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Sample records for global soil moisture

  1. 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.

  2. High-Resolution Global Soil Moisture Map

    NASA Image and Video Library

    2015-05-19

    High-resolution global soil moisture map from NASA SMAP combined radar and radiometer instruments, acquired between May 4 and May 11, 2015 during SMAP commissioning phase. The map has a resolution of 5.6 miles (9 kilometers). The data gap is due to turning the instruments on and off during testing. http://photojournal.jpl.nasa.gov/catalog/PIA19337

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. 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...

  9. NASA Soil Moisture Mission Produces First Global Radar Map

    NASA Image and Video Library

    2015-04-21

    With its antenna now spinning at full speed, NASA new Soil Moisture Active Passive SMAP observatory has successfully re-tested its science instruments and generated its first global maps, a key step to beginning routine science operations in May, 2015

  10. NASA Soil Moisture Mission Produces First Global Radiometer Map

    NASA Image and Video Library

    2015-04-21

    With its antenna now spinning at full speed, NASA new Soil Moisture Active Passive SMAP observatory has successfully re-tested its science instruments and generated its first global maps, a key step to beginning routine science operations in May, 2015

  11. The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale.

    PubMed

    Hursh, Andrew; Ballantyne, Ashley; Cooper, Leila; Maneta, Marco; Kimball, John; Watts, Jennifer

    2017-05-01

    Soil 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, moisture, carbon supply, and other site characteristics are known to regulate soil 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 soil moisture, soil temperature, primary productivity, and soil carbon estimates with observations of annual Rs from the Global Soil Respiration Database (SRDB). We find that calibrating models with parabolic soil moisture functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. Soil 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 soil moisture and soil carbon emerge as dominant predictors of Rs. We identify regions where typical temperature-driven responses are further mediated by soil moisture, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because soil moisture 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 moisture 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

  12. Monitoring the Global Soil Moisture Climatology Using GLDAS/LIS

    NASA Astrophysics Data System (ADS)

    Meng, J.; Mitchell, K.; Wei, H.; Gottschalck, J.

    2006-05-01

    Soil moisture plays a crucial role in the terrestrial water cycle through governing the process of partitioning precipitation among infiltration, runoff and evaporation. Accurate assessment of soil moisture and other land states, namely, soil temperature, snowpack, and vegetation, is critical in numerical environmental prediction systems because of their regulation of surface water and energy fluxes between the surface and atmosphere over a variety of spatial and temporal scales. The Global Land Data Assimilation System (GLDAS) is developed, jointly by NASA Goddard Space Flight Center (GSFC) and NOAA National Centers for Environmental Prediction (NCEP), to perform high-quality global land surface simulation using state-of-art land surface models and further minimizing the errors of simulation by constraining the models with observation- based precipitation, and satellite land data assimilation techniques. The GLDAS-based Land Information System (LIS) infrastructure has been installed on the NCEP supercomputer that serves the operational weather and climate prediction systems. In this experiment, the Noah land surface model is offline executed within the GLDAS/LIS infrastructure, driven by the NCEP Global Reanalysis-2 (GR2) and the CPC Merged Analysis of Precipitation (CMAP). We use the same Noah code that is coupled to the operational NCEP Global Forecast System (GFS) for weather prediction and test bed versions of the NCEP Climate Forecast System (CFS) for seasonal prediction. For assessment, it is crucial that this uncoupled GLDAS/Noah uses exactly the same Noah code (and soil and vegetation parameters therein), and executes with the same horizontal grid, landmask, terrain field, soil and vegetation types, seasonal cycle of green vegetation fraction and surface albedo as in the coupled GFS/Noah and CFS/Noah. This execution is for the 25-year period of 1980-2005, starting with a pre-execution 10-year spin-up. This 25-year GLDAS/Noah global land climatology will be

  13. Homogeneity of a Global Multisatellite Soil Moisture Climate Data Record

    NASA Technical Reports Server (NTRS)

    Su, Chun-Hsu; Ryu, Dongryeol; Dorigo, Wouter; Zwieback, Simon; Gruber, Alexander; Albergel, Clement; Reichle, Rolf H.; Wagner, Wolfgang

    2016-01-01

    Climate Data Records (CDR) that blend multiple satellite products are invaluable for climate studies, trend analysis and risk assessments. Knowledge of any inhomogeneities in the CDR is therefore critical for making correct inferences. This work proposes a methodology to identify the spatiotemporal extent of the inhomogeneities in a 36-year, global multisatellite soil moisture CDR as the result of changing observing systems. Inhomogeneities are detected at up to 24 percent of the tested pixels with spatial extent varying with satellite changeover times. Nevertheless, the contiguous periods without inhomogeneities at changeover times are generally longer than 10 years. Although the inhomogeneities have measurable impact on the derived trends, these trends are similar to those observed in ground data and land surface reanalysis, with an average error less than 0.003 cubic meters per cubic meter per year. These results strengthen the basis of using the product for long-term studies and demonstrate the necessity of homogeneity testing of multisatellite CDRs in general.

  14. 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.

  15. 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.

  16. 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.

  17. 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...

  18. Effects of Recent Regional Soil Moisture Variability on Global Net Ecosystem CO2 Exchange

    NASA Astrophysics Data System (ADS)

    Jones, L. A.; Madani, N.; Kimball, J. S.; Reichle, R. H.; Colliander, A.

    2017-12-01

    Soil moisture exerts a major regional control on the inter-annual variability of the global land sink for atmospheric CO2. In semi-arid regions, annual biomass production is closely coupled to variability in soil moisture availability, while in cold-season-affected regions, summer drought offsets the effects of advancing spring phenology. Availability of satellite solar-induced fluorescence (SIF) observations and improvements in atmospheric inversions has led to unprecedented ability to monitor atmospheric sink strength. However, discrepancies still exist between such top-down estimates as atmospheric inversion and bottom-up process and satellite driven models, indicating that relative strength, mechanisms, and interaction of driving factors remain poorly understood. We use soil moisture fields informed by Soil Moisture Active Passive Mission (SMAP) observations to compare recent (2015-2017) and historic (2000-2014) variability in net ecosystem land-atmosphere CO2 exchange (NEE). The operational SMAP Level 4 Carbon (L4C) product relates ground-based flux tower measurements to other bottom-up and global top-down estimates to underlying soil moisture and other driving conditions using data-assimilation-based SMAP Level 4 Soil Moisture (L4SM). Droughts in coastal Brazil, South Africa, Eastern Africa, and an anomalous wet period in Eastern Australia were observed by L4C. A seasonal seesaw pattern of below-normal sink strength at high latitudes relative to slightly above-normal sink strength for mid-latitudes was also observed. Whereas SMAP-based soil moisture is relatively informative for short-term temporal variability, soil moisture biases that vary in space and with season constrain the ability of the L4C estimates to accurately resolve NEE. Such biases might be caused by irrigation and plant-accessible ground-water. Nevertheless, SMAP L4C daily NEE estimates connect top-down estimates to variability of effective driving factors for accurate estimates of regional-to-global

  19. A Time Series Analysis of Global Soil Moisture Data Products for Water Cycle Studies

    NASA Astrophysics Data System (ADS)

    Zhan, X.; Yin, J.; Liu, J.; Fang, L.; Hain, C.; Ferraro, R. R.; Weng, F.

    2017-12-01

    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, soil moisture 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 soil moisture status and dynamics is crucial for monitoring and predicting the weather, climate, hydrology and ecological processes. Satellite remote sensing has been used for soil moisture observation since the launch of the Scanning Multi-channel Microwave Radiometer (SMMR) on NASA's Nimbus-7 satellite in 1978. Many satellite soil moisture data products have been made available to the science communities and general public. The soil moisture operational product system (SMOPS) of NOAA NESDIS has been operationally providing global soil moisture 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 soil moisture 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 soil moisture data product can be used as a climate data record is evaluated based on the above analyses.

  20. Global Soil Moisture Estimation through a Coupled CLM4-RTM-DART Land Data Assimilation System

    NASA Astrophysics Data System (ADS)

    Zhao, L.; Yang, Z. L.; Hoar, T. J.

    2016-12-01

    Very few frameworks exist that estimate global-scale soil moisture through microwave land data assimilation (DA). Toward this goal, we have developed such a framework by linking the Community Land Model version 4 (CLM4) and a microwave radiative transfer model (RTM) with the Data Assimilation Research Testbed (DART). The deterministic Ensemble Adjustment Kalman Filter (EAKF) within the DART is utilized to estimate global multi-layer soil moisture by assimilating brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). A 40-member of Community Atmosphere Model version 4 (CAM4) reanalysis is adopted to drive CLM4 simulations. Spatial-specific time-invariant microwave parameters are pre-calibrated to minimize uncertainties in RTM. Besides, various methods are designed in consideration of computational efficiency. A series of experiments are conducted to quantify the DA sensitivity to microwave parameters, choice of assimilated observations, and different CLM4 updating schemes. Evaluation results indicate that the newly established CLM4-RTM-DART framework improves the open-loop CLM4 simulated soil moisture. Pre-calibrated microwave parameters, rather than their default values, can ensure a more robust global-scale performance. In addition, updating near-surface soil moisture is capable of improving soil moisture in deeper layers, while simultaneously updating multi-layer soil moisture fails to obtain intended improvements. We will show in this presentation the architecture of the CLM4-RTM-DART system and the evaluations on AMSR-E DA. Preliminary results on multi-sensor DA that integrates various satellite observations including GRACE, MODIS, and AMSR-E will also be presented. ReferenceZhao, L., Z.-L. Yang, and T. J. Hoar, 2016. Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4-RTM-DART System. Journal of Hydrometeorology, DOI: 10.1175/JHM-D-15-0218.1.

  1. 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.

  2. Design of a global soil moisture initialization procedure for the simple biosphere model

    NASA Technical Reports Server (NTRS)

    Liston, G. E.; Sud, Y. C.; Walker, G. K.

    1993-01-01

    Global soil moisture and land-surface evapotranspiration fields are computed using an analysis scheme based on the Simple Biosphere (SiB) soil-vegetation-atmosphere interaction model. The scheme is driven with observed precipitation, and potential evapotranspiration, where the potential evapotranspiration is computed following the surface air temperature-potential evapotranspiration regression of Thomthwaite (1948). The observed surface air temperature is corrected to reflect potential (zero soil moisture stress) conditions by letting the ratio of actual transpiration to potential transpiration be a function of normalized difference vegetation index (NDVI). Soil moisture, evapotranspiration, and runoff data are generated on a daily basis for a 10-year period, January 1979 through December 1988, using observed precipitation gridded at a 4 deg by 5 deg resolution.

  3. Western US high June 2015 temperatures and their relation to global warming and soil moisture

    NASA Astrophysics Data System (ADS)

    Philip, Sjoukje Y.; Kew, Sarah F.; Hauser, Mathias; Guillod, Benoit P.; Teuling, Adriaan J.; Whan, Kirien; Uhe, Peter; Oldenborgh, Geert Jan van

    2018-04-01

    The Western US states Washington (WA), Oregon (OR) and California (CA) experienced extremely high temperatures in June 2015. The temperature anomalies were so extreme that they cannot be explained with global warming alone. We investigate the hypothesis that soil moisture played an important role as well. We use a land surface model and a large ensemble from the weather@home modelling effort to investigate the coupling between soil moisture and temperature in a warming world. Both models show that May was anomalously dry, satisfying a prerequisite for the extreme heat wave, and they indicate that WA and OR are in a wet-to-dry transitional soil moisture regime. We use two different land surface-atmosphere coupling metrics to show that there was strong coupling between temperature, latent heat flux and the effect of soil moisture deficits on the energy balance in June 2015 in WA and OR. June temperature anomalies conditioned on wet/dry conditions show that both the mean and extreme temperatures become hotter for dry soils, especially in WA and OR. Fitting a Gaussian model to temperatures using soil moisture as a covariate shows that the June 2015 temperature values fit well in the extrapolated empirical temperature/drought lines. The high temperature anomalies in WA and OR are thus to be expected, given the dry soil moisture conditions and that those regions are in the transition from a wet to a dry regime. CA is already in the dry regime and therefore the necessity of taking soil moisture into account is of lower importance.

  4. Global retrieval of soil moisture and vegetation properties using data-driven methods

    NASA Astrophysics Data System (ADS)

    Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

    2017-04-01

    Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture 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 soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. 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

  5. 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...

  6. Improving Water Level and Soil Moisture Over Peatlands in a Global Land Modeling System

    NASA Technical Reports Server (NTRS)

    Bechtold, M.; De Lannoy, G. J. M.; Roose, D.; Reichle, R. H.; Koster, R. D.; Mahanama, S. P.

    2017-01-01

    New model structure for peatlands results in improved skill metrics (without any parameter calibration) Simulated surface soil moisture strongly affected by new model, but reliable soil moisture data lacking for validation.

  7. SMAP Global Map of Surface Soil Moisture Aug. 25-27, 2015

    NASA Image and Video Library

    2015-09-02

    A three-day composite global map of surface soil moisture as retrieved from NASA SMAP radiometer instrument between Aug. 25-27, 2015. Dry areas appear yellow/orange, such as the Sahara Desert, western Australia and the western U.S. Wet areas appear blue, representing the impacts of localized storms. White areas indicate snow, ice or frozen ground. http://photojournal.jpl.nasa.gov/catalog/PIA19877

  8. Global observation-based diagnosis of soil moisture control on land surface flux partition

    NASA Astrophysics Data System (ADS)

    Gallego-Elvira, Belen; Taylor, Christopher M.; Harris, Phil P.; Ghent, Darren; Veal, Karen L.; Folwell, Sonja S.

    2016-04-01

    Soil moisture plays a central role in the partition of available energy at the land surface between sensible and latent heat flux to the atmosphere. As soils dry out, evapotranspiration becomes water-limited ("stressed"), and both land surface temperature (LST) and sensible heat flux rise as a result. This change in surface behaviour during dry spells directly affects critical processes in both the land and the atmosphere. Soil water deficits are often a precursor in heat waves, and they control where feedbacks on precipitation become significant. State-of-the-art global climate model (GCM) simulations for the Coupled Model Intercomparison Project Phase 5 (CMIP5) disagree on where and how strongly the surface energy budget is limited by soil moisture. Evaluation of GCM simulations at global scale is still a major challenge owing to the scarcity and uncertainty of observational datasets of land surface fluxes and soil moisture at the appropriate scale. Earth observation offers the potential to test how well GCM land schemes simulate hydrological controls on surface fluxes. In particular, satellite observations of LST provide indirect information about the surface energy partition at 1km resolution globally. Here, we present a potentially powerful methodology to evaluate soil moisture stress on surface fluxes within GCMs. Our diagnostic, Relative Warming Rate (RWR), is a measure of how rapidly the land warms relative to the overlying atmosphere during dry spells lasting at least 10 days. Under clear skies, this is a proxy for the change in sensible heat flux as soil dries out. We derived RWR from MODIS Terra and Aqua LST observations, meteorological re-analyses and satellite rainfall datasets. Globally we found that on average, the land warmed up during dry spells for 97% of the observed surface between 60S and 60N. For 73% of the area, the land warmed faster than the atmosphere (positive RWR), indicating water stressed conditions and increases in sensible heat flux

  9. Soil moisture

    Treesearch

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

    1971-01-01

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

  10. Evaluating soil moisture constraints on surface fluxes in land surface models globally

    NASA Astrophysics Data System (ADS)

    Harris, Phil; Gallego-Elvira, Belen; Taylor, Christopher; Folwell, Sonja; Ghent, Darren; Veal, Karen; Hagemann, Stefan

    2016-04-01

    Soil moisture availability exerts a strong control over land evaporation in many regions. However, global climate models (GCMs) disagree on when and where evaporation is limited by soil moisture. Evaluation of the relevant modelled processes has suffered from a lack of reliable, global observations of land evaporation at the GCM grid box scale. Satellite observations of land surface temperature (LST) offer spatially extensive but indirect information about the surface energy partition and, under certain conditions, about soil moisture availability on evaporation. Specifically, as soil moisture decreases during rain-free dry spells, evaporation may become limited leading to increases in LST and sensible heat flux. We use MODIS Terra and Aqua observations of LST at 1 km from 2000 to 2012 to assess changes in the surface energy partition during dry spells lasting 10 days or longer. The clear-sky LST data are aggregated to a global 0.5° grid before being composited as a function dry spell day across many events in a particular region and season. These composites are then used to calculate a Relative Warming Rate (RWR) between the land surface and near-surface air. This RWR can diagnose the typical strength of short term changes in surface heat fluxes and, by extension, changes in soil moisture limitation on evaporation. Offline land surface model (LSM) simulations offer a relatively inexpensive way to evaluate the surface processes of GCMs. They have the benefits that multiple models, and versions of models, can be compared on a common grid and using unbiased forcing. Here, we use the RWR diagnostic to assess global, offline simulations of several LSMs (e.g., JULES and JSBACH) driven by the WATCH Forcing Data-ERA Interim. Both the observed RWR and the LSMs use the same 0.5° grid, which allows the observed clear-sky sampling inherent in the underlying MODIS LST to be applied to the model outputs directly. This approach avoids some of the difficulties in analysing free

  11. Generating a global soil evaporation dataset using SMAP soil moisture data to estimate components of the surface water balance

    NASA Astrophysics Data System (ADS)

    Carbone, E.; Small, E. E.; Badger, A.; Livneh, B.

