sensing of soil moisture. [Guymon, Oklahoma and Dalhart, Texas

    NASA Technical Reports Server (NTRS)

    Theis, S. W.; Mcfarland, M. J.; Rosenthal, W. D.; Jones, C. L. (Principal Investigator)

    1982-01-01

    Multifrequency sensor data collected at Guymon, Oklahoma and Dalhart, Texas using NASA's C-130 aircraft were used to determine which of the all-weather microwave sensors demonstrated the highest correlation to surface soil moisture over optimal bare soil conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to soil moisture. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate soil moisture over bare fields. In comparison to other active and passive microwave sensors the L-band radiometer (1) was influenced least by ranges in surface roughness; (2) demonstrated the most sensitivity to soil moisture differences in terms of the range of return from the full range of soil moisture; and (3) was less sensitive to errors in measurement in relation to the range of sensor response. L-band emissivity related more strongly to soil moisture when moisture was expressed as percent of field capacity. The perpendicular vegetation index as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to soil moisture.

  6. Is soil moisture initialization important for seasonal to decadal predictions?

    NASA Astrophysics Data System (ADS)

    Stacke, Tobias; Hagemann, Stefan

    2014-05-01

    The state of soil moisture 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 soil moisture data or improving the initial soil moisture 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 soil moisture 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 soil moisture states for different seasons and years. Instead of using common thresholds like wilting point or critical soil moisture, 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 soil hydrology in JSBACH was improved by replacing the bucket-type soil hydrology scheme with a multi-layer scheme. This new scheme is a more realistic representation of the soil, including percolation and diffusion fluxes between up to five separate layers, the limitation of bare soil evaporation to the uppermost soil layer and the addition of a long term water storage below the root zone in regions with deep soil. While the hydrological cycle is not strongly affected by this new scheme, it has some impact on the simulated soil moisture 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

  7. Mapping surface soil moisture with L-band radiometric measurements

    NASA Technical Reports Server (NTRS)

    Wang, James R.; Shiue, James C.; Schmugge, Thomas J.; Engman, Edwin T.

    1989-01-01

    A NASA C-130 airborne remote sensing aircraft was used to obtain four-beam pushbroom microwave radiometric measurements over two small Kansas tall-grass prairie region watersheds, during a dry-down period after heavy rainfall in May and June, 1987. While one of the watersheds had been burned 2 months before these measurements, the other had not been burned for over a year. Surface soil-moisture data were collected at the time of the aircraft measurements and correlated with the corresponding radiometric measurements, establishing a relationship for surface soil-moisture mapping. Radiometric sensitivity to soil moisture variation is higher in the burned than in the unburned watershed; surface soil moisture loss is also faster in the burned watershed.

  8. SMERGE: A multi-decadal root-zone soil moisture product for CONUS

    NASA Astrophysics Data System (ADS)

    Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

    2017-12-01

    Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

  9. Global response of the growing season to soil moisture and topography

    NASA Astrophysics Data System (ADS)

    Guevara, M.; Arroyo, C.; Warner, D. L.; Equihua, J.; Lule, A. V.; Schwartz, A.; Taufer, M.; Vargas, R.

    2017-12-01

    Soil moisture has a direct influence in plant productivity. Plant productivity and its greenness can be inferred by remote sensing with higher spatial detail than soil moisture. The objective was to improve the coarse scale of currently available satellite soil moisture estimates and identify areas of strong coupling between the interannual variability soil moisture and the maximum greenness vegetation fraction (MGVF) at the global scale. We modeled, cross-validated and downscaled remotely sensed soil moisture using machine learning and digital terrain analysis across 23 years (1991-2013) of available data. Improving the accuracy (0.69-0.87 % of cross-validated explained variance) and the spatial detail (from 27 to 15km) of satellite soil moisture, we filled temporal gaps of information across vegetated areas where satellite soil moisture does not work properly. We found that 7.57% of global vegetated area shows strong correlation with our downscaled product (R2>0.5, Fig. 1). We found a dominant positive response of vegetation greenness to topography-based soil moisture across water limited environments, however, the tropics and temperate environments of higher latitudes showed a sparse negative response. We conclude that topography can be used to effectively improve the spatial detail of globally available remotely sensed soil moisture, which is convenient to generate unbiased comparisons with global vegetation dynamics, and better inform land and crop modeling efforts.

  10. The Value of SMAP Soil Moisture Observations For Agricultural Applications

    NASA Astrophysics Data System (ADS)

    Mladenova, I. E.; Bolten, J. D.; Crow, W.; Reynolds, C. A.

    2017-12-01

    Knowledge of the amount of soil moisture (SM) in the root zone (RZ) is critical source of information for crop analysts and agricultural agencies as it controls crop development and crop condition changes and can largely impact end-of-season yield. Foreign Agricultural Services (FAS), a subdivision of U.S. Department of Agriculture (USDA) that is in charge with providing information on current and expected global crop supply and demand estimates, has been relying on RZSM estimates generated by the modified two-layer Palmer model, which has been enhanced to allow the assimilation of satellite-based soil moisture data. Generally the accuracy of model-based soil moisture estimates is dependent on the precision of the forcing data that drives the model and more specifically, the accuracy of the precipitation data. Data assimilation gives the opportunity to correct for such precipitation-related inaccuracies and enhance the quality of the model estimates. Here we demonstrate the value of ingesting passive-based soil moisture observations derived from the Soil Moisture Active Passive (SMAP) mission. In terms of agriculture, general understanding is that the change in soil moisture conditions precede the change in vegetation status, suggesting that soil moisture can be used as an early indicator of expected crop conditions. Therefore, we assess the accuracy of the SMAP enhanced Palmer model by examining the lag rank cross-correlation coefficient between the model generated soil moisture observations and the Normalized Difference Vegetation Index (NDVI).

  11. Evaluation of soil pH and moisture content on in-situ ozonation of pyrene in soils.

    PubMed

    Luster-Teasley, S; Ubaka-Blackmoore, N; Masten, S J

    2009-08-15

    In this study, pyrene spiked soil (300 ppm) was ozonated at pH levels of 2, 6, and 8 and three moisture contents. It was found that soil pH and moisture content impacted the effectiveness of PAH oxidation in unsaturated soils. In air-dried soils, as pH increased, removal increased, such that pyrene removal efficiencies at pH 6 and pH 8 reached 95-97% at a dose of 2.22 mg O(3)/mg pyrene. Ozonation at 16.2+/-0.45 mg O(3)/ppm pyrene in soil resulted in 81-98% removal of pyrene at all pH levels tested. Saturated soils were tested at dry, 5% or 10% moisture conditions. The removal of pyrene was slower in moisturized soils, with the efficiency decreasing as the moisture content increased. Increasing the pH of the soil having a moisture content of 5% resulted in improved pyrene removals. On the contrary, in the soil having a moisture content of 10%, as the pH increased, pyrene removal decreased. Contaminated PAH soils were stored for 6 months to compare the efficiency of PAH removal in freshly contaminated soil and aged soils. PAH adsorption to soil was found to increase with longer exposure times; thus requiring much higher doses of ozone to effectively oxidize pyrene.

  12. The Contribution of Soil Moisture Information to Forecast Skill: Two Studies

    NASA Technical Reports Server (NTRS)

    Koster, Randal

    2010-01-01

    This talk briefly describes two recent studies on the impact of soil moisture information on hydrological and meteorological prediction. While the studies utilize soil moisture derived from the integration of large-scale land surface models with observations-based meteorological data, the results directly illustrate the potential usefulness of satellite-derived soil moisture information (e.g., from SMOS and SMAP) for applications in prediction. The first study, the GEWEX- and ClIVAR-sponsored GLACE-2 project, quantifies the contribution of realistic soil moisture initialization to skill in subseasonal forecasts of precipitation and air temperature (out to two months). The multi-model study shows that soil moisture information does indeed contribute skill to the forecasts, particularly for air temperature, and particularly when the initial local soil moisture anomaly is large. Furthermore, the skill contributions tend to be larger where the soil moisture initialization is more accurate, as measured by the density of the observational network contributing to the initialization. The second study focuses on streamflow prediction. The relative contributions of snow and soil moisture initialization to skill in streamflow prediction at seasonal lead, in the absence of knowledge of meteorological anomalies during the forecast period, were quantified with several land surface models using uniquely designed numerical experiments and naturalized streamflow data covering mUltiple decades over the western United States. In several basins, accurate soil moisture initialization is found to contribute significant levels of predictive skill. Depending on the date of forecast issue, the contributions can be significant out to leads of six months. Both studies suggest that improvements in soil moisture initialization would lead to increases in predictive skill. The relevance of SMOS and SMAP satellite-based soil moisture information to prediction are discussed in the context of these

  13. Microstrip transmission line for soil moisture measurement

    NASA Astrophysics Data System (ADS)

    Chen, Xuemin; Li, Jing; Liang, Renyue; Sun, Yijie; Liu, C. Richard; Rogers, Richard; Claros, German

    2004-12-01

    Pavement life span is often affected by the amount of voids in the base and subgrade soils, especially moisture content in pavement. Most available moisture sensors are based on the capacitive sensing using planar blades. Since the planar sensor blades are fabricated on the same surface to reduce the overall size of the sensor, such structure cannot provide very high accuracy for moisture content measurement. As a consequence, a typical capacitive moisture sensor has an error in the range of 30%. A more accurate measurement is based on the time domain refelctometer (TDR) measurement. However, typical TDR system is fairly expensive equipment, very large in size, and difficult to operate, the moisture content measurement is limited. In this paper, a novel microstrip transmission line based moisture sensor is presented. This sensor uses the phase shift measurement of RF signal going through a transmission line buried in the soil to be measured. Since the amplitude of the transmission measurement is a strong function of the conductivity (loss of the media) and the imaginary part of dielectric constant, and the phase is mainly a strong function of the real part of the dielectric constant, measuring phase shift in transmission mode can directly obtain the soil moisture information. This sensor was designed and implemented. Sensor networking was devised. Both lab and field data show that this sensor is sensitive and accurate.

  14. Aircraft active microwave measurements for estimating soil moisture

    NASA Technical Reports Server (NTRS)

    Jackson, T. J.; Chang, A.; Schmugge, T. J.

    1981-01-01

    Both active and passive microwave sensors are sensitive to variations in near-surface soil moisture. The principal advantage of active microwave systems for soil moisture applications is that high spatial resolution can be retained even at satellite attitudes. The considered investigation is concerned with the use of active microwave scatterometers for estimating near-surface soil moisture. Microwave scatterometer data were obtained during a series of three aircraft flights over a group of Oklahoma research watersheds during May 1978. Data were obtained for the C, L, and P bands at angles of incidence between 5 and 50 degrees. The best results were obtained using C band data at incidence angles of 10 and 15 degrees and soil moisture depth of 0 to 15 cm. These results were in excellent agreement with the conclusions of the truck-mounted scatterometer measurement program reported by Ulaby et al. (1978, 1979).

  15. Soil moisture by extraction and gas chromatography

    NASA Technical Reports Server (NTRS)

    Merek, E. L.; Carle, G. C.

    1973-01-01

    To determine moisture content of soils rapidly and conveniently extract moisture with methanol and determine water content of methanol extract by gas chromatography. Moisture content of sample is calculated from weight of water and methanol in aliquot and weight of methanol added to sample.

  16. Galvanic Cell Type Sensor for Soil Moisture Analysis.

    PubMed

    Gaikwad, Pramod; Devendrachari, Mruthyunjayachari Chattanahalli; Thimmappa, Ravikumar; Paswan, Bhuneshwar; Raja Kottaichamy, Alagar; Makri Nimbegondi Kotresh, Harish; Thotiyl, Musthafa Ottakam

    2015-07-21

    Here we report the first potentiometric sensor for soil moisture 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 soil moisture. Unlike the state of the art soil moisture sensors, a signal derived from the proposed moisture 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 soil moisture 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 moisture levels in the soil 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 soil mechanics, forecasting the risk of natural calamities, and so on.

  17. Soil moisture-soil temperature interrelationships on a sandy-loam soil exposed to full sunlight

    Treesearch

    David A. Marquis

    1967-01-01

    In a study of birch regeneration in New Hampshire, soil moisture and temperature were found to be intimately related. Not only does low moisture lead to high temperature, but high temperature undoubtedly accelerates soil drying, setting up a vicious cycle of heating and drying that may prevent seed germination or kill seedlings.

  18. Using Remotely Sensed Soil Moisture to Estimate Fire Risk in Tropical Peatlands

    NASA Astrophysics Data System (ADS)

    Dadap, N.; Cobb, A.; Hoyt, A.; Harvey, C. F.; Konings, A. G.

    2017-12-01

    Tropical peatlands in Equatorial Asia have become more vulnerable to fire due to deforestation and peatland drainage over the last 30 years. In these regions, water table depth has been shown to play an important role in mediating fire risk as it serves as a proxy for peat moisture content. However, water table depth observations are sparse and expensive. Soil moisture could provide a more direct indicator of fire risk than water table depth. In this study, we use new soil moisture retrievals from the Soil Moisture Active Passive (SMAP) satellite to demonstrate that - contrary to popular wisdom - remotely sensed soil moisture observations are possible over most Southeast Asian peatlands. Soil moisture estimation in this region was previously thought to be impossible over tropical peatlands because of dense vegetation cover. We show that vegetation density is sufficiently low across most Equatorial Asian peatlands to allow soil moisture estimation, and hypothesize that deforestation and other anthropogenic changes in land cover have combined to reduce overall vegetation density sufficient to allow soil moisture estimation. We further combine burned area estimates from the Global Fire Emissions Database and SMAP soil moisture retrievals to show that soil moisture provides a strong signal for fire risk in peatlands, with fires occurring at a much greater rate over drier soils. We will also develop an explicit fire risk model incorporating soil moisture with additional climatic, land cover, and anthropogenic predictor variables.

  19. Preliminary assessment of soil moisture over vegetation

    NASA Technical Reports Server (NTRS)

    Carlson, T. N.

    1986-01-01

    Modeling of surface energy fluxes was combined with in-situ measurement of surface parameters, specifically the surface sensible heat flux and the substrate soil moisture. 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 soil 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 soil moisture availability shows that there is a very high correlation between antecedent precipitation and inferred surface moisture 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 moisture, preparing for participation in the French HAPEX experiment, and analyzing aircraft microwave and radiometric surface temperature data for the 1983 French Beauce experiments.

  20. Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

    NASA Technical Reports Server (NTRS)

    Girotto, Manuela; Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Rodell, Matthew

    2017-01-01

    Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

  1. Joint assimilation of SMOS brightness temperature and GRACE terrestrial water storage observations for improved soil moisture estimation

    NASA Astrophysics Data System (ADS)

    Girotto, M.; Reichle, R. H.; De Lannoy, G.; Rodell, M.

    2017-12-01

    Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0-5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

  2. Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

    NASA Technical Reports Server (NTRS)

    Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian

    2011-01-01

    Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

  3. Use of soil moisture sensors for irrigation scheduling

    USDA-ARS?s Scientific Manuscript database

    Various types of soil moisture sensing devices have been developed and are commercially available for water management applications. Each type of soil moisture sensors has its advantages and shortcomings in terms of accuracy, reliability, and cost. Resistive and capacitive based sensors, and time-d...

  4. Divergent surface and total soil moisture projections under global warming

    USGS Publications Warehouse

    Berg, Alexis; Sheffield, Justin; Milly, Paul C.D.

    2017-01-01

    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 soil moisture. Here we comprehensively analyze soil moisture projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer-by-layer soil moisture. We identify a robust vertical gradient of projected mean soil moisture 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 soil moisture, will tend to overestimate (negatively) changes in total soil water availability.

  5. Investigation of remote sensing techniques of measuring soil moisture

    NASA Technical Reports Server (NTRS)

    Newton, R. W. (Principal Investigator); Blanchard, A. J.; Nieber, J. L.; Lascano, R.; Tsang, L.; Vanbavel, C. H. M.

    1981-01-01

    Major activities described include development and evaluation of theoretical models that describe both active and passive microwave sensing of soil moisture, 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 soil water and soil temperature profile model and the calibration and evaluation of gamma ray attenuation probes for measuring soil moisture profiles are considered. The analysis of spatial variability of soil information as related to remote sensing is discussed as well as the implementation of an instrumented field site for acquisition of soil moisture and meteorologic information for use in validating the soil water profile and soil temperature profile models.

  6. Enhancing SMAP Soil Moisture Retrievals via Superresolution Techniques

    NASA Astrophysics Data System (ADS)

    Beale, K. D.; Ebtehaj, A. M.; Romberg, J. K.; Bras, R. L.

    2017-12-01

    Soil moisture is a key state variable that modulates land-atmosphere interactions and its high-resolution global scale estimates are essential for improved weather forecasting, drought prediction, crop management, and the safety of troop mobility. Currently, NASA's Soil Moisture Active/Passive (SMAP) satellite provides a global picture of soil moisture variability at a resolution of 36 km, which is prohibitive for some hydrologic applications. The goal of this research is to enhance the resolution of SMAP passive microwave retrievals by a factor of 2 to 4 using modern superresolution techniques that rely on the knowledge of high-resolution land surface models. In this work, we explore several super-resolution techniques including an empirical dictionary method, a learned dictionary method, and a three-layer convolutional neural network. Using a year of global high-resolution land surface model simulations as training set, we found that we are able to produce high-resolution soil moisture maps that outperform the original low-resolution observations both qualitatively and quantitatively. In particular, on a patch-by-patch basis we are able to produce estimates of high-resolution soil moisture maps that improve on the original low-resolution patches by on average 6% in terms of mean-squared error, and 14% in terms of the structural similarity index.

  7. Evaluation of Long-term Soil Moisture Proxies in the U.S. Great Plains

    NASA Astrophysics Data System (ADS)

    Yuan, S.; Quiring, S. M.

    2016-12-01

    Soil moisture plays an important role in land-atmosphere interactions through both surface energy and water balances. However, despite its importance, there are few long-term records of observed soil moisture for investigating long-term spatial and temporal variations of soil moisture. Hence, it is necessary to find suitable approximations of soil moisture observations. 5 drought indices will be compared with simulated and observed soil moisture over the U.S. Great Plains during two time periods (1980 - 2012 and 2003 - 2012). Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer Z Index (zindex) and Crop Moisture Index (CMI) will be calculated by PRISM data. The soil moisture simulations will be derived from NLDAS. In situ soil moisture will be obtained from North American Soil Moisture Database. The evaluation will focus on three main aspects: trends, variations and persistence. The results will support further research investigating long-term variations in soil moisture-climate interactions.

  8. The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert.

    PubMed

    Li, Bonan; Wang, Lixin; Kaseke, Kudzai F; Li, Lin; Seely, Mary K

    2016-01-01

    Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture 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 soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months' continuous daily records of rainfall and soil moisture (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 soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture 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 soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture 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., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling

  9. The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert

    PubMed Central

    Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.

    2016-01-01

    Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture 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 soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (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 soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture 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 soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture 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., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling

  10. A multiyear study of soil moisture patterns across agricultural and forested landscapes

    NASA Astrophysics Data System (ADS)

    Georgakakos, C. B.; Hofmeister, K.; O'Connor, C.; Buchanan, B.; Walter, T.

    2017-12-01

    This work compares varying spatial and temporal soil moisture patterns in wet and dry years between forested and agricultural landscapes. This data set spans 6 years (2012-2017) of snow-free soil moisture measurements across multiple watersheds and land covers in New York State's Finger Lakes region. Due to the relatively long sampling period, we have captured fluctuations in soil moisture dynamics across wetter, dryer, and average precipitation years. We can therefore analyze response of land cover types to precipitation under varying climatic and hydrologic conditions. Across the study period, mean soil moisture in forest soils was significantly drier than in agricultural soils, and exhibited a smaller range of moisture conditions. In the drought year of 2016, soil moisture at all sites was significantly drier compared to the other years. When comparing the effects of land cover and year on soil moisture, we found that land cover had a more significant influence. Understanding the difference in landscape soil moisture dynamics between forested and agricultural land will help predict watershed responses to changing precipitation patterns in the future.

  11. Remote sensing of soil moisture with microwave radiometers

    NASA Technical Reports Server (NTRS)

    Schmugge, T.; Wilheit, T.; Webster, W., Jr.; Gloerson, P.

    1976-01-01

    Results are presented that were derived from measurements made by microwave radiometers during the March 1972 and February 1973 flights of National Aeronautics and Space Administration (NASA) Convair-9900 aircraft over agricultural test sites in the southwestern part of United States. The purpose of the missions was to study the use of microwave radiometers for the remote sensing of soil moisture. The microwave radiometers covered the 0.8- to 21-cm wavelength range. The results show a good linear correlation between the observed microwave brightness temperature and moisture content of the 0- to 1-cm layer of the soil. The results at the largest wavelength (21 cm) show the greatest sensitivity to soil moisture variations and indicate the possibility of sensing these variations through a vegetative canopy. The effect of soil texture on the emission from the soil was also studied and it was found that this effect can be compensated for by expressing soil moisture as a percent of field capacity for the soil. The results were compared with calculations based on a radiative transfer model for layered dielectrics and the agreement is very good at the longer wavelengths. At the shorter wavelengths, surface roughness effects are larger and the agreement becomes poorer.

  12. Evaluating Land-Atmosphere Interactions with the North American Soil Moisture Database

    NASA Astrophysics Data System (ADS)

    Giles, S. M.; Quiring, S. M.; Ford, T.; Chavez, N.; Galvan, J.

    2015-12-01

    The North American Soil Moisture Database (NASMD) is a high-quality observational soil moisture database that was developed to study land-atmosphere interactions. It includes over 1,800 monitoring stations the United States, Canada and Mexico. Soil moisture data are collected from multiple sources, quality controlled and integrated into an online database (soilmoisture.tamu.edu). The period of record varies substantially and only a few of these stations have an observation record extending back into the 1990s. Daily soil moisture observations have been quality controlled using the North American Soil Moisture Database QAQC algorithm. The database is designed to facilitate observationally-driven investigations of land-atmosphere interactions, validation of the accuracy of soil moisture simulations in global land surface models, satellite calibration/validation for SMOS and SMAP, and an improved understanding of how soil moisture 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.

  13. Soil moisture inferences from thermal infrared measurements of vegetation temperatures

    NASA Technical Reports Server (NTRS)

    Jackson, R. D. (Principal Investigator)

    1981-01-01

    Thermal infrared measurements of wheat (Triticum durum) canopy temperatures were used in a crop water stress index to infer root zone soil moisture. Results indicated that one time plant temperature measurement cannot produce precise estimates of root zone soil moisture due to complicating plant factors. Plant temperature measurements do yield useful qualitative information concerning soil moisture and plant condition.

  14. Recent advances in (soil moisture) triple collocation analysis

    USDA-ARS?s Scientific Manuscript database

    To date, triple collocation (TC) analysis is one of the most important methods for the global scale evaluation of remotely sensed soil moisture data sets. In this study we review existing implementations of soil moisture TC analysis as well as investigations of the assumptions underlying the method....

  15. Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

    NASA Technical Reports Server (NTRS)

    Akbar, Ruzbeh; Cosh, Michael H.; O'Neill, Peggy E.; Entekhabi, Dara; Moghaddam, Mahta

    2017-01-01

    A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

  16. Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation.

    PubMed

    Akbar, Ruzbeh; Cosh, Michael H; O'Neill, Peggy E; Entekhabi, Dara; Moghaddam, Mahta

    2017-07-01

    A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

  17. SMOS and AMSR-2 soil moisture evaluation using representative monitoring sites in southern Australia

    NASA Astrophysics Data System (ADS)

    Walker, J. P.; Mei Sun, M. S.; Rudiger, C.; Parinussa, R.; Koike, T.; Kerr, Y. H.

    2016-12-01

    The performance of soil moisture products from AMSR-2 and SMOS were evaluated against representative surface soil moisture stations within the Yanco study area in the Murrumbidgee Catchment, in southeast Australia. AMSR-2 Level 3 (L3) soil moisture products retrieved from two sets of brightness temperatures using the Japanese Aerospace exploration Agency (JAXA) and the Land Parameter Retrieval Model (LPRM) algorithms were included. For the LPRM algorithm, two different parameterization methods were applied. In the case of SMOS, two versions of the SMOS L3 soil moisture product were assessed. Results based on using "random" and representative stations to evaluate the products were contrasted. The latest versions of the JAXA (JX2) and LPRM (LP3) products were found to perform better than the earlier versions (JX1, LP1 and LP2). Moreover, soil moisture retrieval based on the latter version of brightness temperature and parameterization scheme improved when C-band observations were used, as opposed to the X-band data. Yet, X-band retrievals were found to perform better than C-band. Inter-comparing AMSR-2 X-band products from different acquisition times showed a better performance for 1:30 pm overpasses whereas SMOS 6:00 am retrievals were found to perform the best. The mean average error (MAE) goal accuracy of the AMSR-2 mission (MAE < 0.08 m3/m3) was met by both versions of the JAXA products, the LPRM X-band products retrieved from the reprocessed version of brightness temperatures, and both versions of SMOS products. Nevertheless, none of the products achieved the SMOS target accuracy of 0.04 m3/m3. Finally, the product performance depended on the statistics used in their evaluation; based on temporal and absolute accuracy JX2 is recommended, whereas LP3 X-band 1:30 pm and SMOS2 6:00 am are recommended based on temporal accuracy alone.

  18. A Citizen Science Soil Moisture Sensor to Support SMAP Calibration/Validation

    NASA Astrophysics Data System (ADS)

    Podest, E.; Das, N. N.

    2016-12-01

    The Soil Moisture Active Passive (SMAP) satellite mission was launched in Jan. 2015 and is currently acquiring global measurements of soil moisture in the top 5 cm of the soil every 3 days. SMAP has partnered with the GLOBE program to engage students from around the world to collect in situ soil moisture and help validate SMAP measurements. The current GLOBE SMAP soil moisture protocol consists in collecting a soil sample, weighing, drying and weighing it again in order to determine the amount of water in the soil. Preparation and soil sample collection can take up to 20 minutes and drying can take up to 3 days. We have hence developed a soil moisture measurement device based on Arduino-like microcontrollers along with off-the-shelf and homemade sensors that are accurate, robust, inexpensive and quick and easy to use so that they can be implemented by the GLOBE community and citizen scientists alike. This talk will discuss building, calibration and validation of the soil moisture measuring device and assessing the quality of the measurements collected. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

  19. Improving long-term global precipitation dataset using multi-sensor surface soil moisture retrievals and the soil moisture analysis rainfall tool (SMART)

    USDA-ARS?s Scientific Manuscript database

    Using multiple historical satellite surface soil moisture products, the Kalman Filtering-based Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain g...

  20. SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

    NASA Technical Reports Server (NTRS)

    Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann

    2011-01-01

    Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS

  1. An inversion method for retrieving soil moisture information from satellite altimetry observations

    NASA Astrophysics Data System (ADS)

    Uebbing, Bernd; Forootan, Ehsan; Kusche, Jürgen; Braakmann-Folgmann, Anne

    2016-04-01

    Soil moisture represents an important component of the terrestrial water cycle that controls., evapotranspiration and vegetation growth. Consequently, knowledge on soil moisture variability is essential to understand the interactions between land and atmosphere. Yet, terrestrial measurements are sparse and their information content is limited due to the large spatial variability of soil moisture. Therefore, over the last two decades, several active and passive radar and satellite missions such as ERS/SCAT, AMSR, SMOS or SMAP have been providing backscatter information that can be used to estimate surface conditions including soil moisture which is proportional to the dielectric constant of the upper (few cm) soil layers . Another source of soil moisture information are satellite radar altimeters, originally designed to measure sea surface height over the oceans. Measurements of Jason-1/2 (Ku- and C-Band) or Envisat (Ku- and S-Band) nadir radar backscatter provide high-resolution along-track information (~ 300m along-track resolution) on backscatter every ~10 days (Jason-1/2) or ~35 days (Envisat). Recent studies found good correlation between backscatter and soil moisture in upper layers, especially in arid and semi-arid regions, indicating the potential of satellite altimetry both to reconstruct and to monitor soil moisture variability. However, measuring soil moisture using altimetry has some drawbacks that include: (1) the noisy behavior of the altimetry-derived backscatter (due to e.g., existence of surface water in the radar foot-print), (2) the strong assumptions for converting altimetry backscatters to the soil moisture storage changes, and (3) the need for interpolating between the tracks. In this study, we suggest a new inversion framework that allows to retrieve soil moisture information from along-track Jason-2 and Envisat satellite altimetry data, and we test this scheme over the Australian arid and semi-arid regions. Our method consists of: (i

  2. Spatiotemporal Variability of Hillslope Soil Moisture Across Steep, Highly Dissected Topography

    NASA Astrophysics Data System (ADS)

    Jarecke, K. M.; Wondzell, S. M.; Bladon, K. D.

    2016-12-01

    Hillslope ecohydrological processes, including subsurface water flow and plant water uptake, are strongly influenced by soil moisture. However, the factors controlling spatial and temporal variability of soil moisture in steep, mountainous terrain are poorly understood. We asked: How do topography and soils interact to control the spatial and temporal variability of soil moisture in steep, Douglas-fir dominated hillslopes in the western Cascades? We will present a preliminary analysis of bimonthly soil moisture variability from July-November 2016 at 0-30 and 0-60 cm depth across spatially extensive convergent and divergent topographic positions in Watershed 1 of the H.J. Andrews Experimental Forest in central Oregon. Soil moisture monitoring locations were selected following a 5 m LIDAR analysis of topographic position, aspect, and slope. Topographic position index (TPI) was calculated as the difference in elevation to the mean elevation within a 30 m radius. Convergent (negative TPI values) and divergent (positive TPI values) monitoring locations were established along northwest to northeast-facing aspects and within 25-55 degree slopes. We hypothesized that topographic position (convergent vs. divergent), as well as soil physical properties (e.g., texture, bulk density), control variation in hillslope soil moisture at the sub-watershed scale. In addition, we expected the relative importance of hillslope topography to the spatial variability in soil moisture to differ seasonally. By comparing the spatiotemporal variability of hillslope soil moisture across topographic positions, our research provides a foundation for additional understanding of subsurface flow processes and plant-available soil-water in forests with steep, highly dissected terrain.

  3. Response of spectral vegetation indices to soil moisture in grasslands and shrublands

    USGS Publications Warehouse

    Zhang, Li; Ji, Lei; Wylie, Bruce K.

    2011-01-01

    The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture.

  4. Mode Decomposition Methods for Soil Moisture Prediction

    NASA Astrophysics Data System (ADS)

    Jana, R. B.; Efendiev, Y. R.; Mohanty, B.

    2014-12-01

    Lack of reliable, well-distributed, long-term datasets for model validation is a bottle-neck for most exercises in soil moisture analysis and prediction. Understanding what factors drive soil hydrological processes at different scales and their variability is very critical to further our ability to model the various components of the hydrologic cycle more accurately. For this, a comprehensive dataset with measurements across scales is very necessary. Intensive fine-resolution sampling of soil moisture over extended periods of time is financially and logistically prohibitive. Installation of a few long term monitoring stations is also expensive, and needs to be situated at critical locations. The concept of Time Stable Locations has been in use for some time now to find locations that reflect the mean values for the soil moisture across the watershed under all wetness conditions. However, the soil moisture variability across the watershed is lost when measuring at only time stable locations. We present here a study using techniques such as Dynamic Mode Decomposition (DMD) and Discrete Empirical Interpolation Method (DEIM) that extends the concept of time stable locations to arrive at locations that provide not simply the average soil moisture values for the watershed, but also those that can help re-capture the dynamics across all locations in the watershed. As with the time stability, the initial analysis is dependent on an intensive sampling history. The DMD/DEIM method is an application of model reduction techniques for non-linearly related measurements. Using this technique, we are able to determine the number of sampling points that would be required for a given accuracy of prediction across the watershed, and the location of those points. Locations with higher energetics in the basis domain are chosen first. We present case studies across watersheds in the US and India. The technique can be applied to other hydro-climates easily.

  5. The NASA Soil Moisture Active Passive (SMAP) Mission: Overview

    NASA Technical Reports Server (NTRS)

    O'Neill, Peggy; Entekhabi, Dara; Njoku, Eni; Kellogg, Kent

    2011-01-01

    The Soil Moisture 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 soil moisture and freeze/thaw state every 2-3 days. The combined active/passive microwave soil moisture 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 soil moisture and net ecosystem exchange of carbon. SMAP is expected to launch in the late 2014 - early 2015 time frame.

  6. SMAP Level 4 Surface and Root Zone Soil Moisture

    NASA Technical Reports Server (NTRS)

    Reichle, R.; De Lannoy, G.; Liu, Q.; Ardizzone, J.; Kimball, J.; Koster, R.

    2017-01-01

    The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, 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 soil moisture 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 soil moisture.

  7. Validation of SMAP surface soil moisture products with core validation sites

    USDA-ARS?s Scientific Manuscript database

    The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diver...

  8. Precipitation estimation using L-Band and C-Band soil moisture retrievals

    USDA-ARS?s Scientific Manuscript database

    An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterome...

  9. Development and Validation of The SMAP Enhanced Passive Soil Moisture Product

    NASA Technical Reports Server (NTRS)

    Chan, S.; Bindlish, R.; O'Neill, P.; Jackson, T.; Chaubell, J.; Piepmeier, J.; Dunbar, S.; Colliander, A.; Chen, F.; Entekhabi, D.; hide

    2017-01-01

    Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 cu m/cu m at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 cu m/cu m. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

  10. Radar for Measuring Soil Moisture Under Vegetation

    NASA Technical Reports Server (NTRS)

    Moghaddam, Mahta; Moller, Delwyn; Rodriguez, Ernesto; Rahmat-Samii, Yahya

    2004-01-01

    A two-frequency, polarimetric, spaceborne synthetic-aperture radar (SAR) system has been proposed for measuring the moisture content of soil as a function of depth, even in the presence of overlying vegetation. These measurements are needed because data on soil moisture 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.

  11. Use of physically-based models and Soil Taxonomy to identify soil moisture classes: Problems and proposals

    NASA Astrophysics Data System (ADS)

    Bonfante, A.; Basile, A.; de Mascellis, R.; Manna, P.; Terribile, F.

    2009-04-01

    Soil classification according to Soil Taxonomy include, as fundamental feature, the estimation of soil moisture regime. The term soil moisture regime refers to the "presence or absence either of ground water or of water held at a tension of less than 1500 kPa in the soil or in specific horizons during periods of the year". In the classification procedure, defining of the soil moisture control section is the primary step in order to obtain the soil moisture regimes classification. Currently, the estimation of soil moisture regimes is carried out through simple calculation schemes, such as Newhall and Billaux models, and only in few cases some authors suggest the use of different more complex models (i.e., EPIC) In fact, in the Soil Taxonomy, the definition of the soil moisture control section is based on the wetting front position in two different conditions: the upper boundary is the depth to which a dry soil will be moistened by 2.5 cm of water within 24 hours and the lower boundary is the depth to which a dry soil will be moistened by 7.5 cm of water within 48 hours. Newhall, Billaux and EPIC models don't use physical laws to describe soil water flows, but they use a simple bucket-like scheme where the soil is divided into several compartments and water moves, instantly, only downward when the field capacity is achieved. On the other side, a large number of one-dimensional hydrological simulation models (SWAP, Cropsyst, Hydrus, MACRO, etc..) are available, tested and successfully used. The flow is simulated according to pressure head gradients through the numerical solution of the Richard's equation. These simulation models can be fruitful used to improve the study of soil moisture regimes. The aims of this work are: (i) analysis of the soil moisture control section concept by a physically based model (SWAP); (ii) comparison of the classification obtained in five different Italian pedoclimatic conditions (Mantova and Lodi in northern Italy; Salerno, Benevento and

  12. Joint microwave and infrared studies for soil moisture determination

    NASA Technical Reports Server (NTRS)

    Njoku, E. G.; Schieldge, J. P.; Kahle, A. B. (Principal Investigator)

    1980-01-01

    The feasibility of using a combined microwave-thermal infrared system to determine soil moisture content is addressed. Of particular concern are bare soils. The theoretical basis for microwave emission from soils and the transport of heat and moisture in soils 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.

  13. Examining diel patterns of soil and xylem moisture using electrical resistivity imaging

    NASA Astrophysics Data System (ADS)

    Mares, Rachel; Barnard, Holly R.; Mao, Deqiang; Revil, André; Singha, Kamini

    2016-05-01

    The feedbacks among forest transpiration, soil moisture, and subsurface flowpaths are poorly understood. We investigate how soil moisture is affected by daily transpiration using time-lapse electrical resistivity imaging (ERI) on a highly instrumented ponderosa pine and the surrounding soil 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 soil moisture due to drying of the soil during moisture uptake. As sap flow decreases during the night, the ground conductivity increases as the soil moisture is replenished. The mean and variance of the ground conductivity decreases into the summer dry season, indicating drier soil and smaller diel fluctuations in soil moisture 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 soil moisture depletion. ERI captured spatiotemporal variability of soil moisture 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.

  14. Multi-Scale Soil Moisture Monitoring and Modeling at ARS Watersheds for NASA's Soil Moisture Active Passive (SMAP) Calibration/Validation Mission

    NASA Astrophysics Data System (ADS)

    Coopersmith, E. J.; Cosh, M. H.

    2014-12-01

    NASA's SMAP satellite, launched in November of 2014, produces estimates of average volumetric soil moisture at 3, 9, and 36-kilometer scales. The calibration and validation process of these estimates requires the generation of an identically-scaled soil moisture product from existing in-situ networks. This can be achieved via the integration of NLDAS precipitation data to perform calibration of models at each ­in-situ gauge. In turn, these models and the gauges' volumetric estimations are used to generate soil moisture estimates at a 500m scale throughout a given test watershed by leveraging, at each location, the gauge-calibrated models deemed most appropriate in terms of proximity, calibration efficacy, soil-textural similarity, and topography. Four ARS watersheds, located in Iowa, Oklahoma, Georgia, and Arizona are employed to demonstrate the utility of this approach. The South Fork watershed in Iowa represents the simplest case - the soil textures and topography are relative constants and the variability of soil moisture is simply tied to the spatial variability of precipitation. The Little Washita watershed in Oklahoma adds soil textural variability (but remains topographically simple), while the Little River watershed in Georgia incorporates topographic classification. Finally, the Walnut Gulch watershed in Arizona adds a dense precipitation network to be employed for even finer-scale modeling estimates. Results suggest RMSE values at or below the 4% volumetric standard adopted for the SMAP mission are attainable over the desired spatial scales via this integration of modeling efforts and existing in-situ networks.

  15. Fiber Optic Thermo-Hygrometers for Soil Moisture Monitoring.

    PubMed

    Leone, Marco; Principe, Sofia; Consales, Marco; Parente, Roberto; Laudati, Armando; Caliro, Stefano; Cutolo, Antonello; Cusano, Andrea

    2017-06-20

    This work deals with the fabrication, prototyping, and experimental validation of a fiber optic thermo-hygrometer-based soil moisture sensor, useful for rainfall-induced landslide prevention applications. In particular, we recently proposed a new generation of fiber Bragg grating (FBGs)-based soil moisture 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 soil moisture sensor for its exploitation in landslide prevention applications. Successively, we present the development, prototyping, and experimental validation of a novel, optimized version of a soil 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 soil.

  16. Fiber Optic Thermo-Hygrometers for Soil Moisture Monitoring

    PubMed Central

    Leone, Marco; Principe, Sofia; Consales, Marco; Parente, Roberto; Laudati, Armando; Caliro, Stefano; Cutolo, Antonello; Cusano, Andrea

    2017-01-01

    This work deals with the fabrication, prototyping, and experimental validation of a fiber optic thermo-hygrometer-based soil moisture sensor, useful for rainfall-induced landslide prevention applications. In particular, we recently proposed a new generation of fiber Bragg grating (FBGs)-based soil moisture 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 soil moisture sensor for its exploitation in landslide prevention applications. Successively, we present the development, prototyping, and experimental validation of a novel, optimized version of a soil 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 soil. PMID:28632172

  17. Round Robin evaluation of soil moisture retrieval models for the MetOp-A ASCAT Instrument

    NASA Astrophysics Data System (ADS)

    Gruber, Alexander; Paloscia, Simonetta; Santi, Emanuele; Notarnicola, Claudia; Pasolli, Luca; Smolander, Tuomo; Pulliainen, Jouni; Mittelbach, Heidi; Dorigo, Wouter; Wagner, Wolfgang

    2014-05-01

    Global soil moisture observations are crucial to understand hydrologic processes, earth-atmosphere interactions and climate variability. ESA's Climate Change Initiative (CCI) project aims to create a global consistent long-term soil moisture data set based on the merging of the best available active and passive satellite-based microwave sensors and retrieval algorithms. Within the CCI, a Round Robin evaluation of existing retrieval algorithms for both active and passive instruments was carried out. In this study we present the comparison of five different retrieval algorithms covering three different modelling principles applied to active MetOp-A ASCAT L1 backscatter data. These models include statistical models (Bayesian Regression and Support Vector Regression, provided by the Institute for Applied Remote Sensing, Eurac Research Viale Druso, Italy, and an Artificial Neural Network, provided by the Institute of Applied Physics, CNR-IFAC, Italy), a semi-empirical model (provided by the Finnish Meteorological Institute), and a change detection model (provided by the Vienna University of Technology). The algorithms were applied on L1 backscatter data within the period of 2007-2011, resampled to a 12.5 km grid. The evaluation was performed over 75 globally distributed, quality controlled in situ stations drawn from the International Soil Moisture Network (ISMN) using surface soil moisture data from the Global Land Data Assimilation System (GLDAS-) Noah land surface model as second independent reference. The temporal correlation between the data sets was analyzed and random errors of the the different algorithms were estimated using the triple collocation method. Absolute soil moisture values as well as soil moisture anomalies were considered including both long-term anomalies from the mean seasonal cycle and short-term anomalies from a five weeks moving average window. Results show a very high agreement between all five algorithms for most stations. A slight

  18. On the temporal and spatial variability of near-surface soil moisture for the identification of representative in situ soil moisture monitoring stations

    USDA-ARS?s Scientific Manuscript database

    The high spatio-temporal variability of soil moisture complicates the validation of remotely sensed soil moisture products using in-situ monitoring stations. Therefore, a standard methodology for selecting the most repre- sentative stations for the purpose of validating satellites and land surface ...

  19. Creating soil moisture maps based on radar satellite imagery

    NASA Astrophysics Data System (ADS)

    Hnatushenko, Volodymyr; Garkusha, Igor; Vasyliev, Volodymyr

    2017-10-01

    The presented work is related to a study of mapping soil moisture basing on radar data from Sentinel-1 and a test of adequacy of the models constructed on the basis of data obtained from alternative sources. Radar signals are reflected from the ground differently, depending on its properties. In radar images obtained, for example, in the C band of the electromagnetic spectrum, soils saturated with moisture usually appear in dark tones. Although, at first glance, the problem of constructing moisture maps basing on radar data seems intuitively clear, its implementation on the basis of the Sentinel-1 data on an industrial scale and in the public domain is not yet available. In the process of mapping, for verification of the results, measurements of soil moisture obtained from logs of the network of climate stations NOAA US Climate Reference Network (USCRN) were used. This network covers almost the entire territory of the United States. The passive microwave radiometers of Aqua and SMAP satellites data are used for comparing processing. In addition, other supplementary cartographic materials were used, such as maps of soil types and ready moisture maps. The paper presents a comparison of the effect of the use of certain methods of roughening the quality of radar data on the result of mapping moisture. Regression models were constructed showing dependence of backscatter coefficient values Sigma0 for calibrated radar data of different spatial resolution obtained at different times on soil moisture values. The obtained soil moisture maps of the territories of research, as well as the conceptual solutions about automation of operations of constructing such digital maps, are presented. The comparative assessment of the time required for processing a given set of radar scenes with the developed tools and with the ESA SNAP product was carried out.

  20. Hydrologic downscaling of soil moisture using global data without site-specific calibration

    USDA-ARS?s Scientific Manuscript database

    Numerous applications require fine-resolution (10-30 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9-60 km) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moistu...