    2016-12-01

    Evapotranspiration (ET) is fundamental to the water, energy and carbon cycles. However, our ability to measure ET and partition the total flux into transpiration and evaporation from soil is limited. This project aims to generate a global, observationally-based soil evaporation dataset (E-SMAP): using SMAP surface soil moisture data in conjunction with models and auxiliary observations to observe or estimate each component of the surface water balance. E-SMAP will enable a better understanding of water balance processes and contribute to forecasts of water resource availability. Here we focus on the flux between the soil surface and root zone layers (qbot), which dictates the proportion of water that is available for soil evaporation. Any water that moves from the surface layer to the root zone contributes to transpiration or groundwater recharge. The magnitude and direction of qbot are driven by gravity and the gradient in matric potential. We use a highly discretized Richards Equation-type model (e.g. Hydrus 1D software) with meteorological forcing from the North American Land Data Assimilation System (NLDAS) to estimate qbot. We verify the simulations using SMAP L4 surface and root zone soil moisture data. These data are well suited for evaluating qbot because they represent the most advanced estimate of the surface to root zone soil moisture gradient at the global scale. Results are compared with similar calculations using NLDAS and in situ soil moisture data. Preliminary calculations show that the greatest amount of variability between qbot determined from NLDAS, in situ and SMAP occurs directly after precipitation events. At these times, uncertainties in qbot calculations significantly affect E-SMAP estimates.

  12. Advances in Measuring Soil Moisture using Global Navigation Satellite Systems Interferometric Reflectometry (GNSS-IR)

    NASA Astrophysics Data System (ADS)

    Moore, A. W.; Small, E. E.; Owen, S. E.; Hardman, S. H.; Wong, C.; Freeborn, D. J.; Larson, K. M.

    2016-12-01

    GNSS Interferometric Reflectometry (GNSS-IR) uses GNSS signals reflected off the land to infer changes in the near-antenna environment and monitor fluctuations in soil moisture, as well as other related hydrologic variables: snow depth/snow water equivalent (SWE), vegetation water content, and water level [Larson and Small, 2013; McCreight, et al., 2014; Larson et al., 2013]. GNSS instruments installed by geoscientists and surveyors to measure land motions can measure soil moisture fluctuations with accuracy (RMSE <0.04 cm3/cm3 [Small et al., 2016]) and latency sufficient for many applications (e.g., weather forecasting, climate studies, satellite validation). The soil moisture products have a unique and complementary footprint intermediate in scale between satellite and standard in situ sensors. Variations in vegetation conditions introduce considerable errors, but algorithms have been developed to address this issue [Small et al., 2016]. A pilot project (PBO H2O) using 100+ GPS sites in the western U.S. (Figure 1) from a single network (the Plate Boundary Observatory) has been operated by the University of Colorado (CU) at http://xenon.colorado.edu/portal since October 2012. JPL and CU are funded by NASA ESTO to refactor the PBO H2O software within an Apache OODT framework for robust operational analysis of soil moisture data and auto-configuration when new stations are added. We will report progress on the new GNSS H2O analysis portal, and plans to expand to global networks and from GPS to other GNSS signals. ReferencesLarson, K. M., & Small, E. E. (2013) Eos, 94(52), 505-512. McCreight, J. L., Small, E. E., & Larson, K. M. (2014). Water Resour. Res., 50(8), 6892-6909. Larson, K. M., Ray, R. D., Nievinski, F. G., & Freymueller, J. T. (2013). IEEE Geosci Remote S, 10(5), 1200-1204. Small, E. E., Larson, K. M., Chew, C. C., Dong, J., & Ochsner, T. E. (2016). IEEE J Sel. Top. Appl. PP(39). Figure 1: (R) Western U.S. GPS-IR soil moisture sites. (L): Products derived

  13. Assessment of SMAP soil moisture for global simulation of gross primary production

    NASA Astrophysics Data System (ADS)

    He, Liming; Chen, Jing M.; Liu, Jane; Bélair, Stéphane; Luo, Xiangzhong

    2017-07-01

    In this study, high-quality soil moisture data derived from the Soil Moisture Active Passive (SMAP) satellite measurements are evaluated from a perspective of improving the estimation of the global gross primary production (GPP) using a process-based ecosystem model, namely, the Boreal Ecosystem Productivity Simulator (BEPS). The SMAP soil moisture data are assimilated into BEPS using an ensemble Kalman filter. The correlation coefficient (r) between simulated GPP from the sunlit leaves and Sun-induced chlorophyll fluorescence (SIF) measured by Global Ozone Monitoring Experiment-2 is used as an indicator to evaluate the performance of the GPP simulation. Areas with SMAP data in low quality (i.e., forests), or with SIF in low magnitude (e.g., deserts), or both are excluded from the analysis. With the assimilated SMAP data, the r value is enhanced for Africa, Asia, and North America by 0.016, 0.013, and 0.013, respectively (p < 0.05). Significant improvement in r appears in single-cropping agricultural land where the irrigation is not considered in the model but well captured by SMAP (e.g., 0.09 in North America, p < 0.05). With the assimilation of SMAP, areas with weak model performances are identified in double or triple cropping cropland (e.g., part of North China Plain) and/or mountainous area (e.g., Spain and Turkey). The correlation coefficient is enhanced by 0.01 in global average for shrub, grass, and cropland. This enhancement is small and insignificant because nonwater-stressed areas are included.

  14. Climate Prediction Center global monthly soil moisture data set at 0.5° resolution for 1948 to present

    NASA Astrophysics Data System (ADS)

    Fan, Yun; van den Dool, Huug

    2004-05-01

    We have produced a 0.5° × 0.5° monthly global soil moisture data set for the period from 1948 to the present. The land model is a one-layer "bucket" water balance model, while the driving input fields are Climate Prediction Center monthly global precipitation over land, which uses over 17,000 gauges worldwide, and monthly global temperature from global Reanalysis. The output consists of global monthly soil moisture, evaporation, and runoff, starting from January 1948. A distinguishing feature of this data set is that all fields are updated monthly, which greatly enhances utility for near-real-time purposes. Data validation shows that the land model does well; both the simulated annual cycle and interannual variability of soil moisture are reasonably good against the limited observations in different regions. A data analysis reveals that, on average, the land surface water balance components have a stronger annual cycle in the Southern Hemisphere than those in the Northern Hemisphere. From the point of view of soil moisture, climates can be characterized into two types, monsoonal and midlatitude climates, with the monsoonal ones covering most of the low-latitude land areas and showing a more prominent annual variation. A global soil moisture empirical orthogonal function analysis and time series of hemisphere means reveal some interesting patterns (like El Niño-Southern Oscillation) and long-term trends in both regional and global scales.

  15. 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...

  16. 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.

  17. The global SMOS Level 3 daily soil moisture and brightness temperature maps

    NASA Astrophysics Data System (ADS)

    Bitar, Ahmad Al; Mialon, Arnaud; Kerr, Yann H.; Cabot, François; Richaume, Philippe; Jacquette, Elsa; Quesney, Arnaud; Mahmoodi, Ali; Tarot, Stéphane; Parrens, Marie; Al-Yaari, Amen; Pellarin, Thierry; Rodriguez-Fernandez, Nemesio; Wigneron, Jean-Pierre

    2017-06-01

    The objective of this paper is to present the multi-orbit (MO) surface soil moisture (SM) and angle-binned brightness temperature (TB) products for the SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS) makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD) compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD) using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an open licence and

  18. Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

    NASA Astrophysics Data System (ADS)

    DY, C. Y.; Fung, J. C. H.

    2016-08-01

    A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture 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 soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

  19. SOIL moisture data intercomparison

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  20. Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products

    USDA-ARS?s Scientific Manuscript database

    Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and comb...

  1. 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

  2. Implementation of a global-scale operational data assimilation system for satellite-based soil moisture retrievals

    NASA Astrophysics Data System (ADS)

    Bolten, J.; Crow, W.; Zhan, X.; Reynolds, C.

    2008-08-01

    Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is presented.

  3. Correlation Between Soil Moisture and Dust Emissions: An Investigation for Global Climate Modeling

    NASA Technical Reports Server (NTRS)

    Fredrickson, Carley; Tan, Qian

    2017-01-01

    This work is using the newly available NASA SMAP soil moisture measurement data to evaluate its impact on the atmospheric dust emissions. Dust is an important component of atmospheric aerosols, which affects both climate and air quality. In this work, we focused on semi-desert regions, where dust emissions show seasonal variations due to soil moisture changes, i.e. in Sahel of Africa. We first identified three Aerosol Robotic Network (AERONET) sites in the Sahel (IER_Cinzana, Banizoumbou, and Zinder_Airport). We then utilized measurements of aerosol optical depth (AOD), fine mode fraction, size distribution, and single-scattering albedo and its wave-length dependence to select dust plumes from the available measurements We matched the latitude and longitude of the AERONET station to the corresponding SMAP data cell in the years 2015 and 2016, and calculated their correlation coefficient. Additionally, we looked at the correlation coefficient with a three-day and a five-day shift to check the impact of soil moisture on dust plumes with some time delay. Due to the arid nature of Banizoumbou and Zinder_Airport, no correlation was found to exist between local soil moisture and dust aerosol load. While IER_Cinzana had soil moisture levels above the satellite threshold of 0.02cm3/cm3, R-value approaching zero indicated no presence of a correlation. On the other hand, Ilorin demonstrated a significant negative correlation between aerosol optical depth and soil moisture. When isolating the analysis to Ilorin's dry season, a negative correlation of -0.593 was the largest dust-isolated R-value recorded, suggesting that soil moisture is driven the dust emission in this semi-desert region during transitional season.

  4. 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.

  5. 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...

  6. Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

    NASA Technical Reports Server (NTRS)

    Lievens, H.; Martens, B.; Verhoest, N. E. C.; Hahn, S.; Reichle, R. H.; Miralles, D. G.

    2017-01-01

    Active radar backscatter (s?) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s? and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s? or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s? and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

  7. Enhancing the USDA Global Crop Assessment Decision Support System Using SMAP Soil Moisture Data

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

    The Foreign Agricultural Services (FAS) is a subdivision of U.S. Department of Agriculture (USDA) that is in charge with providing information on current and expected crop supply and demand estimates. Knowledge of the amount of water in the root zone is an essential source of information for the crop analysts as it governs the crop development and crop growth, which in turn determine the end-of-season yields. USDA FAS currently relies on root zone soil moisture (RZSM) estimates generated using the modified two-layer Palmer Model (PM). PM is a simple water-balance hydrologic model that is driven by daily precipitation observations and minimum and maximum temperature data. These forcing data are based on ground meteorological station measurements from the World Meteorological Organization (WMO), and gridded weather data from the former U.S. Air Force Weather Agency (AFWA), currently called U.S. Air Force 557th Weather Wing. The PM was extended by adding a data assimilation (DA) unit that provides the opportunity to routinely ingest satellite-based soil moisture observations. This allows us to adjust for precipitation-related inaccuracies and enhance the quality of the PM soil moisture estimates. The current operational DA system is based on a 1-D Ensample Kalman Filter approach and relies on observations obtained from the Soil Moisture Ocean Salinity Mission (SMOS). Our talk will demonstrate the value of assimilating two satellite products (i.e. a passive and active) and discuss work that is done in preparation for ingesting soil moisture observations from the Soil Moisture Active Passive (SMAP) mission.

  8. Global soil moisture from the aquarius satellite: Description and initial assessment

    USDA-ARS?s Scientific Manuscript database

    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 scop...

  9. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation

    USDA-ARS?s Scientific Manuscript database

    The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over 5 years while the retrieval algorithms from Level 1 to Level 2 underw...

  10. Homogeneity testing of the global ESA CCI multi-satellite soil moisture climate data record

    NASA Astrophysics Data System (ADS)

    Preimesberger, Wolfgang; Su, Chun-Hsu; Gruber, Alexander; Dorigo, Wouter

    2017-04-01

    ESA's Climate Change Initiative (CCI) creates a global, long-term data record by merging multiple available earth observation products with the goal to provide a product for climate studies, trend analysis, and risk assessments. The blending of soil moisture (SM) time series derived from different active and passive remote sensing instruments with varying sensor characteristics, such as microwave frequency, signal polarization or radiometric accuracy, could potentially lead to inhomogeneities in the merged long-term data series, undercutting the usefulness of the product. To detect the spatio-temporal extent of contiguous periods without inhomogeneities as well as subsequently minimizing their negative impact on the data records, different relative homogeneity tests (namely Fligner-Killeen test of homogeneity of variances and Wilcoxon rank-sums test) are implemented and tested on the combined active-passive ESA CCI SM data set. Inhomogeneities are detected by comparing the data against reference data from in-situ data from ISMN, and model-based estimates from GLDAS-Noah and MERRA-Land. Inhomogeneity testing is performed over the ESA CCI SM data time frame of 38 years (from 1978 to 2015), on a global quarter-degree grid and with regard to six alterations in the combination of observation systems used in the data blending process. This study describes and explains observed variations in the spatial and temporal patterns of inhomogeneities in the combined products. Besides we proposes methodologies for measuring and reducing the impact of inhomogeneities on trends derived from the ESA CCI SM data set, and suggest the use of inhomogeneity-corrected data for future trend studies. This study is supported by the European Union's FP7 EartH2Observe "Global Earth Observation for Integrated Water Resource Assessment" project (grant agreement number 331 603608).

  11. Monitoring Multitemporal Soil Moisture, Rainfall, and ET in Lake Manatee Watershed, South Florida under Global Changes

    NASA Astrophysics Data System (ADS)

    Chang, N.

    2009-12-01

    temporal distributions of key variables in the hydrological cycle, such as soil moisture, evapotranspiration (ET) and precipitation. The multi-sensor platform may include not only in-situ sensor network, ground-based radar, air-borne aircraft, but also even space-borne satellites. The use of a decadal-scale historical record from 1998 to 2008 to support such a trend analysis via NEXRAD (Rainfall), GOES (ET), and MODIS (soil moisture) satellite images may uniquely support middle-term and long-term water resources management in the near future. This study confirms that the potential of using remotely sensed time-series biophysical and ecohydrological states of landscape to characterize soil moisture condition, ET, and other states should be further investigated based on the pros and cons of each type of satellite imageries so as to maximize the beneficial use of remote sensing.

  12. 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.

  13. 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.

  14. Simulated long-term changes in river discharge and soil moisture due to global warming

    USGS Publications Warehouse

    Manabe, S.; Milly, P.C.D.; Wetherald, R.

    2004-01-01

    By use of a coupled ocean atmosphere-land model, this study explores the changes of water availability, as measured by river discharge and soil moisture, that could occur by the middle of the 21st century in response to combined increases of greenhouse gases and sulphate aerosols based upon the "IS92a" scenario. In addition, it presents the simulated change in water availability that might be realized in a few centuries in response to a quadrupling of CO2 concentration in the atmosphere. Averaging the results over extended periods, the radiatively forced changes, which are very similar between the two sets of experiments, were successfully extracted. The analysis indicates that the discharges from Arctic rivers such as the Mackenzie and Ob' increase by up to 20% (of the pre-Industrial Period level) by the middle of the 21st century and by up to 40% or more in a few centuries. In the tropics, the discharges from the Amazonas and Ganga-Brahmaputra rivers increase substantially. However, the percentage changes in runoff from other tropical and many mid-latitude rivers are smaller, with both positive and negative signs. For soil moisture, the results of this study indicate reductions during much of the year in many semiarid regions of the world, such as the southwestern region of North America, the northeastern region of China, the Mediterranean coast of Europe, and the grasslands of Australia and Africa. As a percentage, the reduction is particularly large during the dry season. From middle to high latitudes of the Northern Hemisphere, soil moisture decreases in summer but increases in winter.

  15. 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 ...

  16. NASA's Soil Moisture Active Passive (SMAP) Observatory

    NASA Technical Reports Server (NTRS)

    Kellogg, Kent; Thurman, Sam; Edelstein, Wendy; Spencer, Michael; Chen, Gun-Shing; Underwood, Mark; Njoku, Eni; Goodman, Shawn; Jai, Benhan

    2013-01-01

    The SMAP mission will produce high-resolution and accurate global maps of soil moisture and its freeze/thaw state using data from a non-imaging synthetic aperture radar and a radiometer, both operating at L-band.

  17. Global fields of soil moisture and land surface evapotranspiration derived from observed precipitation and surface air temperature

    NASA Technical Reports Server (NTRS)

    Mintz, Y.; Walker, G. K.

    1993-01-01

    The global fields of normal monthly soil moisture and land surface evapotranspiration are derived with a simple water budget model that has precipitation and potential evapotranspiration as inputs. The precipitation is observed and the potential evapotranspiration is derived from the observed surface air temperature with the empirical regression equation of Thornthwaite (1954). It is shown that at locations where the net surface radiation flux has been measured, the potential evapotranspiration given by the Thornthwaite equation is in good agreement with those obtained with the radiation-based formulations of Priestley and Taylor (1972), Penman (1948), and Budyko (1956-1974), and this provides the justification for the use of the Thornthwaite equation. After deriving the global fields of soil moisture and evapotranspiration, the assumption is made that the potential evapotranspiration given by the Thornthwaite equation and by the Priestley-Taylor equation will everywhere be about the same; the inverse of the Priestley-Taylor equation is used to obtain the normal monthly global fields of net surface radiation flux minus ground heat storage. This and the derived evapotranspiration are then used in the equation for energy conservation at the surface of the earth to obtain the global fields of normal monthly sensible heat flux from the land surface to the atmosphere.

  18. On the Comparison of the Global Surface Soil Moisture product and Land Surface Modeling

    NASA Astrophysics Data System (ADS)

    Delorme, B., Jr.; Ottlé, C.; Peylin, P.; Polcher, J.

    2016-12-01

    Thanks to its large spatio-temporal coverage, the new ESA CCI multi-instruments dataset offers a good opportunity to assess and improve land surface models parametrization. In this study, the ESA CCI surface soil moisture (SSM) combined product (v2.2) has been compared to the simulated top first layers of the ORCHIDEE LSM (the continental part of the IPSL earth system model), in order to evaluate its potential of improvements with data assimilation techniques. The ambition of the work was to develop a comprehensive comparison methodology by analyzing simultaneously the temporal and spatial structures of both datasets. We analyzed the SSM synoptic, seasonal, and inter-annual variations by decomposing the signals into fast and slow components. ORCHIDEE was shown to adequately reproduce the observed SSM dynamics in terms of temporal correlation. However, these correlation scores are supposed to be strongly influenced by SSM seasonal variability and the quality of the model input forcing. Autocorrelation and spectral analyses brought out disagreements in the temporal inertia of the upper soil moisture reservoirs. By linking our results to land cover maps, we found that ORCHIDEE is more dependent on rainfall events compared to the observations in regions with sparse vegetation cover. These diflerences might be due to a wrong partition of rainfall between soil evaporation, transpiration, runofl and drainage in ORCHIDEE. To refine this analysis, a single value decomposition (SVD) of the co-variability between rainfall provided by WFDEI and soil moisture was pursued over Central Europe and South Africa. It showed that spatio-temporal co-varying patterns between ORCHIDEE and rainfall and the ESA-CCI product and rainfall are in relatively good agreement. However, the leading SVD pattern, which exhibits a strong annual cycle and explains the same portion of covariance for both datasets, explains a much larger fraction of variance for ORCHIDEE than for the ESA-CCI product

  19. 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.

  20. Global Soil Moisture Estimation from L-Band Satellite Data: The Impact of Radiative Transfer Modeling in Assimilation and Retrieval Systems

    NASA Technical Reports Server (NTRS)

    De Lannoy, Gabrielle; Reichle, Rolf; Gruber, Alexander; Bechtold, Michel; Quets, Jan; Vrugt, Jasper; Wigneron, Jean-Pierre

    2018-01-01

    The SMOS and SMAP missions have collected a wealth of global L-band Brightness temperature (Tb) observations. The retrieval of surface Soil moisture estimates, and the estimation of other geophysical Variables, such as root-zone soil moisture and temperature, via data Assimilation into land surface models largely depends on accurate Radiative transfer modeling (RTM). This presentation will focus on various configuration aspects of the RTM (i) for the inversion of SMOS Tb to surface soil moisture, and (ii) for the forward modeling as part of a SMOS Tb data assimilation System to estimate a consistent set of geophysical land surface Variables, using the GEOS-5 Catchment Land Surface Model.

  1. 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...

  2. 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.

  3. 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...

  4. Bridging the Global Precipitation and Soil Moisture Active Passive Missions: Variability of Microwave Surface Emissivity from In situ and Remote Sensing Perspectives

    NASA Astrophysics Data System (ADS)

    Zheng, Y.; Kirstetter, P.; Hong, Y.; Turk, J.

    2016-12-01

    The overland precipitation retrievals from satellite passive microwave (PMW) sensors such as the Global Precipitation Mission (GPM) microwave imager (GMI) are impacted by the land surface emissivity. The estimation of PMW emissivity faces challenges because it is highly variable under the influence of surface properties such as soil moisture, surface roughness and vegetation. This study proposes an improved quantitative understanding of the relationship between the emissivity and surface parameters. Surface parameter information is obtained through (i) in-situ measurements from the International Soil Moisture Network and (ii) satellite measurements from the Soil Moisture Active and Passive mission (SMAP) which provides global scale soil moisture estimates. The variation of emissivity is quantified with soil moisture, surface temperature and vegetation at various frequencies/polarization and over different types of land surfaces to sheds light into the processes governing the emission of the land. This analysis is used to estimate the emissivity under rainy conditions. The framework built with in-situ measurements serves as a benchmark for satellite-based analyses, which paves a way toward global scale emissivity estimates using SMAP.

  5. 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.

  6. 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).

  7. Toxicity of a metal(loid)-polluted agricultural soil to Enchytraeus crypticus changes under a global warming perspective: Variations in air temperature and soil moisture content.

    PubMed

    González-Alcaraz, M Nazaret; van Gestel, Cornelis A M

    2016-12-15

    This study aimed to assess how the current global warming perspective, with increasing air temperature (20°C vs. 25°C) and decreasing soil moisture content (50% vs. 30% of the soil water holding capacity, WHC), affected the toxicity of a metal(loid)-polluted agricultural soil to Enchytraeus crypticus. Enchytraeids were exposed for 21d to a dilution series of the agricultural soil with Lufa 2.2 control soil under four climate situations: 20°C+50% WHC (standard conditions), 20°C+30% WHC, 25°C+50% WHC, and 25°C+30% WHC. Survival, reproduction and bioaccumulation of As, Cd, Co, Cu, Fe, Mn, Ni, Pb and Zn were obtained as endpoints. Reproduction was more sensitive to both climate factors and metal(loid) pollution. High soil salinity (electrical conductivity~3dSm -1 ) and clay texture, even without the presence of high metal(loid) concentrations, affected enchytraeid performance especially at drier conditions (≥80% reduction in reproduction). The toxicity of the agricultural soil increased at drier conditions (10% reduction in EC10 and EC50 values for the effect on enchytraeid reproduction). Changes in enchytraeid performance were accompanied by changes in As, Fe, Mn, Pb and Zn bioaccumulation, with lower body concentrations at drier conditions probably due to greater competition with soluble salts in the case of Fe, Mn, Pb and Zn. This study shows that apart from high metal(loid) concentrations other soil properties (e.g. salinity and texture) may be partially responsible for the toxicity of metal(loid)-polluted soils to soil invertebrates, especially under changing climate conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. 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.

  9. Soil Moisture Active Passive (SMAP) Media Briefing

    NASA Image and Video Library

    2015-01-09

    Dara Entekhabi, SMAP science team lead, Massachusetts Institute of Technology, center, speaks during a briefing about the upcoming launch of the Soil Moisture 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 soil moisture 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)

  10. Soil Moisture Active Passive (SMAP) Media Briefing

    NASA Image and Video Library

    2015-01-09

    Dara Entekhabi, SMAP science team lead, Massachusetts Institute of Technology, speaks during a briefing about the upcoming launch of the Soil Moisture 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 soil moisture 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)

  11. Soil Moisture Active Passive (SMAP) Media Briefing

    NASA Image and Video Library

    2015-01-09

    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 Soil Moisture 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 soil moisture 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)

  12. Soil Moisture Active Passive (SMAP) Media Briefing

    NASA Image and Video Library

    2015-01-09

    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 Soil Moisture 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 soil moisture 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)

  13. Soil Moisture Active Passive (SMAP) Media Briefing

    NASA Image and Video Library

    2015-01-09

    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 Soil Moisture 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 soil moisture 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)

  14. 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...

  15. A quasi-global approach to improve day-time satellite surface soil moisture anomalies through land surface temperature input

    USDA-ARS?s Scientific Manuscript database

    Passive microwave observations from various space borne sensors have been linked to soil moisture of the Earth’s surface layer. The new generation passive microwave sensors are dedicated to retrieving this variable and make observations in the single, theoretically optimal L-band frequency (1-2 GHz)...

  16. 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.

  17. 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

  18. Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products

    NASA Astrophysics Data System (ADS)

    Kim, Hyunglok; Parinussa, Robert; Konings, Alexandra G.; Wagner, Wolfgang; Cosh, Michael H.; Lakshmi, Venkat; Zohaib, Muhammad; Choi, Minha

    2018-01-01

    Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016. The Global Land Data Assimilation System (GLDAS) and global in situ observations were utilized to investigate and to compare the quality of satellite-based SSM products. The average R-values of SMAP, ASCAT, and AMSR2 were 0.74, 0.64, and 0.65 when they compared with in situ networks, respectively. The ubRMSD values were (0.0411, 0.0625, and 0.0708) m3 m- 3; and the bias values were (- 0.0460, 0.0010, and 0.0418) m3 m- 3 for SMAP, ASCAT, and AMSR2, respectively. The highest average R-values from SMAP against the in situ results are very encouraging; only SMAP showed higher R-values than GLDAS in several in situ networks with low ubRMSD (0.0438 m3 m- 3). Overall, SMAP showed a dry bias (- 0.0460 m3 m- 3) and AMSR2 had a wet bias (0.0418 m3 m- 3); while ASCAT showed the least bias (0.0010 m3 m- 3) among all the products. Each product was evaluated using TC metrics with respect to the different ranges of vegetation optical depth (VOD). Under vegetation scarce conditions (VOD < 0.10), such as desert and semi-desert regions, all products have difficulty obtaining SSM information. In regions with moderately vegetated areas (0.10 < VOD < 0.40), SMAP showed the highest Signal-to-Noise Ratio. Over highly vegetated regions (VOD > 0.40) ASCAT showed comparatively better performance than did the other products. Using the maximized R method, SMAP, ASCAT, and AMSR2 products were combined one by one using the

  19. 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...

  20. 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.

  1. The Hydrosphere State (Hydros) Satellite Mission: An Earth System Pathfinder for Global Mapping of Soil Moisture and Land Freeze/Thaw

    NASA Technical Reports Server (NTRS)

    Entekhabi, D.; Njoku, E. G.; Spencer, M.; Kim, Y.; Smith, J.; McDonald, K. C.; vanZyl, J.; Houser, P.; Dorion, T.; Koster, R.; hide

    2004-01-01

    The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39 with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

  2. Soil Moisture Retrieval from Aquarius

    USDA-ARS?s Scientific Manuscript database

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

  3. 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...

  4. 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....

  5. Assessment of the SMAP level 2 passive soil moisture product

    USDA-ARS?s Scientific Manuscript database

    The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on Jan 31, 2015. The observatory was developed 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. SMAP provides ...

  6. 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...

  7. 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...

  8. SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture

    NASA Astrophysics Data System (ADS)

    Ciabatta, Luca; Massari, Christian; Brocca, Luca; Gruber, Alexander; Reimer, Christoph; Hahn, Sebastian; Paulik, Christoph; Dorigo, Wouter; Kidd, Richard; Wagner, Wolfgang

    2018-02-01

    Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998-2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value < 10.34 mm over 5 days) and bias (median value < -14.44 %) during the evaluation period. The validation has been carried out at original resolution (0.25°) over Europe, Australia and five other areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.

  9. 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...

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. Influence of soil moisture on soil respiration

    NASA Astrophysics Data System (ADS)

    Fer, Miroslav; Kodesova, Radka; Nikodem, Antonin; Klement, Ales; Jelenova, Klara

    2015-04-01

    The aim of this work was to describe an impact of soil moisture on soil respiration. Study was performed on soil samples from morphologically diverse study site in loess region of Southern Moravia, Czech Republic. The original soil type is Haplic Chernozem, which was due to erosion changed into Regosol (steep parts) and Colluvial soil (base slope and the tributary valley). Soil samples were collected from topsoils at 5 points of the selected elevation transect and also from the parent material (loess). Grab soil samples, undisturbed soil samples (small - 100 cm3, and large - 713 cm3) and undisturbed soil blocks were taken. Basic soil properties were determined on grab soil samples. Small undisturbed soil samples were used to determine the soil water retention curves and the hydraulic conductivity functions using the multiple outflow tests in Tempe cells and a numerical inversion with HYDRUS 1-D. During experiments performed in greenhouse dry large undisturbed soil samples were wetted from below using a kaolin tank and cumulative water inflow due to capillary rise was measured. Simultaneously net CO2 exchange rate and net H2O exchange rate were measured using LCi-SD portable photosynthesis system with Soil Respiration Chamber. Numerical inversion of the measured cumulative capillary rise data using the HYDRUS-1D program was applied to modify selected soil hydraulic parameters for particular conditions and to simulate actual soil water distribution within each soil column in selected times. Undisturbed soil blocks were used to prepare thin soil sections to study soil-pore structure. Results for all soil samples showed that at the beginning of soil samples wetting the CO2 emission increased because of improving condition for microbes' activity. The maximum values were reached for soil column average soil water content between 0.10 and 0.15 cm3/cm3. Next CO2 emission decreased since the pore system starts filling by water (i.e. aggravated conditions for microbes

  17. 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...

  18. A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

    NASA Technical Reports Server (NTRS)

    Parinussa, Robert M.; de Jeu, Richard A. M.; van Der Schalie, Robin; Crow, Wade T.; Lei, Fangni; Holmes, Thomas R. H.

    2016-01-01

    Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative

  19. Seasonal soil moisture patterns in contrasting habitats in the Willamette Valley, Oregon

    EPA Science Inventory

    Changing seasonal soil moisture 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 soil moisture regimes is necessary. The primary objective...

  20. 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

  1. 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.

  2. Soil Moisture Active Passive (SMAP) Media Briefing

    NASA Image and Video Library

    2015-01-09

    Christine Bonniksen, SMAP program executive with the Science Mission Directorate’s Earth Science Division, NASA Headquarters, left, Kent Kellogg, SMAP project manager, NASA Jet Propulsion Laboratory (JPL), second from left, Dara Entekhabi, SMAP science team lead, Massachusetts Institute of Technology, second from right, and Brad Doorn, SMAP applications lead, Science Mission Directorate’s Applied Sciences Program, NASA Headquarters, right, are seen during a briefing about the upcoming launch of the Soil Moisture 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 soil moisture 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)

  3. NASA Soil Moisture Active Passive (SMAP) Applications

    NASA Astrophysics Data System (ADS)

    Orr, Barron; Moran, M. Susan; Escobar, Vanessa; Brown, Molly E.

    2014-05-01

    The launch of the NASA Soil Moisture Active Passive (SMAP) mission in 2014 will provide global soil moisture 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 soil moisture 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 soil moisture as an Essential Climate Variable (ECV), and the UN Food and Agriculture Organization (FAO) which reported a food and nutrition crisis in the Sahel.

  4. 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.

  5. 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).

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

    USDA-ARS?s Scientific Manuscript database

    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 f...

  7. 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...

  8. 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

  9. Anthropogenic warming exacerbates European soil moisture droughts

    NASA Astrophysics Data System (ADS)

    Samaniego, L.; Thober, S.; Kumar, R.; Wanders, N.; Rakovec, O.; Pan, M.; Zink, M.; Sheffield, J.; Wood, E. F.; Marx, A.

    2018-05-01

    Anthropogenic warming is anticipated to increase soil moisture 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 soil moisture 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 soil moisture drought, presenting new challenges for adaptation across the continent.

  10. 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.

  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. 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.

  13. Evaluation of SMOS soil moisture products over the CanEx-SM10 area

    USDA-ARS?s Scientific Manuscript database

    The Soil Moisture and Ocean Salinity (SMOS) Earth observation satellite was launched in November 2009 to provide global soil moisture and ocean salinity measurements based on L-Band passive microwave measurements. Since its launch, different versions of SMOS soil moisture products processors have be...

  14. Estimating error cross-correlations in soil moisture data sets using extended collocation analysis

    USDA-ARS?s Scientific Manuscript database

    Consistent global soil moisture 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 soil moisture data into water balance models or merging multi-source soil moisture retrievals int...

  15. 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...

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  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. 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...

  5. 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...

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. NASA Soil Moisture Mapper Takes First SMAPshots

    NASA Image and Video Library

    2015-03-09

    Fresh off the recent successful deployment of its 20-foot (6-meter) reflector antenna and associated boom arm, NASA's new Soil Moisture Active Passive (SMAP) observatory has successfully completed a two-day test of its science instruments. On Feb. 27 and 28, SMAP's radar and radiometer instruments were successfully operated for the first time with SMAP's antenna in a non-spinning mode. The test was a key step in preparation for the planned spin-up of SMAP's antenna to approximately 15 revolutions per minute in late March. The spin-up will be performed in a two-step process after additional tests and maneuvers adjust the observatory to its final science orbit over the next couple of weeks. Based on the data received, mission controllers at NASA's Jet Propulsion Laboratory, Pasadena, California; and NASA's Goddard Space Flight Center, Greenbelt, Maryland; concluded that the radar and radiometer performed as expected. SMAP launched Jan. 31 on a minimum three-year mission to map global soil moisture and detect whether soils are frozen or thawed. The mission will help scientists understand the links in Earth's water, energy and carbon cycles, help reduce uncertainties in predicting weather and climate, and enhance our ability to monitor and predict natural hazards such as floods and droughts The first test image illustrates the significance of SMAP's spinning instrument design. For this initial test with SMAP's antenna not yet spinning, the observatory's measurement swath width -- the strips observed on Earth in the image -- was limited to 25 miles (40 kilometers). When fully spun up and operating, SMAP's antenna will measure a 620-mile-wide (1,000-kilometer) swath of the ground as it flies above Earth at an altitude of 426 miles (685 kilometers). This will allow SMAP to map the entire globe with high-resolution radar data every two to three days, filling in all of the land surface detail that is not available in this first image. The radar data illustrated in the upper

  16. Water vs. carbon: An evaluation of SMAP soil moisture and OCO-2 solar-induced fluorescence to characterize global plant stress

    NASA Astrophysics Data System (ADS)

    Purdy, A. J.; Fisher, J.; Goulden, M.; Randerson, J. T.; Famiglietti, J. S.