  1. Influence of freeze-thaw events on carbon dioxide emission from soils at different moisture and land use.

    PubMed

    Kurganova, Irina; Teepe, Robert; Loftfield, Norman

    2007-02-19

    The repeated freeze-thaw events during cold season, freezing of soils in autumn and thawing in spring are typical for the tundra, boreal, and temperate soils. The thawing of soils during winter-summer transitions induces the release of decomposable organic carbon and acceleration of soil respiration. The winter-spring fluxes of CO2 from permanently and seasonally frozen soils are essential part of annual carbon budget varying from 5 to 50%. The mechanisms of the freeze-thaw activation are not absolutely clear and need clarifying. We investigated the effect of repeated freezing-thawing events on CO2 emission from intact arable and forest soils (Luvisols, loamy silt; Central Germany) at different moisture (65% and 100% of WHC). Due to the measurement of the CO2 flux in two hours intervals, the dynamics of CO2 emission during freezing-thawing events was described in a detailed way. At +10 degrees C (initial level) in soils investigated, carbon dioxide emission varied between 7.4 to 43.8 mg C m-2h-1 depending on land use and moisture. CO2 flux from the totally frozen soil never reached zero and amounted to 5 to 20% of the initial level, indicating that microbial community was still active at -5 degrees C. Significant burst of CO2 emission (1.2-1.7-fold increase depending on moisture and land use) was observed during thawing. There was close linear correlation between CO2 emission and soil temperature (R2 = 0.86-0.97, P < 0.001). Our investigations showed that soil moisture and land use governed the initial rate of soil respiration, duration of freezing and thawing of soil, pattern of CO2 dynamics and extra CO2 fluxes. As a rule, the emissions of CO2 induced by freezing-thawing were more significant in dry soils and during the first freezing-thawing cycle (FTC). The acceleration of CO2 emission was caused by different processes: the liberation of nutrients upon the soil freezing, biological activity occurring in unfrozen water films, and respiration of cold

  2. On the assimilation of satellite derived soil moisture in numerical weather prediction models

    NASA Astrophysics Data System (ADS)

    Drusch, M.

    2006-12-01

    Satellite derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analysed from the modelled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. Three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-range Weather Forecasts (ECMWF) have been performed for the two months period of June and July 2002: A control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating bias corrected TMI (TRMM Microwave Imager) derived soil moisture over the southern United States through a nudging scheme using 6-hourly departures. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analysed in the nudging experiment is the most accurate estimate when compared against in-situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage. The transferability of the results to other satellite derived soil moisture data sets will be discussed.

  3. SMALT - Soil Moisture from Altimetry

    NASA Astrophysics Data System (ADS)

    Smith, Richard; Salloway, Mark; Berry, Philippa; Hahn, Sebastian; Wagner, Wolfgang; Egido, Alejandro; Dinardo, Salvatore; Lucas, Bruno Manuel; Benveniste, Jerome

    2014-05-01

    Soil surface moisture 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 soil moisture 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 soil moisture 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 soil moisture 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

  4. Calibration and validation of the COSMOS rover for surface soil moisture

    USDA-ARS?s Scientific Manuscript database

    The mobile COsmic-ray Soil Moisture Observing System (COSMOS) rover may be useful for validating satellite-based estimates of near surface soil moisture, but the accuracy with which the rover can measure 0-5 cm soil moisture has not been previously determined. Our objectives were to calibrate and va...

  5. Downscaling SMAP Radiometer Soil Moisture over the CONUS using Soil-Climate Information and Ensemble Learning

    NASA Astrophysics Data System (ADS)

    Abbaszadeh, P.; Moradkhani, H.

    2017-12-01

    Soil moisture contributes significantly towards the improvement of weather and climate forecast and understanding terrestrial ecosystem processes. It is known as a key hydrologic variable in the agricultural drought monitoring, flood modeling and irrigation management. While satellite retrievals can provide an unprecedented information on soil moisture at global-scale, the products are generally at coarse spatial resolutions (25-50 km2). This often hampers their use in regional or local studies, which normally require a finer resolution of the data set. This work presents a new framework based on an ensemble learning method while using soil-climate information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental U.S. (CONUS) at 1 km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets in addition to soil texture information and topography data among others were used. The downscaled product was validated against in situ soil moisture measurements collected from a limited number of core validation sites and several hundred sparse soil moisture networks throughout the CONUS. The obtained results indicated a great potential of the proposed methodology to derive the fine resolution soil moisture information applicable for fine resolution hydrologic modeling, data assimilation and other regional studies.

  6. Soil moisture remote sensing: State of the science

    USDA-ARS?s Scientific Manuscript database

    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) soil moisture at a spatial resolution of 25-40 km and temporal resolution of 2-3 days. C- and X-band soil moisture records date bac...

  7. Soil Moisture Dynamics under Corn, Soybean, and Perennial Kura Clover

    NASA Astrophysics Data System (ADS)

    Ochsner, T.; Venterea, R. T.

    2009-12-01

    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-soil water interactions are central to meeting these twin challenges. The objective of this research was to compare the temporal dynamics of soil moisture under contrasting cropping systems suited for the Midwestern region of the United States. Precipitation, infiltration, drainage, evapotranspiration, soil 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. Soil moisture utilization also continued later into the fall under the clover than under the annual crops. In the annual cropping systems, crop sequence influenced the soil moisture 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, soil moisture depletion was up to 30 mm greater under corn than soybean. Crop residue also played an important role in the soil moisture dynamics. Higher amounts of residue were associated with reduced soil freezing. This presentation will highlight key aspects of the soil moisture dynamics for these contrasting cropping systems across temporal scales ranging from hours to years. The links between soil moisture dynamics, crop yields, and nutrient leaching

  8. A new Downscaling Approach for SMAP, SMOS and ASCAT by predicting sub-grid Soil Moisture Variability based on Soil Texture

    NASA Astrophysics Data System (ADS)

    Montzka, C.; Rötzer, K.; Bogena, H. R.; Vereecken, H.

    2017-12-01

    Improving the coarse spatial resolution of global soil moisture products from SMOS, SMAP and ASCAT is currently an up-to-date topic. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. A method has been developed that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. Here, we predict for each SMOS, SMAP and ASCAT grid cell the sub-grid soil moisture variability based on the SoilGrids1km data set. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean. The resulting data set provides important information for downscaling coarse soil moisture observations of the SMOS, SMAP and ASCAT missions. Downscaling SMAP data by a field capacity proxy indicates adequate accuracy of the sub-grid soil moisture patterns.

  9. Development of an Objective High Spatial Resolution Soil Moisture Index

    NASA Astrophysics Data System (ADS)

    Zavodsky, B.; Case, J.; White, K.; Bell, J. R.

    2015-12-01

    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, soil moisture, 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 soil moisture climatology has been developed in an attempt to place near real-time soil moisture values in historical context at county- and/or watershed-scale resolutions. This LIS-Noah soil moisture 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 soil moisture features. Daily soil moisture histograms are used to identify the real-time soil moisture 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 soil moisture index, comparison to subjective

  10. BOREAS HYD-6 Ground Gravimetric Soil Moisture Data

    NASA Technical Reports Server (NTRS)

    Carroll, Thomas; Knapp, David E. (Editor); Hall, Forrest G. (Editor); Peck, Eugene L.; Smith, David E. (Technical Monitor)

    2000-01-01

    The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-6 team collected several data sets related to the moisture content of soil and overlying humus layers. This data set contains percent soil moisture ground measurements. These data were collected on the ground along the various flight lines flown in the Southern Study Area (SSA) and Northern Study Area (NSA) during 1994 by the gamma ray instrument. The data are available in tabular ASCII files. The HYD-06 ground gravimetric soil moisture 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).

  11. Soil moisture and properties estimation by assimilating soil temperatures using particle batch smoother: A new perspective for DTS

    NASA Astrophysics Data System (ADS)

    Dong, J.; Steele-Dunne, S. C.; Ochsner, T. E.; Van De Giesen, N.

    2015-12-01

    Soil moisture, hydraulic and thermal properties are critical for understanding the soil surface energy balance and hydrological processes. Here, we will discuss the potential of using soil temperature observations from Distributed Temperature Sensing (DTS) to investigate the spatial variability of soil moisture and soil properties. With DTS soil temperature can be measured with high resolution (spatial <1m, and temporal < 1min) in cables up to kilometers in length. Soil temperature evolution is primarily controlled by the soil thermal properties, and the energy balance at the soil surface. Hence, soil moisture, which affects both soil thermal properties and the energy that participates the evaporation process, is strongly correlated to the soil temperatures. In addition, the dynamics of the soil moisture is determined by the soil hydraulic properties.Here we will demonstrate that soil moisture, hydraulic and thermal properties can be estimated by assimilating observed soil temperature at shallow depths using the Particle Batch Smoother (PBS). The PBS can be considered as an extension of the particle filter, which allows us to infer soil moisture and soil properties using the dynamics of soil temperature within a batch window. Both synthetic and real field data will be used to demonstrate the robustness of this approach. We will show that the proposed method is shown to be able to handle different sources of uncertainties, which may provide a new view of using DTS observations to estimate sub-meter resolution soil moisture and properties for remote sensing product validation.

  12. Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

    NASA Astrophysics Data System (ADS)

    Todisco, F.; Brocca, L.; Termite, L. F.; Wagner, W.

    2015-09-01

    The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha-1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

  13. Combined radar-radiometer surface soil moisture and roughness estimation

    USDA-ARS?s Scientific Manuscript database

    A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution rad...

  14. Predicting Soil Salinity with Vis–NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization

    PubMed Central

    Liu, Ya; Pan, Xianzhang; Wang, Changkun; Li, Yanli; Shi, Rongjie

    2015-01-01

    Robust models for predicting soil salinity that use visible and near-infrared (vis–NIR) reflectance spectroscopy are needed to better quantify soil salinity in agricultural fields. Currently available models are not sufficiently robust for variable soil moisture contents. Thus, we used external parameter orthogonalization (EPO), which effectively projects spectra onto the subspace orthogonal to unwanted variation, to remove the variations caused by an external factor, e.g., the influences of soil moisture on spectral reflectance. In this study, 570 spectra between 380 and 2400 nm were obtained from soils with various soil moisture contents and salt concentrations in the laboratory; 3 soil types × 10 salt concentrations × 19 soil moisture levels were used. To examine the effectiveness of EPO, we compared the partial least squares regression (PLSR) results established from spectra with and without EPO correction. The EPO method effectively removed the effects of moisture, and the accuracy and robustness of the soil salt contents (SSCs) prediction model, which was built using the EPO-corrected spectra under various soil moisture conditions, were significantly improved relative to the spectra without EPO correction. This study contributes to the removal of soil moisture effects from soil salinity estimations when using vis–NIR reflectance spectroscopy and can assist others in quantifying soil salinity in the future. PMID:26468645

  15. Quantifying soil moisture impacts on light use efficiency across biomes.

    PubMed

    Stocker, Benjamin D; Zscheischler, Jakob; Keenan, Trevor F; Prentice, I Colin; Peñuelas, Josep; Seneviratne, Sonia I

    2018-06-01

    Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple soil moisture datasets and remotely sensed greenness. This reveals substantial impacts of soil moisture alone that reduce GPP by up to 40% at sites located in sub-humid, semi-arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying soil, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly-based drought indices. Counter to common assumptions, fLUE reductions are largest in drought-deciduous vegetation, including grasslands. Our results highlight the necessity to account for soil moisture limitation in terrestrial primary productivity data products, especially for drought-related assessments. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

  16. Ecosystem-scale plant hydraulic strategies inferred from remotely-sensed soil moisture

    NASA Astrophysics Data System (ADS)

    Bassiouni, M.; Good, S. P.; Higgins, C. W.

    2017-12-01

    Characterizing plant hydraulic strategies at the ecosystem scale is important to improve estimates of evapotranspiration and to understand ecosystem productivity and resilience. However, quantifying plant hydraulic traits beyond the species level is a challenge. The probability density function of soil moisture observations provides key information about the soil moisture states at which evapotranspiration is reduced by water stress. Here, an inverse Bayesian approach is applied to a standard bucket model of soil column hydrology forced with stochastic precipitation inputs. Through this approach, we are able to determine the soil moisture thresholds at which stomata are open or closed that are most consistent with observed soil moisture probability density functions. This research utilizes remotely-sensed soil moisture data to explore global patterns of ecosystem-scale plant hydraulic strategies. Results are complementary to literature values of measured hydraulic traits of various species in different climates and previous estimates of ecosystem-scale plant isohydricity. The presented approach provides a novel relation between plant physiological behavior and soil-water dynamics.

  17. Towards an improved soil moisture retrieval for organic-rich soils from SMOS passive microwave L-band observations

    NASA Astrophysics Data System (ADS)

    Bircher, Simone; Richaume, Philippe; Mahmoodi, Ali; Mialon, Arnaud; Fernandez-Moran, Roberto; Wigneron, Jean-Pierre; Demontoux, François; Jonard, François; Weihermüller, Lutz; Andreasen, Mie; Rautiainen, Kimmo; Ikonen, Jaakko; Schwank, Mike; Drusch, Mattias; Kerr, Yann H.

    2017-04-01

    From the passive L-band microwave radiometer onboard the Soil Moisture and Ocean Salinity (SMOS) space mission global surface soil moisture data is retrieved every 2 - 3 days. Thus far, the empirical L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model applied in the SMOS soil moisture retrieval algorithm is exclusively calibrated over test sites in dry and temperate climate zones. Furthermore, the included dielectric mixing model relating soil moisture to relative permittivity accounts only for mineral soils. However, soil moisture monitoring over the higher Northern latitudes is crucial since these regions are especially sensitive to climate change. A considerable positive feedback is expected if thawing of these extremely organic soils supports carbon decomposition and release to the atmosphere. Due to differing structural characteristics and thus varying bound water fractions, the relative permittivity of organic material is lower than that of the most mineral soils at a given water content. This assumption was verified by means of L-band relative permittivity laboratory measurements of organic and mineral substrates from various sites in Denmark, Finland, Scotland and Siberia using a resonant cavity. Based on these data, a simple empirical dielectric model for organic soils was derived and implemented in the SMOS Soil Moisture Level 2 Prototype Processor (SML2PP). Unfortunately, the current SMOS retrieved soil moisture product seems to show unrealistically low values compared to in situ soil moisture data collected from organic surface layers in North America, Europe and the Tibetan Plateau so that the impact of the dielectric model for organic soils cannot really be tested. A simplified SMOS processing scheme yielding higher soil moisture levels has recently been proposed and is presently under investigation. Furthermore, recalibration of the model parameters accounting for vegetation and roughness effects that were thus far only

  18. Variation in microbial activity in histosols and its relationship to soil moisture.

    PubMed

    Tate, R L; Terry, R E

    1980-08-01

    Microbial biomass, dehydrogenase activity, carbon metabolism, and aerobic bacterial populations were examined in cropped and fallow Pahokee muck (a lithic medisaprist) of the Florida Everglades. Dehydrogenase activity was two- to sevenfold greater in soil cropped to St. Augustinegrass (Stenotaphrum secundatum (Walt) Kuntz) compared with uncropped soil, whereas biomass ranged from equivalence in the two soils to a threefold stimulation in the cropped soil. Biomass in soil cropped to sugarcane (Saccharum spp. L) approximated that from the grass field, whereas dehydrogenase activities of the cane soil were nearly equivalent to those of the fallow soil. Microbial biomass, dehydrogenase activity, aerobic bacterial populations, and salicylate oxidation rates all correlated with soil moisture levels. These data indicate that within the moisture ranges detected in the surface soils, increased moisture stimulated microbial activity, whereas within the soil profile where moisture ranges reached saturation, increased moisture inhibited aerobic activities and stimulated anaerobic processes.

  19. Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

    NASA Technical Reports Server (NTRS)

    Bolten, John; Crow, Wade

    2012-01-01

    The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

  20. Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree-Ring Chronologies

    NASA Astrophysics Data System (ADS)

    Tootle, G.; Anderson, S.; Grissino-Mayer, H.

    2012-12-01

    Soil moisture is an important factor in the global hydrologic cycle, but existing reconstructions of historic soil moisture are limited. Tree-ring chronologies (TRCs) were used to reconstruct annual soil moisture in the Upper Colorado River Basin (UCRB). Gridded soil moisture data were spatially regionalized using principal components analysis and k-nearest neighbor techniques. Moisture sensitive tree-ring chronologies in and adjacent to the UCRB were correlated with regional soil moisture and tested for temporal stability. TRCs that were positively correlated and stable for the calibration period were retained. Stepwise linear regression was applied to identify the best predictor combinations for each soil moisture region. The regressions explained 42-78% of the variability in soil moisture data. We performed reconstructions for individual soil moisture grid cells to enhance understanding of the disparity in reconstructive skill across the regions. Reconstructions that used chronologies based on ponderosa pines (Pinus ponderosa) and pinyon pines (Pinus edulis) explained increased variance in the datasets. Reconstructed soil moisture was standardized and compared with standardized reconstructed streamflow and snow water equivalent from the same region. Soil moisture reconstructions were highly correlated with streamflow and snow water equivalent reconstructions, indicating reconstructions of soil moisture in the UCRB using TRCs successfully represent hydrologic trends, including the identification of periods of prolonged drought.

  1. Using high-resolution soil moisture modelling to assess the uncertainty of microwave remotely sensed soil moisture products at the correct spatial and temporal support

    NASA Astrophysics Data System (ADS)

    Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.

    2012-04-01

    Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture content throughout the river basin. Space-borne remote sensing may provide this information with a high temporal and spatial resolution and with a global coverage. Currently three microwave soil moisture products are available: AMSR-E, ASCAT and SMOS. The quality of these satellite-based products is often assessed by comparing them with in-situ observations of soil moisture. This comparison is however hampered by the difference in spatial and temporal support (i.e., resolution, scale), because the spatial resolution of microwave satellites is rather low compared to in-situ field measurements. Thus, the aim of this study is to derive a method to assess the uncertainty of microwave satellite soil moisture products at the correct spatial support. To overcome the difference in support size between in-situ soil moisture observations and remote sensed soil moisture, we used a stochastic, distributed unsaturated zone model (SWAP, van Dam (2000)) that is upscaled to the support of different satellite products. A detailed assessment of the SWAP model uncertainty is included to ensure that the uncertainty in satellite soil moisture is not overestimated due to an underestimation of the model uncertainty. We simulated unsaturated water flow up to a depth of 1.5m with a vertical resolution of 1 to 10 cm and on a horizontal grid of 1 km2 for the period Jan 2010 - Jun 2011. The SWAP model was first calibrated and validated on in-situ data of the REMEDHUS soil moisture network (Spain). Next, to evaluate the satellite products, the model was run for areas in the proximity of 79 meteorological stations in Spain, where model results were aggregated to the correct support of the satellite

  2. NASA Soil Moisture Active Passive (SMAP) Mission Formulation

    NASA Technical Reports Server (NTRS)

    Entekhabi, Dara; Njoku, Eni; ONeill, Peggy; Kellogg, Kent; Entin, Jared

    2011-01-01

    The Soil Moisture Active Passive (SMAP) Mission is one of the first Earth observation satellites being formulated by NASA in response to the 2007 National Research Council s Earth Science Decadal Survey [1]. SMAP s measurement objectives are high-resolution global measurements of near-surface soil moisture and its freeze-thaw state. These measurements would allow significantly improved estimates of water, energy and carbon transfers between the land and atmosphere. The soil moisture control of these fluxes is a key factor in the performance of atmospheric models used for weather forecasts and climate projections. Soil moisture measurements are also of great importance in assessing flooding and monitoring drought. Knowledge gained from SMAP s planned observations can help mitigate these natural hazards, resulting in potentially great economic and societal benefits. SMAP measurements would also yield high resolution spatial and temporal mapping of the frozen or thawed condition of the surface soil and vegetation. Observations of soil moisture and freeze/thaw timing over the boreal latitudes will contribute to reducing a major uncertainty in quantifying the global carbon balance and help resolve an apparent missing carbon sink over land. The SMAP mission would utilize an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna (see Figure 1) [2]. The radar and radiometer instruments would be carried onboard a 3-axis stabilized spacecraft in a 680 km polar orbit with an 8-day repeating ground track. The instruments are planned to provide high-resolution and high-accuracy global maps of soil moisture at 10 km resolution and freeze/thaw at 3 km resolution, every two to three days (see Table 1 for a list of science data products). The mission is adopting a number of approaches to identify and mitigate potential terrestrial radio frequency interference (RFI). These approaches are being incorporated into the radiometer and radar flight hardware and

  3. Use of satellite and modelled soil moisture data for predicting event soil loss at plot scale

    NASA Astrophysics Data System (ADS)

    Todisco, F.; Brocca, L.; Termite, L. F.; Wagner, W.

    2015-03-01

    The potential of coupling soil moisture and a~USLE-based model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in Central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e. the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the RUSLE/USLE, enhances the capability of the model to account for variations in event soil losses, being the soil moisture an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to of ~ 0.35 and a root-mean-square error (RMSE) of ~ 2.8 Mg ha-1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

  4. Estimating Soil Moisture from Satellite Microwave Observations

    NASA Technical Reports Server (NTRS)

    Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.

    1998-01-01

    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 soil moisture. 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 soil moisture 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 soil moisture 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 soil moisture 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

  5. A review of spatial downscaling of satellite remotely sensed soil moisture

    NASA Astrophysics Data System (ADS)

    Peng, Jian; Loew, Alexander; Merlin, Olivier; Verhoest, Niko E. C.

    2017-06-01

    Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

  6. Effects of Soil Temperature and Moisture on Soil Respiration on the Tibetan Plateau

    PubMed Central

    Chang, Xiaofeng; Wang, Shiping; Xu, Burenbayin; Luo, Caiyun; Zhang, Zhenhua; Wang, Qi; Rui, Yichao; Cui, Xiaoying

    2016-01-01

    Understanding of effects of soil temperature and soil moisture on soil respiration (Rs) under future warming is critical to reduce uncertainty in predictions of feedbacks to atmospheric CO2 concentrations from grassland soil carbon. Intact cores with roots taken from a full factorial, 5-year alpine meadow warming and grazing experiment in the field were incubated at three different temperatures (i.e. 5, 15 and 25°C) with two soil moistures (i.e. 30 and 60% water holding capacity (WHC)) in our study. Another experiment of glucose-induced respiration (GIR) with 4 h of incubation was conducted to determine substrate limitation. Our results showed that high temperature increased Rs and low soil moisture limited the response of Rs to temperature only at high incubation temperature (i.e. 25°C). Temperature sensitivity (Q10) did not significantly decrease over the incubation period, suggesting that substrate depletion did not limit Rs. Meanwhile, the carbon availability index (CAI) was higher at 5°C compared with 15 and 25°C incubation, but GIR increased with increasing temperature. Therefore, our findings suggest that warming-induced decrease in Rs in the field over time may result from a decrease in soil moisture rather than from soil substrate depletion, because warming increased root biomass in the alpine meadow. PMID:27798671

  7. Investigating local controls on soil moisture temporal stability using an inverse modeling approach

    NASA Astrophysics Data System (ADS)

    Bogena, Heye; Qu, Wei; Huisman, Sander; Vereecken, Harry

    2013-04-01

    A better understanding of the temporal stability of soil moisture and its relation to local and nonlocal controls is a major challenge in modern hydrology. Both local controls, such as soil and vegetation properties, and non-local controls, such as topography and climate variability, affect soil moisture dynamics. Wireless sensor networks are becoming more readily available, which opens up opportunities to investigate spatial and temporal variability of soil moisture with unprecedented resolution. In this study, we employed the wireless sensor network SoilNet developed by the Forschungszentrum Jülich to investigate soil moisture variability of a grassland headwater catchment in Western Germany within the framework of the TERENO initiative. In particular, we investigated the effect of soil hydraulic parameters on the temporal stability of soil moisture. For this, the HYDRUS-1D code coupled with a global optimizer (DREAM) was used to inversely estimate Mualem-van Genuchten parameters from soil moisture 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 soil hydraulic conductivity, pore size distribution, air entry suction and soil depth between these 83 locations controlled the temporal stability of soil moisture, which was independently determined from the observed soil moisture data. It was found that the saturated hydraulic conductivity (Ks) was the most significant attribute to explain temporal stability of soil moisture as expressed by the mean relative difference (MRD).

  8. Global Soil Moisture from the Aquarius/SAC-D Satellite: Description and Initial Assessment

    NASA Technical Reports Server (NTRS)

    Bindlish, Rajat; Jackson, Thomas; Cosh, Michael; Zhao, Tianjie; O'Neil, Peggy

    2015-01-01

    Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = -0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

  9. Measuring Soil Moisture using the Signal Strength of Buried Bluetooth Devices.

    NASA Astrophysics Data System (ADS)

    Hut, R.; Campbell, C. S.

    2015-12-01

    A low power bluetooth Low Energy (BLE) device is burried 20cm into the soil and a smartphone is placed on top of the soil to test if bluetooth signal strength can be related to soil moisture. The smartphone continuesly records and stores bluetooth signal strength of the device. The soil is artifcially wetted and drained. Results show a relation between BLE signal strength and soil moisture that could be used to measure soil moisture using these off-the-shelf consumer electronics. This opens the possibily to develop sensors that can be buried into the soil, possibly below the plow-line. These sensors can measure local parameters such as electric conductivity, ph, pressure, etc. Readings would be uploaded to a device on the surface using BLE. The signal strength of this BLE would be an (additional) measurement of soil moisture.

  10. Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

    NASA Technical Reports Server (NTRS)

    Blankenship, Clay; Case, Jonathan; Zavodsky, Bradley; Jedlovec, Gary

    2014-01-01

    Soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) instrument are assimilated into the Noah land surface model (LSM) within the NASA Land Information System (LIS). Before assimilation, SMOS retrievals are bias-corrected to match the model climatological distribution using a Cumulative Distribution Function (CDF) matching approach. Data assimilation is done via the Ensemble Kalman Filter. The goal is to improve the representation of soil moisture within the LSM, and ultimately to improve numerical weather forecasts through better land surface initialization. We present a case study showing a large area of irrigation in the lower Mississippi River Valley, in an area with extensive rice agriculture. High soil moisture value in this region are observed by SMOS, but not captured in the forcing data. After assimilation, the model fields reflect the observed geographic patterns of soil moisture. Plans for a modeling experiment and operational use of the data are given. This work helps prepare for the assimilation of Soil Moisture Active/Passive (SMAP) retrievals in the near future.

  11. A study of the influence of soil moisture on future precipitation

    NASA Technical Reports Server (NTRS)

    Fennessy, M. J.; Sud, Y. C.

    1983-01-01

    Forty years of precipitation and surface temperature data observed over 261 Local Climatic Data (LCD) stations in the Continental United States was utilized in a ground hydrology model to yield soil moisture time series at each station. A month-by-month soil moisture dataset was constructed for each year. The monthly precipitation was correlated with antecedent monthly precipitation, soil moisture and vapotranspiration separately. The maximum positive correlation is found to be in the drought prone western Great Plains region during the latter part of summer. There is also some negative correlation in coastal regions. The correlations between soil moisture and precipitation particularly in the latter part of summer, suggest that large scale droughts over extended periods may be partially maintained by the feedback influence of soil moisture on rainfall. In many other regions the lack of positive correlation shows that there is no simple answer such as higher land-surface evapotranspiration leads to more precipitation, and points out the complexity of the influence of soil moisture on the ensuring precipitation.

  12. Assessment of Version 4 of the SMAP Passive Soil Moisture Standard Product

    NASA Technical Reports Server (NTRS)

    O'neill, P. O.; Chan, S.; Bindlish, R.; Jackson, T.; Colliander, A.; Dunbar, R.; Chen, F.; Piepmeier, Jeffrey R.; Yueh, S.; Entekhabi, D.; hide

    2017-01-01

    NASAs Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAPs radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core calval sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

  13. Relation Between the Rainfall and Soil Moisture During Different Phases of Indian Monsoon

    NASA Astrophysics Data System (ADS)

    Varikoden, Hamza; Revadekar, J. V.

    2018-03-01

    Soil moisture is a key parameter in the prediction of southwest monsoon rainfall, hydrological modelling, and many other environmental studies. The studies on relationship between the soil moisture and rainfall in the Indian subcontinent are very limited; hence, the present study focuses the association between rainfall and soil moisture during different monsoon seasons. The soil moisture data used for this study are the ESA (European Space Agency) merged product derived from four passive and two active microwave sensors spanning over the period 1979-2013. The rainfall data used are India Meteorological Department gridded daily data. Both of these data sets are having a spatial resolution of 0.25° latitude-longitude grid. The study revealed that the soil moisture is higher during the southwest monsoon period similar to rainfall and during the pre-monsoon period, the soil moisture is lower. The annual cycle of both the soil moisture and rainfall has the similitude of monomodal variation with a peak during the month of August. The interannual variability of soil moisture and rainfall shows that they are linearly related with each other, even though they are not matched exactly for individual years. The study of extremes also exhibits the surplus amount of soil moisture during wet monsoon years and also the regions of surplus soil moisture are well coherent with the areas of high rainfall.

  14. Assessment of Multi-frequency Electromagnetic Induction for Determining Soil Moisture Patterns at the Hillslope Scale

    NASA Astrophysics Data System (ADS)

    Tromp-van Meerveld, I.; McDonnell, J.

    2009-05-01

    We present an assessment of electromagnetic induction (EM) as a potential rapid and non-invasive method to map soil moisture patterns at the Panola (GA, USA) hillslope. We address the following questions regarding the applicability of EM measurements for hillslope hydrological investigations: (1) Can EM be used for soil moisture measurements in areas with shallow soils?; (2) Can EM represent the temporal and spatial patterns of soil moisture throughout the year?; and (3) can multiple frequencies be used to extract additional information content from the EM approach and explain the depth profile of soil moisture? We found that the apparent conductivity measured with the multi-frequency GEM-300 was linearly related to soil moisture measured with an Aqua-pro capacitance sensor below a threshold conductivity and represented the temporal patterns in soil moisture well. During spring rainfall events that wetted only the surface soil layers the apparent conductivity measurements explained the soil moisture dynamics at depth better than the surface soil moisture dynamics. All four EM frequencies (7290, 9090, 11250, and 14010 Hz) were highly correlated and linearly related to each other and could be used to predict soil moisture. This limited our ability to use the four different EM frequencies to obtain a soil moisture profile with depth. The apparent conductivity patterns represented the observed spatial soil moisture patterns well when the individually fitted relationships between measured soil moisture and apparent conductivity were used for each measurement point. However, when the same (master) relationship was used for all measurement locations, the soil moisture patterns were smoothed and did not resemble the observed soil moisture patterns very well. In addition, the range in calculated soil moisture values was reduced compared to observed soil moisture. Part of the smoothing was likely due to the much larger measurement area of the GEM-300 compared to the Aqua

  15. Surface Soil Moisture Memory Estimated from Models and SMAP Observations

    NASA Astrophysics Data System (ADS)

    He, Q.; Mccoll, K. A.; Li, C.; Lu, H.; Akbar, R.; Pan, M.; Entekhabi, D.

    2017-12-01

    Soil moisture memory(SMM), which is loosely defined as the time taken by soil to forget an anomaly, has been proved to be important in land-atmosphere interaction. There are many metrics to calculate the SMM timescale, for example, the timescale based on the time-series autocorrelation, the timescale ignoring the soil moisture time series and the timescale which only considers soil moisture increment. Recently, a new timescale based on `Water Cycle Fraction' (Kaighin et al., 2017), in which the impact of precipitation on soil moisture memory is considered, has been put up but not been fully evaluated in global. In this study, we compared the surface SMM derived from SMAP observations with that from land surface model simulations (i.e., the SMAP Nature Run (NR) provided by the Goddard Earth Observing System, version 5) (Rolf et al., 2014). Three timescale metrics were used to quantify the surface SMM as: T0 based on the soil moisture time series autocorrelation, deT0 based on the detrending soil moisture time series autocorrelation, and tHalf based on the Water Cycle Fraction. The comparisons indicate that: (1) there are big gaps between the T0 derived from SMAP and that from NR (2) the gaps get small for deT0 case, in which the seasonality of surface soil moisture was removed with a moving average filter; (3) the tHalf estimated from SMAP is much closer to that from NR. The results demonstrate that surface SMM can vary dramatically among different metrics, while the memory derived from land surface model differs from the one from SMAP observation. tHalf, with considering the impact of precipitation, may be a good choice to quantify surface SMM and have high potential in studies related to land atmosphere interactions. References McColl. K.A., S.H. Alemohammad, R. Akbar, A.G. Konings, S. Yueh, D. Entekhabi. The Global Distribution and Dynamics of Surface Soil Moisture, Nature Geoscience, 2017 Reichle. R., L. Qing, D.L. Gabrielle, A. Joe. The "SMAP_Nature_v03" Data

  16. Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

    NASA Technical Reports Server (NTRS)

    Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

    2012-01-01

    , and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

  17. Challenges in Interpreting and Validating Satellite Soil Moisture Information

    USDA-ARS?s Scientific Manuscript database

    Global soil moisture products are now being generated routinely using microwave-based satellite observing systems. These include the NASA Soil Moisture Active Passive (SMAP) mission. In order to fully exploit these observations they must be integrated with both in situ measurements and model-based e...

  18. Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data

    DTIC Science & Technology

    2011-01-01

    the relationship between reflec- tance and soil moisture where there is ground cover and ascertain the Normalized Difference Vegetation Index ( NDVI ...in those areas. This could establish a minimum NDVI for ground cover that would allow for estimation of soil moisture. Alternatively, they could

  19. Uncertain soil moisture feedbacks in model projections of Sahel precipitation

    NASA Astrophysics Data System (ADS)