    2017-12-01

    Plants link the carbon and water cycles through photosynthesis and evapotranspiration (ET). When plants take in CO2 for photosynthesis, water evaporates to the atmosphere. This exchange of carbon and water is sensitive to a number of environmental variables including: soil water availability, temperature, atmospheric water vapor, and radiation. When the atmospheric demand for water is high, plants avoid hydraulic failure by regulating the amount of water exiting leaves at the expense of inhibiting carbon uptake. Over time, stress caused by this response limits plant growth and can even result in death by carbon starvation. With increasing atmospheric demand for water, impending expansion of arid regions, and more frequent droughts, understanding how vegetation responds to regulate photosynthesis and ET is important to quantify potential feedbacks between the carbon and water cycles. Despite its importance, to what extent plants respond to stressful conditions is an open science question. An important step forward is to characterize the dominant controls in these stress events and identify geographic areas that are vulnerable to climate change. The 2015-2016 El Nino and subsequent 2016-2017 La Nina transition provides an opportunity to quantify the extent and magnitude of vegetation regulation of these carbon and water variables in response to changes in environmental conditions. We present results from a space-based analysis using global observations of solar induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2), soil moisture from Soil Moisture Active Passive (SMAP), and two widely used ET models (PT-JPL and MOD-16) to characterize the dominant controls on gross primary production and ET.

  17. 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.

  18. 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

  19. 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.)

  20. 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

  1. 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

  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. 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.

  4. 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.

  5. 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

  6. Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

    NASA Astrophysics Data System (ADS)

    Abbaszadeh, P.; Moradkhani, H.; Yan, H.

    2016-12-01

    Soil moisture (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 soil moisture 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 soil moisture 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 soil moisture 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 soil moisture information that is currently used for land data assimilation applications.

  7. 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.

  8. 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...

  9. 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.

  10. Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15

    USDA-ARS?s Scientific Manuscript database

    The SMAP (Soil Moisture Active Passive) mission provides global surface soil moisture product at 36 km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications there are also a lot of applications which would benefit from a higher resolution soil moistu...

  11. Estimating surface soil moisture from SMAP observations using a neural network technique

    USDA-ARS?s Scientific Manuscript database

    A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to June 2016 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observ...

  12. SMAP radiometer-based soil moisture products status and validation

    USDA-ARS?s Scientific Manuscript database

    The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band brightness temperature measurements of the globe since 2015. These are used with retrieval algorithms to generate global products every 2-3 days. SMAP has recently implemented several new products to enhance both the spat...

  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. Incorporating root hydraulic redistribution in CLM4.5: Effects on predicted site and global evapotranspiration, soil moisture, and water storage

    SciTech Connect

    Tang, Jinyun; Riley, William J.; Niu, Jie

    We implemented the Amenu-Kumar model in the Community Land Model (CLM4.5) to simulate plant Root Hydraulic Redistribution (RHR) and analyzed its influence on CLM hydrology from site to global scales. We evaluated two numerical implementations: the first solved the coupled equations of root and soil water transport concurrently, while the second solved the two equations sequentially. Through sensitivity analysis, we demonstrate that the sequentially coupled implementation (SCI) is numerically incorrect, whereas the tightly coupled implementation (TCI) is numerically robust with numerical time steps varying from 1 to 30 min. At the site-level, we found the SCI approach resulted in bettermore » agreement with measured evapotranspiration (ET) at the AmeriFlux Blodgett Forest site, California, whereas the two approaches resulted in equally poor agreement between predicted and measured ET at the LBA Tapajos KM67 Mature Forest site in Amazon, Brazil. Globally, the SCI approach overestimated annual land ET by as much as 3.5 mm d -1 in some grid cells when compared to the TCI estimates. These comparisons demonstrate that TCI is a more robust numerical implementation of RHR. However, we found, even with TCI, that incorporating RHR resulted in worse agreement with measured soil moisture at both the Blodgett Forest and Tapajos sites and degraded the agreement between simulated terrestrial water storage anomaly and Gravity Recovery and Climate Experiment (GRACE) observations. We find including RHR in CLM4.5 improved ET predictions compared with the FLUXNET-MTE estimates north of 20° N but led to poorer predictions in the tropics. The biases in ET were robust and significant regardless of the four different pedotransfer functions or of the two meteorological forcing data sets we applied. We also found that the simulated water table was unrealistically sensitive to RHR. Therefore, we contend that further structural and data improvements are warranted to improve the

  15. Incorporating root hydraulic redistribution in CLM4.5: Effects on predicted site and global evapotranspiration, soil moisture, and water storage

    DOE PAGES

    Tang, Jinyun; Riley, William J.; Niu, Jie

    2015-11-12

    We implemented the Amenu-Kumar model in the Community Land Model (CLM4.5) to simulate plant Root Hydraulic Redistribution (RHR) and analyzed its influence on CLM hydrology from site to global scales. We evaluated two numerical implementations: the first solved the coupled equations of root and soil water transport concurrently, while the second solved the two equations sequentially. Through sensitivity analysis, we demonstrate that the sequentially coupled implementation (SCI) is numerically incorrect, whereas the tightly coupled implementation (TCI) is numerically robust with numerical time steps varying from 1 to 30 min. At the site-level, we found the SCI approach resulted in bettermore » agreement with measured evapotranspiration (ET) at the AmeriFlux Blodgett Forest site, California, whereas the two approaches resulted in equally poor agreement between predicted and measured ET at the LBA Tapajos KM67 Mature Forest site in Amazon, Brazil. Globally, the SCI approach overestimated annual land ET by as much as 3.5 mm d -1 in some grid cells when compared to the TCI estimates. These comparisons demonstrate that TCI is a more robust numerical implementation of RHR. However, we found, even with TCI, that incorporating RHR resulted in worse agreement with measured soil moisture at both the Blodgett Forest and Tapajos sites and degraded the agreement between simulated terrestrial water storage anomaly and Gravity Recovery and Climate Experiment (GRACE) observations. We find including RHR in CLM4.5 improved ET predictions compared with the FLUXNET-MTE estimates north of 20° N but led to poorer predictions in the tropics. The biases in ET were robust and significant regardless of the four different pedotransfer functions or of the two meteorological forcing data sets we applied. We also found that the simulated water table was unrealistically sensitive to RHR. Therefore, we contend that further structural and data improvements are warranted to improve the

  16. Incorporating root hydraulic redistribution in CLM4.5: Effects on predicted site and global evapotranspiration, soil moisture, and water storage

    NASA Astrophysics Data System (ADS)

    Tang, Jinyun; Riley, William J.; Niu, Jie

    2015-12-01

    We implemented the Amenu-Kumar model in the Community Land Model (CLM4.5) to simulate plant Root Hydraulic Redistribution (RHR) and analyzed its influence on CLM hydrology from site to global scales. We evaluated two numerical implementations: the first solved the coupled equations of root and soil water transport concurrently, while the second solved the two equations sequentially. Through sensitivity analysis, we demonstrate that the sequentially coupled implementation (SCI) is numerically incorrect, whereas the tightly coupled implementation (TCI) is numerically robust with numerical time steps varying from 1 to 30 min. At the site-level, we found the SCI approach resulted in better agreement with measured evapotranspiration (ET) at the AmeriFlux Blodgett Forest site, California, whereas the two approaches resulted in equally poor agreement between predicted and measured ET at the LBA Tapajos KM67 Mature Forest site in Amazon, Brazil. Globally, the SCI approach overestimated annual land ET by as much as 3.5 mm d-1 in some grid cells when compared to the TCI estimates. These comparisons demonstrate that TCI is a more robust numerical implementation of RHR. However, we found, even with TCI, that incorporating RHR resulted in worse agreement with measured soil moisture at both the Blodgett Forest and Tapajos sites and degraded the agreement between simulated terrestrial water storage anomaly and Gravity Recovery and Climate Experiment (GRACE) observations. We find including RHR in CLM4.5 improved ET predictions compared with the FLUXNET-MTE estimates north of 20° N but led to poorer predictions in the tropics. The biases in ET were robust and significant regardless of the four different pedotransfer functions or of the two meteorological forcing data sets we applied. We also found that the simulated water table was unrealistically sensitive to RHR. Therefore, we contend that further structural and data improvements are warranted to improve the hydrological

  17. 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.

  18. 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...

  19. 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.

  20. 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 <span class="hlt">global</span> 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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.8427B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.8427B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> retrieval from Sentinel-1 satellite data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Benninga, Harm-Jan; van der Velde, Rogier; Su, Zhongbo</p> <p>2016-04-01</p> <p> resolution and the dual-polarized information of Sentinel-1 are utilized. Three candidate algorithms are presented at the conference, which are a data-driven algorithm, inversion of a radar scattering model and downscaling of coarser resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> products. The research is part of the OWAS1S project (Optimizing Water Availability with Sentinel-1 Satellites), which stands for integration of the freely available <span class="hlt">global</span> Sentinel-1 data and local knowledge on <span class="hlt">soil</span> physical processes, to optimize water management of regional water systems and to develop value-added products for agriculture.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H11L..06T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H11L..06T"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> - precipitation feedbacks in observations and models (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Taylor, C.</p> <p>2013-12-01</p> <p>There is considerable uncertainty about the strength, geographical extent, and even the sign of feedbacks between <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation. Whilst precipitation trivially increases <span class="hlt">soil</span> <span class="hlt">moisture</span>, the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span>, via surface fluxes, on convective rainfall is far from straight-forward, and likely depends on space and time scale, <span class="hlt">soil</span> and synoptic conditions, and the nature of the convection itself. In considering how daytime convection responds to surface fluxes, large-scale models based on convective parameterisations may not necessarily provide reliable depictions, particularly given their long-standing inability to reproduce a realistic diurnal cycle of convection. On the other hand, long-term satellite data provide the potential to establish robust relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation across the world, notwithstanding some fundamental weaknesses and uncertainties in the datasets. Here, results from regional and <span class="hlt">global</span> satellite-based analyses are presented. <span class="hlt">Globally</span>, using 3-hourly precipitation and daily <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets, a methodology has been developed to compare the statistics of antecedent <span class="hlt">soil</span> <span class="hlt">moisture</span> in the region of localised afternoon rain events (Taylor et al 2012). Specifically the analysis tests whether there are any significant differences in pre-event <span class="hlt">soil</span> <span class="hlt">moisture</span> between rainfall maxima and nearby (50-100km) minima. The results reveal a clear signal across a number of semi-arid regions, most notably North Africa, indicating a preference for afternoon rain over drier <span class="hlt">soil</span>. Analysis by continent and by climatic zone reveals that this signal (locally a negative feedback) is evident in other continents and climatic zones, but is somewhat weaker. This may be linked to the inherent geographical differences across the world, as detection of a feedback requires water-stressed surfaces coincident with frequent active convective initiations. The differences also reflect the quality and utility of the <span class="hlt">soil</span> <span class="hlt">moisture</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFMIN43B1184L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFMIN43B1184L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation Using Hyperspectral SWIR Imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lewis, D.</p> <p>2007-12-01</p> <p>The U.S. Geological Survey (USGS) is engaged with the U.S. Department of Agriculture's (USDA) Agricultural Research Service (ARS) and the University of Georgia's National Environmentally Sound Production Agriculture Laboratory (NESPAL) both in Tifton, Georgia, USA, to develop transformations for medium and high resolution remotely sensed images to generate <span class="hlt">moisture</span> indicators for <span class="hlt">soil</span>. The Institute for Technology Development (ITD) is located at the Stennis Space Center in southern Mississippi and has developed hyperspectral sensor systems that, when mounted in aircraft, collect electromagnetic reflectance data of the terrain. The sensor suite consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near InfraRed (VNIR) and Short Wave InfraRed (SWIR). The USDA/ ARS' Southeast Watershed Research Laboratory has probes that measure and record <span class="hlt">soil</span> <span class="hlt">moisture</span>. Data taken from the ITD SWIR sensor and the USDA/ARS <span class="hlt">soil</span> <span class="hlt">moisture</span> meters were analyzed to study the informatics relationships between SWIR data and measured <span class="hlt">soil</span> <span class="hlt">moisture</span>. The geographic locations of 29 <span class="hlt">soil</span> <span class="hlt">moisture</span> meters provided by the USDA/ARS are in the vicinity of Tifton, Georgia. Using USGS Digital Ortho Quads (DOQ), flightlines were drawn over the 29 <span class="hlt">soil</span> <span class="hlt">moisture</span> meters. The SWIR sensor was installed into an aircraft. The coordinates for the flightlines were also loaded into the navigational system of the aircraft. This airborne platform was used to collect the data over these flightlines. In order to prepare the data set for analysis, standard preprocessing was performed. These standard processes included sensor calibration, spectral subsetting, and atmospheric calibration. All 60 bands of the SWIR data were collected for each line in the image data, 15 bands of which were stripped from the data set leaving 45 bands of information in the wavelength range of 906 to 1705 nanometers. All the image files were calibrated using the regression equations</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1412724Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412724Z"><span>Impact of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation on NCEP-GFS forecasts</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.; Zheng, W.; Meng, J.; Dong, J.; Ek, M.</p> <p>2012-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> ocean salinity (SMOS) mission in November 2009, about 2 years of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals has been collected. SMOS is believed to be the currently best satellite sensors for <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing. Therefore, it becomes interesting to examine how the collected SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data are compared with other satellite-sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals (such as NASA's Advanced Microwave Scanning Radiometer -AMSR-E and EUMETSAT's Advanced Scatterometer - ASCAT)), in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements, and how these data sets impact numerical weather prediction models such as the <span class="hlt">Global</span> Forecast System of NOAA-NCEP. This study implements the Ensemble Kalman filter in GFS to assimilate the AMSR-E, ASCAT and SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> observations after a quantitative assessment of their error rate based on in situ measurements from ground networks around contiguous United States. in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements from ground networks (such as USDA <span class="hlt">Soil</span> Climate Analysis network - SCAN and NOAA's U.S. Climate Reference Network -USCRN) are used to evaluate the GFS <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> data products over other satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets will be evaluated. The next step toward operationally assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.7936B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.7936B"><span>Investigating local controls on <span class="hlt">soil</span> <span class="hlt">moisture</span> temporal stability using an inverse modeling approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogena, Heye; Qu, Wei; Huisman, Sander; Vereecken, Harry</p> <p>2013-04-01</p> <p>A better understanding of the temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its relation to local and nonlocal controls is a major challenge in modern hydrology. Both local controls, such as <span class="hlt">soil</span> and vegetation properties, and non-local controls, such as topography and climate variability, affect <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. Wireless sensor networks are becoming more readily available, which opens up opportunities to investigate spatial and temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> with unprecedented resolution. In this study, we employed the wireless sensor network <span class="hlt">Soil</span>Net developed by the Forschungszentrum Jülich to investigate <span class="hlt">soil</span> <span class="hlt">moisture</span> variability of a grassland headwater catchment in Western Germany within the framework of the TERENO initiative. In particular, we investigated the effect of <span class="hlt">soil</span> hydraulic parameters on the temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span>. For this, the HYDRUS-1D code coupled with a <span class="hlt">global</span> optimizer (DREAM) was used to inversely estimate Mualem-van Genuchten parameters from <span class="hlt">soil</span> <span class="hlt">moisture</span> observations at three depths under natural (transient) boundary conditions for 83 locations in the headwater catchment. On the basis of the optimized parameter sets, we then evaluated to which extent the variability in <span class="hlt">soil</span> hydraulic conductivity, pore size distribution, air entry suction and <span class="hlt">soil</span> depth between these 83 locations controlled the temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span>, which was independently determined from the observed <span class="hlt">soil</span> <span class="hlt">moisture</span> data. It was found that the saturated hydraulic conductivity (Ks) was the most significant attribute to explain temporal stability of <span class="hlt">soil</span> <span class="hlt">moisture</span> as expressed by the mean relative difference (MRD).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9971Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9971Z"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> in sessile oak forest gaps</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zagyvainé Kiss, Katalin Anita; Vastag, Viktor; Gribovszki, Zoltán; Kalicz, Péter</p> <p>2015-04-01</p> <p>By social demands are being promoted the aspects of the natural forest management. In forestry the concept of continuous forest has been an accepted principle also in Hungary since the last decades. The first step from even-aged stand to continuous forest can be the forest regeneration based on gap cutting, so small openings are formed in a forest due to forestry interventions. This new stand structure modifies the hydrological conditions for the regrowth. Without canopy and due to the decreasing amounts of forest litter the interception is less significant so higher amount of precipitation reaching the <span class="hlt">soil</span>. This research focuses on <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns caused by gaps. The spatio-temporal variability of <span class="hlt">soil</span> water content is measured in gaps and in surrounding sessile oak (Quercus petraea) forest stand. <span class="hlt">Soil</span> <span class="hlt">moisture</span> was determined with manual <span class="hlt">soil</span> <span class="hlt">moisture</span> meter which use Time-Domain Reflectometry (TDR) technology. The three different sizes gaps (G1: 10m, G2: 20m, G3: 30m) was opened next to Sopron on the Dalos Hill in Hungary. First, it was determined that there is difference in <span class="hlt">soil</span> <span class="hlt">moisture</span> between forest stand and gaps. Second, it was defined that how the gap size influences the <span class="hlt">soil</span> <span class="hlt">moisture</span> content. To explore the short term variability of <span class="hlt">soil</span> <span class="hlt">moisture</span>, two 24-hour (in growing season) and a 48-hour (in dormant season) field campaign were also performed in case of the medium-sized G2 gap along two/four transects. Subdaily changes of <span class="hlt">soil</span> <span class="hlt">moisture</span> were performed. The measured <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern was compared with the radiation pattern. It was found that the non-illuminated areas were wetter and in the dormant season the subdaily changes cease. According to our measurements, in the gap there is more available water than under the forest stand due to the less evaporation and interception loss. Acknowledgements: The research was supported by TÁMOP-4.2.2.A-11/1/KONV-2012-0004 and AGRARKLIMA.2 VKSZ_12-1-2013-0034.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980018613','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980018613"><span>Microstrip Ring Resonator for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sarabandi, Kamal; Li, Eric S.</p> <p>1993-01-01</p> <p>Accurate determination of spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution and monitoring its temporal variation have a significant impact on the outcomes of hydrologic, ecologic, and climatic models. Development of a successful remote sensing instrument for <span class="hlt">soil</span> <span class="hlt">moisture</span> relies on the accurate knowledge of the <span class="hlt">soil</span> dielectric constant (epsilon(sub <span class="hlt">soil</span>)) to its <span class="hlt">moisture</span> content. Two existing methods for measurement of dielectric constant of <span class="hlt">soil</span> at low and high frequencies are, respectively, the time domain reflectometry and the reflection coefficient measurement using an open-ended coaxial probe. The major shortcoming of these methods is the lack of accurate determination of the imaginary part of epsilon(sub <span class="hlt">soil</span>). In this paper a microstrip ring resonator is proposed for the accurate measurement of <span class="hlt">soil</span> dielectric constant. In this technique the microstrip ring resonator is placed in contact with <span class="hlt">soil</span> medium and the real and imaginary parts of epsilon(sub <span class="hlt">soil</span>) are determined from the changes in the resonant frequency and the quality factor of the resonator respectively. The solution of the electromagnetic problem is obtained using a hybrid approach based on the method of moments solution of the quasi-static formulation in conjunction with experimental data obtained from reference dielectric samples. Also a simple inversion algorithm for epsilon(sub <span class="hlt">soil</span>) = epsilon'(sub r) + j(epsilon"(sub r)) based on regression analysis is obtained. It is shown that the wide dynamic range of the measured quantities provides excellent accuracy in the dielectric constant measurement. A prototype microstrip ring resonator at L-band is designed and measurements of <span class="hlt">soil</span> with different <span class="hlt">moisture</span> contents are presented and compared with other approaches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19930063678&hterms=Soil+science&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Bscience','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930063678&hterms=Soil+science&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Bscience"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> needs in earth sciences</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Engman, Edwin T.</p> <p>1992-01-01</p> <p>The author reviews the development of passive and active microwave techniques for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> with respect to how the data may be used. New science programs such as the EOS, the GEWEX Continental-Scale International Project (GCIP) and STORM, a mesoscale meteorology and hydrology project, will have to account for <span class="hlt">soil</span> <span class="hlt">moisture</span> either as a storage in water balance computations or as a state variable in-process modeling. The author discusses future <span class="hlt">soil</span> <span class="hlt">moisture</span> needs such as frequency of measurement, accuracy, depth, and spatial resolution, as well as the concomitant model development that must proceed concurrently if the development in microwave technology is to have a major impact in these areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H21I1587H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H21I1587H"><span>Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Memory Estimated from Models and SMAP Observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>He, Q.; Mccoll, K. A.; Li, C.; Lu, H.; Akbar, R.; Pan, M.; Entekhabi, D.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> memory(SMM), which is loosely defined as the time taken by <span class="hlt">soil</span> to forget an anomaly, has been proved to be important in land-atmosphere interaction. There are many metrics to calculate the SMM timescale, for example, the timescale based on the time-series autocorrelation, the timescale ignoring the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series and the timescale which only considers <span class="hlt">soil</span> <span class="hlt">moisture</span> increment. Recently, a new timescale based on `Water Cycle Fraction' (Kaighin et al., 2017), in which the impact of precipitation on <span class="hlt">soil</span> <span class="hlt">moisture</span> memory is considered, has been put up but not been fully evaluated in <span class="hlt">global</span>. In this study, we compared the surface SMM derived from SMAP observations with that from land surface model simulations (i.e., the SMAP Nature Run (NR) provided by the Goddard Earth Observing System, version 5) (Rolf et al., 2014). Three timescale metrics were used to quantify the surface SMM as: T0 based on the <span class="hlt">soil</span> <span class="hlt">moisture</span> time series autocorrelation, deT0 based on the detrending <span class="hlt">soil</span> <span class="hlt">moisture</span> time series autocorrelation, and tHalf based on the Water Cycle Fraction. The comparisons indicate that: (1) there are big gaps between the T0 derived from SMAP and that from NR (2) the gaps get small for deT0 case, in which the seasonality of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> was removed with a moving average filter; (3) the tHalf estimated from SMAP is much closer to that from NR. The results demonstrate that surface SMM can vary dramatically among different metrics, while the memory derived from land surface model differs from the one from SMAP observation. tHalf, with considering the impact of precipitation, may be a good choice to quantify surface SMM and have high potential in studies related to land atmosphere interactions. References McColl. K.A., S.H. Alemohammad, R. Akbar, A.G. Konings, S. Yueh, D. Entekhabi. The <span class="hlt">Global</span> Distribution and Dynamics of Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span>, Nature Geoscience, 2017 Reichle. R., L. Qing, D.L. Gabrielle, A. Joe. The "SMAP_Nature_v03" Data</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=338364','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=338364"><span>Validation of SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> for the SMAPVEX15 field campaign using a hyper-resolution model</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Accurate <span class="hlt">global</span> mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> is the goal of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, which is expected to improve the estimation of water, energy, and carbon exchanges between the land and the atmosphere. Like other satellite products, the SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals need to be...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJAEO..45..187W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJAEO..45..187W"><span>Evaluation of AMSR2 <span class="hlt">soil</span> <span class="hlt">moisture</span> products over the contiguous United States using in situ data from the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Qiusheng; Liu, Hongxing; Wang, Lei; Deng, Chengbin</p> <p>2016-03-01</p> <p>High quality <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets are required for various environmental applications. The launch of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the <span class="hlt">Global</span> Change Observation Mission 1-Water (GCOM-W1) in May 2012 has provided <span class="hlt">global</span> near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> products were evaluated by comparing ascending and descending overpass products to each other as well as comparing them to in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations of 598 monitoring stations obtained from the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network (ISMN). The accuracy of AMSR2 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals are generally lower than in situ measurements. The AMSR2 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> products for different ecoregions. Although AMSR2 <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals represent useful and effective measurements for some regions, further studies are required to improve the data accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EAEJA.....4526W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EAEJA.....4526W"><span>Gravity changes, <span class="hlt">soil</span> <span class="hlt">moisture</span> and data assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walker, J.; Grayson, R.; Rodell, M.; Ellet, K.</p> <p>2003-04-01</p> <p>Remote sensing holds promise for near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and snow mapping, but current techniques do not directly resolve the deeper <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 (<span class="hlt">soil</span> <span class="hlt">moisture</span>, 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> sites. Ground</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810012896','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810012896"><span>Multispectral determination of <span class="hlt">soil</span> <span class="hlt">moisture</span>. [Guymon, Oklahoma</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Estes, J. E.; Simonett, D. S. (Principal Investigator); Hajic, E. J.; Blanchard, B. J.</p> <p>1980-01-01</p> <p>The edited Guymon <span class="hlt">soil</span> <span class="hlt">moisture</span> data collected on August 2, 5, 14, 17, 1978 were grouped into four field cover types for statistical analysis. These are the bare, milo with rows parallel to field of view, milo with rows perpendicular to field of view and alfalfa cover groups. There are 37, 22, 24 and 14 observations respectively in each group for each sensor channel and each <span class="hlt">soil</span> <span class="hlt">moisture</span> layer. A subset of these data called the 'five cover set' (VEG5) limited the scatterometer data to the 15 deg look angle and was used to determine discriminant functions and combined group regressions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8904L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8904L"><span>Spatial variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieved by SMOS satellite</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lukowski, Mateusz; Marczewski, Wojciech; Usowicz, Boguslaw; Rojek, Edyta; Slominski, Jan; Lipiec, Jerzy</p> <p>2015-04-01</p> <p>Standard statistical methods assume that the analysed variables are independent. Since the majority of the processes observed in the nature are continuous in space and time, this assumption introduces a significant limitation for understanding the examined phenomena. In classical approach, valuable information about the locations of examined observations is completely lost. However, there is a branch of statistics, called geostatistics, which is the study of random variables, but taking into account the space where they occur. A common example of so-called "regionalized variable" is <span class="hlt">soil</span> <span class="hlt">moisture</span>. Using in situ methods it is difficult to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution because it is often significantly diversified. Thanks to the geostatistical methods, by employing semivariance analysis, it is possible to get the information about the nature of spatial dependences and their lengths. Since the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity mission launch in 2009, the estimation of <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial distribution for regional up to continental scale started to be much easier. In this study, the SMOS L2 data for Central and Eastern Europe were examined. The statistical and geostatistical features of <span class="hlt">moisture</span> distributions of this area were studied for selected natural <span class="hlt">soil</span> phenomena for 2010-2014 including: freezing, thawing, rainfalls (wetting), drying and drought. Those <span class="hlt">soil</span> water "states" were recognized employing ground data from the agro-meteorological network of ground-based stations SWEX and SMUDP2 data from SMOS. After pixel regularization, without any upscaling, the geostatistical methods were applied directly on Discrete <span class="hlt">Global</span> Grid (15-km resolution) in ISEA 4H9 projection, on which SMOS observations are reported. Analysis of spatial distribution of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span>, carried out for each data set, in most cases did not show significant trends. It was therefore assumed that each of the examined distributions of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the adopted scale satisfies</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H53J1615A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H53J1615A"><span>Downscaling SMAP Radiometer <span class="hlt">Soil</span> <span class="hlt">Moisture</span> over the CONUS using <span class="hlt">Soil</span>-Climate Information and Ensemble Learning</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abbaszadeh, P.; Moradkhani, H.