    Berg, Alexis; Lintner, Benjamin R.; Findell, Kirsten; Giannini, Alessandra

    2017-06-01

    Given the uncertainties in climate model projections of Sahel precipitation, at the northern edge of the West African Monsoon, understanding the factors governing projected precipitation changes in this semiarid region is crucial. This study investigates how long-term soil moisture changes projected under climate change may feedback on projected changes of Sahel rainfall, using simulations with and without soil moisture change from five climate models participating in the Global Land Atmosphere Coupling Experiment-Coupled Model Intercomparison Project phase 5 experiment. In four out of five models analyzed, soil moisture feedbacks significantly influence the projected West African precipitation response to warming; however, the sign of these feedbacks differs across the models. These results demonstrate that reducing uncertainties across model projections of the West African Monsoon requires, among other factors, improved mechanistic understanding and constraint of simulated land-atmosphere feedbacks, even at the large spatial scales considered here.Plain Language SummaryClimate model projections of Sahel rainfall remain notoriously uncertain; understanding the physical processes responsible for this uncertainty is thus crucial. Our study focuses on analyzing the feedbacks of <span class="hlt">soil</span> <span class="hlt">moisture</span> changes on model projections of the West African Monsoon under global warming. <span class="hlt">Soil</span> <span class="hlt">moisture</span>-atmosphere interactions have been shown in prior studies to play an important role in this region, but the potential feedbacks of long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> changes on projected precipitation changes have not been investigated specifically. To isolate these feedbacks, we use targeted simulations from five climate models, with and without <span class="hlt">soil</span> <span class="hlt">moisture</span> change. Importantly, we find that climate models exhibit <span class="hlt">soil</span> <span class="hlt">moisture</span>-precipitation feedbacks of different sign in this region: in some models <span class="hlt">soil</span> <span class="hlt">moisture</span> changes amplify precipitation changes</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760017595','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760017595"><span>Results of <span class="hlt">soil</span> <span class="hlt">moisture</span> flights during April 1974</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schmugge, T. J.; Blanchard, B. J.; Burke, W. J.; Paris, J. F.; Swang, J. R.</p> <p>1976-01-01</p> <p>The results presented here are derived from measurements made during the April 5 and 6, 1974 flights of the NASA P-3A aircraft over the Phoenix, Arizona agricultural test site. The purpose of the mission was to study the use of microwave techniques for the remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. These results include infrared (10-to 12 micrometers) 2.8-cm and 21-cm brightness temperatures for approximately 90 bare fields. These brightness temperatures are compared with surface measurements of the <span class="hlt">soil</span> <span class="hlt">moisture</span> made at the time of the overflights. These data indicate that the combination of the sum and difference of the vertically and the horizontally polarized brightness temperatures yield information on both the <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness conditions.</p> </li> </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('https://www.ncbi.nlm.nih.gov/pubmed/29124249','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29124249"><span>Relating coccidioidomycosis (valley fever) incidence to <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Coopersmith, E J; Bell, J E; Benedict, K; Shriber, J; McCotter, O; Cosh, M H</p> <p>2017-04-17</p> <p>Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp. , in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets, it has not been feasible to perform a direct analysis of the relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower <span class="hlt">soil</span> <span class="hlt">moisture</span> in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to <span class="hlt">soil</span> <span class="hlt">moisture</span> levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950027395','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950027395"><span>Application of IEM model on <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shi, Jiancheng; Wang, J. R.; Oneill, P. E.; Hsu, A. Y.; Engman, E. T.</p> <p>1995-01-01</p> <p>Monitoring spatial and temporal changes of <span class="hlt">soil</span> <span class="hlt">moisture</span> are of importance to hydrology, meteorology, and agriculture. This paper reports a result on study of using L-band SAR imagery to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness for bare fields. Due to limitations of the Small Perturbation Model, it is difficult to apply this model on estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness directly. In this study, we show a simplified model derived from the Integral Equation Model for estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness. We show a test of this model using JPL L-band AIRSAR data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020054243&hterms=seasonal+forecast&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dseasonal%2Bforecast','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020054243&hterms=seasonal+forecast&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dseasonal%2Bforecast"><span>The Impact of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Initialization On Seasonal Precipitation Forecasts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, R. D.; Suarez, M. J.; Tyahla, L.; Houser, Paul (Technical Monitor)</p> <p>2002-01-01</p> <p>Some studies suggest that the proper initialization of <span class="hlt">soil</span> <span class="hlt">moisture</span> in a forecasting model may contribute significantly to the accurate prediction of seasonal precipitation, especially over mid-latitude continents. In order for the initialization to have any impact at all, however, two conditions must be satisfied: (1) the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> anomaly must be "remembered" into the forecasted season, and (2) the atmosphere must respond in a predictable way to the <span class="hlt">soil</span> <span class="hlt">moisture</span> anomaly. In our previous studies, we identified the key land surface and atmospheric properties needed to satisfy each condition. Here, we tie these studies together with an analysis of an ensemble of seasonal forecasts. Initial <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions for the forecasts are established by forcing the land surface model with realistic precipitation prior to the start of the forecast period. As expected, the impacts on forecasted precipitation (relative to an ensemble of runs that do not utilize <span class="hlt">soil</span> <span class="hlt">moisture</span> information) tend to be localized over the small fraction of the earth with all of the required land and atmosphere properties.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3960259','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3960259"><span>Effects of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on the Temperature Sensitivity of <span class="hlt">Soil</span> Heterotrophic Respiration: A Laboratory Incubation Study</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhou, Weiping; Hui, Dafeng; Shen, Weijun</p> <p>2014-01-01</p> <p>The temperature sensitivity (Q10) of <span class="hlt">soil</span> heterotrophic respiration (Rh) is an important ecological model parameter and may vary with temperature and <span class="hlt">moisture</span>. While Q10 generally decreases with increasing temperature, the <span class="hlt">moisture</span> effects on Q10 have been controversial. To address this, we conducted a 90-day laboratory incubation experiment using a subtropical forest <span class="hlt">soil</span> with a full factorial combination of five <span class="hlt">moisture</span> levels (20%, 40%, 60%, 80%, and 100% water holding capacity - WHC) and five temperature levels (10, 17, 24, 31, and 38°C). Under each <span class="hlt">moisture</span> treatment, Rh was measured several times for each temperature treatment to derive Q10 based on the exponential relationships between Rh and temperature. Microbial biomass carbon (MBC), microbial community structure and <span class="hlt">soil</span> nutrients were also measured several times to detect their potential contributions to the <span class="hlt">moisture</span>-induced Q10 variation. We found that Q10 was significantly lower at lower <span class="hlt">moisture</span> levels (60%, 40% and 20% WHC) than at higher <span class="hlt">moisture</span> level (80% WHC) during the early stage of the incubation, but became significantly higher at 20%WHC than at 60% WHC and not significantly different from the other three <span class="hlt">moisture</span> levels during the late stage of incubation. In contrast, <span class="hlt">soil</span> Rh had the highest value at 60% WHC and the lowest at 20% WHC throughout the whole incubation period. Variations of Q10 were significantly associated with MBC during the early stages of incubation, but with the fungi-to-bacteria ratio during the later stages, suggesting that changes in microbial biomass and community structure are related to the <span class="hlt">moisture</span>-induced Q10 changes. This study implies that global warming’s impacts on <span class="hlt">soil</span> CO2 emission may depend upon <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. With the same temperature rise, wetter <span class="hlt">soils</span> may emit more CO2 into the atmosphere via heterotrophic respiration. PMID:24647610</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012SPIE.8531E..14W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012SPIE.8531E..14W"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> retrieval by active/passive microwave remote sensing data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Shengli; Yang, Lijuan</p> <p>2012-09-01</p> <p>This study develops a new algorithm for estimating bare surface <span class="hlt">soil</span> <span class="hlt">moisture</span> using combined active / passive microwave remote sensing on the basis of TRMM (Tropical Rainfall Measuring Mission). Tropical Rainfall Measurement Mission was jointly launched by NASA and NASDA in 1997, whose main task was to observe the precipitation of the area in 40 ° N-40 ° S. It was equipped with active microwave radar sensors (PR) and passive sensor microwave imager (TMI). To accurately estimate bare surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, precipitation radar (PR) and microwave imager (TMI) are simultaneously used for observation. According to the frequency and incident angle setting of PR and TMI, we first need to establish a database which includes a large range of surface conditions; and then we use Advanced Integral Equation Model (AIEM) to calculate the backscattering coefficient and emissivity. Meanwhile, under the accuracy of resolution, we use a simplified theoretical model (GO model) and the semi-empirical physical model (Qp Model) to redescribe the process of scattering and radiation. There are quite a lot of parameters effecting backscattering coefficient and emissivity, including <span class="hlt">soil</span> <span class="hlt">moisture</span>, surface root mean square height, correlation length, and the correlation function etc. Radar backscattering is strongly affected by the surface roughness, which includes the surface root mean square roughness height, surface correlation length and the correlation function we use. And emissivity is differently affected by the root mean square slope under different polarizations. In general, emissivity decreases with the root mean square slope increases in V polarization, and increases with the root mean square slope increases in H polarization. For the GO model, we found that the backscattering coefficient is only related to the root mean square slope and <span class="hlt">soil</span> <span class="hlt">moisture</span> when the incident angle is fixed. And for Qp Model, through the analysis, we found that there is a quite good relationship</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19790008163&hterms=Soil+sampling+radiation&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DSoil%2Bsampling%2Bradiation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19790008163&hterms=Soil+sampling+radiation&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DSoil%2Bsampling%2Bradiation"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> estimation using reflected solar and emitted thermal infrared radiation</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.; Cihlar, J.; Estes, J. E.; Heilman, J. L.; Kahle, A.; Kanemasu, E. T.; Millard, J.; Price, J. C.; Wiegand, C. L.</p> <p>1978-01-01</p> <p>Classical methods of measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> such as gravimetric sampling and the use of neutron <span class="hlt">moisture</span> probes are useful for cases where a point measurement is sufficient to approximate the water content of a small surrounding area. However, there is an increasing need for rapid and repetitive estimations of <span class="hlt">soil</span> <span class="hlt">moisture</span> over large areas. Remote sensing techniques potentially have the capability of meeting this need. The use of reflected-solar and emitted thermal-infrared radiation, measured remotely, to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> is examined.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhDT........24F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT........24F"><span>Disaggregation Of Passive Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> For Use In Watershed Hydrology Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fang, Bin</p> <p></p> <p>In recent years the passive microwave remote sensing has been providing <span class="hlt">soil</span> <span class="hlt">moisture</span> products using instruments on board satellite/airborne platforms. Spatial resolution has been restricted by the diameter of antenna which is inversely proportional to resolution. As a result, typical products have a spatial resolution of tens of kilometers, which is not compatible for some hydrological research applications. For this reason, the dissertation explores three disaggregation algorithms that estimate L-band passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> at the subpixel level by using high spatial resolution remote sensing products from other optical and radar instruments were proposed and implemented in this investigation. The first technique utilized a thermal inertia theory to establish a relationship between daily temperature change and average <span class="hlt">soil</span> <span class="hlt">moisture</span> modulated by the vegetation condition was developed by using NLDAS, AVHRR, SPOT and MODIS data were applied to disaggregate the 25 km AMSR-E <span class="hlt">soil</span> <span class="hlt">moisture</span> to 1 km in Oklahoma. The second algorithm was built on semi empirical physical models (NP89 and LP92) derived from numerical experiments between <span class="hlt">soil</span> evaporation efficiency and <span class="hlt">soil</span> <span class="hlt">moisture</span> over the surface skin sensing depth (a few millimeters) by using simulated <span class="hlt">soil</span> temperature derived from MODIS and NLDAS as well as AMSR-E <span class="hlt">soil</span> <span class="hlt">moisture</span> at 25 km to disaggregate the coarse resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> to 1 km in Oklahoma. The third algorithm modeled the relationship between the change in co-polarized radar backscatter and the remotely sensed microwave change in <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals and assumed that change in <span class="hlt">soil</span> <span class="hlt">moisture</span> was a function of only the canopy opacity. The change detection algorithm was implemented using aircraft based the remote sensing data from PALS and UAVSAR that were collected in SMPAVEX12 in southern Manitoba, Canada. The PALS L-band h-polarization radiometer <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals were disaggregated by combining them with the PALS and UAVSAR L</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('http://adsabs.harvard.edu/abs/2018NatCC...8..421S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018NatCC...8..421S"><span>Anthropogenic warming exacerbates European <span class="hlt">soil</span> <span class="hlt">moisture</span> droughts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Samaniego, L.; Thober, S.; Kumar, R.; Wanders, N.; Rakovec, O.; Pan, M.; Zink, M.; Sheffield, J.; Wood, E. F.; Marx, A.</p> <p>2018-05-01</p> <p>Anthropogenic warming is anticipated to increase <span class="hlt">soil</span> <span class="hlt">moisture</span> drought in the future. However, projections are accompanied by large uncertainty due to varying estimates of future warming. Here, using an ensemble of hydrological and land-surface models, forced with bias-corrected downscaled general circulation model output, we estimate the impacts of 1-3 K global mean temperature increases on <span class="hlt">soil</span> <span class="hlt">moisture</span> droughts in Europe. Compared to the 1.5 K Paris target, an increase of 3 K—which represents current projected temperature change—is found to increase drought area by 40% (±24%), affecting up to 42% (±22%) more of the population. Furthermore, an event similar to the 2003 drought is shown to become twice as frequent; thus, due to their increased occurrence, events of this magnitude will no longer be classified as extreme. In the absence of effective mitigation, Europe will therefore face unprecedented increases in <span class="hlt">soil</span> <span class="hlt">moisture</span> drought, presenting new challenges for adaptation across the continent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AdWR..113...23L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AdWR..113...23L"><span>The impact of fog on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in the Namib Desert</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Vogt, Roland; Li, Lin; Seely, Mary K.</p> <p>2018-03-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a crucial component supporting vegetation dynamics in drylands. Despite increasing attention on fog in dryland ecosystems, the statistical characterization of fog distribution and how fog affects <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics have not been seen in literature. To this end, daily fog records over two years (Dec 1, 2014-Nov 1, 2016) from three sites within the Namib Desert were used to characterize fog distribution. Two sites were located within the Gobabeb Research and Training Center vicinity, the gravel plains and the sand dunes. The third site was located at the gravel plains, Kleinberg. A subset of the fog data during rainless period was used to investigate the effect of fog on <span class="hlt">soil</span> <span class="hlt">moisture</span>. A stochastic modeling framework was used to simulate the effect of fog on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Our results showed that fog distribution can be characterized by a Poisson process with two parameters (arrival rate λ and average depth α (mm)). Fog and <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from eighty (Aug 19, 2015-Nov 6, 2015) rainless days indicated a moderate positive relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and fog in the Gobabeb gravel plains, a weaker relationship in the Gobabeb sand dunes while no relationship was observed at the Kleinberg site. The modeling results suggested that mean and major peaks of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics can be captured by the fog modeling. Our field observations demonstrated the effects of fog on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics during rainless periods at some locations, which has important implications on <span class="hlt">soil</span> biogeochemical processes. The statistical characterization and modeling of fog distribution are of great value to predict fog distribution and investigate the effects of potential changes in fog distribution on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=318185','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=318185"><span>Potential of bias correction for downscaling passive microwave and <span class="hlt">soil</span> <span class="hlt">moisture</span> data</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Passive microwave satellites such as SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity) or SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive) observe brightness temperature (TB) and retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disag...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..12010915G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..12010915G"><span>Dust emission parameterization scheme over the MENA region: Sensitivity analysis to <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> texture</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gherboudj, Imen; Beegum, S. Naseema; Marticorena, Beatrice; Ghedira, Hosni</p> <p>2015-10-01</p> <p>The mineral dust emissions from arid/semiarid <span class="hlt">soils</span> were simulated over the MENA (Middle East and North Africa) region using the dust parameterization scheme proposed by Alfaro and Gomes (2001), to quantify the effect of the <span class="hlt">soil</span> <span class="hlt">moisture</span> and clay fraction in the emissions. For this purpose, an extensive data set of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity <span class="hlt">soil</span> <span class="hlt">moisture</span>, European Centre for Medium-Range Weather Forecasting wind speed at 10 m height, Food Agricultural Organization <span class="hlt">soil</span> texture maps, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index, and erodibility of the <span class="hlt">soil</span> surface were collected for the a period of 3 years, from 2010 to 2013. Though the considered data sets have different temporal and spatial resolution, efforts have been made to make them consistent in time and space. At first, the simulated sandblasting flux over the region were validated qualitatively using MODIS Deep Blue aerosol optical depth and EUMETSAT MSG (Meteosat Seciond Generation) dust product from SEVIRI (Meteosat Spinning Enhanced Visible and Infrared Imager) and quantitatively based on the available ground-based measurements of near-surface particulate mass concentrations (PM10) collected over four stations in the MENA region. Sensitivity analyses were performed to investigate the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> and clay fraction on the emissions flux. The results showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> texture have significant roles in the dust emissions over the MENA region, particularly over the Arabian Peninsula. An inversely proportional dependency is observed between the <span class="hlt">soil</span> <span class="hlt">moisture</span> and the sandblasting flux, where a steep reduction in flux is observed at low friction velocity and a gradual reduction is observed at high friction velocity. Conversely, a directly proportional dependency is observed between the <span class="hlt">soil</span> clay fraction and the sandblasting flux where a steep increase in flux is observed at low friction velocity and a gradual increase is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AdWR..112..124B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AdWR..112..124B"><span>Hydrologic responses to restored wildfire regimes revealed by <span class="hlt">soil</span> <span class="hlt">moisture</span>-vegetation relationships</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Boisramé, Gabrielle; Thompson, Sally; Stephens, Scott</p> <p>2018-02-01</p> <p>Many forested mountain watersheds worldwide evolved with frequent fire, which Twentieth Century fire suppression activities eliminated, resulting in unnaturally dense forests with high water demand. Restoration of pre-suppression forest composition and structure through a variety of management activities could improve forest resilience and water yields. This study explores the potential for "managed wildfire", whereby naturally ignited fires are allowed to burn, to alter the water balance. Interest in this type of managed wildfire is increasing, yet its long-term effects on water balance are uncertain. We use <span class="hlt">soil</span> <span class="hlt">moisture</span> as a spatially-distributed hydrologic indicator to assess the influence of vegetation, fire history and landscape position on water availability in the Illilouette Creek Basin in Yosemite National Park. Over 6000 manual surface <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements were made over a period of three years, and supplemented with continuous <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements over the top 1m of <span class="hlt">soil</span> in three sites. Random forest and linear mixed effects models showed a dominant effect of vegetation type and history of vegetation change on measured <span class="hlt">soil</span> <span class="hlt">moisture</span>. Contemporary and historical vegetation maps were used to upscale the <span class="hlt">soil</span> <span class="hlt">moisture</span> observations to the basin and infer <span class="hlt">soil</span> <span class="hlt">moisture</span> under fire-suppressed conditions. Little change in basin-averaged <span class="hlt">soil</span> <span class="hlt">moisture</span> was inferred due to managed wildfire, but the results indicated that large localized increases in <span class="hlt">soil</span> <span class="hlt">moisture</span> had occurred, which could have important impacts on local ecology or downstream flows.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JHyd..368...56T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JHyd..368...56T"><span>Assessment of multi-frequency electromagnetic induction for determining <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns at the hillslope scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tromp-van Meerveld, H. J.; McDonnell, J. J.</p> <p>2009-04-01</p> <p>SummaryHillslopes are fundamental landscape units, yet represent a difficult scale for measurements as they are well-beyond our traditional point-scale techniques. Here we present an assessment of electromagnetic induction (EM) as a potential rapid and non-invasive method to map <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns at the hillslope scale. We test the new multi-frequency GEM-300 for spatially distributed <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements at the well-instrumented Panola hillslope. EM-based apparent conductivity measurements were linearly related to <span class="hlt">soil</span> <span class="hlt">moisture</span> measured with the Aqua-pro capacitance sensor below a threshold conductivity and represented the temporal patterns in <span class="hlt">soil</span> <span class="hlt">moisture</span> well. During spring rainfall events that wetted only the surface <span class="hlt">soil</span> layers the apparent conductivity measurements explained the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics at depth better than the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. All four EM frequencies (7.290, 9.090, 11.250, and 14.010 kHz) were highly correlated and linearly related to each other and could be used to predict <span class="hlt">soil</span> <span class="hlt">moisture</span>. This limited our ability to use the four different EM frequencies to obtain a <span class="hlt">soil</span> <span class="hlt">moisture</span> profile with depth. The apparent conductivity patterns represented the observed spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns well when the individually fitted relationships between measured <span class="hlt">soil</span> <span class="hlt">moisture</span> and apparent conductivity were used for each measurement point. However, when the same (master) relationship was used for all measurement locations, the <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns were smoothed and did not resemble the observed <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns very well. In addition the range in calculated <span class="hlt">soil</span> <span class="hlt">moisture</span> values was reduced compared to observed <span class="hlt">soil</span> <span class="hlt">moisture</span>. Part of the smoothing was likely due to the much larger measurement area of the GEM-300 compared to the <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/52378','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/52378"><span>Persistence and memory timescales in root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Khaled Ghannam; Taro Nakai; Athanasios Paschalis; Andrew C. Oishi; Ayumi Kotani; Yasunori Igarashi; Tomo' omi Kumagai; Gabriel G. Katul</p> <p>2016-01-01</p> <p>The memory timescale that characterizes root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> remains the dominant measure in seasonal forecasts of land-climate interactions. This memory is a quasi-deterministic timescale associated with the losses (e.g., evapotranspiration) from the <span class="hlt">soil</span> column and is often interpreted as persistence in <span class="hlt">soil</span> <span class="hlt">moisture</span> states. Persistence, however,...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8426H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8426H"><span>Where did my wifi go? Measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> using wifi signal strength</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hut, Rolf; de Jeu, Richard</p> <p>2015-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is tricky to measure. Currently <span class="hlt">soil</span> <span class="hlt">moisture</span> is measured at small footprints using probes and other field devices, or at large footprints using satellites. Promising developments in measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> are using fiber optic cables for measurements along a line, or using cosmos rays for field scale measurements. In this demonstration we present a low cost alternative to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> at footprints of a few square meters. We use a wifi hotspot and a wifi dongle, both mounted in a cantenna for beam forming. We aim the hotspot on a piece of <span class="hlt">soil</span> and put the dongle in the path of the reflection. By logging the signal strength of the wifi netwerk, we have a proxy for <span class="hlt">soil</span> <span class="hlt">moisture</span>. A first proof of concept is presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000038126&hterms=watershed&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dwatershed','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038126&hterms=watershed&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dwatershed"><span>The Impact of Microwave-Derived Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on Watershed Hydrological Modeling</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>ONeill, P. E.; Hsu, A. Y.; Jackson, T. J.; Wood, E. F.; Zion, M.</p> <p>1997-01-01</p> <p>The usefulness of incorporating microwave-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> information in a semi-distributed hydrological model was demonstrated for the Washita '92 experiment in the Little Washita River watershed in Oklahoma. Initializing the hydrological model with surface <span class="hlt">soil</span> <span class="hlt">moisture</span> fields from the ESTAR airborne L-band microwave radiometer on a single wet day at the start of the study period produced more accurate model predictions of <span class="hlt">soil</span> <span class="hlt">moisture</span> than a standard hydrological initialization with streamflow data over an eight-day <span class="hlt">soil</span> <span class="hlt">moisture</span> drydown.</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 affect 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://adsabs.harvard.edu/abs/2017EGUGA..1916217M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1916217M"><span>A Mulitivariate Statistical Model Describing the Compound Nature of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Drought</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manning, Colin; Widmann, Martin; Bevacqua, Emanuele; Maraun, Douglas; Van Loon, Anne; Vrac, Mathieu</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> in Europe acts to partition incoming energy into sensible and latent heat fluxes, thereby exerting a large influence on temperature variability. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is predominantly controlled by precipitation and evapotranspiration. When these meteorological variables are accumulated over different timescales, their joint multivariate distribution and dependence structure can be used to provide information of <span class="hlt">soil</span> <span class="hlt">moisture</span>. We therefore consider <span class="hlt">soil</span> <span class="hlt">moisture</span> drought as a compound event of meteorological drought (deficits of precipitation) and heat waves, or more specifically, periods of high Potential Evapotraspiration (PET). We present here a statistical model of <span class="hlt">soil</span> <span class="hlt">moisture</span> based on Pair Copula Constructions (PCC) that can describe the dependence amongst <span class="hlt">soil</span> <span class="hlt">moisture</span> and its contributing meteorological variables. The model is designed in such a way that it can account for concurrences of meteorological drought and heat waves and describe the dependence between these conditions at a local level. The model is composed of four variables; daily <span class="hlt">soil</span> <span class="hlt">moisture</span> (h); a short term and a long term accumulated precipitation variable (Y1 and Y_2) that account for the propagation of meteorological drought to <span class="hlt">soil</span> <span class="hlt">moisture</span> drought; and accumulated PET (Y_3), calculated using the Penman Monteith equation, which can represent the effect of a heat wave on <span class="hlt">soil</span> conditions. Copula are multivariate distribution functions that allow one to model the dependence structure of given variables separately from their marginal behaviour. PCCs then allow in theory for the formulation of a multivariate distribution of any dimension where the multivariate distribution is decomposed into a product of marginal probability density functions and two-dimensional copula, of which some are conditional. We apply PCC here in such a way that allows us to provide estimates of h and their uncertainty through conditioning on the Y in the form h=h|y_1,y_2,y_3 (1) Applying the model to various</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3697171','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3697171"><span>On the <span class="hlt">Soil</span> Roughness Parameterization Problem in <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval of Bare Surfaces from Synthetic Aperture Radar</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Verhoest, Niko E.C; Lievens, Hans; Wagner, Wolfgang; Álvarez-Mozos, Jesús; Moran, M. Susan; Mattia, Francesco</p> <p>2008-01-01</p> <p>Synthetic Aperture Radar has shown its large potential for retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span> maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span> from radar backscatter usually provides inaccurate estimates. The characterization of <span class="hlt">soil</span> roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing <span class="hlt">soil</span> roughness as well as the reported impact of the errors made on the retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span>. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval accuracy and scale. PMID:27879932</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://adsabs.harvard.edu/abs/2006AGUFM.H11B1259F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.H11B1259F"><span>Modification of <span class="hlt">Soil</span> Temperature and <span class="hlt">Moisture</span> Budgets by Snow Processes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feng, X.; Houser, P.</p> <p>2006-12-01</p> <p>Snow cover significantly influences the land surface energy and surface <span class="hlt">moisture</span> budgets. Snow thermally insulates the <span class="hlt">soil</span> column from large and rapid temperature fluctuations, and snow melting provides an important source for surface runoff and <span class="hlt">soil</span> <span class="hlt">moisture</span>. Therefore, it is important to accurately understand and predict the energy and <span class="hlt">moisture</span> exchange between surface and subsurface associated with snow accumulation and ablation. The objective of this study is to understand the impact of land surface model <span class="hlt">soil</span> layering treatment on the realistic simulation of <span class="hlt">soil</span> temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span>. We seek to understand how many <span class="hlt">soil</span> layers are required to fully take into account <span class="hlt">soil</span> thermodynamic properties and hydrological process while also honoring efficient calculation and inexpensive computation? This work attempts to address this question using field measurements from the Cold Land Processes Field Experiment (CLPX). In addition, to gain a better understanding of surface heat and surface <span class="hlt">moisture</span> transfer process between land surface and deep <span class="hlt">soil</span> involved in snow processes, numerical simulations were performed at several Meso-Cell Study Areas (MSAs) of CLPX using the Center for Ocean-Land-Atmosphere (COLA) Simplified Version of the Simple Biosphere Model (SSiB). Measurements of <span class="hlt">soil</span> temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> were analyzed at several CLPX sites with different vegetation and <span class="hlt">soil</span> features. The monthly mean vertical profile of <span class="hlt">soil</span> temperature during October 2002 to July 2003 at North Park Illinois River exhibits a large near surface variation (<5 cm), reveals a significant transition zone from 5 cm to 25 cm, and becomes uniform beyond 25cm. This result shows us that three <span class="hlt">soil</span> layers are reasonable in solving the vertical variation of <span class="hlt">soil</span> temperature at these study sites. With 6 <span class="hlt">soil</span> layers, SSiB also captures the vertical variation of <span class="hlt">soil</span> temperature during entire winter season, featuring with six <span class="hlt">soil</span> layers, but the bare <span class="hlt">soil</span> temperature is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=244275','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=244275"><span>Remote sensing of an agricultural <span class="hlt">soil</span> <span class="hlt">moisture</span> network in Walnut Creek, Iowa</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The calibration and validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing products is complicated by the logistics of installing a <span class="hlt">soil</span> <span class="hlt">moisture</span> network for a long term period in an active landscape. Usually <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors are added to existing precipitation networks which have as a singular requiremen...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H53H..04S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H53H..04S"><span>Pathways of <span class="hlt">soil</span> <span class="hlt">moisture</span> controls on boundary layer dynamics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Siqueira, M.; Katul, G.; Porporato, A.</p> <p>2007-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> controls on precipitation are now receiving significant attention in climate systems because the memory of their variability is much slower than the memory of the fast atmospheric processes. We propose a new model that integrates <span class="hlt">soil</span> water dynamics, plant hydraulics and stomatal responses to water availability to estimate root water uptake and available energy partitioning, as well as feedbacks to boundary layer dynamics (in terms of water vapor and heat input to the atmospheric system). Using a simplified homogenization technique, the model solves the intrinsically 3-D <span class="hlt">soil</span> water movement equations by two 1-D coupled Richards' equations. The first resolves the radial water flow from bulk <span class="hlt">soil</span> to <span class="hlt">soil</span>-root interface to estimate root uptake (assuming the vertical gradients in <span class="hlt">moisture</span> persist during the rapid lateral flow), and then it solves vertical water movement through the <span class="hlt">soil</span> following the radial <span class="hlt">moisture</span> adjustments. The coupling between these two equations is obtained by area averaging the <span class="hlt">soil</span> <span class="hlt">moisture</span> in the radial domain (i.e. homogenization) to calculate the vertical fluxes. For each vertical layer, the domain is discretized in axi-symmetrical grid with constant <span class="hlt">soil</span> properties. This is deemed to be appropriate given the fact that the root uptake occurs on much shorter time scales closely following diurnal cycles, while the vertical water movement is more relevant to the inter-storm time scale. We show that this approach was able to explicitly simulate known features of root uptake such as diurnal hysteresis of canopy conductance, water redistribution by roots (hydraulic lift) and downward shift of root uptake during drying cycles. The model is then coupled with an atmospheric boundary layer (ABL) growth model thereby permitting us to explore low-dimensional elements of the interaction between <span class="hlt">soil</span> <span class="hlt">moisture</span> and ABL states commensurate with the lifting condensation level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70030715','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70030715"><span>Dependence of <span class="hlt">soil</span> respiration on <span class="hlt">soil</span> temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> in successional forests in Southern China</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tang, X.-L.; Zhou, G.-Y.; Liu, S.-G.; Zhang, D.-Q.; Liu, S.-Z.; Li, Ji; Zhou, C.-Y.</p> <p>2006-01-01</p> <p>The spatial and temporal variations in <span class="hlt">soil</span> respiration and its relationship with biophysical factors in forests near the Tropic of Cancer remain highly uncertain. To contribute towards an improvement of actual estimates, <span class="hlt">soil</span> respiration rates, <span class="hlt">soil</span> temperature, and <span class="hlt">soil</span> <span class="hlt">moisture</span> were measured in three successional subtropical forests at the Dinghushan Nature Reserve (DNR) in southern China from March 2003 to February 2005. The overall objective of the present study was to analyze the temporal variations of <span class="hlt">soil</span> respiration and its biophysical dependence in these forests. The relationships between biophysical factors and <span class="hlt">soil</span> respiration rates were compared in successional forests to test the hypothesis that these forests responded similarly to biophysical factors. The seasonality of <span class="hlt">soil</span> respiration coincided with the seasonal climate pattern, with high respiration rates in the hot humid season (April-September) and with low rates in the cool dry season (October-March). <span class="hlt">Soil</span> respiration measured at these forests showed a clear increasing trend with the progressive succession. Annual mean (±SD) <span class="hlt">soil</span> respiration rate in the DNR forests was (9.0 ± 4.6) Mg CO2-C/hm2per year, ranging from (6.1 ± 3.2) Mg CO2-C/hm2per year in early successional forests to (10.7 ± 4.9) Mg CO2-C/hm2 per year in advanced successional forests. <span class="hlt">Soil</span> respiration was correlated with both <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span>. The T/M model, where the two biophysical variables are driving factors, accounted for 74%-82% of <span class="hlt">soil</span> respiration variation in DNR forests. Temperature sensitivity decreased along progressive succession stages, suggesting that advanced-successional forests have a good ability to adjust to temperature. In contrast, <span class="hlt">moisture</span> increased with progressive succession processes. This increase is caused, in part, by abundant respirators in advanced-successional forest, where more <span class="hlt">soil</span> <span class="hlt">moisture</span> is needed to maintain their activities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://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://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://ntrs.nasa.gov/search.jsp?R=20000032232&hterms=pH+effects+soil&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DpH%2Beffects%2Bsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000032232&hterms=pH+effects+soil&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DpH%2Beffects%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 affected 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/2060/19820016659','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.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5672948','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5672948"><span>Relating coccidioidomycosis (valley fever) incidence to <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Coopersmith, E. J.; Bell, J. E.; Benedict, K.; Shriber, J.; McCotter, O.; Cosh, M. H.</p> <p>2017-01-01</p> <p>Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp., in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets, it has not been feasible to perform a direct analysis of the relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower <span class="hlt">soil</span> <span class="hlt">moisture</span> in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to <span class="hlt">soil</span> <span class="hlt">moisture</span> levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location. PMID:29124249</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020066567','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020066567"><span>Impact of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Initialization on Seasonal Weather Prediction</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal D.; Suarez, Max J.; Houser, Paul (Technical Monitor)</p> <p>2002-01-01</p> <p>The potential role of <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997-2001, we generated two 16-member ensembles of 3-month simulations. The first, "AMIP-style" ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with "realistic" <span class="hlt">soil</span> <span class="hlt">moisture</span> contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation data set and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization has a statistically significant impact on summertime precipitation and temperature over only a handful of continental regions. These regions agree, to first order, with regions that satisfy three conditions: (1) a tendency toward large initial <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies, (2) a strong sensitivity of evaporation to <span class="hlt">soil</span> <span class="hlt">moisture</span>, and (3) a strong sensitivity of precipitation to evaporation. The degree to which the initialization improves forecasts relative to observations is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJAEO..48..146M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJAEO..48..146M"><span>Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS <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>Martens, B.; Miralles, D.; Lievens, H.; Fernández-Prieto, D.; Verhoest, N. E. C.</p> <p>2016-06-01</p> <p>Terrestrial evaporation is an essential variable in the climate system that links the water, energy and carbon cycles over land. Despite this crucial importance, it remains one of the most uncertain components of the hydrological cycle, mainly due to known difficulties to model the constraints imposed by land water availability on terrestrial evaporation. The main objective of this study is to assimilate satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission into an existing evaporation model. Our over-arching goal is to find an optimal use of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> that can help to improve our understanding of evaporation at continental scales. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used to simulate evaporation fields over continental Australia for the period September 2010-December 2013. SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> observations are assimilated using a Newtonian Nudging algorithm in a series of experiments. Model estimates of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and evaporation are validated against <span class="hlt">soil</span> <span class="hlt">moisture</span> probe and eddy-covariance measurements, respectively. Finally, an analogous experiment in which Advanced Microwave Scanning Radiometer (AMSR-E) <span class="hlt">soil</span> <span class="hlt">moisture</span> is assimilated (instead of SMOS) allows to perform a relative assessment of the quality of both satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> products. Results indicate that the modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> from GLEAM can be improved through the assimilation of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span>: the average correlation coefficient between in situ measurements and the modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> over the complete sample of stations increased from 0.68 to 0.71 and a statistical significant increase in the correlations is achieved for 17 out of the 25 individual stations. Our results also suggest a higher accuracy of the ascending SMOS data compared to the descending data, and overall higher quality of SMOS compared to AMSR-E retrievals over Australia. On the other hand, the effect of <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/2014AGUFM.H33F0885M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H33F0885M"><span>Towards Generating Long-term AMSR-based <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data Record</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mladenova, I. E.; Jackson, T. J.; Bindlish, R.; Cosh, M. H.</p> <p>2014-12-01</p> <p>Research done over the past couple of years, such as Jung et al. (Nature, 2010) among others, demonstrates the potential for using <span class="hlt">soil</span> <span class="hlt">moisture</span> as an indicator and parameter for identifying long-term changes in climate trends. The study mentioned links the reduction in global evapotranspiration observed after the 1998 El Nino to decline in <span class="hlt">moisture</span> supplies in the <span class="hlt">soil</span> profile. Due to its crucial role in the terrestrial cycles and the demonstrated strong feedback with other climate variables, <span class="hlt">soil</span> <span class="hlt">moisture</span> has been recognized by the Global Climate Observing System as one of the 50 Essential Climate Variables (ECVs). The most cost and time effective way of monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span> at global scale on routine basis, which is one of the requirements for ECVs, is using satellite technologies. AMSR-E was the first satellite mission to include <span class="hlt">soil</span> <span class="hlt">moisture</span> as an operational product. AMSR-E provided us with almost a decade of <span class="hlt">soil</span> <span class="hlt">moisture</span> data that are now extended by AMSR2, allowing the generation of a consistent and continuous global <span class="hlt">soil</span> <span class="hlt">moisture</span> data record. AMSR-E and AMSR2 are technically alike, thus, they are expected to have similar performance and accuracy, which needs to be confirmed and this the main focus of our research. AMSR-E stopped operating at its optimal rotational speed about 6 months before the launch of AMSR2, which complicates the direct inter-comparison and assessment of AMSR2 performance relative to AMSR-E. The AMSR-E and AMSR2 brightness temperature data and the corresponding <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals derived using the Single Channel Approach were evaluated separately at several ground validation sides located in the US. Brightness temperature inter-comparisons were done using monthly climatology and the low spin AMSR-E data acquired at 2 rpm. Both analyses showed very high agreement between the two instruments and revealed a constant positive bias at all locations in the AMSR2 observations relative to AMSR-E. Removal of this bias is essential</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H51E1317N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H51E1317N"><span>Enhanced <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Initialization Using Blended <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product and Regional Optimization of LSM-RTM Coupled Land Data Assimilation System.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nair, A. S.; Indu, J.</p> <p>2017-12-01</p> <p>Prediction of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics is high priority research challenge because of the complex land-atmosphere interaction processes. <span class="hlt">Soil</span> <span class="hlt">moisture</span> (SM) plays a decisive role in governing water and energy balance of the terrestrial system. An accurate SM estimate is imperative for hydrological and weather prediction models. Though SM estimates are available from microwave remote sensing and land surface model (LSM) simulations, it is affected by uncertainties from several sources during estimation. Past studies have generally focused on land data assimilation (DA) for improving LSM predictions by assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> from single satellite sensor. This approach is limited by the large time gap between two consequent <span class="hlt">soil</span> <span class="hlt">moisture</span> observations due to satellite repeat cycle of more than three days at the equator. To overcome this, in the present study, we have performed DA using ensemble products from the <span class="hlt">soil</span> <span class="hlt">moisture</span> operational product system (SMOPS) blended <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from different satellite sensors into Noah LSM. Before the assimilation period, the Noah LSM is initialized by cycling through seven multiple loops from 2008 to 2010 forcing with Global data assimilation system (GDAS) data over the Indian subcontinent. We assimilated SMOPS into Noah LSM for a period of two years from 2010 to 2011 using Ensemble Kalman Filter within NASA's land information system (LIS) framework. Results show that DA has improved Noah LSM prediction with a high correlation of 0.96 and low root mean square difference of 0.0303 m3/m3 (figure 1a). Further, this study has also investigated the notion of assimilating microwave brightness temperature (Tb) as a proxy for SM estimates owing to the close proximity of Tb and SM. Preliminary sensitivity analysis show a strong need for regional parameterization of radiative transfer models (RTMs) to improve Tb simulation. Towards this goal, we have optimized the forward RTM using swarm optimization technique for direct Tb</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016SPIE.9998E..1OA','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016SPIE.9998E..1OA"><span>Continuous data assimilation for downscaling large-footprint <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Altaf, Muhammad U.; Jana, Raghavendra B.; Hoteit, Ibrahim; McCabe, Matthew F.</p> <p>2016-10-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for <span class="hlt">soil</span> <span class="hlt">moisture</span> is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. <span class="hlt">Soil</span> <span class="hlt">moisture</span> fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale <span class="hlt">soil</span> <span class="hlt">moisture</span> fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170002445','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170002445"><span>Precipitation Estimation Using L-Band and C-Band <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal D.; Brocca, Luca; Crow, Wade T.; Burgin, Mariko S.; De Lannoy, Gabrielle J. M.</p> <p>2016-01-01</p> <p>An established methodology for estimating precipitation amounts from satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals is applied to L-band products from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS data sets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of <span class="hlt">soil</span> and thereby provides more information on the response of <span class="hlt">soil</span> <span class="hlt">moisture</span> to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to approximately100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span> thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150008333&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=20150008333&hterms=Soil+science&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Bscience"><span>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive Mission (SMAP): Science and Applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni</p> <p>2009-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive mission (SMAP) will provide global maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> content and surface freeze/thaw state. Global measurements of these variables are critical for terrestrial water and carbon cycle applications. The SMAP observatory consists of two multipolarization L-band sensors, a radar and radiometer, that share a deployable-mesh reflector antenna. The combined observations from the two sensors will allow accurate estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> at hydrometeorological (10 km) and hydroclimatological (40 km) spatial scales. The rotating antenna configuration provides conical scans of the Earth surface at a constant look angle. The wide-swath (1000 km) measurements will allow global mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its freeze/thaw state with 2-3 days revisit. Freeze/thaw in boreal latitudes will be mapped using the radar at 3 km resolution with 1-2 days revisit. The synergy of active and passive observations enables measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state with unprecedented resolution, sensitivity, area coverage and revisit.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820003640','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820003640"><span>An evaluation of the spatial resolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> information</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hardy, K. R.; Cohen, S. H.; Rogers, L. K.; Burke, H. H. K.; Leupold, R. C.; Smallwood, M. D.</p> <p>1981-01-01</p> <p>Rainfall-amount patterns in the central regions of the U.S. were assessed. The spatial scales of surface features and their corresponding microwave responses in the mid western U.S. were investigated. The usefulness for U.S. government agencies of <span class="hlt">soil</span> <span class="hlt">moisture</span> information at scales of 10 km and 1 km. was ascertained. From an investigation of 494 storms, it was found that the rainfall resulting from the passage of most types of storms produces patterns which can be resolved on a 10 km scale. The land features causing the greatest problem in the sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> over large agricultural areas with a radiometer are bodies of water. Over the mid-western portions of the U.S., water occupies less than 2% of the total area, the consequently, the water bodies will not have a significant impact on the mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Over most of the areas, measurements at a 10-km resolution would adequately define the distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Crop yield models and hydrological models would give improved results if <span class="hlt">soil</span> <span class="hlt">moisture</span> information at scales of 10 km was available.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMEP41C0921A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMEP41C0921A"><span>Downscaling Coarse Scale Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product using Machine Learning</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abbaszadeh, P.; Moradkhani, H.; Yan, H.</p> <p>2016-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of <span class="hlt">soil</span> <span class="hlt">moisture</span> at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> information that is currently used for land data assimilation applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=291573','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=291573"><span>Variation in Microbial Activity in Histosols and Its Relationship to <span class="hlt">Soil</span> <span class="hlt">Moisture</span> †</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tate, Robert L.; Terry, Richard E.</p> <p>1980-01-01</p> <p>Microbial biomass, dehydrogenase activity, carbon metabolism, and aerobic bacterial populations were examined in cropped and fallow Pahokee muck (a lithic medisaprist) of the Florida Everglades. Dehydrogenase activity was two- to sevenfold greater in <span class="hlt">soil</span> cropped to St. Augustinegrass (Stenotaphrum secundatum (Walt) Kuntz) compared with uncropped <span class="hlt">soil</span>, whereas biomass ranged from equivalence in the two <span class="hlt">soils</span> to a threefold stimulation in the cropped <span class="hlt">soil</span>. Biomass in <span class="hlt">soil</span> cropped to sugarcane (Saccharum spp. L) approximated that from the grass field, whereas dehydrogenase activities of the cane <span class="hlt">soil</span> were nearly equivalent to those of the fallow <span class="hlt">soil</span>. Microbial biomass, dehydrogenase activity, aerobic bacterial populations, and salicylate oxidation rates all correlated with <span class="hlt">soil</span> <span class="hlt">moisture</span> levels. These data indicate that within the <span class="hlt">moisture</span> ranges detected in the surface <span class="hlt">soils</span>, increased <span class="hlt">moisture</span> stimulated microbial activity, whereas within the <span class="hlt">soil</span> profile where <span class="hlt">moisture</span> ranges reached saturation, increased <span class="hlt">moisture</span> inhibited aerobic activities and stimulated anaerobic processes. PMID:16345610</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19910035161&hterms=hydra&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dhydra','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910035161&hterms=hydra&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dhydra"><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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080004HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080004HQ.html"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Media Briefing</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-01-09</p> <p>Dara Entekhabi, SMAP science team lead, Massachusetts Institute of Technology, center, speaks during a briefing about the upcoming launch of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, Thursday, Jan. 08, 2015, at NASA Headquarters in Washington DC. The mission is scheduled for a Jan. 29 launch from Vandenberg Air Force Base in California, and will provide the most accurate, highest-resolution global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> ever obtained from space. The data will be used to enhance scientists' understanding of the processes that link Earth's water, energy and carbon cycles. Photo Credit: (NASA/Aubrey Gemignani)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-201501080006HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-201501080006HQ.html"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Media Briefing</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-01-09</p> <p>Dara Entekhabi, SMAP science team lead, Massachusetts Institute of Technology, speaks during a briefing about the upcoming launch of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, Thursday, Jan. 08, 2015, at NASA Headquarters in Washington DC. The mission is scheduled for a Jan. 29 launch from Vandenberg Air Force Base in California, and will provide the most accurate, highest-resolution global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> ever obtained from space. The data will be used to enhance scientists' understanding of the processes that link Earth's water, energy and carbon cycles. Photo Credit: (NASA/Aubrey Gemignani)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27594213','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27594213"><span>The sensitivity of <span class="hlt">soil</span> respiration to <span class="hlt">soil</span> temperature, <span class="hlt">moisture</span>, and carbon supply at the global scale.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hursh, Andrew; Ballantyne, Ashley; Cooper, Leila; Maneta, Marco; Kimball, John; Watts, Jennifer</p> <p>2017-05-01</p> <p><span class="hlt">Soil</span> respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, <span class="hlt">moisture</span>, carbon supply, and other site characteristics are known to regulate <span class="hlt">soil</span> respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of <span class="hlt">soil</span> <span class="hlt">moisture</span>, <span class="hlt">soil</span> temperature, primary productivity, and <span class="hlt">soil</span> carbon estimates with observations of annual Rs from the Global <span class="hlt">Soil</span> Respiration Database (SRDB). We find that calibrating models with parabolic <span class="hlt">soil</span> <span class="hlt">moisture</span> functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. <span class="hlt">Soil</span> temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> carbon emerge as dominant predictors of Rs. We identify regions where typical temperature-driven responses are further mediated by <span class="hlt">soil</span> <span class="hlt">moisture</span>, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because <span class="hlt">soil</span> <span class="hlt">moisture</span> integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global-scale models tend to smooth its spatial heterogeneity by aggregating factors that increase <span class="hlt">moisture</span> variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108 Pg yr -1 , but also highlight regions of uncertainty</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatSR...620018R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatSR...620018R"><span>Experimental evidence for drought induced alternative stable states 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>Robinson, David. A.; Jones, Scott B.; Lebron, Inma; Reinsch, Sabine; Domínguez, María T.; Smith, Andrew R.; Jones, Davey L.; Marshall, Miles R.; Emmett, Bridget A.</p> <p>2016-01-01</p> <p>Ecosystems may exhibit alternative stable states (ASS) in response to environmental change. Modelling and observational data broadly support the theory of ASS, however evidence from manipulation experiments supporting this theory is limited. Here, we provide long-term manipulation and observation data supporting the existence of drought induced alternative stable <span class="hlt">soil</span> <span class="hlt">moisture</span> states (irreversible <span class="hlt">soil</span> wetting) in upland Atlantic heath, dominated by Calluna vulgaris (L.) Hull. Manipulated repeated moderate summer drought, and intense natural summer drought both lowered resilience resulting in shifts in <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. The repeated moderate summer drought decreased winter <span class="hlt">soil</span> <span class="hlt">moisture</span> retention by ~10%. However, intense summer drought, superimposed on the experiment, that began in 2003 and peaked in 2005 caused an unexpected erosion of resilience and a shift to an ASS; both for the experimental drought manipulation and control plots, impairing the <span class="hlt">soil</span> from rewetting in winter. Measurements outside plots, with vegetation removal, showed no evidence of <span class="hlt">moisture</span> shifts. Further independent evidence supports our findings from historical <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring at a long-term upland hydrological observatory. The results herald the need for a new paradigm regarding our understanding of <span class="hlt">soil</span> structure, hydraulics and climate interaction.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4726285','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4726285"><span>Experimental evidence for drought induced alternative stable states of <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Robinson, David. A.; Jones, Scott B.; Lebron, Inma; Reinsch, Sabine; Domínguez, María T.; Smith, Andrew R.; Jones, Davey L.; Marshall, Miles R.; Emmett, Bridget A.</p> <p>2016-01-01</p> <p>Ecosystems may exhibit alternative stable states (ASS) in response to environmental change. Modelling and observational data broadly support the theory of ASS, however evidence from manipulation experiments supporting this theory is limited. Here, we provide long-term manipulation and observation data supporting the existence of drought induced alternative stable <span class="hlt">soil</span> <span class="hlt">moisture</span> states (irreversible <span class="hlt">soil</span> wetting) in upland Atlantic heath, dominated by Calluna vulgaris (L.) Hull. Manipulated repeated moderate summer drought, and intense natural summer drought both lowered resilience resulting in shifts in <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. The repeated moderate summer drought decreased winter <span class="hlt">soil</span> <span class="hlt">moisture</span> retention by ~10%. However, intense summer drought, superimposed on the experiment, that began in 2003 and peaked in 2005 caused an unexpected erosion of resilience and a shift to an ASS; both for the experimental drought manipulation and control plots, impairing the <span class="hlt">soil</span> from rewetting in winter. Measurements outside plots, with vegetation removal, showed no evidence of <span class="hlt">moisture</span> shifts. Further independent evidence supports our findings from historical <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring at a long-term upland hydrological observatory. The results herald the need for a new paradigm regarding our understanding of <span class="hlt">soil</span> structure, hydraulics and climate interaction. PMID:26804897</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/47251','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/47251"><span>Long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns in a northern Minnesota forest</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Salli F. Dymond; Randall K. Kolka; Paul V. Bolstad; Stephen D. Sebestyen</p> <p>2014-01-01</p> <p>Forest hydrological and biogeochemical processes are highly dependent on <span class="hlt">soil</span> water. At the Marcell Experimental Forest, seasonal patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> have been monitored at three forested locations since 1966. This unique, long-term data set was used to analyze seasonal trends in <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as the influence of time-lagged precipitation and modified...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70015963','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70015963"><span>Effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the sorption of trichloroethene vapor to vadose-zone <span class="hlt">soil</span> at picatinny arsenal, New Jersey</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Smith, J.A.; Chiou, C.T.; Kammer, J.A.; Kile, D.E.</p> <p>1990-01-01</p> <p>This report presents data on the sorption of trichloroethene (TCE) vapor to vadose-zone <span class="hlt">soil</span> above a contaminated water-table aquifer at Picatinny Arsenal in Morris County, NJ. To assess the impact of <span class="hlt">moisture</span> on TCE sorption, batch experiments on the sorption of TCE vapor by the field <span class="hlt">soil</span> were carried out as a function of relative humidity. The TCE sorption decreases as <span class="hlt">soil</span> <span class="hlt">moisture</span> content increases from zero to saturation <span class="hlt">soil</span> <span class="hlt">moisture</span> content (the <span class="hlt">soil</span> <span class="hlt">moisture</span> content in equilibrium with 100% relative humidity). The <span class="hlt">moisture</span> content of <span class="hlt">soil</span> samples collected from the vadose zone was found to be greater than the saturation <span class="hlt">soil-moisture</span> content, suggesting that adsorption of TCE by the mineral fraction of the vadose-zone <span class="hlt">soil</span> should be minimal relative to the partition uptake by <span class="hlt">soil</span> organic matter. Analyses of <span class="hlt">soil</span> and <span class="hlt">soil</span>-gas samples collected from the field indicate that the ratio of the concentration of TCE on the vadose-zone <span class="hlt">soil</span> to its concentration in the <span class="hlt">soil</span> gas is 1-3 orders of magnitude greater than the ratio predicted by using an assumption of equilibrium conditions. This apparent disequilibrium presumably results from the slow desorption of TCE from the organic matter of the vadose-zone <span class="hlt">soil</span> relative to the dissipation of TCE vapor from the <span class="hlt">soil</span> gas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.8681K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.8681K"><span><span class="hlt">SOIL</span> <span class="hlt">moisture</span> data intercomparison</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>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</p> <p>2016-04-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> (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 <span class="hlt">soil</span> <span class="hlt">moisture</span> (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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ISPAr42.3.1739W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ISPAr42.3.1739W"><span>Inversion of Farmland <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Large Region Based on Modified Vegetation Index</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. X.; Yu, B. S.; Zhang, G. Z.; Zhao, G. C.; He, S. D.; Luo, W. R.; Zhang, C. C.</p> <p>2018-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important parameter for agricultural production. Efficient and accurate monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> is an important link to ensure the safety of agricultural production. Remote sensing technology has been widely used in agricultural <span class="hlt">moisture</span> monitoring because of its timeliness, cyclicality, dynamic tracking of changes in things, easy access to data, and extensive monitoring. Vegetation index and surface temperature are important parameters for <span class="hlt">moisture</span> monitoring. Based on NDVI, this paper introduces land surface temperature and average temperature for optimization. This article takes the <span class="hlt">soil</span> <span class="hlt">moisture</span> in winter wheat growing area in Henan Province as the research object, dividing Henan Province into three main regions producing winter wheat and dividing the growth period of winter wheat into the early, middle and late stages on the basis of phenological characteristics and regional characteristics. Introducing appropriate correction factor during the corresponding growth period of winter wheat, correcting the vegetation index in the corresponding area, this paper establishes regression models of <span class="hlt">soil</span> <span class="hlt">moisture</span> on NDVI and <span class="hlt">soil</span> <span class="hlt">moisture</span> on modified NDVI based on correlation analysis and compare models. It shows that modified NDVI is more suitable as a indicator of <span class="hlt">soil</span> <span class="hlt">moisture</span> because of the better correlation between <span class="hlt">soil</span> <span class="hlt">moisture</span> and modified NDVI and the higher prediction accuracy of the regression model of <span class="hlt">soil</span> <span class="hlt">moisture</span> on modified NDVI. The research in this paper has certain reference value for winter wheat farmland management and decision-making.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2802917','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2802917"><span>Sensitivity of Polygonum aviculare Seeds to Light as Affected by <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Conditions</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Batlla, Diego; Nicoletta, Marcelo; Benech-Arnold, Roberto</p> <p>2007-01-01</p> <p>Background and Aims It has been hypothesized that <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions could affect the dormancy status of buried weed seeds, and, consequently, their sensitivity to light stimuli. In this study, an investigation is made of the effect of different <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions during cold-induced dormancy loss on changes in the sensitivity of Polygonum aviculare seeds to light. Methods Seeds buried in pots were stored under different constant and fluctuating <span class="hlt">soil</span> <span class="hlt">moisture</span> environments at dormancy-releasing temperatures. Seeds were exhumed at regular intervals during storage and were exposed to different light treatments. Changes in the germination response of seeds to light treatments during storage under the different <span class="hlt">moisture</span> environments were compared in order to determine the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the sensitivity to light of P. aviculare seeds. Key Results Seed acquisition of low-fluence responses during dormancy release was not affected by either <span class="hlt">soil</span> <span class="hlt">moisture</span> fluctuations or different constant <span class="hlt">soil</span> <span class="hlt">moisture</span> contents. On the contrary, different <span class="hlt">soil</span> <span class="hlt">moisture</span> environments affected seed acquisition of very low fluence responses and the capacity of seeds to germinate in the dark. Conclusions The results indicate that under field conditions, the sensitivity to light of buried weed seeds could be affected by the <span class="hlt">soil</span> <span class="hlt">moisture</span> environment experienced during the dormancy release season, and this could affect their emergence pattern. PMID:17430979</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008JHyd..357..405G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008JHyd..357..405G"><span>Landscape complexity and <span class="hlt">soil</span> <span class="hlt">moisture</span> variation in south Georgia, USA, for remote sensing applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giraldo, Mario A.; Bosch, David; Madden, Marguerite; Usery, Lynn; Kvien, Craig</p> <p>2008-08-01</p> <p>SummaryThis research addressed the temporal and spatial variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) in a heterogeneous landscape. The research objective was to investigate <span class="hlt">soil</span> <span class="hlt">moisture</span> variation in eight homogeneous 30 by 30 m plots, similar to the pixel size of a Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper plus (ETM+) image. The plots were adjacent to eight stations of an in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> network operated by the United States Department of Agriculture-Agriculture Research Service USDA-ARS in Tifton, GA. We also studied five adjacent agricultural fields to examine the effect of different landuses/land covers (LULC) (grass, orchard, peanuts, cotton and bare <span class="hlt">soil</span>) on the temporal and spatial variation of <span class="hlt">soil</span> <span class="hlt">moisture</span>. <span class="hlt">Soil</span> <span class="hlt">moisture</span> field data were collected on eight occasions throughout 2005 and January 2006 to establish comparisons within and among eight homogeneous plots. Consistently throughout time, analysis of variance (ANOVA) showed high variation in the <span class="hlt">soil</span> <span class="hlt">moisture</span> behavior among the plots and high homogeneity in the <span class="hlt">soil</span> <span class="hlt">moisture</span> behavior within them. A precipitation analysis for the eight sampling dates throughout the year 2005 showed similar rainfall conditions for the eight study plots. Therefore, <span class="hlt">soil</span> <span class="hlt">moisture</span> variation among locations was explained by in situ local conditions. Temporal stability geostatistical analysis showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> has high temporal stability within the small plots and that a single point reading can be used to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span> status for the plot within a maximum 3% volume/volume (v/v) <span class="hlt">soil</span> <span class="hlt">moisture</span> variation. Similarly, t-statistic analysis showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> status in the upper <span class="hlt">soil</span> layer changes within 24 h. We found statistical differences in the <span class="hlt">soil</span> <span class="hlt">moisture</span> between the different LULC in the agricultural fields as well as statistical differences between these fields and the adjacent 30 by 30 m plots. From this analysis, it was demonstrated that spatial proximity is not enough to produce similar</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70000240','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70000240"><span>Landscape complexity and <span class="hlt">soil</span> <span class="hlt">moisture</span> variation in south Georgia, USA, for remote sensing applications</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Giraldo, M.A.; Bosch, D.; Madden, M.; Usery, L.; Kvien, Craig</p> <p>2008-01-01</p> <p>This research addressed the temporal and spatial variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) in a heterogeneous landscape. The research objective was to investigate <span class="hlt">soil</span> <span class="hlt">moisture</span> variation in eight homogeneous 30 by 30 m plots, similar to the pixel size of a Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper plus (ETM+) image. The plots were adjacent to eight stations of an in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> network operated by the United States Department of Agriculture-Agriculture Research Service USDA-ARS in Tifton, GA. We also studied five adjacent agricultural fields to examine the effect of different landuses/land covers (LULC) (grass, orchard, peanuts, cotton and bare <span class="hlt">soil</span>) on the temporal and spatial variation of <span class="hlt">soil</span> <span class="hlt">moisture</span>. <span class="hlt">Soil</span> <span class="hlt">moisture</span> field data were collected on eight occasions throughout 2005 and January 2006 to establish comparisons within and among eight homogeneous plots. Consistently throughout time, analysis of variance (ANOVA) showed high variation in the <span class="hlt">soil</span> <span class="hlt">moisture</span> behavior among the plots and high homogeneity in the <span class="hlt">soil</span> <span class="hlt">moisture</span> behavior within them. A precipitation analysis for the eight sampling dates throughout the year 2005 showed similar rainfall conditions for the eight study plots. Therefore, <span class="hlt">soil</span> <span class="hlt">moisture</span> variation among locations was explained by in situ local conditions. Temporal stability geostatistical analysis showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> has high temporal stability within the small plots and that a single point reading can be used to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span> status for the plot within a maximum 3% volume/volume (v/v) <span class="hlt">soil</span> <span class="hlt">moisture</span> variation. Similarly, t-statistic analysis showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> status in the upper <span class="hlt">soil</span> layer changes within 24 h. We found statistical differences in the <span class="hlt">soil</span> <span class="hlt">moisture</span> between the different LULC in the agricultural fields as well as statistical differences between these fields and the adjacent 30 by 30 m plots. From this analysis, it was demonstrated that spatial proximity is not enough to produce similar <span class="hlt">soil</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..4411860L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..4411860L"><span>Irrigation Signals Detected From SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lawston, Patricia M.; Santanello, Joseph A.; Kumar, Sujay V.</p> <p>2017-12-01</p> <p>Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled land-atmosphere system. One way to improve irrigation representation in models is to assimilate <span class="hlt">soil</span> <span class="hlt">moisture</span> observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new enhanced <span class="hlt">soil</span> <span class="hlt">moisture</span> product from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive satellite is able to capture irrigation signals over three semiarid regions in the western United States. This marks an advancement in Earth-observing satellite skill and the ability to monitor human impacts on the water cycle.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ISPAr42W4..133K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ISPAr42W4..133K"><span>Estimating <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Using Polsar Data: a Machine Learning Approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khedri, E.; Hasanlou, M.; Tabatabaeenejad, A.</p> <p>2017-09-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important parameter that affects several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for <span class="hlt">moisture</span> calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating <span class="hlt">soil</span> <span class="hlt">moisture</span> in vast territories and high-cost nature as well as spatial and local variability of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating <span class="hlt">soil</span> <span class="hlt">moisture</span>. These images provide a wide field of view and high spatial resolution. For estimating <span class="hlt">soil</span> <span class="hlt">moisture</span>, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. We compare the obtained data with in-situ data. Output results show that the SBS-SVR method results in higher modeling accuracy compared to SFS-SVR model. Statistical parameters obtained from this method show an R2 of 97% and an RMSE of lower than 0.00041 (m3/m3) for P, L, and C channels, which has provided better accuracy compared to other feature selection algorithms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140013012','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140013012"><span>Inferring <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Memory from Streamflow Observations Using a Simple Water Balance Model</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Orth, Rene; Koster, Randal Dean; Seneviratne, Sonia I.</p> <p>2013-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is known for its integrative behavior and resulting memory characteristics. <span class="hlt">Soil</span> <span class="hlt">moisture</span> anomalies can persist for weeks or even months into the future, making initial <span class="hlt">soil</span> <span class="hlt">moisture</span> a potentially important contributor to skill in weather forecasting. A major difficulty when investigating <span class="hlt">soil</span> <span class="hlt">moisture</span> and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We investigate in this study whether such streamflow measurements can be used to infer and characterize <span class="hlt">soil</span> <span class="hlt">moisture</span> memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of <span class="hlt">soil</span> <span class="hlt">moisture</span>; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on <span class="hlt">soil</span> <span class="hlt">moisture</span> memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of <span class="hlt">soil</span> <span class="hlt">moisture</span> memory and to show how memory varies, for example, with altitude and topography.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120009280','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120009280"><span>NASA Giovanni: A Tool for Visualizing, Analyzing, and Inter-Comparing <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>Teng, William; Rui, Hualan; Vollmer, Bruce; deJeu, Richard; Fang, Fan; Lei, Guang-Dih</p> <p>2012-01-01</p> <p>There are many existing satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms and their derived data products, but there is no simple way for a user to inter-compare the products or analyze them together with other related data (e.g., precipitation). An environment that facilitates such inter-comparison and analysis would be useful for validation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals against in situ data and for determining the relationships between different <span class="hlt">soil</span> <span class="hlt">moisture</span> products. The latter relationships are particularly important for applications users, for whom the continuity of <span class="hlt">soil</span> <span class="hlt">moisture</span> data, from whatever source, is critical. A recent example was provided by the sudden demise of EOS Aqua AMSR-E and the end of its <span class="hlt">soil</span> <span class="hlt">moisture</span> data production, as well as the end of other <span class="hlt">soil</span> <span class="hlt">moisture</span> products that had used the AMSR-E brightness temperature data. The purpose of the current effort is to create an environment, as part of the NASA Giovanni family of portals, that facilitates inter-comparisons of <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms and their derived data products.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1810486A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1810486A"><span>A New Approach in Downscaling Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product using Machine Learning</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abbaszadeh, Peyman; Yan, Hongxiang; Moradkhani, Hamid</p> <p>2016-04-01</p> <p>Understating the <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern has significant impact on flood modeling, drought monitoring, and irrigation management. Although satellite retrievals can provide an unprecedented spatial and temporal resolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> at a global-scale, their <span class="hlt">soil</span> <span class="hlt">moisture</span> products (with a spatial resolution of 25-50 km) are inadequate for regional study, where a resolution of 1-10 km is needed. In this study, a downscaling approach using Genetic Programming (GP), a specialized version of Genetic Algorithm (GA), is proposed to improve the spatial resolution of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> products. The GP approach was applied over a test watershed in United States using the coarse resolution satellite data (25 km) from Advanced Microwave Scanning Radiometer - EOS (AMSR-E) <span class="hlt">soil</span> <span class="hlt">moisture</span> products, the fine resolution data (1 km) from Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index, and ground based data including land surface temperature, vegetation and other potential physical variables. The results indicated the great potential of this approach to derive the fine resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> information applicable for data assimilation and other regional studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1999AdSpR..24..935M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1999AdSpR..24..935M"><span>Spatial Estimation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Using Synthetic Aperture Radar in Alaska</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Meade, N. G.; Hinzman, L. D.; Kane, D. L.</p> <p>1999-01-01</p> <p>A spatially distributed Model of Arctic Thermal and Hydrologic processes (MATH) has been developed. One of the attributes of this model is the spatial and temporal prediction of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the active layer. The spatially distributed output from this model required verification data obtained through remote sensing to assess performance at the watershed scale independently. Therefore, a neural network was trained to predict <span class="hlt">soil</span> <span class="hlt">moisture</span> contents near the ground surface. The input to train the neural network is synthetic aperture radar (SAR) pixel value, and field measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span>, and vegetation, which were used as a surrogate for surface roughness. Once the network was trained, <span class="hlt">soil</span> <span class="hlt">moisture</span> predictions were made based on SAR pixel value and vegetation. These results were then used for comparison with results from the hydrologic model. The quality of neural network input was less than anticipated. Our digital elevation model (DEM) was not of high enough resolution to allow exact co-registration with <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements; therefore, the statistical correlations were not as good as hoped. However, the spatial pattern of the SAR derived <span class="hlt">soil</span> <span class="hlt">moisture</span> contents compares favorably with the hydrologic MATH model results. Primary surface parameters that effect SAR include topography, surface roughness, vegetation cover and <span class="hlt">soil</span> texture. Single parameters that are considered to influence SAR include incident angle of the radar, polarization of the radiation, signal strength and returning signal integration, to name a few. These factors influence the reflectance, but if one adequately quantifies the influences of terrain and roughness, it is considered possible to extract information on <span class="hlt">soil</span> <span class="hlt">moisture</span> from SAR imagery analysis and in turn use SAR imagery to validate hydrologic models</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.9844J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.9844J"><span>In situ <span class="hlt">soil</span> <span class="hlt">moisture</span> and matrix potential - what do we measure?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jackisch, Conrad; Durner, Wolfgang</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> and matric potential are often regarded as state variables that are simple to monitor at the Darcy-scale. At the same time unproven believes about the capabilities and reliabilities of specific sensing methods or sensor systems exist. A consortium of ten institutions conducted a comparison study of currently available sensors for <span class="hlt">soil</span> <span class="hlt">moisture</span> and matrix potential at a specially homogenised field site with sandy loam <span class="hlt">soil</span>, which was kept free of vegetation. In total 57 probes of 15 different systems measuring <span class="hlt">soil</span> <span class="hlt">moisture</span>, and 50 probes of 14 different systems measuring matric potential have been installed in a 0.5 meter grid to monitor the <span class="hlt">moisture</span> state in 0.2 meter depth. The results give rise to a series of substantial questions about the state of the art in hydrological monitoring, the heterogeneity problem and the meaning of <span class="hlt">soil</span> water retention at the field scale: A) For <span class="hlt">soil</span> <span class="hlt">moisture</span>, most sensors recorded highly plausible data. However, they do not agree in <span class="hlt">absolute</span> values and reaction timing. For matric potential, only tensiometers were able to capture the quick reactions during rainfall events. All indirect sensors reacted comparably slowly and thus introduced a bias with respect to the sensing of <span class="hlt">soil</span> water state under highly dynamic conditions. B) Under natural field conditions, a better homogeneity than in our setup can hardly be realised. While the homogeneity assumption held for the first weeks, it collapsed after a heavy storm event. The event exceeded the infiltration capacity, initiated the generation of redistribution networks at the surface, which altered the local surface properties on a very small scale. If this is the reality at a 40 m2 plot, what representativity have single point observations referencing the state of whole basins? C) A comparison of in situ and lab-measured retention curves marks systematic differences. Given the general practice of <span class="hlt">soil</span> water retention parameterisation in almost any hydrological model this</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 affected by the hummock-and-...</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/2018HESS...22.2255C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018HESS...22.2255C"><span>Using lagged dependence to identify (de)coupled surface and subsurface <span class="hlt">soil</span> <span class="hlt">moisture</span> values</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Carranza, Coleen D. U.; van der Ploeg, Martine J.; Torfs, Paul J. J. F.</p> <p>2018-04-01</p> <p>Recent advances in radar remote sensing popularized the mapping of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at different spatial scales. Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements are used in combination with hydrological models to determine subsurface <span class="hlt">soil</span> <span class="hlt">moisture</span> values. However, variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> across the <span class="hlt">soil</span> column is important for estimating depth-integrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled <span class="hlt">soil</span> <span class="hlt">moisture</span> values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970014647','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970014647"><span>Retrieval of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Roughness from the Polarimetric Radar Response</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sarabandi, Kamal; Ulaby, Fawwaz T.</p> <p>1997-01-01</p> <p>The main objective of this investigation was the characterization of <span class="hlt">soil</span> <span class="hlt">moisture</span> using imaging radars. In order to accomplish this task, a number of intermediate steps had to be undertaken. In this proposal, the theoretical, numerical, and experimental aspects of electromagnetic scattering from natural surfaces was considered with emphasis on remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. In the general case, the microwave backscatter from natural surfaces is mainly influenced by three major factors: (1) the roughness statistics of the <span class="hlt">soil</span> surface, (2) <span class="hlt">soil</span> <span class="hlt">moisture</span> content, and (3) <span class="hlt">soil</span> surface cover. First the scattering problem from bare-<span class="hlt">soil</span> surfaces was considered and a hybrid model that relates the radar backscattering coefficient to <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness was developed. This model is based on extensive experimental measurements of the radar polarimetric backscatter response of bare <span class="hlt">soil</span> surfaces at microwave frequencies over a wide range of <span class="hlt">moisture</span> conditions and roughness scales in conjunction with existing theoretical surface scattering models in limiting cases (small perturbation, physical optics, and geometrical optics models). Also a simple inversion algorithm capable of providing accurate estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span> content and surface rms height from single-frequency multi-polarization radar observations was developed. The accuracy of the model and its inversion algorithm is demonstrated using independent data sets. Next the hybrid model for bare-<span class="hlt">soil</span> surfaces is made fully polarimetric by incorporating the parameters of the co- and cross-polarized phase difference into the model. Experimental data in conjunction with numerical simulations are used to relate the <span class="hlt">soil</span> <span class="hlt">moisture</span> content and surface roughness to the phase difference statistics. For this purpose, a novel numerical scattering simulation for inhomogeneous dielectric random surfaces was developed. Finally the scattering problem of short vegetation cover above a rough <span class="hlt">soil</span> surface was</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=344643','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=344643"><span>Spatially enhanced passive microwave derived <span class="hlt">soil</span> <span class="hlt">moisture</span>: capabilities and opportunities</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Low frequency passive microwave remote sensing is a proven technique for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval, but its coarse resolution restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution <span class="hlt">soil</span> <span class="hlt">moistur</span>...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://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/2015EGUGA..1712179T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712179T"><span>Coupling rainfall observations and satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> for predicting event <span class="hlt">soil</span> loss in Central Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Todisco, Francesca; Brocca, Luca; Termite, Loris Francesco; Wagner, Wolfgang</p> <p>2015-04-01</p> <p>The accuracy of water <span class="hlt">soil</span> loss prediction depends on the ability of the model to account for effects of the physical phenomena causing the output and the accuracy by which the parameters have been determined. The process based models require considerable effort to obtain appropriate parameter values and their failure to produce better results than achieved using the USLE/RUSLE model, encourages the use of the USLE/RUSLE model in roles of which it was not designed. In particular it is widely used in watershed models even at the event temporal scale. At hillslope scale, spatial variability in <span class="hlt">soil</span> and vegetation result in spatial variations in <span class="hlt">soil</span> <span class="hlt">moisture</span> and consequently in runoff within the area for which <span class="hlt">soil</span> loss estimation is required, so the modeling approach required to produce those estimates needs to be sensitive to those spatial variations in runoff. Some models include explicit consideration of runoff in determining the erosive stresses but this increases the uncertainty of the prediction due to the difficulty in parameterising the models also because the direct measures of surface runoff are rare. The same remarks are effective also for the USLE/RUSLE models including direct consideration of runoff in the erosivity factor (i.e. USLE-M by Kinnell and Risse, 1998, and USLE-MM by Bagarello et al., 2008). Moreover actually most of the rainfall-runoff models are based on the knowledge of the pre-event <span class="hlt">soil</span> <span class="hlt">moisture</span> that is a fundamental variable in the rainfall-runoff transformation. In addiction <span class="hlt">soil</span> <span class="hlt">moisture</span> is a readily available datum being possible to have easily direct pre-event measures of <span class="hlt">soil</span> <span class="hlt">moisture</span> using in situ sensors or satellite observations at larger spatial scale; it is also possible to derive the antecedent water content with <span class="hlt">soil</span> <span class="hlt">moisture</span> simulation models. The attempt made in the study is to use the pre-event <span class="hlt">soil</span> <span class="hlt">moisture</span> to account for the spatial variation in runoff within the area for which the <span class="hlt">soil</span> loss estimates are required. More</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006PhDT........51N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006PhDT........51N"><span>High resolution change estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its assimilation into a land surface model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Narayan, Ujjwal</p> <p></p> <p>Near surface <span class="hlt">soil</span> <span class="hlt">moisture</span> plays an important role in hydrological processes including infiltration, evapotranspiration and runoff. These processes depend non-linearly on <span class="hlt">soil</span> <span class="hlt">moisture</span> and hence sub-pixel scale <span class="hlt">soil</span> <span class="hlt">moisture</span> variability characterization is important for accurate modeling of water and energy fluxes at the pixel scale. Microwave remote sensing has evolved as an attractive technique for global monitoring of near surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. A radiative transfer model has been tested and validated for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval from passive microwave remote sensing data under a full range of vegetation water content conditions. It was demonstrated that <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval errors of approximately 0.04 g/g gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> are attainable with vegetation water content as high as 5 kg/m2. Recognizing the limitation of low spatial resolution associated with passive sensors, an algorithm that uses low resolution passive microwave (radiometer) and high resolution active microwave (radar) data to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> change at the spatial resolution of radar operation has been developed and applied to coincident Passive and Active L and S band (PALS) and Airborne Synthetic Aperture Radar (AIRSAR) datasets acquired during the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiments in 2002 (SMEX02) campaign with root mean square error of 10% and a 4 times enhancement in spatial resolution. The change estimation algorithm has also been used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> change at 5 km resolution using AMSR-E <span class="hlt">soil</span> <span class="hlt">moisture</span> product (50 km) in conjunction with the TRMM-PR data (5 km) for a 3 month period demonstrating the possibility of high resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> change estimation using satellite based data. <span class="hlt">Soil</span> <span class="hlt">moisture</span> change is closely related to precipitation and <span class="hlt">soil</span> hydraulic properties. A simple assimilation framework has been implemented to investigate whether assimilation of surface layer <span class="hlt">soil</span> <span class="hlt">moisture</span> change observations into a hydrologic model will potentially improve it</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/2016EGUGA..18..845M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18..845M"><span>Temporal changes of spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns: controlling factors explained with a multidisciplinary approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Martini, Edoardo; Wollschläger, Ute; Kögler, Simon; Behrens, Thorsten; Dietrich, Peter; Reinstorf, Frido; Schmidt, Karsten; Weiler, Markus; Werban, Ulrike; Zacharias, Steffen</p> <p>2016-04-01</p> <p>Characterizing the spatial patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> is critical for hydrological and meteorological models, as <span class="hlt">soil</span> <span class="hlt">moisture</span> is a key variable that controls matter and energy fluxes and <span class="hlt">soil</span>-vegetation-atmosphere exchange processes. Deriving detailed process understanding at the hillslope scale is not trivial, because of the temporal variability of local <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Nevertheless, it remains a challenge to provide adequate information on the temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its controlling factors. Recent advances in wireless sensor technology allow monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics with high temporal resolution at varying scales. In addition, mobile geophysical methods such as electromagnetic induction (EMI) have been widely used for mapping <span class="hlt">soil</span> water content at the field scale with high spatial resolution, as being related to <span class="hlt">soil</span> apparent electrical conductivity (ECa). The objective of this study was to characterize the spatial and temporal pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the hillslope scale and to infer the controlling hydrological processes, integrating well established and innovative sensing techniques, as well as new statistical methods. We combined <span class="hlt">soil</span> hydrological and pedological expertise with geophysical measurements and methods from digital <span class="hlt">soil</span> mapping for designing a wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring network. For a hillslope site within the Schäfertal catchment (Central Germany), <span class="hlt">soil</span> water dynamics were observed during 14 months, and <span class="hlt">soil</span> ECa was mapped on seven occasions whithin this period of time using an EM38-DD device. Using the Spearman rank correlation coefficient, we described the temporal persistence of a dry and a wet characteristic state of <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as the switching mechanisms, inferring the local properties that control the observed spatial patterns and the hydrological processes driving the transitions. Based on this, we evaluated the use of EMI for mapping the spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> under</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760009508','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760009508"><span>Ground truth report 1975 Phoenix microwave experiment. [Joint <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blanchard, B. J.</p> <p>1975-01-01</p> <p>Direct measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> obtained in conjunction with aircraft data flights near Phoenix, Arizona in March, 1975 are summarized. The data were collected for the Joint <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.1828R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.1828R"><span>Experimental evidence and modelling of drought induced alternative stable <span class="hlt">soil</span> <span class="hlt">moisture</span> states</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Robinson, David; Jones, Scott; Lebron, Inma; Reinsch, Sabine; Dominguez, Maria; Smith, Andrew; Marshal, Miles; Emmett, Bridget</p> <p>2017-04-01</p> <p>The theory of alternative stable states in ecosystems is well established in ecology; however, evidence from manipulation experiments supporting the theory is limited. Developing the evidence base is important because it has profound implications for ecosystem management. Here we show evidence of the existence of alternative stable <span class="hlt">soil</span> <span class="hlt">moisture</span> states induced by drought in an upland wet heath. We used a long-term (15 yrs) climate change manipulation experiment with moderate sustained drought, which reduced the ability of the <span class="hlt">soil</span> to retain <span class="hlt">soil</span> <span class="hlt">moisture</span> by degrading the <span class="hlt">soil</span> structure, reducing <span class="hlt">moisture</span> retention. Moreover, natural intense droughts superimposed themselves on the experiment, causing an unexpected additional alternative <span class="hlt">soil</span> <span class="hlt">moisture</span> state to develop, both for the drought manipulation and control plots; this impaired the <span class="hlt">soil</span> from rewetting in winter. Our results show the coexistence of three stable states. Using modelling with the Hydrus 1D software package we are able to show the circumstances under which shifts in <span class="hlt">soil</span> <span class="hlt">moisture</span> states are likely to occur. Given the new understanding it presents a challenge of how to incorporate feedbacks, particularly related to <span class="hlt">soil</span> structure, into <span class="hlt">soil</span> flow and transport models?</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=254033','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=254033"><span>Evaluating Remotely-Sensed <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals Using Triple Collocation Techniques</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 validation is footprint-scale (~40 km) surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from space is complicated by a lack of ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> instrumentation and challenges associated with up-scaling point-scale measurements from such instrumentation. Recent work has demonstrated the potential of e...</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('https://www.ncbi.nlm.nih.gov/pubmed/28794995','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28794995"><span>A method for <span class="hlt">soil</span> <span class="hlt">moisture</span> probes calibration and validation of satellite estimates.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Holzman, Mauro; Rivas, Raúl; Carmona, Facundo; Niclòs, Raquel</p> <p>2017-01-01</p> <p>Optimization of field techniques is crucial to ensure high quality <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The aim of the work is to present a sampling method for undisturbed <span class="hlt">soil</span> and <span class="hlt">soil</span> water content to calibrated <span class="hlt">soil</span> <span class="hlt">moisture</span> probes, in a context of the SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity) mission MIRAS Level 2 <span class="hlt">soil</span> <span class="hlt">moisture</span> product validation in Pampean Region of Argentina. The method avoids <span class="hlt">soil</span> alteration and is recommended to calibrated probes based on <span class="hlt">soil</span> type under a freely drying process at ambient temperature. A detailed explanation of field and laboratory procedures to obtain reference <span class="hlt">soil</span> <span class="hlt">moisture</span> is shown. The calibration results reflected accurate operation for the Delta-T thetaProbe ML2x probes in most of analyzed cases (RMSE and bias ≤ 0.05 m 3 /m 3 ). Post-calibration results indicated that the accuracy improves significantly applying the adjustments of the calibration based on <span class="hlt">soil</span> types (RMSE ≤ 0.022 m 3 /m 3 , bias ≤ -0.010 m 3 /m 3 ). •A sampling method that provides high quality data of <span class="hlt">soil</span> water content for calibration of probes is described.•Importance of calibration based on <span class="hlt">soil</span> types.•A calibration process for similar <span class="hlt">soil</span> types could be suitable in practical terms, depending on the required accuracy level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29507383','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29507383"><span>Negative <span class="hlt">soil</span> <span class="hlt">moisture</span>-precipitation feedback in dry and wet regions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yang, Lingbin; Sun, Guoqing; Zhi, Lu; Zhao, Jianjun</p> <p>2018-03-05</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span>-precipitation (SM-P) feedback significantly influences the terrestrial water and energy cycles. However, the sign of the feedback and the associated physical mechanism have been debated, leaving a research gap regarding global water and climate changes. Based on Koster's framework, we estimate SM-P feedback using satellite remote sensing and ground observation data sets. Methodologically, the sign of the feedback is identified by the correlation between monthly <span class="hlt">soil</span> <span class="hlt">moisture</span> and next-month precipitation. The physical mechanism is investigated through coupling precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> (P-SM), <span class="hlt">soil</span> <span class="hlt">moisture</span> ad evapotranspiration (SM-E) and evapotranspiration and precipitation (E-P) correlations. Our results demonstrate that although positive SM-P feedback is predominant over land, non-negligible negative feedback occurs in dry and wet regions. Specifically, 43.75% and 40.16% of the negative feedback occurs in the arid and humid climate zones. Physically, negative SM-P feedback depends on the SM-E correlation. In dry regions, evapotranspiration change is <span class="hlt">soil</span> <span class="hlt">moisture</span> limited. In wet regions, evapotranspiration change is energy limited. We conclude that the complex SM-E correlation results in negative SM-P feedback in dry and wet regions, and the cause varies based on the environmental and climatic conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H13I1518M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H13I1518M"><span>Compact polarimetric synthetic aperture radar for monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span> condition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Merzouki, A.; McNairn, H.; Powers, J.; Friesen, M.</p> <p>2017-12-01</p> <p>Coarse resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> maps are currently operationally delivered by ESA's SMOS and NASA's SMAP passive microwaves sensors. Despite this evolution, operational <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring at the field scale remains challenging. A number of factors contribute to this challenge including the complexity of the retrieval that requires advanced SAR systems with enhanced temporal revisit capabilities. Since the launch of RADARSAT-2 in 2007, Agriculture and Agri-Food Canada (AAFC) has been evaluating the accuracy of these data for estimating surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Thus, a hybrid (multi-angle/multi-polarization) retrieval approach was found well suited for the planned RADARSAT Constellation Mission (RCM) considering the more frequent relook expected with the three satellite configuration. The purpose of this study is to evaluate the capability of C-band CP data to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> over agricultural fields, in anticipation of the launch of RCM. In this research we introduce a new CP approach based on the IEM and simulated RCM CP mode intensities from RADARSAT-2 images acquired at different dates. The accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval from the proposed multi-polarization and hybrid methods will be contrasted with that from a more conventional quad-pol approach, and validated against in situ measurements by pooling data collected over AAFC test sites in Ontario, Manitoba and Saskatchewan, Canada.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018WRR....54.1476A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018WRR....54.1476A"><span>Hydrological Storage Length Scales Represented by Remote Sensing Estimates of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Akbar, Ruzbeh; Short Gianotti, Daniel; McColl, Kaighin A.; Haghighi, Erfan; Salvucci, Guido D.; Entekhabi, Dara</p> <p>2018-03-01</p> <p>The <span class="hlt">soil</span> water content profile is often well correlated with the <span class="hlt">soil</span> <span class="hlt">moisture</span> state near the surface. They share mutual information such that analysis of surface-only <span class="hlt">soil</span> <span class="hlt">moisture</span> is, at times and in conjunction with precipitation information, reflective of deeper <span class="hlt">soil</span> fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and <span class="hlt">soil</span> water dynamics evident in surface-only <span class="hlt">soil</span> <span class="hlt">moisture</span> observations. To proceed, first an observation-based technique is presented to estimate the <span class="hlt">soil</span> <span class="hlt">moisture</span> loss function based on analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> dry-downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface <span class="hlt">soil</span> <span class="hlt">moisture</span> observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates are obtained from the NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. <span class="hlt">Soil</span> properties, especially texture in the form of sand fraction, as well as the mean <span class="hlt">soil</span> <span class="hlt">moisture</span> state have a lesser influence on the length scale.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=304546','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=304546"><span>Evaluation of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products over the CanEx-SM10 area</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) Earth observation satellite was launched in November 2009 to provide global <span class="hlt">soil</span> <span class="hlt">moisture</span> and ocean salinity measurements based on L-Band passive microwave measurements. Since its launch, different versions of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products processors have be...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=335031','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=335031"><span>Evaluating <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Two satellites are currently monitoring surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) from L-band observations: SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity), a European Space Agency (ESA) satellite that was launched on November 2, 2009 and SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive), a National Aeronautics and Space Administration...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=320076','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=320076"><span>Estimating error cross-correlations in <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets using extended collocation analysis</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Consistent global <span class="hlt">soil</span> <span class="hlt">moisture</span> records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> data into water balance models or merging multi-source <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals int...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241335&keyword=square&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241335&keyword=square&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Identification of optimal <span class="hlt">soil</span> hydraulic functions and parameters for predicting <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>We examined the accuracy of several commonly used <span class="hlt">soil</span> hydraulic functions and associated parameters for predicting observed <span class="hlt">soil</span> <span class="hlt">moisture</span> data. We used six combined methods formed by three commonly used <span class="hlt">soil</span> hydraulic functions – i.e., Brooks and Corey (1964) (BC), Campbell (19...</p> </li> </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('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-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-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('http://hdl.handle.net/2060/19810012896','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810012896"><span>Multispectral determination of <span class="hlt">soil</span> <span class="hlt">moisture</span>. [Guymon, Oklahoma</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Estes, J. E.; Simonett, D. S. (Principal Investigator); Hajic, E. J.; Blanchard, B. J.</p> <p>1980-01-01</p> <p>The edited Guymon <span class="hlt">soil</span> <span class="hlt">moisture</span> data collected on August 2, 5, 14, 17, 1978 were grouped into four field cover types for statistical analysis. These are the bare, milo with rows parallel to field of view, milo with rows perpendicular to field of view and alfalfa cover groups. There are 37, 22, 24 and 14 observations respectively in each group for each sensor channel and each <span class="hlt">soil</span> <span class="hlt">moisture</span> layer. A subset of these data called the 'five cover set' (VEG5) limited the scatterometer data to the 15 deg look angle and was used to determine discriminant functions and combined group regressions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140012456','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140012456"><span>Assimilation of SMOS Retrieved <span class="hlt">Soil</span> <span class="hlt">Moisture</span> into the Land Information System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.</p> <p>2014-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with <span class="hlt">soil</span> <span class="hlt">moisture</span> observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for <span class="hlt">soil</span> <span class="hlt">moisture</span> is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to <span class="hlt">soil</span> <span class="hlt">moisture</span> concentration in roughly the top 2.5 cm of <span class="hlt">soil</span>. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen <span class="hlt">soil</span>, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H41D0909Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.H41D0909Z"><span>COSMOS: COsmic-ray <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observing System planned for 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>Zweck, C.; Zreda, M.; Shuttleworth, J.; Zeng, X.</p> <p>2008-12-01</p> <p>Because <span class="hlt">soil</span> water exerts a critical control on weather, climate, ecosystem, and water cycle, understanding <span class="hlt">soil</span> <span class="hlt">moisture</span> changes in time and space is crucial for many fields within natural sciences. A serious handicap in <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements is the mismatch between limited point measurements using contact methods and remote sensing estimates over large areas. We present a novel method to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> non- invasively at an intermediate spatial scale that will alleviate this problem. The method takes advantage of the dependence of cosmic-ray neutron intensity on the hydrogen content of <span class="hlt">soils</span> (Zreda et al., Geophysical Research Letters, accepted). Low-energy cosmic-ray neutrons are produced and moderated in the <span class="hlt">soil</span>, transported from the <span class="hlt">soil</span> into the atmosphere where they are measured with a cosmic-ray neutron probe to provide integrated <span class="hlt">soil</span> <span class="hlt">moisture</span> content over a footprint of several hundred meters and a depth of a few decimeters. The method and the instrument are intended for deployment in the continental-scale COSMOS network that is designed to cover the contiguous region of the USA. Fully deployed, the COSMOS network will consist of up to 500 probes, and will provide continuous <span class="hlt">soil</span> <span class="hlt">moisture</span> content (together with atmospheric pressure, temperature and relative humidity) measured and reported hourly. These data will be used for initialization and assimilation of <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions in weather and short-term (seasonal) climate forecasting, and for other land-surface applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H53G1738D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H53G1738D"><span>L-band <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Mapping using Small UnManned Aerial Systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dai, E.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts water resource management, agriculture, and flood runoff prediction. The launch of NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active/Passive (SMAP) mission in 2015 promises to provide global measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface freeze/thaw state at fixed crossing times and spatial resolutions as low as 5 km for some products. However, there exists a need for measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> on smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping <span class="hlt">soil</span> <span class="hlt">moisture</span> on spatial scales as small as several meters (i.e., the height of the platform) .Compared with various other proposed methods of validation based on either situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed (~km scale) coverage at very high spatial resolution (~15 m) suitable for scaling scale studies, and at comparatively low operator cost. The LDCR on Tempest unit can supply the <span class="hlt">soil</span> <span class="hlt">moisture</span> mapping with different resolution which is of order the Tempest altitude.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/5151','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/5151"><span>Using <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> to predict forest <span class="hlt">soil</span> nitrogen mineralization</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Jennifer D. Knoepp; Wayne T. Swank</p> <p>2002-01-01</p> <p>Due to the importance of N in forest productivity ecosystem and nutrient cycling research often includes measurement of <span class="hlt">soil</span> N transformation rates as indices of potential availability and ecosystem losses of N. We examined the feasibility of using <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> content to predict <span class="hlt">soil</span> N mineralization rates (Nmin) at the Coweeta Hydrologic Laboratory...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMEP41C0924C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMEP41C0924C"><span>Estimating <span class="hlt">soil</span> <span class="hlt">moisture</span> exceedance probability from antecedent rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cronkite-Ratcliff, C.; Kalansky, J.; Stock, J. D.; Collins, B. D.