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> contributes significantly towards the improvement of weather and climate forecast and understanding terrestrial ecosystem processes. It is known as a key hydrologic variable in the agricultural drought monitoring, flood modeling and irrigation management. While satellite retrievals can provide an unprecedented information on <span class="hlt">soil</span> <span class="hlt">moisture</span> at <span class="hlt">global</span>-scale, the products are generally at coarse spatial resolutions (25-50 km2). This often hampers their use in regional or local studies, which normally require a finer resolution of the data set. This work presents a new framework based on an ensemble learning method while using <span class="hlt">soil</span>-climate information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer <span class="hlt">soil</span> <span class="hlt">moisture</span> over the Continental U.S. (CONUS) at 1 km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets in addition to <span class="hlt">soil</span> texture information and topography data among others were used. The downscaled product was validated against in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements collected from a limited number of core validation sites and several hundred sparse <span class="hlt">soil</span> <span class="hlt">moisture</span> networks throughout the CONUS. The obtained results indicated a great potential of the proposed methodology to derive the fine resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> information applicable for fine resolution hydrologic modeling, data assimilation and other regional studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESS...19.3845T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESS...19.3845T"><span>Use of satellite and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> data for predicting event <span class="hlt">soil</span> loss at plot scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Todisco, F.; Brocca, L.; Termite, L. F.; Wagner, W.</p> <p>2015-09-01</p> <p>The potential of coupling <span class="hlt">soil</span> <span class="hlt">moisture</span> and a Universal <span class="hlt">Soil</span> Loss Equation-based (USLE-based) model for event <span class="hlt">soil</span> loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named <span class="hlt">Soil</span> <span class="hlt">Moisture</span> for Erosion (SM4E), is applied by considering the unavailability of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements, by using the data predicted by a <span class="hlt">soil</span> water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The <span class="hlt">soil</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> observations in the event rainfall-runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event <span class="hlt">soil</span> losses, the <span class="hlt">soil</span> <span class="hlt">moisture</span> being an effective alternative to the estimated runoff, in the prediction of the event <span class="hlt">soil</span> loss at Masse. The agreement between observed and estimated <span class="hlt">soil</span> 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 <span class="hlt">soil</span> losses. Indeed, currently, <span class="hlt">soil</span> <span class="hlt">moisture</span> is a relatively simple measurement at the field scale and remote sensing data are also widely available on a <span class="hlt">global</span> scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the <span class="hlt">soil</span> erosion process.</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 <span class="hlt">global</span> 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://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, <span class="hlt">global</span> 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.H43C0976J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H43C0976J"><span>Mode Decomposition Methods for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jana, R. B.; Efendiev, Y. R.; Mohanty, B.</p> <p>2014-12-01</p> <p>Lack of reliable, well-distributed, long-term datasets for model validation is a bottle-neck for most exercises in <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis and prediction. Understanding what factors drive <span class="hlt">soil</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> across the watershed under all wetness conditions. However, the <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ACP....1714457P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ACP....1714457P"><span>Modeling the contributions of <span class="hlt">global</span> air temperature, synoptic-scale phenomena and <span class="hlt">soil</span> <span class="hlt">moisture</span> to near-surface static energy variability using artificial neural networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pryor, Sara C.; Sullivan, Ryan C.; Schoof, Justin T.</p> <p>2017-12-01</p> <p>The static energy content of the atmosphere is increasing on a <span class="hlt">global</span> scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called <q>warming holes</q> (i.e., locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the <span class="hlt">global</span> mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θe are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily <span class="hlt">global</span> mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged <span class="hlt">soil</span> <span class="hlt">moisture</span> (<span style="" class="text">SM). <span style="" class="text">SM is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes, confirming the key importance of <span style="" class="text">SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (<span class="hlt">global</span> T, synoptic-scale meteorological conditions and <span style="" class</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..12.2945T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..12.2945T"><span>Use of satellite and modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> data for predicting event <span class="hlt">soil</span> loss at plot scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Todisco, F.; Brocca, L.; Termite, L. F.; Wagner, W.</p> <p>2015-03-01</p> <p>The potential of coupling <span class="hlt">soil</span> <span class="hlt">moisture</span> and a~USLE-based model for event <span class="hlt">soil</span> loss estimation at plot scale is carefully investigated at the Masse area, in Central Italy. The derived model, named <span class="hlt">Soil</span> <span class="hlt">Moisture</span> for Erosion (SM4E), is applied by considering the unavailability of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements, by using the data predicted by a <span class="hlt">soil</span> water balance model (SWBM) and derived from satellite sensors, i.e. the Advanced SCATterometer (ASCAT). The <span class="hlt">soil</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> observations in the event rainfall-runoff erosivity factor of the RUSLE/USLE, enhances the capability of the model to account for variations in event <span class="hlt">soil</span> losses, being the <span class="hlt">soil</span> <span class="hlt">moisture</span> an effective alternative to the estimated runoff, in the prediction of the event <span class="hlt">soil</span> loss at Masse. The agreement between observed and estimated <span class="hlt">soil</span> 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 <span class="hlt">soil</span> losses. Indeed, currently, <span class="hlt">soil</span> <span class="hlt">moisture</span> is a relatively simple measurement at the field scale and remote sensing data are also widely available on a <span class="hlt">global</span> scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the <span class="hlt">soil</span> erosion process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=291074','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=291074"><span>SMAP validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite will be launched by the National Aeronautics and Space Administration in October 2014. SMAP will also incorporate a rigorous calibration and validation program that will support algorithm refinement and provide users with information on the accuracy ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060041588&hterms=tree+olds&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dtree%2Bolds','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060041588&hterms=tree+olds&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dtree%2Bolds"><span>Estimating Subcanopy <span class="hlt">Soil</span> <span class="hlt">Moisture</span> with RADAR</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moghaddam, M.; Saatchi, S.; Cuenca, R. H.</p> <p>1998-01-01</p> <p>The subcanopy <span class="hlt">soil</span> <span class="hlt">moisture</span> of a boreal old jack pine forest is estimated using polarimetric L- and P-band AIRSAR data. Model simulations have shown that for this stand, the principal scattering mechanism responsible for radar backscatter is the double-bounce mechanism between the tree trunks and the ground.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=336620','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=336620"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Remote Sensing: Status and Outlook</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Satellite-based passive microwave sensors have been available for thirty years and provide the basis for <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring and mapping. The approach has reached a level of maturity that is now limited primarily by technology and funding. This is a result of extensive research and development ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=311911','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=311911"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and temperature algorithms and validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Passive microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1916428C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1916428C"><span>Observing and modeling links between <span class="hlt">soil</span> <span class="hlt">moisture</span>, microbes and CH4 fluxes from forest <span class="hlt">soils</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Christiansen, Jesper; Levy-Booth, David; Barker, Jason; Prescott, Cindy; Grayston, Sue</p> <p>2017-04-01</p> <p> community responds different to environmental change dependent on the <span class="hlt">soil</span> <span class="hlt">moisture</span> regime. These results are important to include in future modeling efforts to predict changes in <span class="hlt">soil</span>-atmosphere exchange of CH4 under <span class="hlt">global</span> change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=318163','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=318163"><span>Initial validation of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive mission using USDA-ARS watersheds</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission was launched in January 2015 to measure <span class="hlt">global</span> surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. The calibration and validation program of SMAP relies upon an international cooperative of in situ networks to provide ground truth references across a variety of landscapes. The U...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/53294','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/53294"><span>Evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in a temperate grassland ecosystem in Inner Mongolia China</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>L. Hao; Ge Sun; Yongqiang Liu; G. S. Zhou; J. H.   Wan;  L. B. Zhang; J. L. Niu; Y. H. Sang;  J. J He</p> <p>2015-01-01</p> <p>Precipitation, evapotranspiration (ET), and <span class="hlt">soil</span> <span class="hlt">moisture</span> are the key controls for the productivity and functioning of temperate grassland ecosystems in Inner Mongolia, northern China. Quantifying the <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics and water balances in the grasslands is essential to sustainable grassland management under <span class="hlt">global</span> climate change. We...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=334168','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=334168"><span>Downscaling <span class="hlt">soil</span> <span class="hlt">moisture</span> over regions that include multiple coarse-resolution grid cells</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>Many applications require <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates over large spatial extents (30-300 km) and at fine-resolutions (10-30 m). Remote-sensing methods can provide <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates over very large spatial extents (continental to <span class="hlt">global</span>) at coarse resolutions (10-40 km), but their output must be d...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=338614','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=338614"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> retrieval in forest biomes: field experiment focus for SMAP 2018-2020 and beyond</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) project has made excellent progress in addressing the requirements and science goals of the primary mission. The primary mission baseline requirement is estimates of <span class="hlt">global</span> surface <span class="hlt">soil</span> <span class="hlt">moisture</span> with an error of no greater than 4% volumetric (one sigma) exclud...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011771','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011771"><span>Fostering Application Opportunites for the NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moran, M. Susan; O'Neill, Peggy E.; Entekhabi, Dara; Njoku, Eni G.; Kellogg, Kent H.</p> <p>2010-01-01</p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission will provide <span class="hlt">global</span> observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state from space. We outline how priority applications contributed to the SMAP mission measurement requirements and how the SMAP mission plans to foster applications and applied science.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=235923','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=235923"><span>The Development of Terrestrial Water Cycle Applications for SMAP <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data 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><span class="hlt">Soil</span> <span class="hlt">moisture</span> storage sits at the locus of the terrestrial water cycle and governs the relative partitioning of precipitation into various land surface flux components. Consequently, improved observational constraint of <span class="hlt">soil</span> <span class="hlt">moisture</span> variations should improve our ability to <span class="hlt">globally</span> monitor the te...</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 <span class="hlt">global</span> 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 <span class="hlt">global</span> 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://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 <span class="hlt">global</span> 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/2016cosp...41E1188L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016cosp...41E1188L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Remote Sensing with GNSS-R at the Valencia Anchor Station. The SOMOSTA (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Station) Experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lopez-Baeza, Ernesto</p> <p>2016-07-01</p> <p>In this paper, the SOMOSTA (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring Station) experiment on <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring by<span class="hlt">Global</span> Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced. L-band microwaves have very good advantages in <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing, for being unaffected by clouds and the atmosphere, and for the ability to penetrate vegetation. During this experimental campaign, the ESA GNSS-R Oceanpal antenna was installed on the same tower as the ESA ELBARA-II passive microwave radiometer, both measuring instruments having similar field of view. This experiment is fruitfully framed within the ESA - China Programme of Collaboration on GNSS-R. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and two down-looking antennas for receiving LHCP (left-hand circular polarisation) and RHCP (right-hand circular polarisation) reflected signals from the <span class="hlt">soil</span> surface. We could collect data from the three different antennas through the two channels of Oceanpal and, in addition, calibration could be performed to reduce the impact from the differing channels. Reflectivity was thus measured and <span class="hlt">soil</span> <span class="hlt">moisture</span> could be retrieved by the L- MEB (L-band Microwave Emission of the Biosphere) model considering the effect of vegetation optical thickness and <span class="hlt">soil</span> roughness. By contrasting GNSS-R and ELBARA-II radiometer data, a negative correlation existed between reflectivity measured by GNSS-R and brightness temperature measured by the radiometer. The two parameters represent reflection and absorption of the <span class="hlt">soil</span>. <span class="hlt">Soil</span> <span class="hlt">moisture</span> retrieved by both L-band remote sensing methods shows good agreement. In addition, correspondence with in-situ measurements and rainfall is also good.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820017732','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820017732"><span>Plan of research for integrated <span class="hlt">soil</span> <span class="hlt">moisture</span> studies. Recommendations of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Working Group</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1980-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> information is a potentially powerful tool for applications in agriculture, water resources, and climate. At present, it is difficult for users of this information to clearly define their needs in terms of accuracy, resolution and frequency because of the current sparsity of data. A plan is described for defining and conducting an integrated and coordinated research effort to develop and refine remote sensing techniques which will determine spatial and temporal variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and to utilize <span class="hlt">soil</span> <span class="hlt">moisture</span> information in support of agricultural, water resources, and climate applications. The <span class="hlt">soil</span> <span class="hlt">moisture</span> requirements of these three different application areas were reviewed in relation to each other so that one plan covering the three areas could be formulated. Four subgroups were established to write and compile the plan, namely models, ground-based studies, aircraft experiments, and spacecraft missions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=276404','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=276404"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> mapping for aquarius</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Aquarius is the first satellite to provide both passive and active L-band observations of the Earth. In addition, the instruments on Satelite de Aplicaciones Cientificas-D (SAC-D) provide complementary information for analysis and retrieval algorithms. Our research focuses on the retrieval of <span class="hlt">soil</span> m...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://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 <span class="hlt">global</span> 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 <span class="hlt">global</span> 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/2007AGUFM.H51N..03B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H51N..03B"><span><span class="hlt">Soil</span>Net - A Zigbee based <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogena, H. R.; Weuthen, A.; Rosenbaum, U.; Huisman, J. A.; Vereecken, H.</p> <p>2007-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays a key role in partitioning water and energy fluxes, in providing <span class="hlt">moisture</span> to the atmosphere for precipitation, and controlling the pattern of groundwater recharge. Large-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> variability is driven by variation of precipitation and radiation in space and time. At local scales, land cover, <span class="hlt">soil</span> conditions, and topography act to redistribute <span class="hlt">soil</span> <span class="hlt">moisture</span>. Despite the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span>, it is not yet measured in an operational way, e.g. for a better prediction of hydrological and surface energy fluxes (e.g. runoff, latent heat) at larger scales and in the framework of the development of early warning systems (e.g. flood forecasting) and the management of irrigation systems. The <span class="hlt">Soil</span>Net project aims to develop a sensor network for the near real-time monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> changes at high spatial and temporal resolution on the basis of the new low-cost ZigBee radio network that operates on top of the IEEE 802.15.4 standard. The sensor network consists of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors attached to end devices by cables, router devices and a coordinator device. The end devices are buried in the <span class="hlt">soil</span> and linked wirelessly with nearby aboveground router devices. This ZigBee wireless sensor network design considers channel errors, delays, packet losses, and power and topology constraints. In order to conserve battery power, a reactive routing protocol is used that determines a new route only when it is required. The sensor network is also able to react to external influences, e.g. such as rainfall occurrences. The <span class="hlt">Soil</span>Net communicator, routing and end devices have been developed by the Forschungszentrum Juelich and will be marketed through external companies. We will present first results of experiments to verify network stability and the accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors. Simultaneously, we have developed a data management and visualisation system. We tested the wireless network on a 100 by 100 meter forest plot equipped with 25</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19860018232','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19860018232"><span>Preliminary assessment of <span class="hlt">soil</span> <span class="hlt">moisture</span> over vegetation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carlson, T. N.</p> <p>1986-01-01</p> <p>Modeling of surface energy fluxes was combined with in-situ measurement of surface parameters, specifically the surface sensible heat flux and the substrate <span class="hlt">soil</span> <span class="hlt">moisture</span>. A vegetation component was incorporated in the atmospheric/substrate model and subsequently showed that fluxes over vegetation can be very much different than those over bare <span class="hlt">soil</span> for a given surface-air temperature difference. The temperature signatures measured by a satellite or airborne radiometer should be interpreted in conjunction with surface measurements of modeled parameters. Paradoxically, analyses of the large-scale distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> availability shows that there is a very high correlation between antecedent precipitation and inferred surface <span class="hlt">moisture</span> availability, even when no specific vegetation parameterization is used in the boundary layer model. Preparatory work was begun in streamlining the present boundary layer model, developing better algorithms for relating surface temperatures to substrate <span class="hlt">moisture</span>, preparing for participation in the French HAPEX experiment, and analyzing aircraft microwave and radiometric surface temperature data for the 1983 French Beauce experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=331905','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=331905"><span>Downscaled <span class="hlt">soil</span> <span class="hlt">moisture</span> from SMAP evaluated using high density observations</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Recently, a <span class="hlt">soil</span> <span class="hlt">moisture</span> downscaling algorithm based on a regression relationship between daily temperature changes and daily average <span class="hlt">soil</span> <span class="hlt">moisture</span> was developed to produce an enhanced spatial resolution on <span class="hlt">soil</span> <span class="hlt">moisture</span> product for the Advanced Microwave Scanning Radiometer–EOS (AMSR-E) satellite ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810020962','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810020962"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> inferences from thermal infrared measurements of vegetation temperatures</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jackson, R. D. (Principal Investigator)</p> <p>1981-01-01</p> <p>Thermal infrared measurements of wheat (Triticum durum) canopy temperatures were used in a crop water stress index to infer root zone <span class="hlt">soil</span> <span class="hlt">moisture</span>. Results indicated that one time plant temperature measurement cannot produce precise estimates of root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> due to complicating plant factors. Plant temperature measurements do yield useful qualitative information concerning <span class="hlt">soil</span> <span class="hlt">moisture</span> and plant condition.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=347544','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=347544"><span>Data assimilation to extract <span class="hlt">soil</span> <span class="hlt">moisture</span> information from SMAP 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>This study compares different methods to extract <span class="hlt">soil</span> <span class="hlt">moisture</span> information through the assimilation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) observations. Neural Network(NN) and physically-based SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals were assimilated into the NASA Catchment model over the contiguous United Sta...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H41D1482M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H41D1482M"><span>Measuring <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Skeletal <span class="hlt">Soils</span> Using a COSMOS Rover</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medina, C.; Neely, H.; Desilets, D.; Mohanty, B.; Moore, G. W.</p> <p>2017-12-01</p> <p>The presence of coarse fragments directly influences the volumetric water content of the <span class="hlt">soil</span>. Current surface <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors often do not account for the presence of coarse fragments, and little research has been done to calibrate these sensors under such conditions. The cosmic-ray <span class="hlt">soil</span> <span class="hlt">moisture</span> observation system (COSMOS) rover is a passive, non-invasive surface <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor with a footprint greater than 100 m. Despite its potential, the COSMOS rover has yet to be validated in skeletal <span class="hlt">soils</span>. The goal of this study was to validate measurements of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> as taken by a COSMOS rover on a Texas skeletal <span class="hlt">soil</span>. Data was collected for two <span class="hlt">soils</span>, a Marfla clay loam and Chinati-Boracho-Berrend association, in West Texas. Three levels of data were collected: 1) COSMOS surveys at three different <span class="hlt">soil</span> <span class="hlt">moistures</span>, 2) electrical conductivity surveys within those COSMOS surveys, and 3) ground-truth measurements. Surveys with the COSMOS rover covered an 8000-h area and were taken both after large rain events (>2") and a long dry period. Within the COSMOS surveys, the EM38-MK2 was used to estimate the spatial distribution of coarse fragments in the <span class="hlt">soil</span> around two COSMOS points. Ground truth measurements included coarse fragment mass and volume, bulk density, and water content at 3 locations within each EM38 survey. Ground-truth measurements were weighted using EM38 data, and COSMOS measurements were validated by their distance from the samples. There was a decrease in water content as the percent volume of coarse fragment increased. COSMOS estimations responded to both changes in coarse fragment percent volume and the ground-truth volumetric water content. Further research will focus on creating digital <span class="hlt">soil</span> maps using landform data and water content estimations from the COSMOS rover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=344556','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=344556"><span>Inference of <span class="hlt">soil</span> hydrologic parameters from electronic <span class="hlt">soil</span> <span class="hlt">moisture</span> records</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important control on hydrologic function, as it governs vertical fluxes from and to the atmosphere, groundwater recharge, and lateral fluxes through the <span class="hlt">soil</span>. Historically, the traditional model parameters of saturation, field capacity, and permanent wilting point have been deter...