</p> <p>2016-12-01</p> <p>The first storms of the rainy season in coastal California, USA, add <span class="hlt">moisture</span> to <span class="hlt">soils</span> but rarely trigger landslides. Previous workers proposed that antecedent rainfall, the cumulative seasonal rain from October 1 onwards, had to exceed specific amounts in order to trigger landsliding. Recent monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> upslope of historic landslides in the San Francisco Bay Area shows that storms can cause positive pressure heads once <span class="hlt">soil</span> <span class="hlt">moisture</span> values exceed a threshold of volumetric water content (VWC). We propose that antecedent rainfall could be used to estimate the probability that VWC exceeds this threshold. A major challenge to estimating the probability of exceedance is that rain gauge records are frequently incomplete. We developed a stochastic model to impute (infill) missing hourly precipitation data. This model uses nearest neighbor-based conditional resampling of the gauge record using data from nearby rain gauges. Using co-located VWC measurements, imputed data can be used to estimate the probability that VWC exceeds a specific threshold for a given antecedent rainfall. The stochastic imputation model can also provide an estimate of uncertainty in the exceedance probability curve. Here we demonstrate the method using <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation data from several sites located throughout Northern California. Results show a significant variability between sites in the sensitivity of VWC exceedance probability to antecedent rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20100002940&hterms=soil+layers&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsoil%2Blayers','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20100002940&hterms=soil+layers&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsoil%2Blayers"><span>Role of Subsurface Physics in the Assimilation of Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reichle, R. H.</p> <p>2010-01-01</p> <p>Root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> controls the land-atmosphere exchange of water and energy and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface <span class="hlt">soil</span> <span class="hlt">moisture</span> observations into a land surface model is an effective way to estimate large-scale root zone <span class="hlt">soil</span> <span class="hlt">moisture</span>. The propagation of surface information into deeper <span class="hlt">soil</span> layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments we assimilate synthetic surface <span class="hlt">soil</span> <span class="hlt">moisture</span> observations into four different models (Catchment, Mosaic, Noah and CLM) using the Ensemble Kalman Filter. We demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> observations. The second key result indicates that the potential of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Our experiments also suggest that (faced with unknown true subsurface physics) overestimating surface to root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Finally, the skill improvements through assimilation were found to be sensitive to the regional climate and <span class="hlt">soil</span> types.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H13I1520H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H13I1520H"><span>Evaluation of Remote Sensing and Hydrological Model Based <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Datasets in Drought Perspective</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hüsami Afşar, M.; Bulut, B.; Yilmaz, M. T.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is one of the fundamental parameters of the environment that plays a major role in carbon, energy, and water cycles. Spatial distribution and temporal changes of <span class="hlt">soil</span> <span class="hlt">moisture</span> is one of the important components in climatic, ecological and natural hazards at global, regional and local levels scales. Therefore retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets has a great importance in these studies. Given <span class="hlt">soil</span> <span class="hlt">moisture</span> can be retrieved through different platforms (i.e., in-situ measurements, numerical modeling, and remote sensing) for the same location and time period, it is often desirable to evaluate these different datasets to assign the most accurate estimates for different purposes. During last decades, efforts have been given to provide evaluations about different <span class="hlt">soil</span> <span class="hlt">moisture</span> products based on various statistical analysis of the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series (i.e., comparison of correlation, bias, and their error standard deviation). On the other hand, there is still need for the comparisons of the <span class="hlt">soil</span> <span class="hlt">moisture</span> products in drought analysis context. In this study, LPRM and NOAH Land Surface Model <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets are investigated in drought analysis context using station-based watershed average datasets obtained over four USDA ARS watersheds as ground truth. Here, the drought analysis are performed using the standardized <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets (i.e., zero mean and one standard deviation) while the droughts are defined as consecutive negative anomalies less than -1 for longer than 3 months duration. Accordingly, the drought characteristics (duration and severity) and false alarm and hit/miss ratios of LPRM and NOAH datasets are validated using station-based datasets as ground truth. Results showed that although the NOAH <span class="hlt">soil</span> <span class="hlt">moisture</span> products have better correlations, LPRM based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals show better consistency in drought analysis. This project is supported by TUBITAK Project number 114Y676.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ACP....18.6413H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ACP....18.6413H"><span>Assessing the uncertainty of <span class="hlt">soil</span> <span class="hlt">moisture</span> impacts on convective precipitation using a new ensemble approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Henneberg, Olga; Ament, Felix; Grützun, Verena</p> <p>2018-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> amount and distribution control evapotranspiration and thus impact the occurrence of convective precipitation. Many recent model studies demonstrate that changes in initial <span class="hlt">soil</span> <span class="hlt">moisture</span> content result in modified convective precipitation. However, to quantify the resulting precipitation changes, the chaotic behavior of the atmospheric system needs to be considered. Slight changes in the simulation setup, such as the chosen model domain, also result in modifications to the simulated precipitation field. This causes an uncertainty due to stochastic variability, which can be large compared to effects caused by <span class="hlt">soil</span> <span class="hlt">moisture</span> variations. By shifting the model domain, we estimate the uncertainty of the model results. Our novel uncertainty estimate includes 10 simulations with shifted model boundaries and is compared to the effects on precipitation caused by variations in <span class="hlt">soil</span> <span class="hlt">moisture</span> amount and local distribution. With this approach, the influence of <span class="hlt">soil</span> <span class="hlt">moisture</span> amount and distribution on convective precipitation is quantified. Deviations in simulated precipitation can only be attributed to <span class="hlt">soil</span> <span class="hlt">moisture</span> impacts if the systematic effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> modifications are larger than the inherent simulation uncertainty at the convection-resolving scale. We performed seven experiments with modified <span class="hlt">soil</span> <span class="hlt">moisture</span> amount or distribution to address the effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on precipitation. Each of the experiments consists of 10 ensemble members using the deep convection-resolving COSMO model with a grid spacing of 2.8 km. Only in experiments with very strong modification in <span class="hlt">soil</span> <span class="hlt">moisture</span> do precipitation changes exceed the model spread in amplitude, location or structure. These changes are caused by a 50 % <span class="hlt">soil</span> <span class="hlt">moisture</span> increase in either the whole or part of the model domain or by drying the whole model domain. Increasing or decreasing <span class="hlt">soil</span> <span class="hlt">moisture</span> both predominantly results in reduced precipitation rates. Replacing the <span class="hlt">soil</span> <span class="hlt">moisture</span> with realistic</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170007422','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170007422"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval with Airborne PALS Instrument over Agricultural Areas in SMAPVEX16</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Colliander, Andreas; Jackson, Thomas J.; Cosh, Mike; Misra, Sidharth; Bindlish, Rajat; Powers, Jarrett; McNairn, Heather; Bullock, P.; Berg, A.; Magagi, A.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20170007422'); toggleEditAbsImage('author_20170007422_show'); toggleEditAbsImage('author_20170007422_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20170007422_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20170007422_hide"></p> <p>2017-01-01</p> <p>NASA's SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive) calibration and validation program revealed that the <span class="hlt">soil</span> <span class="hlt">moisture</span> products are experiencing difficulties in meeting the mission requirements in certain agricultural areas. Therefore, the mission organized airborne field experiments at two core validation sites to investigate these anomalies. The SMAP Validation Experiment 2016 included airborne observations with the PALS (Passive Active L-band Sensor) instrument and intensive ground sampling. The goal of the PALS measurements are to investigate the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm formulation and parameterization under the varying (spatially and temporally) conditions of the agricultural domains and to obtain high resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> maps within the SMAP pixels. In this paper the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval using the PALS brightness temperature observations in SMAPVEX16 is presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.epa.gov/watersense/watersense-soil-moisture-based-control-technologies-notice-intent-noi','PESTICIDES'); return false;" href="https://www.epa.gov/watersense/watersense-soil-moisture-based-control-technologies-notice-intent-noi"><span>WaterSense <span class="hlt">Soil</span> <span class="hlt">Moisture</span>-Based Control Technologies Notice of Intent (NOI)</span></a></p> <p><a target="_blank" href="http://www.epa.gov/pesticides/search.htm">EPA Pesticide Factsheets</a></p> <p></p> <p></p> <p>By directly measuring the amount of <span class="hlt">moisture</span> in the <span class="hlt">soil</span>, <span class="hlt">soil</span> <span class="hlt">moisture</span>-based control technologies tailor irrigation schedules to meet landscape water needs based on seasonal patterns, as well as prevailing conditions in the landscape.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170010214','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170010214"><span>Version 3 of the SMAP Level 4 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reichle, Rolf; Liu, Qing; Ardizzone, Joe; Crow, Wade; De Lannoy, Gabrielle; Kolassa, Jana; Kimball, John; Koster, Randy</p> <p>2017-01-01</p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Level 4 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root zone (0-100 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as related land surface states and fluxes from 31 March 2015 to present with a latency of 2.5 days. The ensemble-based L4_SM algorithm is a variant of the Goddard Earth Observing System version 5 (GEOS-5) land data assimilation system and ingests SMAP L-band (1.4 GHz) Level 1 brightness temperature observations into the Catchment land surface model. The <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis is non-local (spatially distributed), performs downscaling from the 36-km resolution of the observations to that of the model, and respects the relative uncertainties of the modeled and observed brightness temperatures. Prior to assimilation, a climatological rescaling is applied to the assimilated brightness temperatures using a 6 year record of SMOS observations. A new feature in Version 3 of the L4_SM data product is the use of 2 years of SMAP observations for rescaling where SMOS observations are not available because of radio frequency interference, which expands the impact of SMAP observations on the L4_SM estimates into large regions of northern Africa and Asia. This presentation investigates the performance and data assimilation diagnostics of the Version 3 L4_SM data product. The L4_SM <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates meet the 0.04 m3m3 (unbiased) RMSE requirement. We further demonstrate that there is little bias in the <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis. Finally, we illustrate where the assimilation system overestimates or underestimates the actual errors in the system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H13C1121A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H13C1121A"><span>Inter-Annual Variability of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Stress Function in the Wheat Field</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Akuraju, V. R.; Ryu, D.; George, B.; Ryu, Y.; Dassanayake, K. B.</p> <p>2014-12-01</p> <p>Root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> content is a key variable that controls the exchange of water and energy fluxes between land and atmosphere. In the <span class="hlt">soil</span>-vegetation-atmosphere transfer (SVAT) schemes, the influence of root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> on evapotranspiration (ET) is parameterized by the <span class="hlt">soil</span> <span class="hlt">moisture</span> stress function (SSF). Dependence of actual ET: potential ET (fPET) or evaporative fraction to the root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> via SSF can also be used inversely to estimate root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> when fPET is estimated by remotely sensed land surface states. In this work we present fPET versus available <span class="hlt">soil</span> water (ASW) in the root zone observed in the experimental farm sites in Victoria, Australia in 2012-2013. In the wheat field site, fPET vs ASW exhibited distinct features for different <span class="hlt">soil</span> depth, net radiation, and crop growth stages. Interestingly, SSF in the wheat field presented contrasting shapes for two cropping years of 2012 and 2013. We argue that different temporal patterns of rainfall (and resulting <span class="hlt">soil</span> <span class="hlt">moisture</span>) during the growing seasons in 2012 and 2013 are responsible for the distinctive SSFs. SSF of the wheat field was simulated by the Agricultural Production Systems sIMulator (APSIM). The APSIM was able to reproduce the observed fPET vs. ASW. We discuss implications of our findings for existing modeling and (inverse) remote sensing approaches relying on SSF and alternative growth-stage-dependent SSFs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..551..203K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..551..203K"><span>Automated general temperature correction method for dielectric <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kapilaratne, R. G. C. Jeewantinie; Lu, Minjiao</p> <p>2017-08-01</p> <p>An effective temperature correction method for dielectric sensors is important to ensure the accuracy of <span class="hlt">soil</span> water content (SWC) measurements of local to regional-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring networks. These networks are extensively using highly temperature sensitive dielectric sensors due to their low cost, ease of use and less power consumption. Yet there is no general temperature correction method for dielectric sensors, instead sensor or site dependent correction algorithms are employed. Such methods become ineffective at <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring networks with different sensor setups and those that cover diverse climatic conditions and <span class="hlt">soil</span> types. This study attempted to develop a general temperature correction method for dielectric sensors which can be commonly used regardless of the differences in sensor type, climatic conditions and <span class="hlt">soil</span> type without rainfall data. In this work an automated general temperature correction method was developed by adopting previously developed temperature correction algorithms using time domain reflectometry (TDR) measurements to ThetaProbe ML2X, Stevens Hydra probe II and Decagon Devices EC-TM sensor measurements. The rainy day effects removal procedure from SWC data was automated by incorporating a statistical inference technique with temperature correction algorithms. The temperature correction method was evaluated using 34 stations from the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring Network and another nine stations from a local <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring network in Mongolia. <span class="hlt">Soil</span> <span class="hlt">moisture</span> monitoring networks used in this study cover four major climates and six major <span class="hlt">soil</span> types. Results indicated that the automated temperature correction algorithms developed in this study can eliminate temperature effects from dielectric sensor measurements successfully even without on-site rainfall data. Furthermore, it has been found that actual daily average of SWC has been changed due to temperature effects of dielectric sensors with a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26098548','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26098548"><span><span class="hlt">Soil</span> Tillage Management Affects Maize Grain Yield by Regulating Spatial Distribution Coordination of Roots, <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Nitrogen Status.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Xinbing; Zhou, Baoyuan; Sun, Xuefang; Yue, Yang; Ma, Wei; Zhao, Ming</p> <p>2015-01-01</p> <p>The spatial distribution of the root system through the <span class="hlt">soil</span> profile has an impact on <span class="hlt">moisture</span> and nutrient uptake by plants, affecting growth and productivity. The spatial distribution of the roots, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and fertility are affected by tillage practices. The combination of high <span class="hlt">soil</span> density and the presence of a <span class="hlt">soil</span> plow pan typically impede the growth of maize (Zea mays L.).We investigated the spatial distribution coordination of the root system, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and N status in response to different <span class="hlt">soil</span> tillage treatments (NT: no-tillage, RT: rotary-tillage, SS: subsoiling) and the subsequent impact on maize yield, and identify yield-increasing mechanisms and optimal <span class="hlt">soil</span> 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 <span class="hlt">soil</span> bulk density in the top 0-20 cm layer of the <span class="hlt">soil</span> profile, while SS significantly decreased <span class="hlt">soil</span> bulk density in the 20-30 cm layer. <span class="hlt">Soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> depth increased. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was reduced in the <span class="hlt">soil</span> profile where root concentration was high. SS had greater <span class="hlt">soil</span> <span class="hlt">moisture</span> depletion and a more concentration root system than RT and NT in deep <span class="hlt">soil</span>. Our results suggest that the SS treatment improved the spatial distribution of root density, <span class="hlt">soil</span> <span class="hlt">moisture</span> and N states, thereby promoting the absorption of <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> profile, played a pivotal role in plants' ability to access nutrients and water. An optimal</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4476672','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4476672"><span><span class="hlt">Soil</span> Tillage Management Affects Maize Grain Yield by Regulating Spatial Distribution Coordination of Roots, <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Nitrogen Status</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wang, Xinbing; Zhou, Baoyuan; Sun, Xuefang; Yue, Yang; Ma, Wei; Zhao, Ming</p> <p>2015-01-01</p> <p>The spatial distribution of the root system through the <span class="hlt">soil</span> profile has an impact on <span class="hlt">moisture</span> and nutrient uptake by plants, affecting growth and productivity. The spatial distribution of the roots, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and fertility are affected by tillage practices. The combination of high <span class="hlt">soil</span> density and the presence of a <span class="hlt">soil</span> plow pan typically impede the growth of maize (Zea mays L.).We investigated the spatial distribution coordination of the root system, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and N status in response to different <span class="hlt">soil</span> tillage treatments (NT: no-tillage, RT: rotary-tillage, SS: subsoiling) and the subsequent impact on maize yield, and identify yield-increasing mechanisms and optimal <span class="hlt">soil</span> 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 <span class="hlt">soil</span> bulk density in the top 0–20 cm layer of the <span class="hlt">soil</span> profile, while SS significantly decreased <span class="hlt">soil</span> bulk density in the 20–30 cm layer. <span class="hlt">Soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> depth increased. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was reduced in the <span class="hlt">soil</span> profile where root concentration was high. SS had greater <span class="hlt">soil</span> <span class="hlt">moisture</span> depletion and a more concentration root system than RT and NT in deep <span class="hlt">soil</span>. Our results suggest that the SS treatment improved the spatial distribution of root density, <span class="hlt">soil</span> <span class="hlt">moisture</span> and N states, thereby promoting the absorption of <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> profile, played a pivotal role in plants’ ability to access nutrients and water. An</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817481V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817481V"><span>Can the normalized <span class="hlt">soil</span> <span class="hlt">moisture</span> index improve the prediction of <span class="hlt">soil</span> organic carbon based on hyperspectral remote sensing data?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>van Wesemael, Bas; Nocita, Marco</p> <p>2016-04-01</p> <p>One of the problems for mapping of <span class="hlt">soil</span> organic carbon (SOC) at large-scale based on visible - near and short wave infrared (VIS-NIR-SWIR) remote sensing techniques is the spatial variation of topsoil <span class="hlt">moisture</span> when the images are collected. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is certainly an aspect causing biased SOC estimations, due to the problems in discriminating reflectance differences due to either variations in organic matter or <span class="hlt">soil</span> <span class="hlt">moisture</span>, or their combination. In addition, the difficult validation procedures make the accurate estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> from optical airborne a major challenge. After all, the first millimeters of the <span class="hlt">soil</span> surface reflect the signal to the airborne sensor and show a large spatial, vertical and temporal variation in <span class="hlt">soil</span> <span class="hlt">moisture</span>. Hence, the difficulty of assessing the <span class="hlt">soil</span> <span class="hlt">moisture</span> of this thin layer at the same moment of the flight. The creation of a <span class="hlt">soil</span> <span class="hlt">moisture</span> proxy, directly retrievable from the hyperspectral data is a priority to improve the large-scale prediction of SOC. This paper aims to verify if the application of the normalized <span class="hlt">soil</span> <span class="hlt">moisture</span> index (NSMI) to Airborne Prima Experiment (APEX) hyperspectral images could improve the prediction of SOC. The study area was located in the loam region of Wallonia, Belgium. About 40 samples were collected from bare fields covered by the flight lines, and analyzed in the laboratory. <span class="hlt">Soil</span> spectra, corresponding to the sample locations, were extracted from the images. Once the NSMI was calculated for the bare fields' pixels, spatial patterns, presumably related to within field <span class="hlt">soil</span> <span class="hlt">moisture</span> variations, were revealed. SOC prediction models, built using raw and pre-treated spectra, were generated from either the full dataset (general model), or pixels belonging to one of the two classes of NSMI values (NSMI models). The best result, with a RMSE after validation of 1.24 g C kg-1, was achieved with a NSMI model, compared to the best general model, characterized by a RMSE of 2.11 g C kg-1. These</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://www.ars.usda.gov/research/publications/publication/?seqNo115=244712','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=244712"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval from 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 observations over land offer an unprecedented opportunity to provide a value-added product, land surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, 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...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1911086P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1911086P"><span>Downscaling <span class="hlt">soil</span> <span class="hlt">moisture</span> over East Asia through multi-sensor data fusion and optimization of regression trees</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Park, Seonyoung; Im, Jungho; Park, Sumin; Rhee, Jinyoung</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is one of the most important keys for understanding regional and global climate systems. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is directly related to agricultural processes as well as hydrological processes because <span class="hlt">soil</span> <span class="hlt">moisture</span> highly influences vegetation growth and determines water supply in the agroecosystem. Accurate monitoring of the spatiotemporal pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> is important. <span class="hlt">Soil</span> <span class="hlt">moisture</span> has been generally provided through in situ measurements at stations. Although field survey from in situ measurements provides accurate <span class="hlt">soil</span> <span class="hlt">moisture</span> with high temporal resolution, it requires high cost and does not provide the spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> over large areas. Microwave satellite (e.g., advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR2), the Advanced Scatterometer (ASCAT), and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP)) -based approaches and numerical models such as Global Land Data Assimilation System (GLDAS) and Modern- Era Retrospective Analysis for Research and Applications (MERRA) provide spatial-temporalspatiotemporally continuous <span class="hlt">soil</span> <span class="hlt">moisture</span> products at global scale. However, since those global <span class="hlt">soil</span> <span class="hlt">moisture</span> products have coarse spatial resolution ( 25-40 km), their applications for agriculture and water resources at local and regional scales are very limited. Thus, <span class="hlt">soil</span> <span class="hlt">moisture</span> downscaling is needed to overcome the limitation of the spatial resolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> products. In this study, GLDAS <span class="hlt">soil</span> <span class="hlt">moisture</span> data were downscaled up to 1 km spatial resolution through the integration of AMSR2 and ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> data, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Moderate Resolution Imaging Spectroradiometer (MODIS) data—Land Surface Temperature, Normalized Difference Vegetation Index, and Land cover—using modified regression trees over East Asia from 2013 to 2015. Modified regression trees were implemented using Cubist, a commercial software tool based on machine learning. An</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..515..330D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..515..330D"><span>Assessing artificial neural networks and statistical methods for infilling missing <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>Dumedah, Gift; Walker, Jeffrey P.; Chik, Li</p> <p>2014-07-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of <span class="hlt">soil</span> <span class="hlt">moisture</span> is required for these applications, the available <span class="hlt">soil</span> <span class="hlt">moisture</span> data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span>, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing <span class="hlt">soil</span> <span class="hlt">moisture</span> records and subsequently validated against known values for 13 <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring stations for three different <span class="hlt">soil</span> layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of <span class="hlt">soil</span> <span class="hlt">moisture</span>, with the capability to account for different <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions.</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('https://www.fs.usda.gov/treesearch/pubs/19797','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/19797"><span>A comparison of <span class="hlt">soil-moisture</span> loss from forested and clearcut areas in West Virginia</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Charles A. Troendle</p> <p>1970-01-01</p> <p><span class="hlt">Soil-moisture</span> losses from forested and clearcut areas were compared on the Fernow Experimental Forest. As expected, hardwood forest <span class="hlt">soils</span> lost most <span class="hlt">moisture</span> while revegetated clearcuttings, clearcuttings, and barren areas lost less, in that order. <span class="hlt">Soil-moisture</span> losses from forested <span class="hlt">soils</span> also correlated well with evapotranspiration and streamflow.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H23J..07B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H23J..07B"><span>Root Water Uptake and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Pattern Dynamics - Capturing Connections, Controls and Causalities</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Blume, T.; Heidbuechel, I.; Hassler, S. K.; Simard, S.; Guntner, A.; Stewart, R. D.; Weiler, M.</p> <p>2015-12-01</p> <p>We hypothesize that there is a shift in controls on landscape scale <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns when plants become active during the growing season. Especially during the summer <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns are not only controlled by <span class="hlt">soils</span>, topography and related abiotic site characteristics but also by root water uptake. Root water uptake influences <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns both in the lateral and vertical direction. Plant water uptake from different <span class="hlt">soil</span> depths is estimated based on diurnal fluctuations in <span class="hlt">soil</span> <span class="hlt">moisture</span> content and was investigated with a unique setup of 46 field sites in Luxemburg and 15 field sites in Germany. These sites cover a range of geologies, <span class="hlt">soils</span>, topographic positions and types of vegetation. Vegetation types include pasture, pine forest (young and old) and different deciduous forest stands. Available data at all sites includes information at high temporal resolution from 3-5 <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature profiles, matrix potential, piezometers and sapflow sensors as well as standard climate data. At sites with access to a stream, discharge or water level is also recorded. The analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns over time indicates a shift in regime depending on season. Depth profiles of root water uptake show strong differences between different forest stands, with maximum depths ranging between 50 and 200 cm. Temporal dynamics of signal strength within the profile furthermore suggest a locally shifting spatial distribution of root water uptake depending on water availability. We will investigate temporal thresholds (under which conditions spatial patterns of root water uptake become most distinct) as well as landscape controls on <span class="hlt">soil</span> <span class="hlt">moisture</span> and root water uptake dynamics.</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>Spatial and temporal variability of the natural environment is its inherent and unavoidable feature. Every element of the environment is characterized by its own variability. One of the kinds of variability in the natural environment is the variability of the <span class="hlt">soil</span> environment. To acquire better and deeper knowledge and understanding of the temporal and spatial variability of the physical, chemical and biological features of the <span class="hlt">soil</span> environment, we should determine the causes that induce a given variability. Relatively stable features of <span class="hlt">soil</span> include its texture and mineral composition; examples of those variables in time are the <span class="hlt">soil</span> pH or organic matter content; an example of a feature with strong dynamics is the <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> content. The aim of this study was to identify the variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the field with and without plants using geostatistical methods. The <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements were taken on the object with plant canopy and without plants (as reference). The measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and meteorological components were taken within the period of April-July. The TDR <span class="hlt">moisture</span> sensors covered 5 cm <span class="hlt">soil</span> layers and were installed in the plots in the <span class="hlt">soil</span> layers of 0-0.05, 0.05-0.1, 0.1-0.15, 0.2-0.25, 0.3-0.35, 0.4-0.45, 0.5-0.55, 0.8-0.85 m. Measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> were taken once a day, in the afternoon hours. For the determination of reciprocal correlation, precipitation data and data from <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements with the TDR meter were used. Calculations of reciprocal correlation of precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> at various depths were made for three objects - spring barley, rye, and bare <span class="hlt">soil</span>, at the level of significance of p<0.05. No significant reciprocal correlation was found between the precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> in the <span class="hlt">soil</span> profile for any of the objects studied. Although the correlation analysis indicates a lack of correlation between the variables under consideration, observation of the <span class="hlt">soil</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20120013141&hterms=kellogg&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dkellogg','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20120013141&hterms=kellogg&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dkellogg"><span>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission Formulation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Entekhabi, Dara; Njoku, Eni; ONeill, Peggy; Kellogg, Kent; Entin, Jared</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-tier projects recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space. The SMAP mission is in formulation phase and it is scheduled for launch in 2014. The SMAP mission is designed to produce high-resolution and accurate global mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its freeze/thaw state using an instrument architecture that incorporates an L-band (1.26 GHz) radar and an L-band (1.41 GHz) radiometer. The simultaneous radar and radiometer measurements will be combined to derive global <span class="hlt">soil</span> <span class="hlt">moisture</span> mapping at 9 [km] resolution with a 2 to 3 days revisit and 0.04 [cm3 cm-3] (1 sigma) <span class="hlt">soil</span> water content accuracy. The radar measurements also allow the binary detection of surface freeze/thaw state. The project science goals address in water, energy and carbon cycle science as well as provide improved capabilities in natural hazards applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1912968W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1912968W"><span>Multi-site assimilation of a terrestrial biosphere model (BETHY) using satellite derived <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>Wu, Mousong; Sholze, Marko</p> <p>2017-04-01</p> <p>We investigated the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span> data on assimilation of a terrestrial biosphere model (BETHY) for a long time period from 2010 to 2015. Totally, 101 parameters related to carbon turnover, <span class="hlt">soil</span> respiration, as well as <span class="hlt">soil</span> texture were selected for optimization within a carbon cycle data assimilation system (CCDAS). <span class="hlt">Soil</span> <span class="hlt">moisture</span> data from <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) product was derived for 10 sites representing different plant function types (PFTs) as well as different climate zones. Uncertainty of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data was also estimated using triple collocation analysis (TCA) method by comparing with ASCAT dataset and BETHY forward simulation results. Assimilation of <span class="hlt">soil</span> <span class="hlt">moisture</span> to the system improved <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as net primary productivity(NPP) and net ecosystem productivity (NEP) when compared with <span class="hlt">soil</span> <span class="hlt">moisture</span> derived from in-situ measurements and fluxnet datasets. Parameter uncertainties were largely reduced relatively to prior values. Using SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data for assimilation of a terrestrial biosphere model proved to be an efficient approach in reducing uncertainty in ecosystem fluxes simulation. It could be further used in regional an global assimilation work to constrain carbon dioxide concentration simulation by combining with other sources of measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC53C1300F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC53C1300F"><span>Developing a <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Index for California Grasslands from Airborne Hyperspectral Imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flamme, H. E.; Roberts, D. A.; Miller, D. L.</p> <p>2016-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key environmental factor controlling vegetation diversity and productivity, evaporation, transpiration, and rainfall runoff. Despite the contribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> to ecological productivity, the hydrologic cycle, and erosion, it is currently not being monitored as accurately or as frequently as other environmental factors. Traditional <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring techniques rely on in situ measurements, which become costly when evaluating areas of unevenly distributed <span class="hlt">soil</span> characteristics and varying topography. Alternatively, satellite remote sensing, such as passive microwave from SMAP, can provide <span class="hlt">soil</span> <span class="hlt">moisture</span> but only at very coarse spatial resolutions. Imagery from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) has the potential to allow better spatial and temporal monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span>. This study established a relationship between plant available water and hyperspectral reflectance via linear regressions of data from 2013-2015 for two grassland field sites: 1) near Santa Barbara, California, at Coal Oil Point Reserve (COPR) and 2) Airstrip station (AIRS) at UC Santa Barbara's Sedgwick Reserve near Santa Ynez, California. Volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements at 10 cm and 20 cm depths were provided by meteorological stations situated in COPR and AIRS while reflectance data were extracted from AVIRIS. We found strong correlations between plant available water and bands centered at wavelengths 704 nm and 831 nm, which we used to create Hyperspectral <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Index (HSMI): 0.38((ρ831-ρ704)/(ρ831+ρ704))-0.02. HSMI demonstrated a coefficient of determination (R2) of 0.71 for linear regressions of reflectance versus plant available water with a lag time of 28 days. We applied HSMI to the AIRS and COPR grasslands for 2011 AVIRIS scenes. Plant available water values predicted by HSMI were 0.039 higher at AIRS and 0.048 higher at COPR than the field measurements at the sites. Differences in grass species, <span class="hlt">soil</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ISPAr42.3..583H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ISPAr42.3..583H"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval Using Convolutional Neural Networks: Application to Passive Microwave Remote Sensing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hu, Z.; Xu, L.; Yu, B.</p> <p>2018-04-01</p> <p>A empirical model is established to analyse the daily retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> from passive microwave remote sensing using convolutional neural networks (CNN). <span class="hlt">Soil</span> <span class="hlt">moisture</span> plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global <span class="hlt">soil</span> <span class="hlt">moisture</span> map can be predicted in less than 10 seconds. What's more, the method of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global <span class="hlt">soil</span> <span class="hlt">moisture</span> can achieve a better performance than the support vector regression (SVR) for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150023285','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150023285"><span>An Overview of Production and Validation 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, S.; O'Neill, P.; Njoku, E.; Jackson, T.; Bindlish, R.</p> <p>2015-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission is an L-band mission scheduled for launch in Jan. 2015. The SMAP instruments consist of a radar and a radiometer to obtain complementary information from space for <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state research and applications. By utilizing novel designs in antenna construction, retrieval algorithms, and acquisition hardware, SMAP provides a capability for global mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state with unprecedented accuracy, resolution, and coverage. This improvement in hydrosphere state measurement is expected to advance our understanding of the processes that link the terrestrial water, energy and carbon cycles, improve our capability in flood prediction and drought monitoring, and enhance our skills in weather and climate forecast. For swath-based <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement, SMAP generates three operational geophysical data products: (1) the radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product (L2_SM_P) posted at 36-kilometer resolution, (2) the radar-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product (L2_SM_A) posted at 3-kilometers resolution, and (3) the radar-radiometer combined <span class="hlt">soil</span> <span class="hlt">moisture</span> product (L2_SM_AP) posted at 9-kilometers resolution. Each product draws on the strengths of the underlying sensor(s) and plays a unique role in hydroclimatological and hydrometeorological applications. A full suite of SMAP data products is given in Table 1.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRD..12111516K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRD..12111516K"><span>A novel approach to validate satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals using precipitation data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Karthikeyan, L.; Kumar, D. Nagesh</p> <p>2016-10-01</p> <p>A novel approach is proposed that attempts to validate passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals using precipitation data (applied over India). It is based on the concept that the expectation of precipitation conditioned on <span class="hlt">soil</span> <span class="hlt">moisture</span> follows a sigmoidal convex-concave-shaped curve, the characteristic of which was recently shown to be represented by mutual information estimated between <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation. On this basis, with an emphasis over distribution-free nonparametric computations, a new measure called Copula-Kernel Density Estimator based Mutual Information (CKDEMI) is introduced. The validation approach is generic in nature and utilizes CKDEMI in tandem with a couple of proposed bootstrap strategies, to check accuracy of any two <span class="hlt">soil</span> <span class="hlt">moisture</span> products (here Advanced Microwave Scanning Radiometer-EOS sensor's Vrije Universiteit Amsterdam-NASA (VUAN) and University of Montana (MONT) products) using precipitation (India Meteorological Department) data. The proposed technique yields a "best choice <span class="hlt">soil</span> <span class="hlt">moisture</span> product" map which contains locations where any one of the two/none of the two/both the products have produced accurate retrievals. The results indicated that in general, VUA-NASA product has performed well over University of Montana's product for India. The best choice <span class="hlt">soil</span> <span class="hlt">moisture</span> map is then integrated with land use land cover and elevation information using a novel probability density function-based procedure to gain insight on conditions under which each of the products has performed well. Finally, the impact of using a different precipitation (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) data set over the best choice <span class="hlt">soil</span> <span class="hlt">moisture</span> product map is also analyzed. The proposed methodology assists researchers and practitioners in selecting the appropriate <span class="hlt">soil</span> <span class="hlt">moisture</span> product for various assimilation strategies at both basin and continental scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1413603X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1413603X"><span>The advanced qualtiy control techniques planned for the Internation <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xaver, A.; Gruber, A.; Hegiova, A.; Sanchis-Dufau, A. D.; Dorigo, W. A.</p> <p>2012-04-01</p> <p>In situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations are essential to evaluate and calibrate modeled and remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> products. Although a number of meteorological networks and field campaigns measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> exist on a global and long-term scale, their observations are not easily accessible and lack standardization of both technique and protocol. Thus, handling and especially comparing these datasets with satellite products or land surface models is a demanding issue. To overcome these limitations the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network (ISMN; http://www.ipf.tuwien.ac.at/insitu/) has been initiated to act as a centralized data hosting facility. One advantage of the ISMN is that users are able to access the harmonized datasets easily through a web portal. Another advantage is the fully automated processing chain including the data harmonization in terms of units and sampling interval, but even more important is the advanced quality control system each measurement has to run through. The quality of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements is crucial for the validation of satellite- and model-based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals; therefore a sophisticated quality control system was developed. After a check for plausibility and geophysical limits a quality flag is added to each measurement. An enhanced flagging mechanism was recently defined using a spectrum based approach to detect spurious spikes, jumps and plateaus. The International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network has already evolved to one of the most important distribution platforms for in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations and is still growing. Currently, data from 27 networks in total covering more than 800 stations in Europe, North America, Australia, Asia and Africa is hosted by the ISMN. Available datasets also include historical datasets as well as near real-time measurements. The improved quality control system will provide important information for satellite-based as well as land surface model-based validation studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011346','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011346"><span>NASAs <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission and Opportunities For Applications Users</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Brown, Molly E.; Escobar, Vanessa; Moran, Susan; Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni G.; Doorn, Brad; Entin, Jared K.</p> <p>2013-01-01</p> <p>Water in the <span class="hlt">soil</span>, both its amount (<span class="hlt">soil</span> <span class="hlt">moisture</span>) and its state (freeze/thaw), plays a key role in water and energy cycles, in weather and climate, and in the carbon cycle. Additionally, <span class="hlt">soil</span> <span class="hlt">moisture</span> touches upon human lives in a number of ways from the ravages of flooding to the needs for monitoring agricultural and hydrologic droughts. Because of their relevance to weather, climate, science, and society, accurate and timely measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state with global coverage are critically important.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.9335J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.9335J"><span>Station for spatially distributed measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and ambient temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jankovec, Jakub; Šanda, Martin; Haase, Tomáš; Sněhota, Michal; Wild, Jan</p> <p>2013-04-01</p> <p>Third generation of combined thermal and <span class="hlt">soil</span> <span class="hlt">moisture</span> standalone field station coded TMS3 with wireless communication is presented. The device combines three thermometers (MAXIM/DALLAS Semiconductor DS7505U with -55 to +125°C range and 0.0625°C resolution, 0.5°C precision in 0 to +70°C range and 2°C precision out of this range). <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurement is performed based on time domain transmission (TDT) principle for the full range of <span class="hlt">soil</span> <span class="hlt">moisture</span> with 0.025% resolution within the full possible <span class="hlt">soil</span> <span class="hlt">moisture</span> span for the most typical conditions of dry to saturated <span class="hlt">soils</span> with safe margins to enable measurements in freezing, hot or saline <span class="hlt">soils</span>. Principal compact version is designed for temperature measurements approximately at heights -10, 0 and +15 cm relative to <span class="hlt">soil</span> surface when installed vertically and <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements between 0 and 12 cm below surface. Set of buriable/subsurface stations each with 2.2 meter extension cord with <span class="hlt">soil</span> and surface temperature measurement provides possibility to scan vertical <span class="hlt">soil</span> profile for <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature at desired depths. USB equipped station is designed for streamed direct data acquisition in laboratory use in 1s interval. Station is also equipped with the shock sensor indicating the manipulation. Presented version incorporates life time permanent data storage (0.5 million logs). Current sensor design aims towards improved durability in harsh outdoor environment with reliable functioning in wet conditions withstanding mechanical or electric shock destruction. Insertion into the <span class="hlt">soil</span> is possible by pressing with the use of a simple plastic cover. Data are retrieved by contact portable pocket collector (second generation) or by RFID wireless communication for hundreds meter distance (third generation) in either star pattern of GSM hub to stations or lined up GSM to station to another station both in comprised data packets. This option will allow online data harvesting and real time process</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160008959','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160008959"><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="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>O'Neill, P.; Chan, S.; Colliander, A.; Dunbar, S.; Njoku, E.; Bindlish, R.; Chen, F.; Jackson, T.; Burgin, M.; Piepmeier, J.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20160008959'); toggleEditAbsImage('author_20160008959_show'); toggleEditAbsImage('author_20160008959_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20160008959_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20160008959_hide"></p> <p>2016-01-01</p> <p>NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce 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 SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> product (L2_SM_P) provides <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates posted on a 36 km fixed Earth grid using brightness temperature observations from descending (6 am) passes and ancillary data. A beta quality version of L2_SM_P was released to the public in September, 2015, with the fully validated L2_SM_P <span class="hlt">soil</span> <span class="hlt">moisture</span> data expected to be released in May, 2016. Additional improvements (including optimization of retrieval algorithm parameters and upscaling approaches) and methodology expansions (including increasing the number of core sites, model-based intercomparisons, and results from several intensive field campaigns) are anticipated in moving from accuracy assessment of the beta quality data to an evaluation of the fully validated L2_SM_P data product.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18..846T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18..846T"><span>Use of modeled and satelite <span class="hlt">soil</span> <span class="hlt">moisture</span> to estimate <span class="hlt">soil</span> erosion in central and southern Italy.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Termite, Loris Francesco; Massari, Christian; Todisco, Francesca; Brocca, Luca; Ferro, Vito; Bagarello, Vincenzo; Pampalone, Vincenzo; Wagner, Wolfgang</p> <p>2016-04-01</p> <p>This study presents an accurate comparison between two different approaches aimed to enhance accuracy of the Universal <span class="hlt">Soil</span> Loss Equation (USLE) in estimating the <span class="hlt">soil</span> loss at the single event time scale. Indeed it is well known that including the observed event runoff in the USLE improves its <span class="hlt">soil</span> loss estimation ability at the event scale. In particular, the USLE-M and USLE-MM models use the observed runoff coefficient to correct the rainfall erosivity factor. In the first case, the <span class="hlt">soil</span> loss is linearly dependent on rainfall erosivity, in the second case <span class="hlt">soil</span> loss and erosivity are related by a power law. However, the measurement of the event runoff is not straightforward or, in some cases, possible. For this reason, the first approach used in this study is the use of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> For Erosion (SM4E), a recent USLE-derived model in which the event runoff is replaced by the antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span>. Three kinds of <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets have been separately used: the ERA-Interim/Land reanalysis data of the European Centre for Medium-range Weather Forecasts (ECMWF); satellite retrievals from the European Space Agency - Climate Change Initiative (ESA-CCI); modeled data using a <span class="hlt">Soil</span> Water Balance Model (SWBM). The second approach is the use of an estimated runoff rather than the observed. Specifically, the Simplified Continuous Rainfall-Runoff Model (SCRRM) is used to derive the runoff estimates. SCRMM requires <span class="hlt">soil</span> <span class="hlt">moisture</span> data as input and at this aim the same three <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets used for the SM4E have been separately used. All the examined models have been calibrated and tested at the plot scale, using data from the experimental stations for the monitoring of the erosive processes "Masse" (Central Italy) and "Sparacia" (Southern Italy). Climatic data and runoff and <span class="hlt">soil</span> loss measures at the event time scale are available for the period 2008-2013 at Masse and for the period 2002-2013 at Sparacia. The results show that both the approaches can provide</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H33F0892W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H33F0892W"><span>Why is SMOS Drier than the South Fork In-situ <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walker, V. A.; Hornbuckle, B. K.; Cosh, M. H.</p> <p>2014-12-01</p> <p>Global maps of near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> are currently being produced by the European Space Agency's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite mission at 40 km. Within the next few months NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite mission will begin producing observations of near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at 10 km. Near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> is the water content of the first 3 to 5 cm of the <span class="hlt">soil</span>. Observations of near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> are expected to improve weather and climate forecasts. These satellite observations must be validated. We define validation as determining the space/time statistical characteristics of the uncertainty. A standard that has been used for satellite validation is in-situ measurements of near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> made with a network of sensors spanning the extent of a satellite footprint. Such a network of sensors has been established in the South Fork of the Iowa River in Central Iowa by the USDA ARS. Our analysis of data in 2013 indicates that SMOS has a dry bias: SMOS near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> is between 0.05 to 0.10 m^3m^{-3} lower than what is observed by the South Fork network. A dry bias in SMOS observations has also been observed in other regions of North America. There are many possible explanations for this difference: underestimation of vegetation, or <span class="hlt">soil</span> surface roughness; undetected radio frequency interference (RFI); a retrieval model that is not appropriate for agricultural areas; or the use of an incorrect surface temperature in the retrieval process. We will begin our investigation by testing this last possibility: that SMOS is using a surface temperature that is too low which results in a drier <span class="hlt">soil</span> <span class="hlt">moisture</span> that compensates for this error. We will present a comparison of surface temperatures from the European Center for Medium-range Weather Forecasting (ECMWF) used to retrieve near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> from SMOS measurements of brightness temperature, and surface temperatures in the South Fork</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> </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://adsabs.harvard.edu/abs/2014EGUGA..16.4089A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.4089A"><span>Understanding tree growth in response to <span class="hlt">moisture</span> variability: Linking 32 years of satellite based <span class="hlt">soil</span> <span class="hlt">moisture</span> observations with tree rings</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Albrecht, Franziska; Dorigo, Wouter; Gruber, Alexander; Wagner, Wolfgang; Kainz, Wolfgang</p> <p>2014-05-01</p> <p>Climate change induced drought variability impacts global forest ecosystems and forest carbon cycle dynamics. Physiological drought stress might even become an issue in regions generally not considered water-limited. The water balance at the <span class="hlt">soil</span> surface is essential for forest growth. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key driver linking precipitation and tree development. Tree ring based analyses are a potential approach to study the driving role of hydrological parameters for tree growth. However, at present two major research gaps are apparent: i) <span class="hlt">soil</span> <span class="hlt">moisture</span> records are hardly considered and ii) only a few studies are linking tree ring chronologies and satellite observations. Here we used tree ring chronologies obtained from the International Tree ring Data Bank (ITRDB) and remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> observations (ECV_SM) to analyze the <span class="hlt">moisture</span>-tree growth relationship. The ECV_SM dataset, which is being distributed through ESA's Climate Change Initiative for <span class="hlt">soil</span> <span class="hlt">moisture</span> covers the period 1979 to 2010 at a spatial resolution of 0.25°. First analyses were performed for Mongolia, a country characterized by a continental arid climate. We extracted 13 tree ring chronologies suitable for our analysis from the ITRDB. Using monthly satellite based <span class="hlt">soil</span> <span class="hlt">moisture</span> observations we confirmed previous studies on the seasonality of <span class="hlt">soil</span> <span class="hlt">moisture</span> in Mongolia. Further, we investigated the relationship between tree growth (as reflected by tree ring width index) and remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> records by applying correlation analysis. In terms of correlation coefficient a strong response of tree growth to <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions of current April to August was observed, confirming a strong linkage between tree growth and <span class="hlt">soil</span> water storage. The highest correlation was found for current April (R=0.44), indicating that sufficient water supply is vital for trees at the beginning of the growing season. To verify these results, we related the chronologies to reanalysis precipitation and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H51R..04B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H51R..04B"><span>Empirical <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation with Spaceborne L-band Polarimetric Radars: Aquarius, SMAP, and PALSAR-2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Burgin, M. S.; van Zyl, J. J.</p> <p>2017-12-01</p> <p>Traditionally, substantial ancillary data is needed to parametrize complex electromagnetic models to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> from polarimetric radar data. The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) baseline radar <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm uses a data cube approach, where a cube of radar backscatter values is calculated using sophisticated models. In this work, we utilize the empirical approach by Kim and van Zyl (2009) which is an optional SMAP radar <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm; it expresses radar backscatter of a vegetated scene as a linear function of <span class="hlt">soil</span> <span class="hlt">moisture</span>, hence eliminating the need for ancillary data. We use 2.5 years of L-band Aquarius radar and radiometer derived <span class="hlt">soil</span> <span class="hlt">moisture</span> data to determine two coefficients of a linear model function on a global scale. These coefficients are used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> with 2.5 months of L-band SMAP and L-band PALSAR-2 data. The estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> is compared with the SMAP Level 2 radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product; the global unbiased RMSE of the SMAP derived <span class="hlt">soil</span> <span class="hlt">moisture</span> corresponds to 0.06-0.07 cm3/cm3. In this study, we leverage the three diverse L-band radar data sets to investigate the impact of pixel size and pixel heterogeneity on <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation performance. Pixel sizes range from 100 km for Aquarius, over 3, 9, 36 km for SMAP, to 10m for PALSAR-2. Furthermore, we observe seasonal variation in the radar sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span> which allows the identification and quantification of seasonally changing vegetation. Utilizing this information, we further improve the estimation performance. The research described in this paper is supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Copyright 2017. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014WRR....50.4038S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014WRR....50.4038S"><span>Quantifying the influence of deep <span class="hlt">soil</span> <span class="hlt">moisture</span> on ecosystem albedo: The role of vegetation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sanchez-Mejia, Zulia Mayari; Papuga, Shirley Anne; Swetish, Jessica Blaine; van Leeuwen, Willem Jan Dirk; Szutu, Daphne; Hartfield, Kyle</p> <p>2014-05-01</p> <p>As changes in precipitation dynamics continue to alter the water availability in dryland ecosystems, understanding the feedbacks between the vegetation and the hydrologic cycle and their influence on the climate system is critically important. We designed a field campaign to examine the influence of two-layer <span class="hlt">soil</span> <span class="hlt">moisture</span> control on bare and canopy albedo dynamics in a semiarid shrubland ecosystem. We conducted this campaign during 2011 and 2012 within the tower footprint of the Santa Rita Creosote Ameriflux site. Albedo field measurements fell into one of four Cases within a two-layer <span class="hlt">soil</span> <span class="hlt">moisture</span> framework based on permutations of whether the shallow and deep <span class="hlt">soil</span> layers were wet or dry. Using these Cases, we identified differences in how shallow and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> influence canopy and bare albedo. Then, by varying the number of canopy and bare patches within a gridded framework, we explore the influence of vegetation and <span class="hlt">soil</span> <span class="hlt">moisture</span> on ecosystem albedo. Our results highlight the importance of deep <span class="hlt">soil</span> <span class="hlt">moisture</span> in land surface-atmosphere interactions through its influence on aboveground vegetation characteristics. For instance, we show how green-up of the vegetation is triggered by deep <span class="hlt">soil</span> <span class="hlt">moisture</span>, and link deep <span class="hlt">soil</span> <span class="hlt">moisture</span> to a decrease in canopy albedo. Understanding relationships between vegetation and deep <span class="hlt">soil</span> <span class="hlt">moisture</span> will provide important insights into feedbacks between the hydrologic cycle and the climate system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820005597','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820005597"><span>Analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> extraction algorithm using data from aircraft experiments</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Burke, H. H. K.; Ho, J. H.</p> <p>1981-01-01</p> <p>A <span class="hlt">soil</span> <span class="hlt">moisture</span> extraction algorithm is developed using a statistical parameter inversion method. Data sets from two aircraft experiments are utilized for the test. Multifrequency microwave radiometric data surface temperature, and <span class="hlt">soil</span> <span class="hlt">moisture</span> information are contained in the data sets. The surface and near surface ( or = 5 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> content can be extracted with accuracy of approximately 5% to 6% for bare fields and fields with grass cover by using L, C, and X band radiometer data. This technique is used for handling large amounts of remote sensing data from space.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.9120W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.9120W"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Extremes Observed by METOP ASCAT: Was 2012 an Exceptional Year?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wagner, Wolfgang; Paulik, Christoph; Hahn, Sebastian; Melzer, Thomas; Parinussa, Robert; de Jeu, Richard; Dorigo, Wouter; Chung, Daniel; Enenkel, Markus</p> <p>2013-04-01</p> <p>In summer 2012 the international press reported widely about the severe drought that had befallen large parts of the United States. Yet, the US drought was only one of several major droughts that occurred in 2012: Southeastern Europe, Central Asia, Brazil, India, Southern Australia and several other regions suffered from similarly dry <span class="hlt">soil</span> conditions. This raises the question whether 2012 was an exceptionally dry year? In this presentation we will address this question by analyzing global <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns as observed by the Advanced Scatterometer (ASCAT) flown on board of the METOP-A satellite. We firstly compare the 2012 ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> data to all available ASCAT measurements acquired by the instrument since the launch of METOP-A in November 2006. Secondly, we compare the 2012 data to a long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> data set derived by merging the ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> data with other active and passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals as described by Liu et al. (2012) and Wagner et al. (2012) (see also http://www.esa-soilmoisture-cci.org/). A first trend analysis of the latter long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> data set carried out by Dorigo et al. (2012) has revealed that over the period 1988-2010 significant trends were observed over 27 % of the area covered by the data set, of which 73 % were negative (<span class="hlt">soil</span> drying) and only 27 % were positive (<span class="hlt">soil</span> wetting). In this presentation we will show how the inclusion of the years 2011 and 2012 affects the areal extent and strengths of these significant trends. REFERENCES Dorigo, W., R. de Jeu, D. Chung, R. Parinussa, Y. Liu, W. Wagner, D. Fernández-Prieto (2012) Evaluating global trends (1988-2010) in harmonized multi-satellite surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, Geophysical Research Letters, 39, L18405, 1-7. Liu, Y.Y., W.A. Dorigo, R.M. Parinussa, R.A.M. de Jeu, W. Wagner, M.F. McCabe, J.P. Evans, A.I.J.M. van Dijk (2012) Trend-preserving blending of passive and active microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals, Remote Sensing of Environment</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120013516','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120013516"><span>Advances in Assimilation of Satellite-Based Passive Microwave Observations for <span class="hlt">Soil-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>De Lannoy, Gabrielle J. M.; Pauwels, Valentijn; Reichle, Rolf H.; Draper, Clara; Koster, Randy; Liu, Qing</p> <p>2012-01-01</p> <p>Satellite-based microwave measurements have long shown potential to provide global information about <span class="hlt">soil</span> <span class="hlt">moisture</span>. The European Space Agency (ESA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS, [1]) mission as well as the future National Aeronautics and Space Administration (NASA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP, [2]) mission measure passive microwave emission at L-band frequencies, at a relatively coarse (40 km) spatial resolution. In addition, SMAP will measure active microwave signals at a higher spatial resolution (3 km). These new L-band missions have a greater sensing depth (of -5cm) compared with past and present C- and X-band microwave sensors. ESA currently also disseminates retrievals of SMOS surface <span class="hlt">soil</span> <span class="hlt">moisture</span> that are derived from SMOS brightness temperature observations and ancillary data. In this research, we address two major challenges with the assimilation of recent/future satellite-based microwave measurements: (i) assimilation of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals versus brightness temperatures for surface and root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation and (ii) scale-mismatches between satellite observations, models and in situ validation data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140009183','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140009183"><span>NASA Giovanni: A Tool for Visualizing, Analyzing, and Inter-comparing <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>Teng, William; Rui, Hualan; Vollmer, Bruce; deJeu, Richard; Fang, Fan; Lei, Guang-Dih; Parinussa, Robert</p> <p>2014-01-01</p> <p>There are many existing satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms and their derived data products, but there is no simple way for a user to inter-compare the products or analyze them together with other related data. An environment that facilitates such inter-comparison and analysis would be useful for validation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals against in situ data and for determining the relationships between different <span class="hlt">soil</span> <span class="hlt">moisture</span> products. As part of the NASA Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) family of portals, which has provided users worldwide with a simple but powerful way to explore NASA data, a beta prototype Giovanni Inter-comparison of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products portal has been developed. A number of <span class="hlt">soil</span> <span class="hlt">moisture</span> data products are currently included in the prototype portal. More will be added, based on user requirements and feedback and as resources become available. Two application examples for the portal are provided. The NASA Giovanni <span class="hlt">Soil</span> <span class="hlt">Moisture</span> portal is versatile and extensible, with many possible uses, for research and applications, as well as for the education community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19740011869','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19740011869"><span>Use of visible, near-infrared, and thermal infrared remote sensing to study <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>Blanchard, M. B.; Greeley, R.; Goettelman, R.</p> <p>1974-01-01</p> <p>Two methods are described which are used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> remotely using the 0.4- to 14.0 micron wavelength region: (1) measurement of spectral reflectance, and (2) measurement of <span class="hlt">soil</span> temperature. The reflectance method is based on observations which show that directional reflectance decreases as <span class="hlt">soil</span> <span class="hlt">moisture</span> increases for a given material. The <span class="hlt">soil</span> temperature method is based on observations which show that differences between daytime and nighttime <span class="hlt">soil</span> temperatures decrease as <span class="hlt">moisture</span> content increases for a given material. In some circumstances, separate reflectance or temperature measurements yield ambiguous data, in which case these two methods may be combined to obtain a valid <span class="hlt">soil</span> <span class="hlt">moisture</span> determination. In this combined approach, reflectance is used to estimate low <span class="hlt">moisture</span> levels; and thermal inertia (or thermal diffusivity) is used to estimate higher levels. The reflectance method appears promising for surface estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span>, whereas the temperature method appears promising for estimates of near-subsurface (0 to 10 cm).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19750033072&hterms=Soil+use&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DSoil%2Buse','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19750033072&hterms=Soil+use&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DSoil%2Buse"><span>Use of visible, near-infrared, and thermal infrared remote sensing to study <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>Blanchard, M. B.; Greeley, R.; Goettelman, R.</p> <p>1974-01-01</p> <p>Two methods are used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> remotely using the 0.4- to 14.0-micron wavelength region: (1) measurement of spectral reflectance, and (2) measurement of <span class="hlt">soil</span> temperature. The reflectance method is based on observations which show that directional reflectance decreases as <span class="hlt">soil</span> <span class="hlt">moisture</span> increases for a given material. The <span class="hlt">soil</span> temperature method is based on observations which show that differences between daytime and nighttime <span class="hlt">soil</span> temperatures decrease as <span class="hlt">moisture</span> content increases for a given material. In some circumstances, separate reflectance or temperature measurements yield ambiguous data, in which case these two methods may be combined to obtain a valid <span class="hlt">soil</span> <span class="hlt">moisture</span> determination. In this combined approach, reflectance is used to estimate low <span class="hlt">moisture</span> levels; and thermal inertia (or thermal diffusivity) is used to estimate higher levels. The reflectance method appears promising for surface estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span>, whereas the temperature method appears promising for estimates of near-subsurface (0 to 10 cm).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26592040','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26592040"><span>[Effects of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on Phytoremediation of As-Containinated <span class="hlt">Soils</span> Using As-Hyperaccumulator Pteris vittata L].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Qiu-xin; Yan, Xiu-lan; Liao, Xiao-yong; Lin, Long-yong; Yang, Jing</p> <p>2015-08-01</p> <p>A pot experiment was carried out to study the effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the growth and arsenic uptake of As-hyperaccumulator Pteris vittata L. The results showed that the remediation efficiency of As was the highest when the <span class="hlt">soil</span> <span class="hlt">moisture</span> was between 35%-45%. P. vittata grew best under 45% water content, and its aboveground and underground plant dry weights were 2.95 g x plant(-1) and 11.95 g x plant(-1), respectively; the arsenic concentration in aboveground and roots was the highest under 35% water content, and 40% content was the best for accumulation of arsenic in P. vittata. Moreover, controlling the <span class="hlt">soil</span> <span class="hlt">moisture</span> to 35%-45% enhanced the conversion of As(V) to As(III) in aboveground plant, and promoted arsenic detoxification in P. vittata. These above results showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> played an important role in the absorption and transport of arsenic by P. vittata. The results of this study can provide important guidance for the large-scale planting of P. vittata and the <span class="hlt">moisture</span> management measures in engineering application.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=215525&keyword=bond&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=215525&keyword=bond&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><span class="hlt">Soil</span> <span class="hlt">moisture</span> effects on the carbon isotopic composition of <span class="hlt">soil</span> respiration</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 carbon isotopic composition ( 13C) of recently assimilated plant carbon is known to depend on water-stress, caused either by low <span class="hlt">soil</span> <span class="hlt">moisture</span> or by low atmospheric humidity. Air humidity has also been shown to correlate with the 13C of <span class="hlt">soil</span> respiration, which suggests indir...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H31F1177M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H31F1177M"><span>Agricultural Decision Support Through Robust Assimilation of Satellite Derived <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimates</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mishra, V.; Cruise, J.; Mecikalski, J. R.</p> <p>2012-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> is a key component in the hydrological process, affects surface and boundary layer energy fluxes and is the driving factor in agricultural production. Multiple in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measuring instruments such as Time-domain Reflectrometry (TDR), Nuclear Probes etc. are in use along with remote sensing methods like Active and Passive Microwave (PM) sensors. In situ measurements, despite being more accurate, can only be obtained at discrete points over small spatial scales. Remote sensing estimates, on the other hand, can be obtained over larger spatial domains with varying spatial and temporal resolutions. <span class="hlt">Soil</span> <span class="hlt">moisture</span> profiles derived from satellite based thermal infrared (TIR) imagery can overcome many of the problems associated with laborious in-situ observations over large spatial domains. An area where <span class="hlt">soil</span> <span class="hlt">moisture</span> observation and assimilation is receiving increasing attention is agricultural crop modeling. This study revolves around the use of the Decision Support System for Agrotechnology Transfer (DSSAT) crop model to simulate corn yields under various forcing scenarios. First, the model was run and calibrated using observed precipitation and model generated <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Next, the modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> was updated using estimates derived from satellite based TIR imagery and the Atmospheric Land Exchange Inverse (ALEXI) model. We selected three climatically different locations to test the concept. Test Locations were selected to represent varied climatology. Bell Mina, Alabama - South Eastern United States, representing humid subtropical climate. Nabb, Indiana - Mid Western United States, representing humid continental climate. Lubbok, Texas - Southern United States, representing semiarid steppe climate. A temporal (2000-2009) correlation analysis of the <span class="hlt">soil</span> <span class="hlt">moisture</span> values from both DSSAT and ALEXI were performed and validated against the Land Information System (LIS) <span class="hlt">soil</span> <span class="hlt">moisture</span> dataset. The results clearly show strong</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000038138&hterms=modeling+hydrological+basins&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dmodeling%2Bhydrological%2Bbasins','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038138&hterms=modeling+hydrological+basins&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dmodeling%2Bhydrological%2Bbasins"><span>Examination of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval Using SIR-C Radar Data and a Distributed Hydrological Model</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hsu, A. Y.; ONeill, P. E.; Wood, E. F.; Zion, M.</p> <p>1997-01-01</p> <p>A major objective of <span class="hlt">soil</span> <span class="hlt">moisture</span>-related hydrological-research during NASA's SIR-C/X-SAR mission was to determine and compare <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns within humid watersheds using SAR data, ground-based measurements, and hydrologic modeling. Currently available <span class="hlt">soil</span> <span class="hlt">moisture</span>-inversion methods using active microwave data are only accurate when applied to bare and slightly vegetated surfaces. Moreover, as the surface dries down, the number of pixels that can provide estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> by these radar inversion methods decreases, leading to less accuracy and, confidence in the retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span> fields at the watershed scale. The impact of these errors in microwave- derived <span class="hlt">soil</span> <span class="hlt">moisture</span> on hydrological modeling of vegetated watersheds has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework is used to predict surface <span class="hlt">soil</span> <span class="hlt">moisture</span> for both bare and vegetated areas. In the first model run, the hydrological model is initialized using a standard baseflow approach, while in the second model run, <span class="hlt">soil</span> <span class="hlt">moisture</span> values derived from SIR-C radar data are used for initialization. The results, which compare favorably with ground measurements, demonstrate the utility of combining radar-derived surface <span class="hlt">soil</span> <span class="hlt">moisture</span> information with basin-scale hydrological modeling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B51G1897R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B51G1897R"><span>Modelling of Space-Time <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Savannas and its Relation to Vegetation Patterns</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rodriguez-Iturbe, I.; Mohanty, B.; Chen, Z.</p> <p>2017-12-01</p> <p>A physically derived space-time representation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> field is presented. It includes the incorporation of a "jitter" process acting over the space-time <span class="hlt">soil</span> <span class="hlt">moisture</span> field and accounting for the short distance heterogeneities in topography, <span class="hlt">soil</span>, and vegetation characteristics. The modelling scheme allows for the representation of spatial random fluctuations of <span class="hlt">soil</span> <span class="hlt">moisture</span> at small spatial scales and reproduces quite well the space-time correlation structure of <span class="hlt">soil</span> <span class="hlt">moisture</span> from a field study in Oklahoma. It is shown that the islands of <span class="hlt">soil</span> <span class="hlt">moisture</span> above different thresholds have sizes which follow power distributions over an extended range of scales. A discussion is provided about the possible links of this feature with the observed power law distributions of the clusters of trees in savannas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1915682R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1915682R"><span>Can we quantify the variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> across scales using Electromagnetic Induction ?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Robinet, Jérémy; von Hebel, Christian; van der Kruk, Jan; Govers, Gerard; Vanderborght, Jan</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable in many natural processes. Therefore, technological and methodological advancements are of primary importance to provide accurate measurements of spatial and temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span>. In that context, ElectroMagnetic Induction (EMI) instruments are often cited as a hydrogeophysical method with a large potential, through the measurement of the <span class="hlt">soil</span> apparent electrical conductivity (ECa). To our knowledge, no studies have evaluated the potential of EMI to characterize variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> on both agricultural and forested land covers in a (sub-) tropical environment. These differences in land use could be critical as differences in temperature, transpiration and root water uptake can have significant effect, notably on the electrical conductivity of the pore water. In this study, we used an EMI instrument to carry out a first assessment of the impact of deforestation and agriculture on <span class="hlt">soil</span> <span class="hlt">moisture</span> in a subtropical region in the south of Brazil. We selected slopes of different topographies (gentle vs. steep) and contrasting land uses (natural forest vs. agriculture) within two nearby catchments. At selected locations on the slopes, we measured simultaneously ECa using EMI and a depth-weighted average of the <span class="hlt">soil</span> <span class="hlt">moisture</span> using TDR probes installed within <span class="hlt">soil</span> pits. We found that the temporal variability of the <span class="hlt">soil</span> <span class="hlt">moisture</span> could not be measured accurately with EMI, probably because of important temporal variations of the pore water electrical conductivity and the relatively small temporal variations in <span class="hlt">soil</span> <span class="hlt">moisture</span> content. However, we found that its spatial variability could be effectively quantified using a non-linear relationship, for both intra- and inter-slopes variations. Within slopes, the ECa could explained between 67 and 90% of the variability of the <span class="hlt">soil</span> <span class="hlt">moisture</span>, while a single non-linear model for all the slopes could explain 55% of the <span class="hlt">soil</span> <span class="hlt">moisture</span> variability. We eventually showed that combining</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27859221','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27859221"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> mediates alpine life form and community productivity responses to warming.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Winkler, Daniel E; Chapin, Kenneth J; Kueppers, Lara M</p> <p>2016-06-01</p> <p>Climate change is expected to alter primary production and community composition in alpine ecosystems, but the direction and magnitude of change is debated. Warmer, wetter growing seasons may increase productivity; however, in the absence of additional precipitation, increased temperatures may decrease <span class="hlt">soil</span> <span class="hlt">moisture</span>, thereby diminishing any positive effect of warming. Since plant species show individual responses to environmental change, responses may depend on community composition and vary across life form or functional groups. We warmed an alpine plant community at Niwot Ridge, Colorado continuously for four years to test whether warming increases or decreases productivity of life form groups and the whole community. We provided supplemental water to a subset of plots to alleviate the drying effect of warming. We measured annual above-ground productivity and <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span>, from which we calculated <span class="hlt">soil</span> degree days and adequate <span class="hlt">soil</span> <span class="hlt">moisture</span> days. Using an information-theoretic approach, we observed that positive productivity responses to warming at the community level occur only when warming is combined with supplemental watering; otherwise we observed decreased productivity. Watering also increased community productivity in the absence of warming. Forbs accounted for the majority of the productivity at the site and drove the contingent community response to warming, while cushions drove the generally positive response to watering and graminoids muted the community response. Warming advanced snowmelt and increased <span class="hlt">soil</span> degree days, while watering increased adequate <span class="hlt">soil</span> <span class="hlt">moisture</span> days. Heated and watered plots had more adequate <span class="hlt">soil</span> <span class="hlt">moisture</span> days than heated plots. Overall, measured changes in <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> in response to treatments were consistent with expected productivity responses. We found that available <span class="hlt">soil</span> <span class="hlt">moisture</span> largely determines the responses of this forb-dominated alpine community to simulated climate warming. © 2016</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H13I1517F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H13I1517F"><span>Remote Sensing of <span class="hlt">Soil</span> <span class="hlt">Moisture</span>: A Comparison of Optical and Thermal Methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Foroughi, H.; Naseri, A. A.; Boroomandnasab, S.; Sadeghi, M.; Jones, S. B.; Tuller, M.; Babaeian, E.</p> <p>2017-12-01</p> <p>Recent technological advances in satellite and airborne remote sensing have provided new means for large-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring. Traditional methods for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval require thermal and optical RS observations. In this study we compared the traditional trapezoid model parameterized based on the land surface temperature - normalized difference vegetation index (LST-NDVI) space with the recently developed optical trapezoid model OPTRAM parameterized based on the shortwave infrared transformed reflectance (STR)-NDVI space for an extensive sugarcane field located in Southwestern Iran. Twelve Landsat-8 satellite images were acquired during the sugarcane growth season (April to October 2016). Reference in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data were obtained at 22 locations at different depths via core sampling and oven-drying. The obtained results indicate that the thermal/optical and optical prediction methods are comparable, both with volumetric <span class="hlt">moisture</span> content estimation errors of about 0.04 cm3 cm-3. However, the OPTRAM model is more efficient because it does not require thermal data and can be universally parameterized for a specific location, because unlike the LST-<span class="hlt">soil</span> <span class="hlt">moisture</span> relationship, the reflectance-<span class="hlt">soil</span> <span class="hlt">moisture</span> relationship does not significantly vary with environmental variables (e.g., air temperature, wind speed, etc.).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160008107','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160008107"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission Level 4 Surface and Root Zone <span class="hlt">Soil</span> <span class="hlt">Moisture</span> (L4_SM) Product Specification Document</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.; Ardizzone, Joseph V.; Kim, Gi-Kong; Lucchesi, Robert A.; Smith, Edmond B.; Weiss, Barry H.</p> <p>2015-01-01</p> <p>This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone <span class="hlt">Soil</span> <span class="hlt">Moisture</span> (L4_SM) data for the Science Data System (SDS) of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project. The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission will enhance the accuracy and the resolution of space-based measurements of terrestrial <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze-thaw state. SMAP data products will have a noteworthy impact on multiple relevant and current Earth Science endeavors. These include: Understanding of the processes that link the terrestrial water, the energy and the carbon cycles, Estimations of global water and energy fluxes over the land surfaces, Quantification of the net carbon flux in boreal landscapes Forecast skill of both weather and climate, Predictions and monitoring of natural disasters including floods, landslides and droughts, and Predictions of agricultural productivity. To provide these data, the SMAP mission will deploy a satellite observatory in a near polar, sun synchronous orbit. The observatory will house an L-band radiometer that operates at 1.40 GHz and an L-band radar that operates at 1.26 GHz. The instruments will share a rotating reflector antenna with a 6 meter aperture that scans over a 1000 km swath.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170002761','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170002761"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Mission Status and Science Performance</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Yueh, Simon H.; Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni; Entin, Jared K.</p> <p>2016-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational since mid-April 2015. The SMAP radiometer has been operating flawlessly, but the radar transmitter ceased operation on July 7. This paper provides a status summary of the calibration and validation of the SMAP instruments and the quality assessment of its <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw products. Since the loss of the radar in July, the SMAP project has been conducting two parallel activities to enhance the resolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> products. One of them explores the Backus Gilbert optimum interpolation and de-convolution techniques based on the oversampling characteristics of the SMAP radiometer. The other investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic radar data to obtain <span class="hlt">soil</span> <span class="hlt">moisture</span> products at about 1 to 3 kilometers resolution. In addition, SMAP's L-band data have found many new applications, including vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20050182714&hterms=gravimetric+methods&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dgravimetric%2Bmethods','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20050182714&hterms=gravimetric+methods&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dgravimetric%2Bmethods"><span>A comparison of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors for space flight applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Norikane, J. H.; Prenger, J. J.; Rouzan-Wheeldon, D. T.; Levine, H. G.</p> <p>2005-01-01</p> <p>Plants will be an important part of future long-term space missions. Automated plant growth systems require accurate and reliable methods of monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span> levels. There are a number of different methods to accomplish this task. This study evaluated sensors using the capacitance method (ECH2O), the heat-pulse method (TMAS), and tensiometers, compared to <span class="hlt">soil</span> water loss measured gravimetrically in a side-by-side test. The experiment monitored evaporative losses from substrate compartments filled with 1- to 2-mm baked calcinated clay media. The ECH2O data correlated well with the gravimetric measurements, but over a limited range of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The averaged TMAS sensor data overstated <span class="hlt">soil</span> <span class="hlt">moisture</span> content levels. The tensiometer data appeared to track evaporative losses in the 0.5- to 2.5-kPa range of matric potential that corresponds to the water content needed to grow plants. This small range is characteristic of large particle media, and thus high-resolution tensiometers are required to distinguish changing <span class="hlt">moisture</span> contents in this range.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.2464B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.2464B"><span>Rainfall estimation from <span class="hlt">soil</span> <span class="hlt">moisture</span> data: crash test for SM2RAIN algorithm</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brocca, Luca; Albergel, Clement; Massari, Christian; Ciabatta, Luca; Moramarco, Tommaso; de Rosnay, Patricia</p> <p>2015-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> governs the partitioning of mass and energy fluxes between the land surface and the atmosphere and, hence, it represents a key variable for many applications in hydrology and earth science. In recent years, it was demonstrated that <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from ground and satellite sensors contain important information useful for improving rainfall estimation. Indeed, <span class="hlt">soil</span> <span class="hlt">moisture</span> data have been used for correcting rainfall estimates from state-of-the-art satellite sensors (e.g. Crow et al., 2011), and also for improving flood prediction through a dual data assimilation approach (e.g. Massari et al., 2014; Chen et al., 2014). Brocca et al. (2013; 2014) developed a simple algorithm, called SM2RAIN, which allows estimating rainfall directly from <span class="hlt">soil</span> <span class="hlt">moisture</span> data. SM2RAIN has been applied successfully to in situ and satellite observations. Specifically, by using three satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> products from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation) and SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity); it was found that the SM2RAIN-derived rainfall products are as accurate as state-of-the-art products, e.g., the real-time version of the TRMM (Tropical Rainfall Measuring Mission) product. Notwithstanding these promising results, a detailed study investigating the physical basis of the SM2RAIN algorithm, its range of applicability and its limitations on a global scale has still to be carried out. In this study, we carried out a crash test for SM2RAIN algorithm on a global scale by performing a synthetic experiment. Specifically, modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> data are obtained from HTESSEL model (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land) forced by ERA-Interim near-surface meteorology. Afterwards, the modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> data are used as input into SM2RAIN algorithm for testing weather or not the resulting rainfall estimates are able to reproduce ERA-Interim rainfall data. Correlation, root</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JARS...12a6030Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JARS...12a6030Z"><span>On the relationship between land surface infrared emissivity and <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhou, Daniel K.; Larar, Allen M.; Liu, Xu</p> <p>2018-01-01</p> <p>The relationship between surface infrared (IR) emissivity and <span class="hlt">soil</span> <span class="hlt">moisture</span> content has been investigated based on satellite measurements. Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> content can be estimated by IR remote sensing, namely using the surface parameters of IR emissivity, temperature, vegetation coverage, and <span class="hlt">soil</span> texture. It is possible to separate IR emissivity from other parameters affecting surface <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. The main objective of this paper is to examine the correlation between land surface IR emissivity and <span class="hlt">soil</span> <span class="hlt">moisture</span>. To this end, we have developed a simple yet effective scheme to estimate volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> (VSM) using IR land surface emissivity retrieved from satellite IR spectral radiance measurements, assuming those other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme is applied to a decade of global IR emissivity data retrieved from MetOp-A infrared atmospheric sounding interferometer measurements. The VSM estimated from these IR emissivity data (denoted as IR-VSM) is used to demonstrate its measurement-to-measurement variations. Representative 0.25-deg spatially-gridded monthly-mean IR-VSM global datasets are then assembled to compare with those routinely provided from satellite microwave (MW) multisensor measurements (denoted as MW-VSM), demonstrating VSM spatial variations as well as seasonal-cycles and interannual variability. Initial positive agreement is shown to exist between IR- and MW-VSM (i.e., R2 = 0.85). IR land surface emissivity contains surface water content information. So, when IR measurements are used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span>, this correlation produces results that correspond with those customarily achievable from MW measurements. A decade-long monthly-gridded emissivity atlas is used to estimate IR-VSM, to demonstrate its seasonal-cycle and interannual variation, which is spatially</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820016726','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820016726"><span>Evaluation of HCMM data for assessing <span class="hlt">soil</span> <span class="hlt">moisture</span> and water table depth. [South Dakota</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moore, D. G.; Heilman, J. L.; Tunheim, J. A.; Westin, F. C.; Heilman, W. E.; Beutler, G. A.; Ness, S. D. (Principal Investigator)</p> <p>1981-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> in the 0-cm to 4-cm layer could be estimated with 1-mm <span class="hlt">soil</span> temperatures throughout the growing season of a rainfed barley crop in eastern South Dakota. Empirical equations were developed to reduce the effect of canopy cover when radiometrically estimating the <span class="hlt">soil</span> temperature. Corrective equations were applied to an aircraft simulation of HCMM data for a diversity of crop types and land cover conditions to estimate the <span class="hlt">soil</span> <span class="hlt">moisture</span>. The average difference between observed and measured <span class="hlt">soil</span> <span class="hlt">moisture</span> was 1.6% of field capacity. Shallow alluvial aquifers were located with HCMM predawn data. After correcting the data for vegetation differences, equations were developed for predicting water table depths within the aquifer. A finite difference code simulating <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature shows that <span class="hlt">soils</span> with different <span class="hlt">moisture</span> profiles differed in <span class="hlt">soil</span> temperatures in a well defined functional manner. A significant surface thermal anomaly was found to be associated with shallow water tables.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18..925R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18..925R"><span>Mapping patterns of <span class="hlt">soil</span> properties and <span class="hlt">soil</span> <span class="hlt">moisture</span> using electromagnetic induction to investigate the impact of land use changes on <span class="hlt">soil</span> processes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Robinet, Jérémy; von Hebel, Christian; van der Kruk, Jan; Govers, Gerard; Vanderborght, Jan</p> <p>2016-04-01</p> <p>As highlighted by many authors, classical or geophysical techniques for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> such as destructive <span class="hlt">soil</span> sampling, neutron probes or Time Domain Reflectometry (TDR) have some major drawbacks. Among other things, they provide point scale information, are often intrusive and time-consuming. ElectroMagnetic Induction (EMI) instruments are often cited as a promising alternative hydrogeophysical methods providing more efficiently <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements ranging from hillslope to catchment scale. The overall objective of our research project is to investigate whether a combination of geophysical techniques at various scales can be used to study the impact of land use change on temporal and spatial variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> properties. In our work, apparent electrical conductivity (ECa) patterns are obtained with an EM multiconfiguration system. Depth profiles of ECa were subsequently inferred through a calibration-inversion procedure based on TDR data. The obtained spatial patterns of these profiles were linked to <span class="hlt">soil</span> profile and <span class="hlt">soil</span> water content distributions. Two catchments with contrasting land use (agriculture vs. natural forest) were selected in a subtropical region in the south of Brazil. On selected slopes within the catchments, combined EMI and TDR measurements were carried out simultaneously, under different atmospheric and <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. Ground-truth data for <span class="hlt">soil</span> properties were obtained through <span class="hlt">soil</span> sampling and auger profiles. The comparison of these data provided information about the potential of the EMI technique to deliver qualitative and quantitative information about the variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> properties.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H33K1729L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H33K1729L"><span>Impact of Tropical Cyclones on <span class="hlt">Soil</span> <span class="hlt">Moisture</span> over East Asia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liess, S.</p> <p>2016-12-01</p> <p>A simulation of a series of three strong typhoons (Frankie, Gloria, and Herb) during the 1996 typhoon season shows that the Weather Research and Forecasting (WRF) model is representing the general characteristics of each typhoon, including sharp right turns by Gloria and Herb over the Philippine Sea. These sharp right turns can be attributed to tropical easterly waves and they are responsible for landfall over Taiwan, instead of following the general direction toward the Philippines. A second simulation where the typhoon signal is removed before landfall over East Asia shows that both rainfall and <span class="hlt">soil</span> <span class="hlt">moisture</span> is increased by up to 30% in coastal regions after landfall, mostly to the north of the landfall region. However, despite the noisier signal in rainfall, significant increases in <span class="hlt">soil</span> <span class="hlt">moisture</span> related to the paths of the simulated typhoons occur as far west as western China and Myanmar. Strong winds associated with the typhoons can also increase local evaporation and thus locally reduce <span class="hlt">soil</span> <span class="hlt">moisture</span>, especially south of the landfall region. Detailed observations of hydrologic variables such as <span class="hlt">soil</span> <span class="hlt">moisture</span> are needed to evaluate these model studies not only over coastal regions but also further inland where typhoon signals are weaker but local <span class="hlt">moisture</span> availability is still influenced by increased rainfall and stronger winds.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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://hdl.handle.net/2060/19840005567','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19840005567"><span>A simulation study of scene confusion factors in sensing <span class="hlt">soil</span> <span class="hlt">moisture</span> from orbital radar</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. (Principal Investigator); Dobson, M. C.; Moezzi, S.; Roth, F. T.</p> <p>1983-01-01</p> <p>Simulated C-band radar imagery for a 124-km by 108-km test site in eastern Kansas is used to classify <span class="hlt">soil</span> <span class="hlt">moisture</span>. Simulated radar resolutions are 100 m by 100 m, 1 km by 1km, and 3 km by 3 km. Distributions of actual near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> are established daily for a 23-day accounting period using a water budget model. Within the 23-day period, three orbital radar overpasses are simulated roughly corresponding to generally moist, wet, and dry <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions. The radar simulations are performed by a target/sensor interaction model dependent upon a terrain model, land-use classification, and near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution. The accuracy of <span class="hlt">soil-moisture</span> classification is evaluated for each single-date radar observation and also for multi-date detection of relative <span class="hlt">soil</span> <span class="hlt">moisture</span> change. In general, the results for single-date <span class="hlt">moisture</span> detection show that 70% to 90% of cropland can be correctly classified to within +/- 20% of the true percent of field capacity. For a given radar resolution, the expected classification accuracy is shown to be dependent upon both the general <span class="hlt">soil</span> <span class="hlt">moisture</span> condition and also the geographical distribution of land-use and topographic relief. An analysis of cropland, urban, pasture/rangeland, and woodland subregions within the test site indicates that multi-temporal detection of relative <span class="hlt">soil</span> <span class="hlt">moisture</span> change is least sensitive to classification error resulting from scene complexity and topographic effects.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100031160','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100031160"><span>Evaluating the Utility of Remotely-Sensed <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals for Operational Agricultural Drought Monitoring</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt</p> <p>2010-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> using a two-layer modified Palmer <span class="hlt">soil</span> <span class="hlt">moisture</span> model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer <span class="hlt">soil</span> <span class="hlt">moisture</span> model. An assessment of <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> by comparing EnKF <span class="hlt">soil</span> <span class="hlt">moisture</span> results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.</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/2017AGUFM.H42B..02K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H42B..02K"><span>Land-atmosphere coupling and <span class="hlt">soil</span> <span class="hlt">moisture</span> memory contribute to long-term agricultural drought</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kumar, S.; Newman, M.; Lawrence, D. M.; Livneh, B.; Lombardozzi, D. L.</p> <p>2017-12-01</p> <p>We assessed the contribution of land-atmosphere coupling and <span class="hlt">soil</span> <span class="hlt">moisture</span> memory on long-term agricultural droughts in the US. We performed an ensemble of climate model simulations to study <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics under two atmospheric forcing scenarios: active and muted land-atmosphere coupling. Land-atmosphere coupling contributes to a 12% increase and 36% decrease in the decorrelation time scale of <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies in the US Great Plains and the Southwest, respectively. These differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> memory affect the length and severity of modeled drought. Consequently, long-term droughts are 10% longer and 3% more severe in the Great Plains, and 15% shorter and 21% less severe in the Southwest. An analysis of Coupled Model Intercomparsion Project phase 5 data shows four fold uncertainty in <span class="hlt">soil</span> <span class="hlt">moisture</span> memory across models that strongly affects simulated long-term droughts and is potentially attributable to the differences in <span class="hlt">soil</span> water storage capacity across models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29176688','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29176688"><span>Elevated <span class="hlt">moisture</span> stimulates carbon loss from mineral <span class="hlt">soils</span> by releasing protected organic matter.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Huang, Wenjuan; Hall, Steven J</p> <p>2017-11-24</p> <p><span class="hlt">Moisture</span> response functions for <span class="hlt">soil</span> microbial carbon (C) mineralization remain a critical uncertainty for predicting ecosystem-climate feedbacks. Theory and models posit that C mineralization declines under elevated <span class="hlt">moisture</span> and associated anaerobic conditions, leading to <span class="hlt">soil</span> C accumulation. Yet, iron (Fe) reduction potentially releases protected C, providing an under-appreciated mechanism for C destabilization under elevated <span class="hlt">moisture</span>. Here we incubate Mollisols from ecosystems under C 3 /C 4 plant rotations at <span class="hlt">moisture</span> levels at and above field capacity over 5 months. Increased <span class="hlt">moisture</span> and anaerobiosis initially suppress <span class="hlt">soil</span> C mineralization, consistent with theory. However, after 25 days, elevated <span class="hlt">moisture</span> stimulates cumulative gaseous C-loss as CO 2 and CH 4 to >150% of the control. Stable C isotopes show that mineralization of older C 3 -derived C released following Fe reduction dominates C losses. Counter to theory, elevated <span class="hlt">moisture</span> may significantly accelerate C losses from mineral <span class="hlt">soils</span> over weeks to months-a critical mechanistic deficiency of current Earth system models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=344150','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=344150"><span>Estimating surface <span class="hlt">soil</span> <span class="hlt">moisture</span> from SMAP observations using a neural network technique</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 Neural Network (NN) algorithm was developed to estimate global surface <span class="hlt">soil</span> <span class="hlt">moisture</span> for April 2015 to June 2016 with a 2-3 day repeat frequency using passive microwave observations from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite, surface <span class="hlt">soil</span> temperatures from the NASA Goddard Earth Observ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B51G1895B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B51G1895B"><span>Landscape-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> heterogeneity and its influence on surface fluxes at the Jornada LTER site: Evaluating a new model parameterization for subgrid-scale <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>Baker, I. T.; Prihodko, L.; Vivoni, E. R.; Denning, A. S.</p> <p>2017-12-01</p> <p>Arid and semiarid regions represent a large fraction of global land, with attendant importance of surface energy and trace gas flux to global totals. These regions are characterized by strong seasonality, especially in precipitation, that defines the level of ecosystem stress. Individual plants have been observed to respond non-linearly to increasing <span class="hlt">soil</span> <span class="hlt">moisture</span> stress, where plant function is generally maintained as <span class="hlt">soils</span> dry down to a threshold at which rapid closure of stomates occurs. Incorporating this nonlinear mechanism into landscape-scale models can result in unrealistic binary "on-off" behavior that is especially problematic in arid landscapes. Subsequently, models have `relaxed' their simulation of <span class="hlt">soil</span> <span class="hlt">moisture</span> stress on evapotranspiration (ET). Unfortunately, these relaxations are not physically based, but are imposed upon model physics as a means to force a more realistic response. Previously, we have introduced a new method to represent <span class="hlt">soil</span> <span class="hlt">moisture</span> regulation of ET, whereby the landscape is partitioned into `BINS' of <span class="hlt">soil</span> <span class="hlt">moisture</span> wetness, each associated with a fractional area of the landscape or grid cell. A physically- and observationally-based nonlinear <span class="hlt">soil</span> <span class="hlt">moisture</span> stress function is applied, but when convolved with the relative area distribution represented by wetness BINS the system has the emergent property of `smoothing' the landscape-scale response without the need for non-physical impositions on model physics. In this research we confront BINS simulations of Bowen ratio, <span class="hlt">soil</span> <span class="hlt">moisture</span> variability and trace gas flux with <span class="hlt">soil</span> <span class="hlt">moisture</span> and eddy covariance observations taken at the Jornada LTER dryland site in southern New Mexico. We calculate the mean annual wetting cycle and associated variability about the mean state and evaluate model performance against this variability and time series of land surface fluxes from the highly instrumented Tromble Weir watershed. The BINS simulations capture the relatively rapid reaction to wetting</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/13771','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/13771"><span>Determining <span class="hlt">soil</span> volumetric <span class="hlt">moisture</span> content using time domain reflectometry</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>1998-02-01</p> <p>Time domain reflectometry (TDR) is a technique used to measure indirectly the in situ volumetric <span class="hlt">moisture</span> content of <span class="hlt">soil</span>. Current research provides a variety of prediction equations that estimate the volumetric <span class="hlt">moisture</span> content using the dielectric ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1614609O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1614609O"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Orr, Barron; Moran, M. Susan; Escobar, Vanessa; Brown, Molly E.</p> <p>2014-05-01</p> <p>The launch of the NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission in 2014 will provide global <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze-thaw measurements at moderate resolution (9 km) with latency as short as 24 hours. The resolution, latency and global coverage of SMAP products will enable new applications in the fields of weather, climate, drought, flood, agricultural production, human health and national security. To prepare for launch, the SMAP mission has engaged more than 25 Early Adopters. Early Adopters are users who have a need for SMAP-like <span class="hlt">soil</span> <span class="hlt">moisture</span> or freeze-thaw data, and who agreed to apply their own resources to demonstrate the utility of SMAP data for their particular system or model. In turn, the SMAP mission agreed to provide Early Adopters with simulated SMAP data products and pre-launch calibration and validation data from SMAP field campaigns, modeling, and synergistic studies. The applied research underway by Early Adopters has provided fundamental knowledge of how SMAP data products can be scaled and integrated into users' policy, business and management activities to improve decision-making efforts. This presentation will cover SMAP applications including weather and climate forecasting, vehicle mobility estimation, quantification of greenhouse gas emissions, management of urban potable water supply, and prediction of crop yield. The presentation will end with a discussion of potential international applications with focus on the ESA/CEOS TIGER Initiative entitled "looking for water in Africa", the United Nations (UN) Convention to Combat Desertification (UNCCD) which carries a specific mandate focused on Africa, the UN Framework Convention on Climate Change (UNFCCC) which lists <span class="hlt">soil</span> <span class="hlt">moisture</span> as an Essential Climate Variable (ECV), and the UN Food and Agriculture Organization (FAO) which reported a food and nutrition crisis in the Sahel.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817862S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817862S"><span>Combined evaluation of optical and microwave satellite dataset for <span class="hlt">soil</span> <span class="hlt">moisture</span> deficit estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Singh, Sudhir Kumar; Gupta, Manika; Gupta, Dileep Kumar; Kumar, Pradeep</p> <p>2016-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable responsible for water and energy exchanges from land surface to the atmosphere (Srivastava et al., 2014). On the other hand, <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit (or SMD) can help regulating the proper use of water at specified time to avoid any agricultural losses (Srivastava et al., 2013b) and could help in preventing natural disasters, e.g. flood and drought (Srivastava et al., 2013a). In this study, evaluation of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and <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) satellites are attempted for prediction of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit (SMD). Sophisticated algorithm like Adaptive Neuro Fuzzy Inference System (ANFIS) is used for prediction of SMD using the MODIS and SMOS dataset. The benchmark SMD estimated from Probability Distributed Model (PDM) over the Brue catchment, Southwest of England, U.K. is used for all the validation. The performances are assessed in terms of Nash Sutcliffe Efficiency, Root Mean Square Error and the percentage of bias between ANFIS simulated SMD and the benchmark. The performance statistics revealed a good agreement between benchmark and the ANFIS estimated SMD using the MODIS dataset. The assessment of the products with respect to this peculiar evidence is an important step for successful development of hydro-meteorological model and forecasting system. The analysis of the satellite products (viz. SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> and MODIS LST) towards SMD prediction is a crucial step for successful hydrological modelling, agriculture and water resource management, and can provide important assistance in policy and decision making. Keywords: Land Surface Temperature, MODIS, SMOS, <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit, Fuzzy Logic System References: Srivastava, P.K., Han, D., Ramirez, M.A., Islam, T., 2013a. Appraisal of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> at a catchment scale in a temperate maritime climate. Journal of Hydrology 498, 292-304. Srivastava, P.K., Han, D., Rico</p> </li> <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 affect 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 affect <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/2016AGUFM.H51H1598F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H51H1598F"><span>SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Disaggregation using Land Surface Temperature and Vegetation Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fang, B.; Lakshmi, V.</p> <p>2016-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> (SM) is a key parameter in agriculture, hydrology and ecology studies. The global SM retrievals have been providing by microwave remote sensing technology since late 1970s and many SM retrieval algorithms have been developed, calibrated and applied on satellite sensors such as AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System), AMSR-2 (Advanced Microwave Scanning Radiometer 2) and SMOS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity). Particularly, SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active/Passive) satellite, which was developed by NASA, was launched in January 2015. SMAP provides <span class="hlt">soil</span> <span class="hlt">moisture</span> products of 9 km and 36 km spatial resolutions which are not capable for research and applications of finer scale. Toward this issue, this study applied a SM disaggregation algorithm to disaggregate SMAP passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> 36 km product. This algorithm was developed based on the thermal inertial relationship between daily surface temperature variation and daily average <span class="hlt">soil</span> <span class="hlt">moisture</span> which is modulated by vegetation condition, by using remote sensing retrievals from AVHRR (Advanced Very High Resolution Radiometer, MODIS (Moderate Resolution Imaging Spectroradiometer), SPOT (Satellite Pour l'Observation de la Terre), as well as Land Surface Model (LSM) output from NLDAS (North American Land Data Assimilation System). The disaggregation model was built at 1/8o spatial resolution on monthly basis and was implemented to calculate and disaggregate SMAP 36 km SM retrievals to 1 km resolution in Oklahoma. The SM disaggregation results were also validated using MESONET (Mesoscale Network) and MICRONET (Microscale Network) ground SM measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110023466','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110023466"><span>A Comparison of Methods for a Priori Bias Correction in <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data Assimilation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.</p> <p>2011-01-01</p> <p>Data assimilation is being increasingly used to merge remotely sensed land surface variables such as <span class="hlt">soil</span> <span class="hlt">moisture</span>, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation experiments is used to compare two approaches that address typical biases in <span class="hlt">soil</span> <span class="hlt">moisture</span> prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the <span class="hlt">soil</span> <span class="hlt">moisture</span> observations, and (ii) scaling of the observations to the model s <span class="hlt">soil</span> <span class="hlt">moisture</span> climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation and confirms that both approaches adequately address the issue.