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.9543P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.9543P"><span>Evaluation of <span class="hlt">soil</span> and vegetation response to drought using SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> satellite observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Piles, Maria; Sánchez, Nilda; Vall-llossera, Mercè; Ballabrera, Joaquim; Martínez, Justino; Martínez-Fernández, José; Camps, Adriano; Font, Jordi</p> <p>2014-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays an important role in determining the likelihood of droughts and floods that may affect an area. Knowledge of <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution as a function of time and space is highly relevant for hydrological, ecological and agricultural applications, especially in water-limited or drought-prone regions. However, measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> is challenging because of its high variability; point-scale in-situ measurements are scarce being remote sensing the only practical means to obtain regional- and <span class="hlt">global</span>-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates. The ESA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) is the first satellite mission ever designed to measuring the Earth's surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at near daily time scales with levels of accuracy previously not attained. Since its launch in November 2009, significant efforts have been dedicated to validate and fine-tune the retrieval algorithms so that SMOS-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates meet the standards required for a wide variety of applications. In this line, the SMOS Barcelona Expert Center (BEC) is distributing daily, monthly, and annual temporal averages of 0.25-deg <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> maps, which have proved useful for assessing drought and water-stress conditions. In addition, a downscaling algorithm has been developed to combine SMOS and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data into fine-scale (< 1km) <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates, which permits extending the applicability of the data to regional and local studies. Fine-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> maps are currently limited to the Iberian Peninsula but the algorithm is dynamic and can be transported to any region. <span class="hlt">Soil</span> <span class="hlt">moisture</span> maps are generated in a near real-time fashion at BEC facilities and are used by Barcelona's fire prevention services to detect extremely dry <span class="hlt">soil</span> and vegetation conditions posing a risk of fire. Recently, they have been used to explain drought-induced tree mortality episodes and forest decline in the Catalonia region. These</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080038045','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080038045"><span>Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval Under Trees</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>O'Neill, P.; Lang, R.; Kurum, M.; Joseph, A.; Jackson, T.; Cosh, M.</p> <p>2008-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is recognized as an important component of the water, energy, and carbon cycles at the interface between the Earth's surface and atmosphere. Current baseline <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithms for microwave space missions have been developed and validated only over grasslands, agricultural crops, and generally light to moderate vegetation. Tree areas have commonly been excluded from operational <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval plans due to the large expected impact of trees on masking the microwave response to the underlying <span class="hlt">soil</span> <span class="hlt">moisture</span>. Our understanding of the microwave properties of trees of various sizes and their effect on <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithms at L band is presently limited, although research efforts are ongoing in Europe, the United States, and elsewhere to remedy this situation. As part of this research, a coordinated sequence of field measurements involving the ComRAD (for Combined Radar/Radiometer) active/passive microwave truck instrument system has been undertaken. Jointly developed and operated by NASA Goddard Space Flight Center and George Washington University, ComRAD consists of dual-polarized 1.4 GHz total-power radiometers (LH, LV) and a quad-polarized 1.25 GHz L band radar sharing a single parabolic dish antenna with a novel broadband stacked patch dual-polarized feed, a quad-polarized 4.75 GHz C band radar, and a single channel 10 GHz XHH radar. The instruments are deployed on a mobile truck with an 19-m hydraulic boom and share common control software; real-time calibrated signals, and the capability for automated data collection for unattended operation. Most microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithms developed for use at L band frequencies are based on the tau-omega model, a simplified zero-order radiative transfer approach where scattering is largely ignored and vegetation canopies are generally treated as a bulk attenuating layer. In this approach, vegetation effects are parameterized by tau and omega, the microwave</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990064542&hterms=desertification&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Ddesertification','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064542&hterms=desertification&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Ddesertification"><span>Estimating <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from Satellite Microwave Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.</p> <p>1998-01-01</p> <p>Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of <span class="hlt">soil</span> <span class="hlt">moisture</span> from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017RvGeo..55..341P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017RvGeo..55..341P"><span>A review of spatial downscaling of satellite remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, Jian; Loew, Alexander; Merlin, Olivier; Verhoest, Niko E. C.</p> <p>2017-06-01</p> <p>Satellite remote sensing technology has been widely used to estimate surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Numerous efforts have been devoted to develop <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> products. However, these <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span>. 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H11O..04S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H11O..04S"><span>High-resolution multimodel projections of <span class="hlt">soil</span> <span class="hlt">moisture</span> drought in Europe under 1.5, 2 and 3 degree <span class="hlt">global</span> warming</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. E.; Kumar, R.; Zink, M.; Pan, M.; Wanders, N.; Marx, A.; Sheffield, J.; Wood, E. F.; Thober, S.</p> <p>2017-12-01</p> <p> HMs are, however, similar to those of the GCMs in the Iberian peninsula due to different representation of evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics. And, 3) despite the large uncertainty in the full ensemble, significant positive trends have been observed in all drought characteristics that intensify with increased <span class="hlt">global</span> warming.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160003391','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160003391"><span>Technical Report Series on <span class="hlt">Global</span> Modeling and Data Assimilation. Volume 42; <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Project Calibration and Validation for the L4_C Beta-Release Data Product</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. (Editor); Kimball, John S.; Jones, Lucas A.; Glassy, Joseph; Stavros, E. Natasha; Madani, Nima (Editor); Reichle, Rolf H.; Jackson, Thomas; Colliander, Andreas</p> <p>2015-01-01</p> <p>During the post-launch Cal/Val Phase of SMAP there are two objectives for each science product team: 1) calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the L1 science requirements according to the Cal/Val timeline. This report provides analysis and assessment of the SMAP Level 4 Carbon (L4_C) product specifically for the beta release. The beta-release version of the SMAP L4_C algorithms utilizes a terrestrial carbon flux model informed by SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> inputs along with optical remote sensing (e.g. MODIS) vegetation indices and other ancillary biophysical data to estimate <span class="hlt">global</span> daily NEE and component carbon fluxes, particularly vegetation gross primary production (GPP) and ecosystem respiration (Reco). Other L4_C product elements include surface (<10 cm depth) <span class="hlt">soil</span> organic carbon (SOC) stocks and associated environmental constraints to these processes, including <span class="hlt">soil</span> <span class="hlt">moisture</span> and landscape FT controls on GPP and Reco (Kimball et al. 2012). The L4_C product encapsulates SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and <span class="hlt">soil</span> <span class="hlt">moisture</span> constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity in northern ecosystems.</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 <span class="hlt">global</span> 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 <span class="hlt">global</span> <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://hdl.handle.net/2060/20080004233','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080004233"><span>Method for evaluating <span class="hlt">moisture</span> tensions of <span class="hlt">soils</span> using spectral data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peterson, John B. (Inventor)</p> <p>1982-01-01</p> <p>A method is disclosed which permits evaluation of <span class="hlt">soil</span> <span class="hlt">moisture</span> utilizing remote sensing. Spectral measurements at a plurality of different wavelengths are taken with respect to sample <span class="hlt">soils</span> and the bidirectional reflectance factor (BRF) measurements produced are submitted to regression analysis for development therefrom of predictable equations calculated for orderly relationships. <span class="hlt">Soil</span> of unknown reflective and unknown <span class="hlt">soil</span> <span class="hlt">moisture</span> tension is thereafter analyzed for bidirectional reflectance and the resulting data utilized to determine the <span class="hlt">soil</span> <span class="hlt">moisture</span> tension of the <span class="hlt">soil</span> as well as providing a prediction as to the bidirectional reflectance of the <span class="hlt">soil</span> at other <span class="hlt">moisture</span> tensions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1616875A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1616875A"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> under contrasted atmospheric conditions in Eastern Spain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Azorin-Molina, César; Cerdà, Artemi; Vicente-Serrano, Sergio M.</p> <p>2014-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> plays a key role on the recently abandoned agriculture land where determine the recovery and the erosion rates (Cerdà, 1995), on the <span class="hlt">soil</span> water repellency degree (Bodí et al., 2011) and on the hydrological cycle (Cerdà, 1999), the plant development (García Fayos et al., 2000) and the seasonality of the geomorphological processes (Cerdà, 2002). Moreover, <span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key factor on the semiarid land (Ziadat and Taimeh, 2013), on the productivity of the land (Qadir et al., 2013) and <span class="hlt">soils</span> treated with amendments (Johnston et al., 2013) and on <span class="hlt">soil</span> reclamation on drained saline-sodic <span class="hlt">soils</span> (Ghafoor et al., 2012). In previous study (Azorin-Molina et al., 2013) we investigated the intraannual evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> in <span class="hlt">soils</span> under different land managements in the Valencia region, Eastern Spain, and concluded that <span class="hlt">soil</span> <span class="hlt">moisture</span> recharges are much controlled by few heavy precipitation events; 23 recharge episodes during 2012. Most of the <span class="hlt">soil</span> <span class="hlt">moisture</span> recharge events occurred during the autumn season under Back-Door cold front situations. Additionally, sea breeze front episodes brought isolated precipitation and <span class="hlt">moisture</span> to mountainous areas within summer (Azorin-Molina et al., 2009). We also evidenced that the intraanual evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> changes are positively and significatively correlated (at p<0.01) with the amount of measured precipitation. In this study we analyze the role of other crucial atmospheric parameters (i.e., temperature, relative humidity, <span class="hlt">global</span> solar radiation, and wind speed and wind direction) in the intraanual evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span>; focussing our analyses on the <span class="hlt">soil</span> <span class="hlt">moisture</span> discharge episodes. Here we present 1-year of <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements at two experimental sites in the Valencia region, one representing rainfed orchard typical from the Mediterranean mountains (El Teularet-Sierra de Enguera), and a second site corresponding to an irrigated orange crop (Alcoleja). Key Words: <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Discharges</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8129E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8129E"><span>Impacts of <span class="hlt">soil</span> <span class="hlt">moisture</span> content on visual <span class="hlt">soil</span> evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Emmet-Booth, Jeremy; Forristal, Dermot; Fenton, Owen; Bondi, Giulia; Creamer, Rachel; Holden, Nick</p> <p>2017-04-01</p> <p>Visual <span class="hlt">Soil</span> Examination and Evaluation (VSE) techniques offer tools for <span class="hlt">soil</span> quality assessment. They involve the visual and tactile assessment of <span class="hlt">soil</span> properties such as aggregate size and shape, porosity, redox morphology, <span class="hlt">soil</span> colour and smell. An increasing body of research has demonstrated the reliability and utility of VSE techniques. However a number of limitations have been identified, including the potential impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> variation during sampling. As part of a national survey of grassland <span class="hlt">soil</span> quality in Ireland, an evaluation of the impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> on two widely used VSE techniques was conducted. The techniques were Visual Evaluation of <span class="hlt">Soil</span> Structure (VESS) (Guimarães et al., 2011) and Visual <span class="hlt">Soil</span> Assessment (VSA) (Shepherd, 2009). Both generate summarising numeric scores that indicate <span class="hlt">soil</span> structural quality, though employ different scoring mechanisms. The former requires the assessment of properties concurrently and the latter separately. Both methods were deployed on 20 sites across Ireland representing a range of <span class="hlt">soils</span>. Additional samples were taken for <span class="hlt">soil</span> volumetric water (θ) determination at 5-10 and 10-20 cm depth. No significant correlation was observed between θ 5-10 cm and either VSE technique. However, VESS scores were significantly related to θ 10-20 cm (rs = 0.40, sig = 0.02) while VSA scores were not (rs = -0.33, sig = 0.06). VESS and VSA scores can be grouped into quality classifications (good, moderate and poor). No significant mean difference was observed between θ 5-10 cm or θ 10-20 cm according to quality classification by either method. It was concluded that VESS scores may be affected by <span class="hlt">soil</span> <span class="hlt">moisture</span> variation while VSA appear unaffected. The different scoring mechanisms, where the separate assessment and scoring of individual properties employed by VSA, may limit <span class="hlt">soil</span> <span class="hlt">moisture</span> effects. However, <span class="hlt">moisture</span> content appears not to affect overall structural quality classification by either method. References</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 <span class="hlt">global</span> 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 <span class="hlt">Global</span> 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 <span class="hlt">global</span> scale. However, since those <span class="hlt">global</span> <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('https://ntrs.nasa.gov/search.jsp?R=19930036630&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=19930036630&hterms=Soil+sampling+radiation&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DSoil%2Bsampling%2Bradiation"><span>An overview of the measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and modeling of <span class="hlt">moisture</span> flux in FIFE</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, J. R.</p> <p>1992-01-01</p> <p>Measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and calculations of <span class="hlt">moisture</span> transfer in the <span class="hlt">soil</span> medium and at the air-<span class="hlt">soil</span> interface were performed over a 15-km by 15-km test site during FIFE in 1987 and 1989. The measurements included intensive <span class="hlt">soil</span> <span class="hlt">moisture</span> sampling at the ground level and surveys at aircraft altitudes by several passive and active microwave sensors as well as a gamma radiation device.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6692U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6692U"><span>An inversion method for retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span> information from satellite altimetry observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Uebbing, Bernd; Forootan, Ehsan; Kusche, Jürgen; Braakmann-Folgmann, Anne</p> <p>2016-04-01</p> <p>) deriving time-invariant spatial patterns (base-functions) by applying principal component analysis (PCA) to simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> from a large-scale land surface model. (ii) Estimating time-variable <span class="hlt">soil</span> <span class="hlt">moisture</span> evolution by fitting these base functions of (i) to the along-track retracked backscatter coefficients in a least squares sense. (iii) Combining the estimated time-variable amplitudes and the pre-computed base-functions, which results in reconstructed (spatio-temporal) <span class="hlt">soil</span> <span class="hlt">moisture</span> information. We will show preliminary results that are compared to available high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> model data over the region (the Australian Water Resource Assessment, AWRA model). We discuss the possibility of using altimetry-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> estimations to improve the simulation skill of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the <span class="hlt">Global</span> Land Data Assimilation System (GLDAS) over Australia.</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 <span class="hlt">global</span> 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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050238481','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050238481"><span>NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data Products and Their Incorporation in DREAM</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blonski, Slawomir; Holland, Donald; Henderson, Vaneshette</p> <p>2005-01-01</p> <p>NASA provides <span class="hlt">soil</span> <span class="hlt">moisture</span> data products that include observations from the Advanced Microwave Scanning Radiometer on the Earth Observing System Aqua satellite, field measurements from the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment campaigns, and model predictions from the Land Information System and the Goddard Earth Observing System Data Assimilation System. Incorporation of the NASA <span class="hlt">soil</span> <span class="hlt">moisture</span> products in the Dust Regional Atmospheric Model is possible through use of the satellite observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> to set initial conditions for the dust simulations. An additional comparison of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> observations with mesoscale atmospheric dynamics modeling is recommended. Such a comparison would validate the use of NASA <span class="hlt">soil</span> <span class="hlt">moisture</span> data in applications and support acceptance of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation in weather and climate modeling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/wsp/1619u/report.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/wsp/1619u/report.pdf"><span>Methods of measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> in the field</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Johnson, A.I.</p> <p>1962-01-01</p> <p>For centuries, the amount of <span class="hlt">moisture</span> in the <span class="hlt">soil</span> has been of interest in agriculture. The subject of <span class="hlt">soil</span> <span class="hlt">moisture</span> is also of great importance to the hydrologist, forester, and <span class="hlt">soils</span> engineer. Much equipment and many methods have been developed to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> under field conditions. This report discusses and evaluates the various methods for measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> and describes the equipment needed for each method. The advantages and disadvantages of each method are discussed and an extensive list of references is provided for those desiring to study the subject in more detail. The gravimetric method is concluded to be the most satisfactory method for most problems requiring onetime <span class="hlt">moisture</span>-content data. The radioactive method is normally best for obtaining repeated measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> in place. It is concluded that all methods have some limitations and that the ideal method for measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> under field conditions has yet to be perfected.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://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 <span class="hlt">global</span> 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://hdl.handle.net/2060/20170007420','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170007420"><span>Development and Validation of The SMAP Enhanced 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.; Bindlish, R.; O'Neill, P.; Jackson, T.; Chaubell, J.; Piepmeier, J.; Dunbar, S.; Colliander, A.; Chen, F.; Entekhabi, D.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20170007420'); toggleEditAbsImage('author_20170007420_show'); toggleEditAbsImage('author_20170007420_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20170007420_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20170007420_hide"></p> <p>2017-01-01</p> <p>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 <span class="hlt">global</span> mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> product, the enhanced passive <span class="hlt">soil</span> <span class="hlt">moisture</span> product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve <span class="hlt">global</span> mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> with enhanced clarity. The resulting enhanced <span class="hlt">soil</span> <span class="hlt">moisture</span> product was assessed using long-term in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170007421','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170007421"><span>Assessment of Version 4 of the SMAP Passive <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Standard Product</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. O.; Chan, S.; Bindlish, R.; Jackson, T.; Colliander, A.; Dunbar, R.; Chen, F.; Piepmeier, Jeffrey R.; Yueh, S.; Entekhabi, D.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20170007421'); toggleEditAbsImage('author_20170007421_show'); toggleEditAbsImage('author_20170007421_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20170007421_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20170007421_hide"></p> <p>2017-01-01</p> <p>NASAs <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am6 pm orbit with an objective to produce <span class="hlt">global</span> mapping of high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> product (L2SMP) provides <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithms as previous versions, but now include retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">global</span> core calval sites. Accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120013591','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120013591"><span>Assimilation of Passive and Active Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Draper, C. S.; Reichle, R. H.; DeLannoy, G. J. M.; Liu, Q.