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.3052C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.3052C"><span>Effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on diurnal convection and precipitation in Large-Eddy Simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cioni, Guido; Hohenegger, Cathy</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> and convective precipitation are generally thought to be strongly coupled, although limitations in the modeling set-up of past studies due to coarse resolutions, and thus poorly resolved convective processes, have prevented a trustful determination of the strength and sign of this coupling. In this work the <span class="hlt">soil</span> <span class="hlt">moisture</span>-precipitation feedback is investigated by means of high-resolution simulations where convection is explicitly resolved. To that aim we use the LES (Large Eddy Simulation) version of the ICON model with a grid spacing of 250 m, coupled to the TERRA-ML <span class="hlt">soil</span> model. We use homogeneous initial <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions and focus on the precipitation response to increase/decrease of the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> for various atmospheric profiles. The experimental framework proposed by Findell and Eltahir (2003) is revisited by using the same atmospheric soundings as initial condition but allowing a full interaction of the atmosphere with the land-surface over a complete diurnal cycle. In agreement with Findell and Eltahir (2003) the triggering of convection can be favoured over dry <span class="hlt">soils</span> or over wet <span class="hlt">soils</span> depending on the initial atmospheric sounding. However, total accumulated precipitation is found to always decrease over dry <span class="hlt">soils</span> regardless of the employed sounding, thus highlighting a positive <span class="hlt">soil</span> <span class="hlt">moisture</span>-precipitation feedback (more rain over wetter <span class="hlt">soils</span>) for the considered cases. To understand these differences and to infer under which conditions a negative feedback may occur, the total accumulated precipitation is split into its magnitude and duration component. While the latter can exhibit a dry <span class="hlt">soil</span> advantage, the precipitation magnitude strongly correlates with the surface latent heat flux and thus always exhibits a wet <span class="hlt">soil</span> advantage. The dependency is so strong that changes in duration cannot offset it. This simple argument shows that, in our idealised setup, a negative feedback is unlikely to be observed. The effects of other</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GMD....10.1903M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GMD....10.1903M"><span>GLEAM v3: satellite-based land evaporation and 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>Martens, Brecht; Miralles, Diego G.; Lievens, Hans; van der Schalie, Robin; de Jeu, Richard A. M.; Fernández-Prieto, Diego; Beck, Hylke E.; Dorigo, Wouter A.; Verhoest, Niko E. C.</p> <p>2017-05-01</p> <p>The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span>, including a 36-year data set spanning 1980-2015, referred to as v3a (based on satellite-observed <span class="hlt">soil</span> <span class="hlt">moisture</span>, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and <span class="hlt">soil</span> <span class="hlt">moisture</span>, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003-2015) and observations from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011-2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors across a broad range of ecosystems. Results indicate that the quality of the v3 <span class="hlt">soil</span> <span class="hlt">moisture</span> is consistently better than the one from v2: average correlations against in situ surface <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of <span class="hlt">soil</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20120002049&hterms=Soil+solution&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Bsolution','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20120002049&hterms=Soil+solution&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Bsolution"><span>In Situ Validation of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Satellite Mission</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jackson, T.; Cosh, M.; Crow, W.; Colliander, A.; Walker, J.</p> <p>2011-01-01</p> <p>SMAP is a new NASA mission proposed for 2014 that would provide a number of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw products. The <span class="hlt">soil</span> <span class="hlt">moisture</span> products span spatial resolutions from 3 to 40 km. In situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations will be one of the key elements of the validation program for SMAP. Data from the currently available set of <span class="hlt">soil</span> <span class="hlt">moisture</span> observing sites and networks need improvement if they are to be useful. Problems include a lack of standardization of instrumentation and installation and the disparity in spatial scale between the point-scale in situ data (a few centimeters) and the coarser satellite products. SMAP has initiated activities to resolve these issues for some of the existing resources. The other challenge to <span class="hlt">soil</span> <span class="hlt">moisture</span> validation is the need to expand the number of sites and their geographic distribution. SMAP is attempting to increase the number of sites and their value in validation through collaboration. The issues and solutions involving in situ validation being investigated will be described along with recent results from SMAP validation projects.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=311907','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=311907"><span>Aquarius/SAC-D <span class="hlt">soil</span> <span class="hlt">moisture</span> product using V3.0 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>Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. The <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm development ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JHyd..561..833S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JHyd..561..833S"><span>The impact of non-isothermal <span class="hlt">soil</span> <span class="hlt">moisture</span> transport on evaporation fluxes in a maize cropland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shao, Wei; Coenders-Gerrits, Miriam; Judge, Jasmeet; Zeng, Yijian; Su, Ye</p> <p>2018-06-01</p> <p>The process of evaporation interacts with the <span class="hlt">soil</span>, which has various comprehensive mechanisms. Multiphase flow models solve air, vapour, water, and heat transport equations to simulate non-isothermal <span class="hlt">soil</span> <span class="hlt">moisture</span> transport of both liquid water and vapor flow, but are only applied in non-vegetated <span class="hlt">soils</span>. For (sparsely) vegetated <span class="hlt">soils</span> often energy balance models are used, however these lack the detailed information on non-isothermal <span class="hlt">soil</span> <span class="hlt">moisture</span> transport. In this study we coupled a multiphase flow model with a two-layer energy balance model to study the impact of non-isothermal <span class="hlt">soil</span> <span class="hlt">moisture</span> transport on evaporation fluxes (i.e., interception, transpiration, and <span class="hlt">soil</span> evaporation) for vegetated <span class="hlt">soils</span>. The proposed model was implemented at an experimental agricultural site in Florida, US, covering an entire maize-growing season (67 days). As the crops grew, transpiration and interception became gradually dominated, while the fraction of <span class="hlt">soil</span> evaporation dropped from 100% to less than 20%. The mechanisms of <span class="hlt">soil</span> evaporation vary depending on the <span class="hlt">soil</span> <span class="hlt">moisture</span> content. After precipitation the <span class="hlt">soil</span> <span class="hlt">moisture</span> content increased, exfiltration of the liquid water flow could transport sufficient water to sustain evaporation from <span class="hlt">soil</span>, and the <span class="hlt">soil</span> vapor transport was not significant. However, after a sufficient dry-down period, the <span class="hlt">soil</span> <span class="hlt">moisture</span> content significantly reduced, and the <span class="hlt">soil</span> vapour flow significantly contributed to the upward <span class="hlt">moisture</span> transport in topmost <span class="hlt">soil</span>. A sensitivity analysis found that the simulations of <span class="hlt">moisture</span> content and temperature at the <span class="hlt">soil</span> surface varied substantially when including the advective (i.e., advection and mechanical dispersion) vapour transport in simulation, including the mechanism of advective vapour transport decreased <span class="hlt">soil</span> evaporation rate under wet condition, while vice versa under dry condition. The results showed that the formulation of advective <span class="hlt">soil</span> vapor transport in a <span class="hlt">soil</span>-vegetation-atmosphere transfer continuum can</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JHyd..559..684D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JHyd..559..684D"><span>Using repeat electrical resistivity surveys to assess heterogeneity in <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics under contrasting vegetation types</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dick, Jonathan; Tetzlaff, Doerthe; Bradford, John; Soulsby, Chris</p> <p>2018-04-01</p> <p>As the relationship between vegetation and <span class="hlt">soil</span> <span class="hlt">moisture</span> is complex and reciprocal, there is a need to understand how spatial patterns in <span class="hlt">soil</span> <span class="hlt">moisture</span> influence the distribution of vegetation, and how the structure of vegetation canopies and root networks regulates the partitioning of precipitation. Spatial patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> are often difficult to visualise as usually, <span class="hlt">soil</span> <span class="hlt">moisture</span> is measured at point scales, and often difficult to extrapolate. Here, we address the difficulties in collecting large amounts of spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> data through a study combining plot- and transect-scale electrical resistivity tomography (ERT) surveys to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> in a 3.2 km2 upland catchment in the Scottish Highlands. The aim was to assess the spatio-temporal variability in <span class="hlt">soil</span> <span class="hlt">moisture</span> under Scots pine forest (Pinus sylvestris) and heather moorland shrubs (Calluna vulgaris); the two dominant vegetation types in the Scottish Highlands. The study focussed on one year of fortnightly ERT surveys. The surveyed resistivity data was inverted and Archie's law was used to calculate volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> by estimating parameters and comparing against field measured data. Results showed that spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns were more heterogeneous in the forest site, as were patterns of wetting and drying, which can be linked to vegetation distribution and canopy structure. The heather site showed a less heterogeneous response to wetting and drying, reflecting the more uniform vegetation cover of the shrubs. Comparing <span class="hlt">soil</span> <span class="hlt">moisture</span> temporal variability during growing and non-growing seasons revealed further contrasts: under the heather there was little change in <span class="hlt">soil</span> <span class="hlt">moisture</span> during the growing season. Greatest changes in the forest were in areas where the trees were concentrated reflecting water uptake and canopy partitioning. Such differences have implications for climate and land use changes; increased forest cover can lead to greater spatial variability, greater</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JESS..127...24M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JESS..127...24M"><span>Estimation of improved resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> in vegetated areas using passive AMSR-E data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moradizadeh, Mina; Saradjian, Mohammad R.</p> <p>2018-03-01</p> <p>Microwave remote sensing provides a unique capability for <span class="hlt">soil</span> parameter retrievals. Therefore, various <span class="hlt">soil</span> parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the <span class="hlt">soil</span> parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimations that have been validated against in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data. Advance Microwave Scanning Radiometer-EOS BT data in <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the <span class="hlt">soil</span> <span class="hlt">moisture</span> variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=342425','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=342425"><span><span class="hlt">Absolute</span> <span class="hlt">moisture</span> sensing for cotton bales</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 recent prevalence of <span class="hlt">moisture</span> restoration systems in cotton gins, more and more gins are putting <span class="hlt">moisture</span> back into the bales immediately before the packaging operation. There are two main reasons for this recent trend, the first is that it has been found that added <span class="hlt">moisture</span> at the bale pre...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830007480','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830007480"><span>Microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>, volume 1. [Guymon, Oklahoma and Dalhart, Texas</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mcfarland, M. J. (Principal Investigator); Theis, S. W.; Rosenthal, W. D.; Jones, C. L.</p> <p>1982-01-01</p> <p>Multifrequency sensor data from NASA's C-130 aircraft were used to determine which of the all weather microwave sensors demonstrated the highest correlation to surface <span class="hlt">soil</span> <span class="hlt">moisture</span> over optimal bare <span class="hlt">soil</span> conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to <span class="hlt">soil</span> <span class="hlt">moisture</span>. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> over bare fields. The perpendicular vegetation index (PVI) as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to <span class="hlt">soil</span> <span class="hlt">moisture</span>. A linear equation was developed to estimate percent field capacity as a function of L-band emissivity and the vegetation index. The prediction algorithm improves the estimation of <span class="hlt">moisture</span> significantly over predictions from L-band emissivity alone.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=330164','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=330164"><span>SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> drying more rapid than observed in situ following rainfall events</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>We examine <span class="hlt">soil</span> drying rates by comparing observations from the NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission to surface <span class="hlt">soil</span> <span class="hlt">moisture</span> from in situ probes during drydown periods at SMAP validation sites. SMAP and in situ probes record different <span class="hlt">soil</span> drying dynamics after rainfall. We modeled this...</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 affected <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('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308353&keyword=water+AND+sensor&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308353&keyword=water+AND+sensor&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>Spatial Distribution of Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in a Small Forested Catchment</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>Predicting the spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> is an important hydrological question. We measured the spatial distribution of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (upper 6 cm) using an Amplitude Domain Reflectometry sensor at the plot scale (2 × 2 m) and small catchment scale (0.84 ha) in...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=340301','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=340301"><span>Development and assessment of the SMAP enhanced 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>Launched in January 2015, the National Aeronautics and Space Administration (NASA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) observatory was designed to provide frequent 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 a radar and a radiometer operating a...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.5695J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.5695J"><span>Validation of Distributed <span class="hlt">Soil</span> <span class="hlt">Moisture</span>: Airborne Polarimetric SAR vs. Ground-based Sensor Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jagdhuber, T.; Kohling, M.; Hajnsek, I.; Montzka, C.; Papathanassiou, K. P.</p> <p>2012-04-01</p> <p>The knowledge of spatially distributed <span class="hlt">soil</span> <span class="hlt">moisture</span> is highly desirable for an enhanced hydrological modeling in terms of flood prevention and for yield optimization in combination with precision farming. Especially in mid-latitudes, the growing agricultural vegetation results in an increasing <span class="hlt">soil</span> coverage along the crop cycle. For a remote sensing approach, this vegetation influence has to be separated from the <span class="hlt">soil</span> contribution within the resolution cell to extract the actual <span class="hlt">soil</span> <span class="hlt">moisture</span>. Therefore a hybrid decomposition was developed for estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> under vegetation cover using fully polarimetric SAR data. The novel polarimetric decomposition combines a model-based decomposition, separating the volume component from the ground components, with an eigen-based decomposition of the two ground components into a surface and a dihedral scattering contribution. Hence, this hybrid decomposition, which is based on [1,2], establishes an innovative way to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> under vegetation. The developed inversion algorithm for <span class="hlt">soil</span> <span class="hlt">moisture</span> under vegetation cover is applied on fully polarimetric data of the TERENO campaign, conducted in May and June 2011 for the Rur catchment within the Eifel/Lower Rhine Valley Observatory. The fully polarimetric SAR data were acquired in high spatial resolution (range: 1.92m, azimuth: 0.6m) by DLR's novel F-SAR sensor at L-band. The inverted <span class="hlt">soil</span> <span class="hlt">moisture</span> product from the airborne SAR data is validated with corresponding distributed ground measurements for a quality assessment of the developed algorithm. The in situ measurements were obtained on the one hand by mobile FDR probes from agricultural fields near the towns of Merzenhausen and Selhausen incorporating different crop types and on the other hand by distributed wireless sensor networks (<span class="hlt">Soil</span>Net clusters) from a grassland test site (near the town of Rollesbroich) and from a forest stand (within the Wüstebach sub-catchment). Each <span class="hlt">Soil</span>Net cluster</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/50485','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/50485"><span>Seedling establishment and physiological responses to temporal and spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> changes</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Jeremy Pinto; John D. Marshall; Kas Dumroese; Anthony S. Davis; Douglas R. Cobos</p> <p>2016-01-01</p> <p>In many forests of the world, the summer season (temporal element) brings drought conditions causing low <span class="hlt">soil</span> <span class="hlt">moisture</span> in the upper <span class="hlt">soil</span> profile (spatial element) - a potentially large barrier to seedling establishment. We evaluated the relationship between initial seedling root depth, temporal and spatial changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> during drought after...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22315556','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22315556"><span>ELBARA II, an L-band radiometer system for <span class="hlt">soil</span> <span class="hlt">moisture</span> research.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Schwank, Mike; Wiesmann, Andreas; Werner, Charles; Mätzler, Christian; Weber, Daniel; Murk, Axel; Völksch, Ingo; Wegmüller, Urs</p> <p>2010-01-01</p> <p>L-band (1-2 GHz) microwave radiometry is a remote sensing technique that can be used to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span>, and is deployed in the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) Mission of the European Space Agency (ESA). Performing ground-based radiometer campaigns before launch, during the commissioning phase and during the operative SMOS mission is important for validating the satellite data and for the further improvement of the radiative transfer models used in the <span class="hlt">soil-moisture</span> retrieval algorithms. To address these needs, three identical L-band radiometer systems were ordered by ESA. They rely on the proven architecture of the ETH L-Band radiometer for <span class="hlt">soil</span> <span class="hlt">moisture</span> research (ELBARA) with major improvements in the microwave electronics, the internal calibration sources, the data acquisition, the user interface, and the mechanics. The purpose of this paper is to describe the design of the instruments and the main characteristics that are relevant for the user.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3270857','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3270857"><span>ELBARA II, an L-Band Radiometer System for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Research</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Schwank, Mike; Wiesmann, Andreas; Werner, Charles; Mätzler, Christian; Weber, Daniel; Murk, Axel; Völksch, Ingo; Wegmüller, Urs</p> <p>2010-01-01</p> <p>L-band (1–2 GHz) microwave radiometry is a remote sensing technique that can be used to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span>, and is deployed in the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) Mission of the European Space Agency (ESA). Performing ground-based radiometer campaigns before launch, during the commissioning phase and during the operative SMOS mission is important for validating the satellite data and for the further improvement of the radiative transfer models used in the <span class="hlt">soil-moisture</span> retrieval algorithms. To address these needs, three identical L-band radiometer systems were ordered by ESA. They rely on the proven architecture of the ETH L-Band radiometer for <span class="hlt">soil</span> <span class="hlt">moisture</span> research (ELBARA) with major improvements in the microwave electronics, the internal calibration sources, the data acquisition, the user interface, and the mechanics. The purpose of this paper is to describe the design of the instruments and the main characteristics that are relevant for the user. PMID:22315556</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3587384','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3587384"><span>Evaluation of the predicted error of the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval from C-band SAR by comparison against modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates over Australia</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter</p> <p>2012-01-01</p> <p>The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring. Currently, updated <span class="hlt">soil</span> <span class="hlt">moisture</span> data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in <span class="hlt">soil</span> <span class="hlt">moisture</span>. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated <span class="hlt">soil</span> <span class="hlt">moisture</span> over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ESASP.729E..15J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ESASP.729E..15J"><span>Estimation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Under Vegetation Cover at Multiple Frequencies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jadghuber, Thomas; Hajnsek, Irena; Weiß, Thomas; Papathanassiou, Konstantinos P.</p> <p>2015-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> under vegetation cover was estimated by a polarimetric, iterative, generalized, hybrid decomposition and inversion approach at multiple frequencies (X-, C- and L-band). Therefore the algorithm, originally designed for longer wavelength (L-band), was adapted to deal with the short wavelength scattering scenarios of X- and C-band. The Integral Equation Method (IEM) was incorporated together with a pedo-transfer function of Dobson et al. to account for the peculiarities of short wavelength scattering at X- and C-band. DLR's F-SAR system acquired fully polarimetric SAR data in X-, C- and L-band over the Wallerfing test site in Lower Bavaria, Germany in 2014. Simultaneously, <span class="hlt">soil</span> and vegetation measurements were conducted on different agricultural test fields. The results indicate a spatially continuous inversion of <span class="hlt">soil</span> <span class="hlt">moisture</span> in all three frequencies (inversion rates >92%), mainly due to the careful adaption of the vegetation volume removal including a physical constraining of the decomposition algorithm. However, for X- and C-band the inversion results reveal <span class="hlt">moisture</span> pattern inconsistencies and in some cases an incorrectly high inversion of <span class="hlt">soil</span> <span class="hlt">moisture</span> at X-band. The validation with in situ measurements states a stable performance of 2.1- 7.6vol.% at L-band for the entire growing period. At C- and X-band a reliable performance of 3.7-13.4vol.% in RMSE can only be achieved after distinct filtering (X- band) leading to a loss of almost 60% in spatial inversion rate. Hence, a robust inversion for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation under vegetation cover can only be conducted at L-band due to a constant availability of the <span class="hlt">soil</span> signal in contrast to higher frequencies (X- and C-band).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170007429&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170007429&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dsoil"><span>Validation and Scaling of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in a Semi-Arid Environment: SMAP Validation Experiment 2015 (SMAPVEX15)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Colliander, Andreas; Cosh, Michael H.; Misra, Sidharth; Jackson, Thomas J.; Crow, Wade T.; Chan, Steven; Bindlish, Rajat; Chae, Chun; Holifield Collins, Chandra; Yueh, Simon H.</p> <p>2017-01-01</p> <p>The NASA SMAP (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of <span class="hlt">soil</span> <span class="hlt">moisture</span> products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the ground and the coarse resolution radiometer-based <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS <span class="hlt">soil</span> <span class="hlt">moisture</span> acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors. It was also concluded that the spatial distribution of the <span class="hlt">soil</span> <span class="hlt">moisture</span> resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately10 of the observations) the following metrics were obtained: RMSD (root mean square difference) of0.016m3m3 and correlation of 0.83. The</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920044615&hterms=soil+environment&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsoil%2Benvironment','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920044615&hterms=soil+environment&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsoil%2Benvironment"><span>Multifrequency passive microwave observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> in an arid rangeland environment</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.; Schmugge, T. J.; Parry, R.; Kustas, W. P.; Ritchie, J. C.; Shutko, A. M.; Khaldin, A.; Reutov, E.; Novichikhin, E.; Liberman, B.</p> <p>1992-01-01</p> <p>A cooperative experiment was conducted by teams from the U.S. and U.S.S.R. to evaluate passive microwave instruments and algorithms used to estimate surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Experiments were conducted as part of an interdisciplinary experiment in an arid rangeland watershed located in the southwest United States. Soviet microwave radiometers operating at wavelengths of 2.25, 21 and 27 cm were flown on a U.S. aircraft. Radio frequency interference limited usable data to the 2.25 and 21 cm systems. Data have been calibrated and compared to ground observations of <span class="hlt">soil</span> <span class="hlt">moisture</span>. These analyses showed that the 21 cm system could produce reliable and useful <span class="hlt">soil</span> <span class="hlt">moisture</span> information and that the 2.25 cm system was of no value for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation in this experiment.</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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4366536','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4366536"><span>Reconciling spatial and temporal <span class="hlt">soil</span> <span class="hlt">moisture</span> effects on afternoon rainfall</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Guillod, Benoit P.; Orlowsky, Boris; Miralles, Diego G.; Teuling, Adriaan J.; Seneviratne, Sonia I.</p> <p>2015-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> impacts on precipitation have been strongly debated. Recent observational evidence of afternoon rain falling preferentially over land parcels that are drier than the surrounding areas (negative spatial effect), contrasts with previous reports of a predominant positive temporal effect. However, whether spatial effects relating to <span class="hlt">soil</span> <span class="hlt">moisture</span> heterogeneity translate into similar temporal effects remains unknown. Here we show that afternoon precipitation events tend to occur during wet and heterogeneous <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions, while being located over comparatively drier patches. Using remote-sensing data and a common analysis framework, spatial and temporal correlations with opposite signs are shown to coexist within the same region and data set. Positive temporal coupling might enhance precipitation persistence, while negative spatial coupling tends to regionally homogenize land surface conditions. Although the apparent positive temporal coupling does not necessarily imply a causal relationship, these results reconcile the notions of <span class="hlt">moisture</span> recycling with local, spatially negative feedbacks. PMID:25740589</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170005499&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsoil','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170005499&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsoil"><span>Validation of SMAP Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products with Core Validation Sites</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Colliander, A.; Jackson, T. J.; Bindlish, R.; Chan, S.; Das, N.; Kim, S. B.; Cosh, M. H.; Dunbar, R. S.; Dang, L.; Pashaian, L.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20170005499'); toggleEditAbsImage('author_20170005499_show'); toggleEditAbsImage('author_20170005499_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20170005499_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20170005499_hide"></p> <p>2017-01-01</p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm performance. Those sites provide well calibrated in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements within SMAP product grid pixels for diverse conditions and locations.The estimation of the average <span class="hlt">soil</span> <span class="hlt">moisture</span> within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution. SMAP fulfilled these requirements through a collaborative CalVal Partner program.This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34 candidate sites 18 sites fulfilled all the requirements at one of the resolution scales (at least). The rest of the sites are used as secondary information in algorithm evaluation. The results indicate that the SMAP radiometer-based <span class="hlt">soil</span> <span class="hlt">moisture</span> data product meets its expected performance of 0.04 cu m/cu m volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> (unbiased root mean square error); the combined radar-radiometer product is close to its expected performance of 0.04 cu m/cu m, and the radar-based product meets its target accuracy of 0.06 cu m/cu m (the lengths of the combined and radar-based products are truncated to about 10 weeks because of the SMAP radar failure). Upon completing the intensive CalVal phase of the mission the SMAP project will continue to enhance the products in the primary and extended geographic domains, in co-operation with the CalVal Partners, by continuing the comparisons over the existing core validation sites and inclusion of candidate sites that can address shortcomings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70162445','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70162445"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> response to experimentally altered snowmelt timing is mediated by <span class="hlt">soil</span>, vegetation, and regional climate patterns</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Conner, Lafe G; Gill, Richard A.; Belnap, Jayne</p> <p>2016-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> in seasonally snow-covered environments fluctuates seasonally between wet and dry states. Climate warming is advancing the onset of spring snowmelt and may lengthen the summer-dry state and ultimately cause drier <span class="hlt">soil</span> conditions. The magnitude of either response may vary across elevation and vegetation types. We situated our study at the lower boundary of persistent snow cover and the upper boundary of subalpine forest with paired treatment blocks in aspen forest and open meadow. In treatments plots, we advanced snowmelt timing by an average of 14 days by adding dust to the snow surface during spring melt. We specifically wanted to know whether early snowmelt would increase the duration of the summer-dry period and cause <span class="hlt">soils</span> to be drier in the early-snowmelt treatments compared with control plots. We found no difference in the onset of the summer-dry state and no significant differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> between treatments. To better understand the reasons <span class="hlt">soil</span> <span class="hlt">moisture</span> did not respond to early snowmelt as expected, we examined the mediating influences of <span class="hlt">soil</span> organic matter, texture, temperature, and the presence or absence of forest. In our study, late-spring precipitation may have moderated the effects of early snowmelt on <span class="hlt">soil</span> <span class="hlt">moisture</span>. We conclude that landscape characteristics, including <span class="hlt">soil</span>, vegetation, and regional weather patterns, may supersede the effects of snowmelt timing in determining growing season <span class="hlt">soil</span> <span class="hlt">moisture</span>, and efforts to anticipate the impacts of climate change on seasonally snow-covered ecosystems should take into account these mediating factors. </p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1911899D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1911899D"><span>Estimating <span class="hlt">soil</span> hydraulic properties from <span class="hlt">soil</span> <span class="hlt">moisture</span> time series by inversion of a dual-permeability model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dalla Valle, Nicolas; Wutzler, Thomas; Meyer, Stefanie; Potthast, Karin; Michalzik, Beate</p> <p>2017-04-01</p> <p>Dual-permeability type models are widely used to simulate water fluxes and solute transport in structured <span class="hlt">soils</span>. These models contain two spatially overlapping flow domains with different parameterizations or even entirely different conceptual descriptions of flow processes. They are usually able to capture preferential flow phenomena, but a large set of parameters is needed, which are very laborious to obtain or cannot be measured at all. Therefore, model inversions are often used to derive the necessary parameters. Although these require sufficient input data themselves, they can use measurements of state variables instead, which are often easier to obtain and can be monitored by automated measurement systems. In this work we show a method to estimate <span class="hlt">soil</span> hydraulic parameters from high frequency <span class="hlt">soil</span> <span class="hlt">moisture</span> time series data gathered at two different measurement depths by inversion of a simple one dimensional dual-permeability model. The model uses an advection equation based on the kinematic wave theory to describe the flow in the fracture domain and a Richards equation for the flow in the matrix domain. The <span class="hlt">soil</span> <span class="hlt">moisture</span> time series data were measured in mesocosms during sprinkling experiments. The inversion consists of three consecutive steps: First, the parameters of the water retention function were assessed using vertical <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles in hydraulic equilibrium. This was done using two different exponential retention functions and the Campbell function. Second, the <span class="hlt">soil</span> sorptivity and diffusivity functions were estimated from Boltzmann-transformed <span class="hlt">soil</span> <span class="hlt">moisture</span> data, which allowed the calculation of the hydraulic conductivity function. Third, the parameters governing flow in the fracture domain were determined using the whole <span class="hlt">soil</span> <span class="hlt">moisture</span> time series. The resulting retention functions were within the range of values predicted by pedotransfer functions apart from very dry conditions, where all retention functions predicted lower matrix potentials</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1121134','SCIGOV-DOEDE'); return false;" href="https://www.osti.gov/servlets/purl/1121134"><span><span class="hlt">Soil</span> temperature, <span class="hlt">soil</span> <span class="hlt">moisture</span> and thaw depth, Barrow, Alaska, Ver. 1</span></a></p> <p><a target="_blank" href="http://www.osti.gov/dataexplorer">DOE Data Explorer</a></p> <p>Sloan, V.L.; J.A. Liebig; M.S. Hahn; J.B. Curtis; J.D. Brooks; A. Rogers; C.M. Iversen; R.J. Norby</p> <p>2014-01-10</p> <p>This dataset consists of field measurements of <span class="hlt">soil</span> properties made during 2012 and 2013 in areas A-D of Intensive Site 1 at the Next-Generation Ecosystem Experiments (NGEE) Arctic site near Barrow, Alaska. Included are i) weekly measurements of thaw depth, <span class="hlt">soil</span> <span class="hlt">moisture</span>, presence and depth of standing water, and <span class="hlt">soil</span> temperature made during the 2012 and 2013 growing seasons (June - September) and ii) half-hourly measurements of <span class="hlt">soil</span> temperature logged continuously during the period June 2012 to September 2013.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUSM.B42A..02S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUSM.B42A..02S"><span>Investigating <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Feedbacks on Precipitation With Tests of Granger Causality</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salvucci, G. D.; Saleem, J. A.; Kaufmann, R.</p> <p>2002-05-01</p> <p>Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a <span class="hlt">soil</span> <span class="hlt">moisture</span> (S) feedback on precipitation (P) using data from Illinois. In this framework S is said to Granger cause P if F(Pt;At-dt)does not equal F(P;(A-S)t-dt) where F denotes the conditional distribution of P at time t, At-dt represents the set of all knowledge available at time t-dt, and (A-S)t-dt represents all knowledge available at t-dt except S. Critical for land-atmosphere interaction research is that At-dt includes all past information on P as well as S. Therefore that part of the relation between past <span class="hlt">soil</span> <span class="hlt">moisture</span> and current precipitation which results from precipitation autocorrelation and <span class="hlt">soil</span> water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span>) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on <span class="hlt">soil</span> <span class="hlt">moisture</span>. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple <span class="hlt">soil</span> <span class="hlt">moisture</span>-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in <span class="hlt">moisture</span> not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no <span class="hlt">moisture</span> influence) is evaluated by comparing observed</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AdWR...25.1305S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AdWR...25.1305S"><span>Investigating <span class="hlt">soil</span> <span class="hlt">moisture</span> feedbacks on precipitation with tests of Granger causality</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salvucci, Guido D.; Saleem, Jennifer A.; Kaufmann, Robert</p> <p></p> <p>Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a <span class="hlt">soil</span> <span class="hlt">moisture</span> ( S) feedback on precipitation ( P) using data from Illinois. In this framework S is said to Granger cause P if F(P t|Ω t- Δt )≠F(P t|Ω t- Δt -S t- Δt ) where F denotes the conditional distribution of P, Ω t- Δt represents the set of all knowledge available at time t-Δ t, and Ω t- Δt -S t- Δt represents all knowledge except S. Critical for land-atmosphere interaction research is that Ω t- Δt includes all past information on P as well as S. Therefore that part of the relation between past <span class="hlt">soil</span> <span class="hlt">moisture</span> and current precipitation which results from precipitation autocorrelation and <span class="hlt">soil</span> water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span>) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on <span class="hlt">soil</span> <span class="hlt">moisture</span>. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple <span class="hlt">soil</span> <span class="hlt">moisture</span>-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in <span class="hlt">moisture</span> not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no <span class="hlt">moisture</span> influence) is evaluated by comparing observed</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/7767','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/7767"><span>Effects of neutron source type on <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Irving Goldberg; Norman A. MacGillivray; Robert R. Ziemer</p> <p>1967-01-01</p> <p>A number of radioisotopes have recently become commercially available as alternatives to radium-225 in <span class="hlt">moisture</span> gauging devices using alpha-neutron sources for determining <span class="hlt">soil</span> <span class="hlt">moisture</span>, for well logging, and for other industrial applications in which hydrogenous materials are measured.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913830S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913830S"><span>Evaluation of uncertainty in field <span class="hlt">soil</span> <span class="hlt">moisture</span> estimations by cosmic-ray neutron sensing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Scheiffele, Lena Maria; Baroni, Gabriele; Schrön, Martin; Ingwersen, Joachim; Oswald, Sascha E.</p> <p>2017-04-01</p> <p>Cosmic-ray neutron sensing (CRNS) has developed into a valuable, indirect and non-invasive method to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> at the field scale. It provides continuous temporal data (hours to days), relatively large depth (10-70 cm), and intermediate spatial scale measurements (hundreds of meters), thereby overcoming some of the limitations in point measurements (e.g., TDR/FDR) and of remote sensing products. All these characteristics make CRNS a favorable approach for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation, especially for applications in cropped fields and agricultural water management. Various studies compare CRNS measurements to <span class="hlt">soil</span> sensor networks and show a good agreement. However, CRNS is sensitive to more characteristics of the land-surface, e.g. additional hydrogen pools, <span class="hlt">soil</span> bulk density, and biomass. Prior to calibration the standard atmospheric corrections are accounting for the effects of air pressure, humidity and variations in incoming neutrons. In addition, the standard calibration approach was further extended to account for hydrogen in lattice water and <span class="hlt">soil</span> organic material. Some corrections were also proposed to account for water in biomass. Moreover, the sensitivity of the probe was found to decrease with distance and a weighting procedure for the calibration datasets was introduced to account for the sensors' radial sensitivity. On the one hand, all the mentioned corrections showed to improve the accuracy in estimated <span class="hlt">soil</span> <span class="hlt">moisture</span> values. On the other hand, they require substantial additional efforts in monitoring activities and they could inherently contribute to the overall uncertainty of the CRNS product. In this study we aim (i) to quantify the uncertainty in the field <span class="hlt">soil</span> <span class="hlt">moisture</span> estimated by CRNS and (ii) to understand the role of the different sources of uncertainty. To this end, two experimental sites in Germany were equipped with a CRNS probe and compared to values of a <span class="hlt">soil</span> <span class="hlt">moisture</span> network. The agricultural fields were cropped with winter</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29726183','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29726183"><span>[Detecting the <span class="hlt">moisture</span> content of forest surface <span class="hlt">soil</span> based on the microwave remote sensing technology.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Ming Ze; Gao, Yuan Ke; Di, Xue Ying; Fan, Wen Yi</p> <p>2016-03-01</p> <p>The <span class="hlt">moisture</span> content of forest surface <span class="hlt">soil</span> is an important parameter in forest ecosystems. It is practically significant for forest ecosystem related research to use microwave remote sensing technology for rapid and accurate estimation of the <span class="hlt">moisture</span> content of forest surface <span class="hlt">soil</span>. With the aid of TDR-300 <span class="hlt">soil</span> <span class="hlt">moisture</span> content measuring instrument, the <span class="hlt">moisture</span> contents of forest surface <span class="hlt">soils</span> of 120 sample plots at Tahe Forestry Bureau of Daxing'anling region in Heilongjiang Province were measured. Taking the <span class="hlt">moisture</span> content of forest surface <span class="hlt">soil</span> as the dependent variable and the polarization decomposition parameters of C band Quad-pol SAR data as independent variables, two types of quantitative estimation models (multilinear regression model and BP-neural network model) for predicting <span class="hlt">moisture</span> content of forest surface <span class="hlt">soils</span> were developed. The spatial distribution of <span class="hlt">moisture</span> content of forest surface <span class="hlt">soil</span> on the regional scale was then derived with model inversion. Results showed that the model precision was 86.0% and 89.4% with RMSE of 3.0% and 2.7% for the multilinear regression model and the BP-neural network model, respectively. It indicated that the BP-neural network model had a better performance than the multilinear regression model in quantitative estimation of the <span class="hlt">moisture</span> content of forest surface <span class="hlt">soil</span>. The spatial distribution of forest surface <span class="hlt">soil</span> <span class="hlt">moisture</span> content in the study area was then obtained by using the BP neural network model simulation with the Quad-pol SAR data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170010181','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170010181"><span>Uncertainty Assessment of Space-Borne Passive <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Quets, Jan; De Lannoy, Gabrielle; Reichle, Rolf; Cosh, Michael; van der Schalie, Robin; Wigneron, Jean-Pierre</p> <p>2017-01-01</p> <p>The uncertainty associated with passive <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval is hard to quantify, and known to be underlain by various, diverse, and complex causes. Factors affecting space-borne retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation include: (i) the optimization or inversion method applied to the radiative transfer model (RTM), such as e.g. the Single Channel Algorithm (SCA), or the Land Parameter Retrieval Model (LPRM), (ii) the selection of the observed brightness temperatures (Tbs), e.g. polarization and incidence angle, (iii) the definition of the cost function and the impact of prior information in it, and (iv) the RTM parameterization (e.g. parameterizations officially used by the SMOS L2 and SMAP L2 retrieval products, ECMWF-based SMOS assimilation product, SMAP L4 assimilation product, and perturbations from those configurations). This study aims at disentangling the relative importance of the above-mentioned sources of uncertainty, by carrying out <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval experiments, using SMOS Tb observations in different settings, of which some are mentioned above. The ensemble uncertainties are evaluated at 11 reference CalVal sites, over a time period of more than 5 years. These experimental retrievals were inter-compared, and further confronted with in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements and operational SMOS L2 retrievals, using commonly used skill metrics to quantify the temporal uncertainty in the retrievals.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000038011&hterms=How+soil+form&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DHow%2Bsoil%2Bform','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038011&hterms=How+soil+form&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DHow%2Bsoil%2Bform"><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=20000032337&hterms=Hydrology&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DHydrology','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000032337&hterms=Hydrology&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DHydrology"><span>Estimation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Profile using a Simple Hydrology Model and Passive Microwave Remote Sensing</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Soman, Vishwas V.; Crosson, William L.; Laymon, Charles; Tsegaye, Teferi</p> <p>1998-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important component of analysis in many Earth science disciplines. <span class="hlt">Soil</span> <span class="hlt">moisture</span> information can be obtained either by using microwave remote sensing or by using a hydrologic model. In this study, we combined these two approaches to increase the accuracy of profile <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. A hydrologic model was used to analyze the errors in the estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> using the data collected during Huntsville '96 microwave remote sensing experiment in Huntsville, Alabama. Root mean square errors (RMSE) in <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation increase by 22% with increase in the model input interval from 6 hr to 12 hr for the grass-covered plot. RMSEs were reduced for given model time step by 20-50% when model <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates were updated using remotely-sensed data. This methodology has a potential to be employed in <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation using rainfall data collected by a space-borne sensor, such as the Tropical Rainfall Measuring Mission (TRMM) satellite, if remotely-sensed data are available to update the model estimates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/1415421-manipulative-experiments-demonstrate-how-long-term-soil-moisture-changes-alter-controls-plant-water-use','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1415421-manipulative-experiments-demonstrate-how-long-term-soil-moisture-changes-alter-controls-plant-water-use"><span>Manipulative experiments demonstrate how long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> changes alter controls of plant water use</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Grossiord, Charlotte; Sevanto, Sanna Annika; Limousin, Jean -Marc</p> <p></p> <p>Tree transpiration depends on biotic and abiotic factors that might change in the future, including precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> status. Although short-term sap flux responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> and evaporative demand have been the subject of attention before, the relative sensitivity of sap flux to these two factors under long-term changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions has rarely been determined experimentally. We tested how long-term artificial change in <span class="hlt">soil</span> <span class="hlt">moisture</span> affects the sensitivity of tree-level sap flux to daily atmospheric vapor pressure deficit ( VPD) and <span class="hlt">soil</span> <span class="hlt">moisture</span> variations, and the generality of these effects across forest types and environments usingmore » four manipulative sites in mature forests. Exposure to relatively long-term (two to six years) <span class="hlt">soil</span> <span class="hlt">moisture</span> reduction decreases tree sap flux sensitivity to daily VPD and relative extractable water ( REW) variations, leading to lower sap flux even under high <span class="hlt">soil</span> <span class="hlt">moisture</span> and optimal VPD. Inversely, trees subjected to long-term irrigation showed a significant increase in their sensitivity to daily VPD and REW, but only at the most water-limited site. The ratio between the relative change in <span class="hlt">soil</span> <span class="hlt">moisture</span> manipulation and the relative change in sap flux sensitivity to VPD and REW variations was similar across sites suggesting common adjustment mechanisms to long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> status across environments for evergreen tree species. Altogether, our results show that long-term changes in <span class="hlt">soil</span> water availability, and subsequent adjustments to these novel conditions, could play a critical and increasingly important role in controlling forest water use in the future.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1415421-manipulative-experiments-demonstrate-how-long-term-soil-moisture-changes-alter-controls-plant-water-use','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1415421-manipulative-experiments-demonstrate-how-long-term-soil-moisture-changes-alter-controls-plant-water-use"><span>Manipulative experiments demonstrate how long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> changes alter controls of plant water use</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Grossiord, Charlotte; Sevanto, Sanna Annika; Limousin, Jean -Marc; ...</p> <p>2017-12-14</p> <p>Tree transpiration depends on biotic and abiotic factors that might change in the future, including precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> status. Although short-term sap flux responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> and evaporative demand have been the subject of attention before, the relative sensitivity of sap flux to these two factors under long-term changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions has rarely been determined experimentally. We tested how long-term artificial change in <span class="hlt">soil</span> <span class="hlt">moisture</span> affects the sensitivity of tree-level sap flux to daily atmospheric vapor pressure deficit ( VPD) and <span class="hlt">soil</span> <span class="hlt">moisture</span> variations, and the generality of these effects across forest types and environments usingmore » four manipulative sites in mature forests. Exposure to relatively long-term (two to six years) <span class="hlt">soil</span> <span class="hlt">moisture</span> reduction decreases tree sap flux sensitivity to daily VPD and relative extractable water ( REW) variations, leading to lower sap flux even under high <span class="hlt">soil</span> <span class="hlt">moisture</span> and optimal VPD. Inversely, trees subjected to long-term irrigation showed a significant increase in their sensitivity to daily VPD and REW, but only at the most water-limited site. The ratio between the relative change in <span class="hlt">soil</span> <span class="hlt">moisture</span> manipulation and the relative change in sap flux sensitivity to VPD and REW variations was similar across sites suggesting common adjustment mechanisms to long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> status across environments for evergreen tree species. Altogether, our results show that long-term changes in <span class="hlt">soil</span> water availability, and subsequent adjustments to these novel conditions, could play a critical and increasingly important role in controlling forest water use in the future.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/1457599-manipulative-experiments-demonstrate-how-long-term-soil-moisture-changes-alter-controls-plant-water-use','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1457599-manipulative-experiments-demonstrate-how-long-term-soil-moisture-changes-alter-controls-plant-water-use"><span>Manipulative experiments demonstrate how long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> changes alter controls of plant water use</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Grossiord, Charlotte; Sevanto, Sanna; Limousin, Jean-Marc</p> <p></p> <p>Tree transpiration depends on biotic and abiotic factors that might change in the future, including precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> status. Although short-term sap flux responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> and evaporative demand have been the subject of attention before, the relative sensitivity of sap flux to these two factors under long-term changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions has rarely been determined experimentally. We tested how long-term artificial change in <span class="hlt">soil</span> <span class="hlt">moisture</span> affects the sensitivity of tree-level sap flux to daily atmospheric vapor pressure deficit (VPD) and <span class="hlt">soil</span> <span class="hlt">moisture</span> variations, and the generality of these effects across forest types and environments using fourmore » manipulative sites in mature forests. Exposure to relatively long-term (two to six years) <span class="hlt">soil</span> <span class="hlt">moisture</span> reduction decreases tree sap flux sensitivity to daily VPD and relative extractable water (REW) variations, leading to lower sap flux even under high <span class="hlt">soil</span> <span class="hlt">moisture</span> and optimal VPD. Inversely, trees subjected to long-term irrigation showed a significant increase in their sensitivity to daily VPD and REW, but only at the most water-limited site. The ratio between the relative change in <span class="hlt">soil</span> <span class="hlt">moisture</span> manipulation and the relative change in sap flux sensitivity to VPD and REW variations was similar across sites suggesting common adjustment mechanisms to long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> status across environments for evergreen tree species. Overall, our results show that long-term changes in <span class="hlt">soil</span> water availability, and subsequent adjustments to these novel conditions, could play a critical and increasingly important role in controlling forest water use in the future.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760009510','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760009510"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> ground truth, Lafayette, Indiana, site; St. Charles Missouri, site; Centralia, Missouri, site</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>1975-01-01</p> <p>The <span class="hlt">soil</span> <span class="hlt">moisture</span> ground-truth measurements and ground-cover descriptions taken at three <span class="hlt">soil</span> <span class="hlt">moisture</span> survey sites located near Lafayette, Indiana; St. Charles, Missouri; and Centralia, Missouri are given. The data were taken on November 10, 1975, in connection with airborne remote sensing missions being flown by the Environmental Research Institute of Michigan under the auspices of the National Aeronautics and Space Administration. Emphasis was placed on the <span class="hlt">soil</span> <span class="hlt">moisture</span> in bare fields. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was sampled in the top 0 to 1 in. and 0 to 6 in. by means of a <span class="hlt">soil</span> sampling push tube. These samples were then placed in plastic bags and awaited gravimetric analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/39672','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/39672"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> effects on the carbon isotope composition of <span class="hlt">soil</span> respiration</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Claire L. Phillips; Nick Nickerson; David Risk; Zachary E. Kayler; Chris Andersen; Alan Mix; Barbara J. Bond</p> <p>2010-01-01</p> <p>The carbon isotopic composition (δ13C) of recently assimilated plant carbon is known to depend on water-stress, caused either by low <span class="hlt">soil</span> <span class="hlt">moisture</span> or by low atmospheric humidity. Air humidity has also been shown to correlate with the δ13C of <span class="hlt">soil</span> respiration, which suggests indirectly that recently fixed photosynthates...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SPIE10444E..10A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SPIE10444E..10A"><span>Tracing dynamics of relative volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> content using SAR data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Avetisyan, Daniela; Velizarova, Emiliya; Nedkov, Roumen</p> <p>2017-09-01</p> <p><span class="hlt">Soil</span> is a dominant factor of the terrestrial geosystems in the dry sub-humid zones, particularly through its effect on biomass production. Due to the climate changes and industrial development, <span class="hlt">soil</span> resources in these zones are prone to degradation. Mitigation of the negative effects of land degradation requires in-depth knowledge of the ongoing in the geosystems processes and application of innovative end effective methods for their investigation. The recent study aims to evaluate the relative <span class="hlt">soil</span> <span class="hlt">moisture</span> content in various <span class="hlt">soil</span> differences and to trace its dynamics during growing season. In order to achieve this aim, Relative <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Index (RSMI) based on Synthetic Aperture Radar (SAR) data was calculated. The index estimates the relative variation of volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> content in a given time period and enables determination of its change in relative values. The generated results show very high level of correlation for the investigated pilot areas which testifies that the RSMI is applicable in different territories.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.H13J..04B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.H13J..04B"><span>Long-Term Evaluation of the AMSR-E <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product Over the Walnut Gulch Watershed, AZ</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bolten, J. D.; Jackson, T. J.; Lakshmi, V.; Cosh, M. H.; Drusch, M.</p> <p>2005-12-01</p> <p>The Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) was launched aboard NASA's Aqua satellite on May 4th, 2002. Quantitative estimates of <span class="hlt">soil</span> <span class="hlt">moisture</span> using the AMSR-E provided data have required routine radiometric data calibration and validation using comparisons of satellite observations, extended targets and field campaigns. The currently applied NASA EOS Aqua ASMR-E <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithm is based on a change detection approach using polarization ratios (PR) of the calibrated AMSR-E channel brightness temperatures. To date, the accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithm has been investigated on short time scales during field campaigns such as the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiments in 2004 (SMEX04). Results have indicated self-consistency and calibration stability of the observed brightness temperatures; however the performance of the <span class="hlt">moisture</span> retrieval algorithm has been poor. The primary objective of this study is to evaluate the quality of the current version of the AMSR-E <span class="hlt">soil</span> <span class="hlt">moisture</span> product for a three year period over the Walnut Gulch Experimental Watershed (150 km2) near Tombstone, AZ; the northern study area of SMEX04. This watershed is equipped with hourly and daily recording of precipitation, <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature via a network of raingages and a USDA-NRCS <span class="hlt">Soil</span> Climate Analysis Network (SCAN) site. Surface wetting and drying are easily distinguished in this area due to the moderately-vegetated terrain and seasonally intense precipitation events. Validation of AMSR-E derived <span class="hlt">soil</span> <span class="hlt">moisture</span> is performed from June 2002 to June 2005 using watershed averages of precipitation, and <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature data from the SCAN site supported by a surface <span class="hlt">soil</span> <span class="hlt">moisture</span> network. Long-term assessment of <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithm performance is investigated by comparing temporal variations of <span class="hlt">moisture</span> estimates with seasonal changes and precipitation events. Further comparisons are made with a standard <span class="hlt">soil</span> dataset from the European</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_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><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" 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><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></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="461"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H43F1017B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H43F1017B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> dynamics and their effect on bioretention performance in Northeast Ohio</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bush, S. A.; Jefferson, A.; Jarden, K.; Kinsman-Costello, L. E.; Grieser, J.</p> <p>2014-12-01</p> <p>Urban impervious surfaces lead to increases in stormwater runoff. Green infrastructure, like bioretention cells, is being used to mitigate negative impacts of runoff by disconnecting impervious surfaces from storm water systems and redirecting flow to decentralized treatment areas. While bioretention <span class="hlt">soil</span> characteristics are carefully designed, little research is available on <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics within the cells and how these might relate to inter-storm variability in performance. Bioretentions have been installed along a residential street in Parma, Ohio to determine the impact of green infrastructure on the West Creek watershed, a 36 km2 subwatershed of the Cuyahoga River. Bioretentions were installed in two phases (Phase I in 2013 and Phase II in 2014); design and vegetation density vary slightly between the two phases. Our research focuses on characterizing <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics of multiple bioretentions and assessing their impact on stormwater runoff at the street scale. <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurements were collected in transects for eight bioretentions over the course of one summer. Vegetation indices of canopy height, percent vegetative cover, species richness and NDVI were also measured. A flow meter in the storm drain at the end of the street measured storm sewer discharge. Precipitation was recorded from a meteorological station 2 km from the research site. <span class="hlt">Soil</span> <span class="hlt">moisture</span> increased in response to precipitation and decreased to relatively stable conditions within 3 days following a rain event. Phase II bioretentions exhibited greater <span class="hlt">soil</span> <span class="hlt">moisture</span> and less vegetation than Phase I bioretentions, though the relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and vegetative cover is inconclusive for bioretentions constructed in the same phase. Data from five storms suggest that pre-event <span class="hlt">soil</span> <span class="hlt">moisture</span> does not control the runoff-to-rainfall ratio, which we use as a measure of bioretention performance. However, discharge data indicate that hydrograph characteristics, such as lag</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27337651','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27337651"><span><span class="hlt">Moisture</span> effect in prompt gamma measurements from <span class="hlt">soil</span> samples.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Naqvi, A A; Khiari, F Z; Liadi, F A; Khateeb-Ur-Rehman; Raashid, M A; Isab, A H</p> <p>2016-09-01</p> <p>The variation in intensity of 1.78MeV silicon, 6.13MeV oxygen, and 2.22MeV hydrogen prompt gamma rays from <span class="hlt">soil</span> samples due to the addition of 5.1, 7.4, 9.7, 11.9 and 14.0wt% water was studied for 14MeV incident neutron beams utilizing a LaBr3:Ce gamma ray detector. The intensities of 1.78MeV and 6.13MeV gamma rays from silicon and oxygen, respectively, decreased with increasing sample <span class="hlt">moisture</span>. The intensity of 2.22MeV hydrogen gamma rays increases with <span class="hlt">moisture</span>. The decrease in intensity of silicon and oxygen gamma rays with <span class="hlt">moisture</span> concentration indicates a loss of 14MeV neutron flux, while the increase in intensity of 2.22MeV gamma rays with <span class="hlt">moisture</span> indicates an increase in thermal neutron flux due to increasing concentration of <span class="hlt">moisture</span>. The experimental intensities of silicon, oxygen and hydrogen prompt gamma rays, measured as a function of <span class="hlt">moisture</span> concentration in the <span class="hlt">soil</span> samples, are in good agreement with the theoretical results obtained through Monte Carlo calculations. Copyright © 2016 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=291074','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=291074"><span>SMAP validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite will be launched by the National Aeronautics and Space Administration in October 2014. SMAP will also incorporate a rigorous calibration and validation program that will support algorithm refinement and provide users with information on the accuracy ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715065F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715065F"><span>Upscaling of <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements in NW Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ferraris, Stefano; Canone, Davide; Previati, Maurizio; Brunod, Christian; Ratto, Sara; Cauduro, Marco</p> <p>2015-04-01</p> <p>There is large mismatch in spatial scale between the climate and meteorological model grid, and the scale of <span class="hlt">soil</span> and vegetation measurements. Remote sensing data can help to fit the model scale, but they cannot provide rootzone data. In this work some <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets are analysed for the sake of providing larger scale estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and water and energy fluxes. The first dataset refers to a plain site near Torino, where measurements are taken since 1997 (Baudena et al., 2012), and a mountain site close to the town. The second one is a dataset in the mountains of Valle d'Aosta (Brocca et al., 2013), where 4 years of data are available. The use of digital elevation models and vegetation maps is shown in this work. Some <span class="hlt">soil</span> processes (e.g. Whalley et al., 2012) are usually disregarded, but in this work their possible impact is considered. References L. Brocca, A. Tarpanelli, T. Moramarco, F. Melone, S.M. Ratto, M. Cauduro, S. Ferraris, N. Berni, F. Ponziani, W. Wagner, T. Melzer (2013). <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation in Alpine Catchments through Modeling and Satellite Observations VADOSE ZONE JOURNAL, vol. 8-2, p. 1-10, doi:10.2136/vzj2012.0102 M. Baudena, I. Bevilacqua, D. Canone, S. Ferraris, M. Previati, A. Provenzale (2012). <span class="hlt">Soil</span> water dynamics at a midlatitude test site: Field measurements and box modeling approaches. JOURNAL OF HYDROLOGY, vol. 414-415, p. 329-340, ISSN: 0022-1694, doi: 10.1016/j.jhydrol.2011.11.009 W.R. Whalley, G.P. Matthews, S. Ferraris (2012). The effect of compaction and shear deformation of saturated <span class="hlt">soil</span> on hydraulic conductivity. <span class="hlt">SOIL</span> & TILLAGE RESEARCH, vol. 125, p. 23-29, ISSN: 0167-1987</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H21D1442H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H21D1442H"><span>Vegetation-induced turbulence influencing evapotranspiration-<span class="hlt">soil</span> <span class="hlt">moisture</span> coupling: Implications for semiarid regions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haghighi, E.; Kirchner, J. W.; Entekhabi, D.</p> <p>2016-12-01</p> <p>The relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and evapotranspiration (ET) fluxes is an important component of land-atmosphere interactions controlling hydrology-climate feedback processes. Important as this relationship is, it remains empirical and physical mechanisms governing its dynamics are insufficiently studied. This is particularly of importance for semiarid regions (currently comprising about half of the Earth's land surface) where the shallow surface <span class="hlt">soil</span> layer is the primary source of ET and direct evaporation from bare <span class="hlt">soil</span> is likely a large component of the total flux. Hence, ET-<span class="hlt">soil</span> <span class="hlt">moisture</span> coupling in these regions is hypothesized to be strongly influenced by <span class="hlt">soil</span> evaporation and associated mechanisms. Motivated by recent progress in mechanistic modeling of localized heat and mass exchange rates from bare <span class="hlt">soil</span> surfaces covered by cylindrical bluff-body elements, we developed a physically based ET model explicitly incorporating coupled impacts of <span class="hlt">soil</span> <span class="hlt">moisture</span> and vegetation-induced turbulence in the near-surface region. Model predictions of ET and its partitioning were in good agreement with measured data and suggest that the strength and nature of ET-<span class="hlt">soil</span> <span class="hlt">moisture</span> interactions in sparsely vegetated areas are strongly influenced by aerodynamic (rather than radiative) forcing namely wind speed and near-surface turbulence generation as a function of vegetation type and cover fraction. The results demonstrated that the relationship between ET and <span class="hlt">soil</span> <span class="hlt">moisture</span> varies from a nonlinear function (the dual regime behavior) to a single <span class="hlt">moisture</span>-limited regime (linear relationship) by increasing wind velocity and enhancing turbulence generation in the near-surface region (small-scale woody vegetation species of low cover fraction). Potential benefits of this study for improving accuracy and predictive capabilities of remote sensing techniques when applied to semiarid environments will also be discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B41E2008Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B41E2008Y"><span>A <span class="hlt">Moisture</span> Function of <span class="hlt">Soil</span> Heterotrophic Respiration Derived from Pore-scale Mechanisms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yan, Z.; Todd-Brown, K. E.; Bond-Lamberty, B. P.; Bailey, V.; Liu, C.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> heterotrophic respiration (HR) is an important process controlling carbon (C) flux, but its response to changes in <span class="hlt">soil</span> water content (θ) is poorly understood. Earth system models (ESMs) use empirical <span class="hlt">moisture</span> functions developed from specific sites to describe the HR-θ relationship in <span class="hlt">soils</span>, introducing significant uncertainty. Generalized models derived from mechanisms that control substrate availability and microbial respiration are thus urgently needed. Here we derive, present, and test a novel <span class="hlt">moisture</span> function fp developed from pore-scale mechanisms. This fp encapsulates primary physicochemical and biological processes controlling HR response to <span class="hlt">moisture</span> variation in <span class="hlt">soils</span>. We tested fp against a wide range of published data for different <span class="hlt">soil</span> types, and found that fp reliably predicted diverse HR- relationships. The mathematical relationship between the parameters in fp and macroscopic <span class="hlt">soil</span> properties such as porosity and organic C content was also established, enabling to estimate fp using <span class="hlt">soil</span> properties. Compared with empirical <span class="hlt">moisture</span> functions used in ESMs, this derived fp could reduce uncertainty in predicting the response of <span class="hlt">soil</span> organic C stock to climate changes. In addition, this work is one of the first studies to upscale a mechanistic <span class="hlt">soil</span> HR model based on pore-scale processes, thus linking the pore-scale mechanisms with macroscale observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19940015982&hterms=neural+net&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dneural%2Bnet','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19940015982&hterms=neural+net&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dneural%2Bnet"><span>Application of neural network to remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> using theoretical polarimetric backscattering coefficients</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, L.; Shin, R. T.; Kong, J. A.; Yueh, S. H.</p> <p>1993-01-01</p> <p>This paper investigates the potential application of neural network to inversion of <span class="hlt">soil</span> <span class="hlt">moisture</span> using polarimetric remote sensing data. The neural network used for the inversion of <span class="hlt">soil</span> parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of <span class="hlt">soil</span> parameters which are comprised of the <span class="hlt">soil</span> permittivity and surface roughness parameters. <span class="hlt">Soil</span> permittivity is calculated from the <span class="hlt">soil</span> <span class="hlt">moisture</span> and the assumed <span class="hlt">soil</span> texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the <span class="hlt">soil</span> surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired <span class="hlt">soil</span> parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the <span class="hlt">soil</span> parameters which are compared with the desired <span class="hlt">soil</span> parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the <span class="hlt">soil</span> surface are fixed while <span class="hlt">soil</span> <span class="hlt">moisture</span> is varied. <span class="hlt">Soil</span> <span class="hlt">moisture</span> obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired <span class="hlt">soil</span> <span class="hlt">moisture</span>. The neural net output matches the desired output for the <span class="hlt">soil</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=263656','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=263656"><span>Enhancing begetation productivity forecasting using remotely-sensed 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..1913336R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913336R"><span>Global retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> and vegetation properties using data-driven methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann</p> <p>2017-04-01</p> <p>Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> with the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of <span class="hlt">soil</span> <span class="hlt">moisture</span> from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span>. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19800018214','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19800018214"><span>Microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> content over bare and vegetated fields</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.; Shiue, J. C.; Mcmurtrey, J. E., III (Principal Investigator)</p> <p>1980-01-01</p> <p>The author has identified the following significant results. Ground truth of <span class="hlt">soil</span> <span class="hlt">moisture</span> content, and ambient air and <span class="hlt">soil</span> temperatures were acquired concurrently with measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> in bare fields and fields covered with grass, corn, and soybeans obtained with 1.4 GHz and 5 GHz radiometers mounted on a truck. The biomass of the vegetation was sampled about once a week. The measured brightness temperatures over the bare fields were compared with those of radiative transfer model calculations using as inputs the acquired <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperatures data with appropriate values of dielectric constants for <span class="hlt">soil</span>-water mixtures. A good agreement was found between the calculated and measured results over 10 deg to 70 deg incident angles. The presence of vegetation reduced the sensitivity of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing. At 1.4 GHz the sensitivity reduction ranged from about 20% for 10 cm tall grassland cover to over 50 to 60% for the dense soybean field. At 5 GHz corresponding reduction in sensitivity ranged from approximately 70% to approximately 90%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820014726','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820014726"><span>Evaluation of HCMM data for assessing <span class="hlt">soil</span> <span class="hlt">moisture</span> and water table depth</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moore, D. G.; Heilman, J. L.; Tunheim, J. A.; Westin, F. C.; Heilman, W. E.; Beutler, G. A.; Ness, S. D. (Principal Investigator)</p> <p>1981-01-01</p> <p>Data were analyzed for variations in eastern South Dakota. <span class="hlt">Soil</span> <span class="hlt">moisture</span> in the 0-4 cm layer could be estimated with 1-mm <span class="hlt">soil</span> temperatures throughout the growing season of a rainfed barley crop (% cover ranging from 30% to 90%) with an r squared = 0.81. Empirical equations were developed to reduce the effect of canopy cover when radiometrically estimating the 1-mm <span class="hlt">soil</span> temperature, r squared = 0.88. The corrective equations were applied to an aircraft simulation of HCMM data for a diversity of crop types and land cover conditions to estimate the 0-4 cm <span class="hlt">soil</span> <span class="hlt">moisture</span>. The average difference between observed and measured <span class="hlt">soil</span> <span class="hlt">moisture</span> was 1.6% of field capacity. HCMM data were used to estimate the <span class="hlt">soil</span> <span class="hlt">moisture</span> for four dates with an r squared = 0.55 after correction for crop conditions. Location of shallow alluvial aquifers could be accomplished with HCMM predawn data. After correction of HCMM day data for vegetation differences, equations were developed for predicting water table depths within the aquifer (r=0.8).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19810025886&hterms=801&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3D801','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19810025886&hterms=801&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3D801"><span>Microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> content over bare and vegetated fields</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.; Shiue, J. C.; Mcmurtrey, J. E., III</p> <p>1980-01-01</p> <p>Remote measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> contents over bare fields and fields covered with orchard grass, corn, and soybean were made during October 1979 with 1.4 GHz and 5 GHz microwave radiometers mounted on a truck. Ground truth of <span class="hlt">soil</span> <span class="hlt">moisture</span> content, ambient air, and <span class="hlt">soil</span> temperatures was acquired concurrently with the radiometric measurements. The biomass of the vegetation was sampled about once a week. The measured brightness temperatures over bare fields were compared with those of radiative transfer model calculations using as inputs the acquired <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature data with appropriate values of dielectric constants for <span class="hlt">soil</span>-water mixtures. Good agreement was found between the calculated and the measured results over 10-70 deg incident angles. The presence of vegetation was found to reduce the sensitivity of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing. At 1.4 GHz the sensitivity reduction ranged from approximately 20% for 10-cm tall grassland to over 60% for the dense soybean field. At 5 GHz the corresponding reduction in sensitivity ranged from approximately 70 to approximately 90%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H33A0855C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33A0855C"><span>The Temporal Dynamics of Spatial Patterns of Observed <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Interpreted Using the Hydrus 1-D Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, M.; Willgoose, G. R.; Saco, P. M.</p> <p>2009-12-01</p> <p>This paper investigates the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics over two subcatchments (Stanley and Krui) in the Goulburn River in NSW during a three year period (2005-2007) using the Hydrus 1-D unsaturated <span class="hlt">soil</span> water flow model. The model was calibrated to the seven Stanley microcatchment sites (1 sqkm site) using continuous time surface 30cm and full profile <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements. <span class="hlt">Soil</span> type, leaf area index and <span class="hlt">soil</span> depth were found to be the key parameters changing model fit to the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series. They either shifted the time series up or down, changed the steepness of dry-down recessions or determined the lowest point of <span class="hlt">soil</span> <span class="hlt">moisture</span> dry-down respectively. Good correlations were obtained between observed and simulated <span class="hlt">soil</span> water storage (R=0.8-0.9) when calibrated parameters for one site were applied to the other sites. <span class="hlt">Soil</span> type was also found to be the main determinant (after rainfall) of the mean of modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> time series. Simulations of top 30cm were better than those of the whole <span class="hlt">soil</span> profile. Within the Stanley microcatchment excellent <span class="hlt">soil</span> <span class="hlt">moisture</span> matches could be generated simply by adjusting the mean of <span class="hlt">soil</span> <span class="hlt">moisture</span> up or down slightly. Only minor modification of <span class="hlt">soil</span> properties from site to site enable good fits for all of the Stanley sites. We extended the predictions of <span class="hlt">soil</span> <span class="hlt">moisture</span> to a larger spatial scale of the Krui catchment (sites up to 30km distant from Stanley) using <span class="hlt">soil</span> and vegetation parameters from Stanley but the locally recorded rainfall at the <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement site. The results were encouraging (R=0.7~0.8). These results show that it is possible to use a calibrated <span class="hlt">soil</span> <span class="hlt">moisture</span> model to extrapolate the <span class="hlt">soil</span> <span class="hlt">moisture</span> to other sites for a catchment with an area of up to 1000km2. This paper demonstrates the potential usefulness of continuous time, point scale <span class="hlt">soil</span> <span class="hlt">moisture</span> (typical of that measured by permanently installed TDR probes) in predicting the <span class="hlt">soil</span> wetness status over a catchment of significant size.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25016467','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25016467"><span>Quantifying the effects of <span class="hlt">soil</span> temperature, <span class="hlt">moisture</span> and sterilization on elemental mercury formation in boreal <span class="hlt">soils</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Pannu, Ravinder; Siciliano, Steven D; O'Driscoll, Nelson J</p> <p>2014-10-01</p> <p><span class="hlt">Soils</span> are a source of elemental mercury (Hg(0)) to the atmosphere, however the effects of <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> on Hg(0) formation is not well defined. This research quantifies the effect of varying <span class="hlt">soil</span> temperature (278-303 K), <span class="hlt">moisture</span> (15-80% water filled pore space (WFPS)) and sterilization on the kinetics of Hg(0) formation in forested <span class="hlt">soils</span> of Nova Scotia, Canada. Both, the logarithm of cumulative mass of Hg(0) formed in <span class="hlt">soils</span> and the reduction rate constants (k values) increased with temperature and <span class="hlt">moisture</span> respectively. Sterilizing <span class="hlt">soils</span> significantly (p < 0.05, n = 10) decreased the percent of total Hg reduced to Hg(0). We describe the fundamentals of Hg(0) formation in <span class="hlt">soils</span> and our results highlight two key processes: (i) a fast abiotic process that peaks at 45% WFPS and depletes a small pool of Hg(0) and; (ii) a slower, rate limiting biotic process that generates a large pool of reducible Hg(II). Copyright © 2014 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AdAtS..22..337L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AdAtS..22..337L"><span>A nonlinear coupled <span class="hlt">soil</span> <span class="hlt">moisture</span>-vegetation model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Shikuo; Liu, Shida; Fu, Zuntao; Sun, Lan</p> <p>2005-06-01</p> <p>Based on the physical analysis that the <span class="hlt">soil</span> <span class="hlt">moisture</span> and vegetation depend mainly on the precipitation and evaporation as well as the growth, decay and consumption of vegetation a nonlinear dynamic coupled system of <span class="hlt">soil</span> <span class="hlt">moisture</span>-vegetation is established. Using this model, the stabilities of the steady states of vegetation are analyzed. This paper focuses on the research of the vegetation catastrophe point which represents the transition between aridness and wetness to a great extent. It is shown that the catastrophe point of steady states of vegetation depends mainly on the rainfall P and saturation value v0, which is selected to balance the growth and decay of vegetation. In addition, when the consumption of vegetation remains constant, the analytic solution of the vegetation equation is obtained.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3681150','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3681150"><span>Assessment of Evapotranspiration and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Content Across Different Scales of Observation</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Verstraeten, Willem W.; Veroustraete, Frank; Feyen, Jan</p> <p>2008-01-01</p> <p>The proper assessment of evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> content are fundamental in food security research, land management, pollution detection, nutrient flows, (wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc. This paper takes an extensive, though not exhaustive sample of international scientific literature to discuss different approaches to estimate land surface and ecosystem related evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> content. This review presents: (i)a summary of the generally accepted cohesion theory of plant water uptake and transport including a shortlist of meteorological and plant factors influencing plant transpiration;(ii)a summary on evapotranspiration assessment at different scales of observation (sap-flow, porometer, lysimeter, field and catchment water balance, Bowen ratio, scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii)a summary on data assimilation schemes conceived to estimate evapotranspiration using optical and thermal remote sensing; and(iv)for <span class="hlt">soil</span> <span class="hlt">moisture</span> content, a summary on <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval techniques at different spatial and temporal scales is presented. Concluding remarks on the best available approaches to assess evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> content with and emphasis on remote sensing data assimilation, are provided. PMID:27879697</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EOSTr..84..233N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EOSTr..84..233N"><span>New DEMs may stimulate significant advancements in remote sensing 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>Nolan, Matt; Fatland, Dennis R.</p> <p></p> <p>From Napoleon's defeat at Waterloo to increasing corn yields in Kansas to greenhouse gas flux in the Arctic, the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span> is endemic to world affairs and merits the considerable attention it receives from the scientific community. This importance can hardly be overstated, though it often goes unstated.<span class="hlt">Soil</span> <span class="hlt">moisture</span> is one of the key variables in a variety of broad areas critical to the conduct of societies' economic and political affairs and their well-being; these include the health of agricultural crops, global climate dynamics, military trafficability planning, and hazards such as flooding and forest fires. Unfortunately the in situ measurement of the spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> on a watershed-scale is practically impossible. And despite decades of international effort, a satellite remote sensing technique that can reliably measure <span class="hlt">soil</span> <span class="hlt">moisture</span> with a spatial resolution of meters has not yet been identified or implemented. Due to the lack of suitable measurement techniques and, until recently digital elevation models (DEMs), our ability to understand and predict <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics through modeling has largely remained crippled from birth [Grayson and Bloschl, 200l].</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..325a2019G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..325a2019G"><span>Characterization of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Level for Rice and Maize Crops using GSM Shield and Arduino Microcontroller</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gines, G. A.; Bea, J. G.; Palaoag, T. D.</p> <p>2018-03-01</p> <p><span class="hlt">Soil</span> serves a medium for plants growth. One factor that affects <span class="hlt">soil</span> <span class="hlt">moisture</span> is drought. Drought has been a major cause of agricultural disaster. Agricultural drought is said to occur when <span class="hlt">soil</span> <span class="hlt">moisture</span> is insufficient to meet crop water requirements, resulting in yield losses. In this research, it aimed to characterize <span class="hlt">soil</span> <span class="hlt">moisture</span> level for Rice and Maize Crops using Arduino and applying fuzzy logic. System architecture for <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor and water pump were the basis in developing the equipment. The data gathered was characterized by applying fuzzy logic. Based on the results, applying fuzzy logic in validating the characterization of <span class="hlt">soil</span> <span class="hlt">moisture</span> level for Rice and Maize crops is accurate as attested by the experts. This will help the farmers in monitoring the <span class="hlt">soil</span> <span class="hlt">moisture</span> level of the Rice and Maize crops.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRD..121.8777D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRD..121.8777D"><span>Updated global <span class="hlt">soil</span> map for the Weather Research and Forecasting model and <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization for the Noah land surface model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>DY, C. Y.; Fung, J. C. H.</p> <p>2016-08-01</p> <p>A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and <span class="hlt">moisture</span> exchanges, which can be determined by the physical properties of the <span class="hlt">soil</span> and <span class="hlt">soil</span> state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of <span class="hlt">soil</span> <span class="hlt">moisture</span> are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the <span class="hlt">soil</span> map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) <span class="hlt">soil</span> database, which combines several <span class="hlt">soil</span> surveys from around the world. Both <span class="hlt">soil</span> <span class="hlt">moisture</span> from the FNL analysis data and the default <span class="hlt">soil</span> map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global <span class="hlt">soil</span> map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the <span class="hlt">soil</span> <span class="hlt">moisture</span> improves near-surface temperature and relative humidity prediction using different types of <span class="hlt">soil</span> <span class="hlt">moisture</span> initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26328425','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26328425"><span>Bioactivity of Several Herbicides on the Nanogram Level Under Different <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Conditions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jung, S C; Kuk, Y I; Senseman, S A; Ahn, H G; Seong, C N; Lee, D J</p> <p>2015-01-01</p> <p>In this study, a double-tube centrifuge method was employed to determine the effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the bioactivity of cafenstrole, pretilachlor, benfuresate, oxyfluorfen and simetryn. In general, the available herbicide concentration in <span class="hlt">soil</span> solution (ACSS) showed little change as <span class="hlt">soil</span> <span class="hlt">moisture</span> increased for herbicides. The total available herbicide in <span class="hlt">soil</span> solution (TASS) typically increased as <span class="hlt">soil</span> <span class="hlt">moisture</span> increased for all herbicides. The relationship between TASS and % growth rate based on dry weight showed strong linear relationships for both cafenstrole and pretilachlor, with r2 values of 0.95 and 0.84, respectively. Increasing TASS values were consistent with increasing herbicide water solubility, with the exception of the ionizable herbicide simetryn. Plant absorption and % growth rate exhibited a strong linear relationship with TASS. According to the results suggested that TASS was a better predictor of herbicidal bioactivity than ACSS for all herbicides under unsaturated <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions.</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_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></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><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_24 --> <div id="page_25" 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><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></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="481"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=265877','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=265877"><span>Field scale spatiotemporal analysis of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> for evaluating point-scale in situ 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><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an intrinsic state variable that varies considerably in space and time. From a hydrologic viewpoint, <span class="hlt">soil</span> <span class="hlt">moisture</span> controls runoff, infiltration, storage and drainage. <span class="hlt">Soil</span> <span class="hlt">moisture</span> determines the partitioning of the incoming radiation between latent and sensible heat fluxes. Althou...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H51R..01Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H51R..01Z"><span>A Time Series Analysis of Global <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data Products for Water Cycle Studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhan, X.; Yin, J.; Liu, J.; Fang, L.; Hain, C.; Ferraro, R. R.; Weng, F.</p> <p>2017-12-01</p> <p>Water is essential for sustaining life on our planet Earth and water cycle is one of the most important processes of out weather and climate system. As one of the major components of the water cycle, <span class="hlt">soil</span> <span class="hlt">moisture</span> impacts significantly the other water cycle components (e.g. evapotranspiration, runoff, etc) and the carbon cycle (e.g. plant/crop photosynthesis and respiration). Understanding of <span class="hlt">soil</span> <span class="hlt">moisture</span> status and dynamics is crucial for monitoring and predicting the weather, climate, hydrology and ecological processes. Satellite remote sensing has been used for <span class="hlt">soil</span> <span class="hlt">moisture</span> observation since the launch of the Scanning Multi-channel Microwave Radiometer (SMMR) on NASA's Nimbus-7 satellite in 1978. Many satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data products have been made available to the science communities and general public. The <span class="hlt">soil</span> <span class="hlt">moisture</span> operational product system (SMOPS) of NOAA NESDIS has been operationally providing global <span class="hlt">soil</span> <span class="hlt">moisture</span> data products from each of the currently available microwave satellite sensors and their blends. This presentation will provide an update of SMOPS products. The time series of each of these <span class="hlt">soil</span> <span class="hlt">moisture</span> data products are analyzed against other data products, such as precipitation and evapotranspiration from other independent data sources such as the North America Land Data Assimilation System (NLDAS). Temporal characteristics of these water cycle components are explored against some historical events, such as the 2010 Russian, 2010 China and 2012 United States droughts, 2015 South Carolina floods, etc. Finally whether a merged global <span class="hlt">soil</span> <span class="hlt">moisture</span> data product can be used as a climate data record is evaluated based on the above analyses.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018EurSS..51...73I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018EurSS..51...73I"><span>Numerical Investigations of <span class="hlt">Moisture</span> Distribution in a Selected Anisotropic <span class="hlt">Soil</span> Medium</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Iwanek, M.</p> <p>2018-01-01</p> <p>The <span class="hlt">moisture</span> of <span class="hlt">soil</span> profile changes both in time and space and depends on many factors. Changes of the quantity of water in <span class="hlt">soil</span> can be determined on the basis of in situ measurements, but numerical methods are increasingly used for this purpose. The quality of the results obtained using pertinent software packages depends on appropriate description and parameterization of <span class="hlt">soil</span> medium. Thus, the issue of providing for the <span class="hlt">soil</span> anisotropy phenomenon gains a big importance. Although anisotropy can be taken into account in many numerical models, isotopic <span class="hlt">soil</span> is often assumed in the research process. However, this assumption can be a reason for incorrect results in the simulations of water changes in <span class="hlt">soil</span> medium. In this article, results of numerical simulations of <span class="hlt">moisture</span> distribution in the selected <span class="hlt">soil</span> profile were presented. The calculations were conducted assuming isotropic and anisotropic conditions. Empirical verification of the results obtained in the numerical investigations indicated statistical essential discrepancies for the both analyzed conditions. However, better fitting measured and calculated <span class="hlt">moisture</span> values was obtained for the case of providing for anisotropy in the simulation model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H44D..04H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H44D..04H"><span>Large-area <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Surveys Using a Cosmic-ray Rover: Approaches and Results from Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hawdon, A. A.; McJannet, D. L.; Renzullo, L. J.; Baker, B.; Searle, R.</p> <p>2017-12-01</p> <p>Recent improvements in satellite instrumentation has increased the resolution and frequency of <span class="hlt">soil</span> <span class="hlt">moisture</span> observations, and this in turn has supported the development of higher resolution land surface process models. Calibration and validation of these products is restricted by the mismatch of scales between remotely sensed and contemporary ground based observations. Although the cosmic ray neutron <span class="hlt">soil</span> <span class="hlt">moisture</span> probe can provide estimates <span class="hlt">soil</span> <span class="hlt">moisture</span> at a scale useful for the calibration and validation purposes, it is spatially limited to a single, fixed location. This scaling issue has been addressed with the development of mobile <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring systems that utilizes the cosmic ray neutron method, typically referred to as a `rover'. This manuscript describes a project designed to develop approaches for undertaking rover surveys to produce <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates at scales comparable to satellite observations and land surface process models. A custom designed, trailer-mounted rover was used to conduct repeat surveys at two scales in the Mallee region of Victoria, Australia. A broad scale survey was conducted at 36 x 36 km covering an area of a standard SMAP pixel and an intensive scale survey was conducted over a 10 x 10 km portion of the broad scale survey, which is at a scale equivalent to that used for national water balance modelling. We will describe the design of the rover, the methods used for converting neutron counts into <span class="hlt">soil</span> <span class="hlt">moisture</span> and discuss factors controlling <span class="hlt">soil</span> <span class="hlt">moisture</span> variability. We found that the intensive scale rover surveys produced reliable <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates at 1 km resolution and the broad scale at 9 km resolution. We conclude that these products are well suited for future analysis of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals and finer scale <span class="hlt">soil</span> <span class="hlt">moisture</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26620951','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26620951"><span>Linking the <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution pattern to dynamic processes along slope transects in the Loess Plateau, China.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Shuai; Fu, Bojie; Gao, Guangyao; Zhou, Ji; Jiao, Lei; Liu, Jianbo</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> pulses are a prerequisite for other land surface pulses at various spatiotemporal scales in arid and semi-arid areas. The temporal dynamics and profile variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> in relation to land cover combinations were studied along five slopes transect on the Loess Plateau during the rainy season of 2011. Within the 3 months of the growing season coupled with the rainy season, all of the <span class="hlt">soil</span> <span class="hlt">moisture</span> was replenished in the area, proving that a type stability exists between different land cover <span class="hlt">soil</span> <span class="hlt">moisture</span> levels. Land cover combinations disturbed the trend determined by topography and increased <span class="hlt">soil</span> <span class="hlt">moisture</span> variability in space and time. The stability of <span class="hlt">soil</span> <span class="hlt">moisture</span> resulting from the dynamic processes could produce stable patterns on the slopes. The relationships between the mean <span class="hlt">soil</span> <span class="hlt">moisture</span> and vertical standard deviation (SD) and coefficient of variation (CV) were more complex, largely due to the fact that different land cover types had distinctive vertical patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The spatial SD of each layer had a positive correlation and the spatial CV exhibited a negative correlation with the increase in mean <span class="hlt">soil</span> <span class="hlt">moisture</span>. The <span class="hlt">soil</span> <span class="hlt">moisture</span> stability implies that sampling comparisons in this area can be conducted at different times to accurately compare different land use types.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/46775','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/46775"><span>Sensitivity of <span class="hlt">soil</span> respiration to variability in <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature in a humid tropical forest</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Tana Wood; M. Detto; W.L. Silver</p> <p>2013-01-01</p> <p>Precipitation and temperature are important drivers of <span class="hlt">soil</span> respiration. The role of <span class="hlt">moisture</span> and temperature are generally explored at seasonal or inter-annual timescales; however, significant variability also occurs on hourly to daily time-scales. We used small (1.54 m2), throughfall exclusion shelters to evaluate the role <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature as temporal...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMED31D0310P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMED31D0310P"><span>An Arduino Based Citizen Science <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensor in Support of SMAP and GLOBE</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Podest, E.; Das, N. N.; Rajasekaran, E.; Jeyaram, R.; Lohrli, C.; Hovhannesian, H.; Fairbanks, G.</p> <p>2017-12-01</p> <p>Citizen science allows individuals anywhere in the world to engage in science by collecting environmental variables. One of the longest running platforms for the collection of in situ variables is the GLOBE program, which is international in scope and encourages students and citizen scientists alike to collect in situ measurements. NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite mission, which has been acquiring global <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements every 3 days of the top 5 cm of the <span class="hlt">soil</span> since 2015, has partnered with the GLOBE program to engage students from around the world to collect in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> and help validate SMAP measurements. The current GLOBE SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> protocol consists in collecting a <span class="hlt">soil</span> sample, weighing, drying and weighing it again in order to determine the amount of water in the <span class="hlt">soil</span>. Preparation and <span class="hlt">soil</span> sample collection can take up to 20 minutes and drying can take up to 3 days. We have hence developed a <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement device based on Arduino- microcontrollers along with off-the-shelf and homemade sensors that are accurate, robust, inexpensive and quick and easy to use so that they can be implemented by the GLOBE community and citizen scientists alike. In addition, we have developed a phone app, which interfaces with the Arduino, displays the <span class="hlt">soil</span> <span class="hlt">moisture</span> value and send the measurement to the GLOBE database. This talk will discuss building, calibration and validation of the <span class="hlt">soil</span> <span class="hlt">moisture</span> measuring device and assessing the quality of the measurements collected. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.</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('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=338181&Lab=NERL&keyword=public+AND+relations&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=338181&Lab=NERL&keyword=public+AND+relations&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>Interaction between <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Air Temperature in the Mississippi River Basin</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>Increasing air temperatures are expected to continue in the future. The relation between <span class="hlt">soil</span> <span class="hlt">moisture</span> and near surface air temperature is significant for climate change and climate extremes. Evaluation of the relations between <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature was performed by devel...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=254216&keyword=Global+AND+warming&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=254216&keyword=Global+AND+warming&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50"><span>Seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns in contrasting habitats in the Willamette Valley, Oregon</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Changing seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes caused by global warming may alter plant community composition in sensitive habitats such as wetlands and oak savannas. To evaluate such changes, an understanding of typical seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span> regimes is necessary. The primary objective...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJAEO..45..110Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJAEO..45..110Z"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> variability over Odra watershed: Comparison between SMOS and GLDAS data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zawadzki, Jaroslaw; Kędzior, Mateusz</p> <p>2016-03-01</p> <p>Monitoring of temporal and spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> variability is an important issue, both from practical and scientific point of view. It is well known that passive, L-band, radiometric measurements provide best <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates. Unfortunately as it was observed during <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission, which was specially dedicated to measure <span class="hlt">soil</span> <span class="hlt">moisture</span>, these measurements suffer significant data loss. It is caused mainly by radio frequency interference (RFI) which strongly contaminates Central Europe and even in particularly unfavorable conditions, might prevent these data from being used for regional or watershed scale analysis. Nevertheless, it is highly awaited by researchers to receive statistically significant information on <span class="hlt">soil</span> <span class="hlt">moisture</span> over the area of a big watershed. One of such watersheds, the Odra (Oder) river watershed, lies in three European countries - Poland, Germany and the Czech Republic. The area of the Odra river watershed is equal to 118,861 km2 making it the second most important river in Poland as well as one of the most significant one in Central Europe. This paper examines the SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data in the Odra river watershed in the period from 2010 to 2012. This attempt was made to check the possibility of assessing, from the low spatial resolution observations of SMOS, useful information that could be exploited for practical aims in watershed scale, for example, in water storage models even while moderate RFI takes place. Such studies, performed over the area of a large watershed, were recommended by researchers in order to obtain statistically significant results. To meet these expectations, Centre Aval de Traitement des Donnes SMOS (CATDS), 3-days averaged data, together with Global Land Data Assimilation System (GLDAS) National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrologic Research Lab (NOAH) model 0.25 <span class="hlt">soil</span> <span class="hlt">moisture</span> values were used for statistical analyses and mutual</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JGRG..123...46W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JGRG..123...46W"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Controls the Thermal Habitat of Active Layer <span class="hlt">Soils</span> in the McMurdo Dry Valleys, Antarctica</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wlostowski, A. N.; Gooseff, M. N.; Adams, B. J.</p> <p>2018-01-01</p> <p>Antarctic <span class="hlt">soil</span> ecosystems are strongly controlled by abiotic habitat variables. Regional climate change in the McMurdo Dry Valleys is expected to cause warming over the next century, leading to an increase in frequency of freeze-thaw cycling in the <span class="hlt">soil</span> habitat. Previous studies show that physiological stress associated with freeze-thaw cycling adversely affects invertebrate populations by decreasing abundance and positively selecting for larger body sizes. However, it remains unclear whether or not climate warming will indeed enhance the frequency of annual freeze-thaw cycling and associated physiological stresses. This research quantifies the frequency, rate, and spatial heterogeneity of active layer freezing to better understand how regional climate change may affect active layer <span class="hlt">soil</span> thermodynamics, and, in turn, affect <span class="hlt">soil</span> macroinvertebrate communities. Shallow active layer temperature, specific conductance, and <span class="hlt">soil</span> <span class="hlt">moisture</span> were observed along natural wetness gradients. Field observations show that the frequency and rate of freeze events are nonlinearly related to freezable <span class="hlt">soil</span> <span class="hlt">moisture</span> (θf). Over a 2 year period, <span class="hlt">soils</span> at θf < 0.080 m3/m3 experienced between 15 and 35 freeze events and froze rapidly compared to <span class="hlt">soils</span> with θf > 0.080 m3/m3, which experienced between 2 and 6 freeze events and froze more gradually. A numerical <span class="hlt">soil</span> thermodynamic model is able to simulate observed freezing rates across a range of θf, reinforcing a well-known causal relationship between <span class="hlt">soil</span> <span class="hlt">moisture</span> and active layer freezing dynamics. Findings show that slight increases in <span class="hlt">soil</span> <span class="hlt">moisture</span> can potentially offset the effect of climate warming on exacerbating <span class="hlt">soil</span> freeze-thaw cycling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=309870','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=309870"><span>Generating large-scale estimates from sparse, in-situ networks: multi-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> modeling at ARS watersheds for NASA’s <span class="hlt">soil</span> <span class="hlt">moisture</span> active passive (SMAP) calibration/validation 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 SMAP satellite, launched in November of 2014, produces estimates of average volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> at 3, 9, and 36-kilometer scales. The calibration and validation process of these estimates requires the generation of an identically-scaled <span class="hlt">soil</span> <span class="hlt">moisture</span> product from existing in-situ networ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1210783B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1210783B"><span>Analysis of spatiotemporal <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns at the catchment scale using a wireless 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, Heye R.; Huisman, Johan A.; Rosenbaum, Ulrike; Weuthen, Ansgar; Vereecken, Harry</p> <p>2010-05-01</p> <p><span class="hlt">Soil</span> water content plays a key role in partitioning water and energy fluxes and controlling the pattern of groundwater recharge. Despite the importance of <span class="hlt">soil</span> water content, it is not yet measured in an operational way at larger scales. The aim of this paper is to present the potential of real-time monitoring for the analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns at the catchment scale using the recently developed wireless sensor network <span class="hlt">Soil</span>Net [1], [2]. <span class="hlt">Soil</span>Net is designed to measure <span class="hlt">soil</span> <span class="hlt">moisture</span>, salinity and temperature in several depths (e.g. 5, 20 and 50 cm). Recently, a small forest catchment Wüstebach (~27 ha) has been instrumented with 150 sensor nodes and more than 1200 <span class="hlt">soil</span> sensors in the framework of the Transregio32 and the Helmholtz initiative TERENO (Terrestrial Environmental Observatories). From August to November 2009, more than 6 million <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements have been performed. We will present first results from a statistical and geostatistical analysis of the data. The observed spatial variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> corresponds well with the 800-m scale variability described in [3]. The very low scattering of the standard deviation versus mean <span class="hlt">soil</span> <span class="hlt">moisture</span> plots indicates that sensor network data shows less artificial <span class="hlt">soil</span> <span class="hlt">moisture</span> variations than <span class="hlt">soil</span> <span class="hlt">moisture</span> data originated from measurement campaigns. The variograms showed more or less the same nugget effect, which indicates that the sum of the sub-scale variability and the measurement error is rather time-invariant. Wet situations showed smaller spatial variability, which is attributed to saturated <span class="hlt">soil</span> water content, which poses an upper limit and is typically not strongly variable in headwater catchments with relatively homogeneous <span class="hlt">soil</span>. The spatiotemporal variability in <span class="hlt">soil</span> <span class="hlt">moisture</span> at 50 cm depth was significantly lower than at 5 and 20 cm. This finding indicates that the considerable variability of the top <span class="hlt">soil</span> is buffered deeper in the <span class="hlt">soil</span> due to lateral and vertical water fluxes</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=257436','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=257436"><span>Information and Complexity Measures Applied to Observed and Simulated <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Time Series</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>Time series of <span class="hlt">soil</span> <span class="hlt">moisture</span>-related parameters provides important insights in functioning of <span class="hlt">soil</span> water systems. Analysis of patterns within these time series has been used in several studies. The objective of this work was to compare patterns in observed and simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> contents to u...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=299203','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=299203"><span>Error characterization of microwave satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets using fourier 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><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key geophysical variable in hydrological and meteorological processes. Accurate and current observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> over meso to global scales used as inputs to hydrological, weather and climate modelling will benefit the predictability and understanding of these processes. ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=300706','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=300706"><span>Error characterization of microwave satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets using fourier 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>Abstract: <span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key geophysical variable in hydrological and meteorological processes. Accurate and current observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> over mesoscale to global scales as inputs to hydrological, weather and climate modelling will benefit the predictability and understanding of these p...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.B13N0076H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.B13N0076H"><span>The role of tree species and <span class="hlt">soil</span> <span class="hlt">moisture</span> in <span class="hlt">soil</span> organic matter stabilization and destabilization</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hatten, J. A.; Dewey, J.; Roberts, S.; McNeal, K.; Shaman, A.</p> <p>2014-12-01</p> <p>Inputs of labile organic substrates to <span class="hlt">soils</span> are commonly associated with elevated <span class="hlt">soil</span> organic carbon mineralization rates; this process is known as the priming effect. Plant presence and <span class="hlt">soil</span> conditions (i.e. water regime, nutrient status) are known to be interacting factors governing priming. In this study, we examine the role of differing species, loblolly pine (Pinus taeda L.) and nuttall oak (Quercus texana B.), and <span class="hlt">moisture</span> regimes (low and high) upon the <span class="hlt">soil</span> priming effect in a fine textured <span class="hlt">soil</span>. We explore whether there is depletion of original <span class="hlt">soil</span> carbon and concurrent replacement through addition of fresh organic matter from the planted tree species. By employing a series of planted and plant-free pots in a greenhouse mesocosm study, we were able to characterize the composition of <span class="hlt">soil</span> organic matter and its carbon with the use of CuO oxidation products (e.g. lignin, cutin/suberin biomarkers). Carbon was elevated on the low <span class="hlt">moisture</span> samples relative to all other treatments, and the C:N ratio suggests that newly produced plant carbon replaced original <span class="hlt">soil</span> carbon. The <span class="hlt">soil</span> lignin content of the planted treatments was lower than the plant-free treatments suggesting that lignin present in the original <span class="hlt">soil</span> may have been preferentially degraded by priming and not replaced. We will discuss the utility of CuO oxidation products to explore <span class="hlt">soil</span> organic carbon dynamics and the implications of understanding the role of species and <span class="hlt">soil</span> <span class="hlt">moisture</span> in predicting the response of <span class="hlt">soil</span> carbon to land use and climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120008162','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120008162"><span>The Impact of AMSR-E <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Assimilation on Evapotranspiration Estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peters-Lidard, Christa D.; Kumar, Sujay; Mocko, David; Tian, Yudong</p> <p>2012-01-01</p> <p>An assessment ofETestimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) <span class="hlt">soil</span> <span class="hlt">moisture</span> products into the Noah LSM Version 3.2 with the North American LDAS phase 2 CNLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM <span class="hlt">soil</span> <span class="hlt">moisture</span> product.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H33F1393K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H33F1393K"><span>Multiscale analysis of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in a mesoscale catchment utilizing an integrated ecohydrological model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Korres, W.; Reichenau, T. G.; Schneider, K.</p> <p>2012-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is one of the fundamental variables in hydrology, meteorology and agriculture, influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Numerous studies have shown that in addition to natural factors (rainfall, <span class="hlt">soil</span>, topography etc.) agricultural management is one of the key drivers for spatio-temporal patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> in agricultural landscapes. Interactions between plant growth, <span class="hlt">soil</span> hydrology and <span class="hlt">soil</span> nitrogen transformation processes are modeled by using a dynamically coupled modeling approach. The process-based ecohydrological model components of the integrated decision support system DANUBIA are used to identify the important processes and feedbacks determining <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns in agroecosystems. Integrative validation of plant growth and surface <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics serves as a basis for a spatially distributed modeling analysis of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns in the northern part of the Rur catchment (1100 sq km), Western Germany. An extensive three year dataset (2007-2009) of surface <span class="hlt">soil</span> <span class="hlt">moisture</span>-, plant- (LAI, organ specific biomass and N) and <span class="hlt">soil</span>- (texture, N, C) measurements was collected. Plant measurements were carried out biweekly for winter wheat, maize, and sugar beet during the growing season. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was measured with three FDR <span class="hlt">soil</span> <span class="hlt">moisture</span> stations. Meteorological data was measured with an eddy flux station. The results of the model validation showed a very good agreement between the modeled plant parameters (biomass, green LAI) and the measured parameters with values between 0.84 and 0.98 (Willmotts index of agreement). The modeled surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (0 - 20 cm) showed also a very favorable agreement with the measurements for winter wheat and sugar beet with an RMSE between 1.68 and 3.45 Vol.-%. For maize, the RMSE was less favorable particularly in the 1.5 months prior to harvest. 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