</p> <p>2012-01-01</p> <p>Root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> is an important control over the partition of land surface energy and <span class="hlt">moisture</span>, and the assimilation of remotely sensed near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> has been shown to improve model profile <span class="hlt">soil</span> <span class="hlt">moisture</span> [1]. To date, efforts to assimilate remotely sensed near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at large scales have focused on <span class="hlt">soil</span> <span class="hlt">moisture</span> derived from the passive microwave Advanced Microwave Scanning Radiometer (AMSR-E) and the active Advanced Scatterometer (ASCAT; together with its predecessor on the European Remote Sensing satellites (ERS. The assimilation of passive and active microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> observations has not yet been directly compared, and so this study compares the impact of assimilating ASCAT and AMSR-E <span class="hlt">soil</span> <span class="hlt">moisture</span> data, both separately and together. Since the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval skill from active and passive microwave data is thought to differ according to surface characteristics [2], the impact of each assimilation on the model <span class="hlt">soil</span> <span class="hlt">moisture</span> skill is assessed according to land cover type, by comparison to in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014cosp...40E3147S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014cosp...40E3147S"><span>SMALT - <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from Altimetry project</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smith, Richard; Benveniste, Jérôme; Dinardo, Salvatore; Lucas, Bruno Manuel; Berry, Philippa; Wagner, Wolfgang; Hahn, Sebastian; Egido, Alejandro</p> <p></p> <p><span class="hlt">Soil</span> surface <span class="hlt">moisture</span> is a key scientific parameter; however, it is extremely difficult to measure remotely, particularly in arid and semi-arid terrain. This paper outlines the development of a novel methodology to generate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates in these regions from multi-mission satellite radar altimetry. Key to this approach is the development of detailed DRy Earth ModelS (DREAMS), which encapsulate the detailed and intricate surface brightness variations over the Earth’s land surface, resulting from changes in surface roughness and composition. DREAMS have been created over a number of arid and semi-arid deserts worldwide to produce historical SMALT timeseries over <span class="hlt">soil</span> <span class="hlt">moisture</span> variation. These products are available in two formats - a high resolution track product which utilises the altimeter’s high frequency content alongtrack and a multi-looked 6” gridded product at facilitate easy comparison/integeration with other remote sensing techniques. An overview of the SMALT processing scheme, covering the progression of the data from altimeter sigma0 through to final <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate, is included along with example SMALT products. Validation has been performed over a number of deserts by comparing SMALT products with other remote sensing techniques, results of the comparison between SMALT and Metop Warp 5.5 are presented here. Comparisons with other remote sensing techniques have been limited in scope due to differences in the operational aspects of the instruments, the restricted geographical coverage of the DREAMS and the low repeat temporal sampling rate of the altimeter. The potential to expand the SMALT technique into less arid areas has been investigated. Small-scale comparison with in-situ and GNSS-R data obtained by the LEiMON experimental campaign over Tuscany, where historical trends exist within both SMALT and SMC probe datasets. A qualitative analysis of unexpected backscatter characteristics in dedicated dry environments is performed</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913930C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913930C"><span>Using satellite image data to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chuang, Chi-Hung; Yu, Hwa-Lung</p> <p>2017-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is considered as an important parameter in various study fields, such as hydrology, phenology, and agriculture. In hydrology, <span class="hlt">soil</span> <span class="hlt">moisture</span> is an significant parameter to decide how much rainfall that will infiltrate into permeable layer and become groundwater resource. Although <span class="hlt">soil</span> <span class="hlt">moisture</span> is a critical role in many environmental studies, so far the measurement of <span class="hlt">soil</span> <span class="hlt">moisture</span> is using ground instrument such as electromagnetic <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor. Use of ground instrumentation can directly obtain the information, but the instrument needs maintenance and consume manpower to operation. If we need wide range region information, ground instrumentation probably is not suitable. To measure wide region <span class="hlt">soil</span> <span class="hlt">moisture</span> information, we need other method to achieve this purpose. Satellite remote sensing techniques can obtain satellite image on Earth, this can be a way to solve the spatial restriction on instrument measurement. In this study, we used MODIS data to retrieve daily <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern estimation, i.e., crop water stress index (cwsi), over the year of 2015. The estimations are compared with the observations at the <span class="hlt">soil</span> <span class="hlt">moisture</span> stations from Taiwan Bureau of <span class="hlt">soil</span> and water conservation. Results show that the satellite remote sensing data can be helpful to the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. Further analysis can be required to obtain the optimal parameters for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation in Taiwan.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://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 <span class="hlt">global</span> coverage are critically important.</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 <span class="hlt">Global</span> 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/2005AGUFM.P51D0945K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.P51D0945K"><span>Microbiology and <span class="hlt">Moisture</span> Uptake of Desert <span class="hlt">Soils</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kress, M. E.; Bryant, E. P.; Morgan, S. W.; Rech, S.; McKay, C. P.</p> <p>2005-12-01</p> <p>We have initiated an interdisciplinary study of the microbiology and water content of desert <span class="hlt">soils</span> to better understand microbial activity in extreme arid environments. Water is the one constituent that no organism can live without; nevertheless, there are places on Earth with an annual rainfall near zero that do support microbial ecosystems. These hyperarid deserts (e.g. Atacama and the Antarctic Dry Valleys) are the closest terrestrial analogs to Mars, which is the subject of future exploration motivated by the search for life beyond Earth. We are modeling the <span class="hlt">moisture</span> uptake by <span class="hlt">soils</span> in hyperarid environments to quantify the environmental constraints that regulate the survival and growth of micro-organisms. Together with the studies of <span class="hlt">moisture</span> uptake, we are also characterizing the microbial population in these <span class="hlt">soils</span> using molecular and culturing methods. We are in the process of extracting DNA from these <span class="hlt">soils</span> using MoBio extraction kits. This DNA will be used as a template to amplify bacterial and eukaryotic ribosomal DNA to determine the diversity of the microbial population. We also have been attempting to determine the density of organisms by culturing on one-half strength R2A agar. The long-range goal of this research is to identify special adaptations of terrestrial life that allow them to inhabit extreme arid environments, while simultaneously quantifying the environmental parameters that enforce limits on these organisms' growth and survival.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160009650&hterms=soil+environment&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dsoil%2Benvironment','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160009650&hterms=soil+environment&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dsoil%2Benvironment"><span>Spacecraft Environmental Testing SMAP (<span class="hlt">Soil</span>, <span class="hlt">Moisture</span>, Active, Passive)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fields, Keith</p> <p>2014-01-01</p> <p>Testing a complete full up spacecraft to verify it will survive the environment, in which it will be exposed to during its mission, is a formidable task in itself. However, the ''test like you fly'' philosophy sometimes gets compromised because of cost, design and or time. This paper describes the thermal-vacuum and mass properties testing of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) earth orbiting satellite. SMAP will provide <span class="hlt">global</span> observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state (the hydrosphere state). SMAP hydrosphere state measurements will be used to enhance understanding of processes that link the water, energy, and carbon cycles, and to extend the capabilities of weather and climate prediction models. It will explain the problems encountered, and the solutions developed, which minimized the risk typically associated with such an arduous process. Also discussed, the future of testing on expensive long lead-time spacecraft. Will we ever reach the ''build and shoot" scenario with minimal or no verification testing?</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008257','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008257"><span>Contributions of Precipitation and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observations to the Skill of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimates in a Land Data Assimilation System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.</p> <p>2011-01-01</p> <p>The contributions of precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> observations to the skill of <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates <span class="hlt">soil</span> <span class="hlt">moisture</span> skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). <span class="hlt">Soil</span> <span class="hlt">moisture</span> skill is measured against in situ observations in the continental United States at 44 single-profile sites within the <span class="hlt">Soil</span> Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure <span class="hlt">soil</span> <span class="hlt">moisture</span> at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface <span class="hlt">moisture</span> skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals increases surface <span class="hlt">soil</span> <span class="hlt">moisture</span> skill levels by IDDeltaR=0.06-0.08, and root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface <span class="hlt">soil</span> <span class="hlt">moisture</span> skill levels by DeltaR=0.13, and root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals contribute similar and largely independent amounts of information.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813805M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813805M"><span>Capacitive <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensor for Plant Watering</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maier, Thomas; Kamm, Lukas</p> <p>2016-04-01</p> <p>How can you realize a water saving and demand-driven plant watering device? To achieve this you need a sensor, which precisely detects the <span class="hlt">soil</span> <span class="hlt">moisture</span>. Designing such a sensor is the topic of this poster. We approached this subject with comparing several physical properties of water, e.g. the conductivity, permittivity, heat capacity and the <span class="hlt">soil</span> water potential, which are suitable to detect the <span class="hlt">soil</span> <span class="hlt">moisture</span> via an electronic device. For our project we have developed a sensor device, which measures the <span class="hlt">soil</span> <span class="hlt">moisture</span> and provides the measured values for a plant watering system via a wireless bluetooth 4.0 network. Different sensor setups have been analyzed and the final sensor is the result of many iterative steps of improvement. In the end we tested the precision of our sensor and compared the results with theoretical values. The sensor is currently being used in the Botanical Garden of the Friedrich-Alexander-University in a long-term test. This will show how good the usability in the real field is. On the basis of these findings a marketable sensor will soon be available. Furthermore a more specific type of this sensor has been designed for the EU:CROPIS Space Project, where tomato plants will grow at different gravitational forces. Due to a very small (15mm x 85mm x 1.5mm) and light (5 gramm) realisation, our sensor has been selected for the space program. Now the scientists can monitor the water content of the substrate of the tomato plants in outer space and water the plants on demand.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H51H1601B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H51H1601B"><span>Inter-Comparison of SMAP, SMOS and GCOM-W <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bindlish, R.; Jackson, T. J.; Chan, S.; Burgin, M. S.; Colliander, A.; Cosh, M. H.</p> <p>2016-12-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission was launched on Jan 31, 2015. The goal of the SMAP mission is to produce <span class="hlt">soil</span> <span class="hlt">moisture</span> with accuracy better than 0.04 m3/m3 with a revisit frequency of 2-3 days. The validated standard SMAP passive <span class="hlt">soil</span> <span class="hlt">moisture</span> product (L2SMP) with a spatial resolution of 36 km was released in May 2016. <span class="hlt">Soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> products. SMAP and SMOS operate at L-band but GCOM-W uses X-band observations for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. All these missions use different ancillary data sources, parameterization and algorithm to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span>. Therefore, it is important to validate and to compare the consistency of these products. <span class="hlt">Soil</span> <span class="hlt">moisture</span> products from the different missions will be compared with the in situ observations. SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> products will be inter-compared at <span class="hlt">global</span> scales with SMOS and GCOM-W <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> product from all the missions.</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 <span class="hlt">global</span>, 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/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 <span class="hlt">global</span> <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 <span class="hlt">global</span> <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 <span class="hlt">global</span> <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('https://rosap.ntl.bts.gov/view/dot/13771','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/13771"><span>Determining <span class="hlt">soil</span> volumetric <span class="hlt">moisture</span> content using time domain reflectometry</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>1998-02-01</p> <p>Time domain reflectometry (TDR) is a technique used to measure indirectly the in situ volumetric <span class="hlt">moisture</span> content of <span class="hlt">soil</span>. Current research provides a variety of prediction equations that estimate the volumetric <span class="hlt">moisture</span> content using the dielectric ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870052970&hterms=evapotranspiration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Devapotranspiration','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870052970&hterms=evapotranspiration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Devapotranspiration"><span>Concerning the relationship between evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wetzel, Peter J.; Chang, Jy-Tai</p> <p>1987-01-01</p> <p>The relationship between the evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> during the drying, supply-limited phase is studied. A second scaling parameter, based on the evapotranspirational supply and demand concept of Federer (1982), is defined; the parameter, referred to as the threshold evapotranspiration, occurs in vegetation-covered surfaces just before leaf stomata close and when surface tension restricts <span class="hlt">moisture</span> release from bare <span class="hlt">soil</span> pores. A simple model for evapotranspiration is proposed. The effects of natural <span class="hlt">soil</span> heterogeneities on evapotranspiration computed from the model are investigated. It is observed that the natural variability in <span class="hlt">soil</span> <span class="hlt">moisture</span>, caused by the heterogeneities, alters the relationship between regional evapotranspiration and the area average <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H21I1595M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H21I1595M"><span>A new Downscaling Approach for SMAP, SMOS and ASCAT by predicting sub-grid <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variability based on <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>Montzka, C.; Rötzer, K.; Bogena, H. R.; Vereecken, H.</p> <p>2017-12-01</p> <p>Improving the coarse spatial resolution of <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> products from SMOS, SMAP and ASCAT is currently an up-to-date topic. <span class="hlt">Soil</span> texture heterogeneity is known to be one of the main sources of <span class="hlt">soil</span> <span class="hlt">moisture</span> spatial variability. A method has been developed that predicts the <span class="hlt">soil</span> <span class="hlt">moisture</span> standard deviation as a function of the mean <span class="hlt">soil</span> <span class="hlt">moisture</span> based on <span class="hlt">soil</span> 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 <span class="hlt">soil</span> properties such as <span class="hlt">soil</span> texture and bulk density, relevant information to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> variability within a satellite product grid cell is available. Here, we predict for each SMOS, SMAP and ASCAT grid cell the sub-grid <span class="hlt">soil</span> <span class="hlt">moisture</span> variability based on the <span class="hlt">Soil</span>Grids1km data set. We provide a look-up table that indicates the <span class="hlt">soil</span> <span class="hlt">moisture</span> standard deviation for any given <span class="hlt">soil</span> <span class="hlt">moisture</span> mean. The resulting data set provides important information for downscaling coarse <span class="hlt">soil</span> <span class="hlt">moisture</span> observations of the SMOS, SMAP and ASCAT missions. Downscaling SMAP data by a field capacity proxy indicates adequate accuracy of the sub-grid <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.129..305S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.129..305S"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> variations in remotely sensed and reanalysis datasets during weak monsoon conditions over central India and central Myanmar</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shrivastava, Sourabh; Kar, Sarat C.; Sharma, Anu Rani</p> <p>2017-07-01</p> <p>Variation of <span class="hlt">soil</span> <span class="hlt">moisture</span> during active and weak phases of summer monsoon JJAS (June, July, August, and September) is very important for sustenance of the crop and subsequent crop yield. As in situ observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> are few or not available, researchers use data derived from remote sensing satellites or <span class="hlt">global</span> reanalysis. This study documents the intercomparison of <span class="hlt">soil</span> <span class="hlt">moisture</span> from remotely sensed and reanalyses during dry spells within monsoon seasons in central India and central Myanmar. <span class="hlt">Soil</span> <span class="hlt">moisture</span> data from the European Space Agency (ESA)—Climate Change Initiative (CCI) has been treated as observed data and was compared against <span class="hlt">soil</span> <span class="hlt">moisture</span> data from the ECMWF reanalysis-Interim (ERA-I) and the climate forecast system reanalysis (CFSR) for the period of 2002-2011. The ESA <span class="hlt">soil</span> <span class="hlt">moisture</span> correlates rather well with observed gridded rainfall. The ESA data indicates that <span class="hlt">soil</span> <span class="hlt">moisture</span> increases over India from west to east and from north to south during monsoon season. The ERA-I overestimates the <span class="hlt">soil</span> <span class="hlt">moisture</span> over India, while the CFSR <span class="hlt">soil</span> <span class="hlt">moisture</span> agrees well with the remotely sensed observation (ESA). Over Myanmar, both the reanalysis overestimate <span class="hlt">soil</span> <span class="hlt">moisture</span> values and the ERA-I <span class="hlt">soil</span> <span class="hlt">moisture</span> does not show much variability from year to year. Day-to-day variations of <span class="hlt">soil</span> <span class="hlt">moisture</span> in central India and central Myanmar during weak monsoon conditions indicate that, because of the rainfall deficiency, the observed (ESA) and the CFSR <span class="hlt">soil</span> <span class="hlt">moisture</span> values are reduced up to 0.1 m3/m3 compared to climatological values of more than 0.35 m3/m3. This reduction is not seen in the ERA-I data. Therefore, <span class="hlt">soil</span> <span class="hlt">moisture</span> from the CFSR is closer to the ESA observed <span class="hlt">soil</span> <span class="hlt">moisture</span> than that from the ERA-I during weak phases of monsoon in the study region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUSM.H23D..04B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUSM.H23D..04B"><span>Evaluation of a <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data Assimilation System Over West Africa</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.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.</p> <p>2009-05-01</p> <p>A crucial requirement of <span class="hlt">global</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span>. However, due to the spatial heterogeneity and dynamic nature of precipitation events and resulting <span class="hlt">soil</span> <span class="hlt">moisture</span>, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. IPAD estimates <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> using daily estimates of minimum and maximum temperature and precipitation applied to a modified Palmer two-layer <span class="hlt">soil</span> <span class="hlt">moisture</span> model which calculates the daily amount of <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA <span class="hlt">soil</span> <span class="hlt">moisture</span> model. This work aims at evaluating the utility of merging satellite-retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates with the IPAD two-layer <span class="hlt">soil</span> <span class="hlt">moisture</span> model used within the DBMS. We present a quantitative analysis of the assimilated <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> product over multiple land cover types and conditions. 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 assimilated <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 model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model</p> </li> <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 <span class="hlt">global</span> 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://hdl.handle.net/2060/20010000376','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010000376"><span>Ultrasound Algorithm Derivation for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Content Estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Belisle, W.R.; Metzl, R.; Choi, J.; Aggarwal, M. D.; Coleman, T.</p> <p>1997-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> content can be estimated by evaluating the velocity at which sound waves travel through a known volume of solid material. This research involved the development of three <span class="hlt">soil</span> algorithms relating the <span class="hlt">moisture</span> content to the velocity at which sound waves moved through dry and moist media. Pressure and shear wave propagation equations were used in conjunction with <span class="hlt">soil</span> property descriptions to derive algorithms appropriate for describing the effects of <span class="hlt">moisture</span> content variation on the velocity of sound waves in <span class="hlt">soils</span> with and without complete <span class="hlt">soil</span> pore water volumes, An elementary algorithm was used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> contents ranging from 0.08 g/g to 0.5 g/g from sound wave velocities ranging from 526 m/s to 664 m/s. Secondary algorithms were also used to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> content from sound wave velocities through <span class="hlt">soils</span> with pores that were filled predominantly with air or water.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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 <span class="hlt">global</span> 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://hdl.handle.net/2060/19810020956','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810020956"><span>Evaluation of gravimetric ground truth <span class="hlt">soil</span> <span class="hlt">moisture</span> data collected for the agricultural <span class="hlt">soil</span> <span class="hlt">moisture</span> experiment, 1978 Colby, Kansas, aircraft mission</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Arya, L. M.; Phinney, D. E. (Principal Investigator)</p> <p>1980-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> data acquired to support the development of algorithms for estimating surface <span class="hlt">soil</span> <span class="hlt">moisture</span> from remotely sensed backscattering of microwaves from ground surfaces are presented. Aspects of field uniformity and variability of gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements are discussed. <span class="hlt">Moisture</span> distribution patterns are illustrated by frequency distributions and contour plots. Standard deviations and coefficients of variation relative to degree of wetness and agronomic features of the fields are examined. Influence of sampling depth on observed <span class="hlt">moisture</span> content an variability are indicated. For the various sets of measurements, <span class="hlt">soil</span> <span class="hlt">moisture</span> values that appear as outliers are flagged. The distribution and legal descriptions of the test fields are included along with examinations of <span class="hlt">soil</span> types, agronomic features, and sampling plan. Bulk density data for experimental fields are appended, should analyses involving volumetric <span class="hlt">moisture</span> content be of interest to the users of data in this report.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010000505','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010000505"><span>Use of Ultrasonic Technology for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurement</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Choi, J.; Metzl, R.; Aggarwal, M. D.; Belisle, W.; Coleman, T.</p> <p>1997-01-01</p> <p>In an effort to improve existing <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement techniques or find new techniques using physics principles, a new technique is presented in this paper using ultrasonic techniques. It has been found that ultrasonic velocity changes as the <span class="hlt">moisture</span> content changes. Preliminary values of velocities are 676.1 m/s in dry <span class="hlt">soil</span> and 356.8 m/s in 100% moist <span class="hlt">soils</span>. Intermediate values can be calibrated to give exact values for the <span class="hlt">moisture</span> content in an unknown sample.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20100031193&hterms=soil&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsoil%26Nf%3DPublication-Date%257CBTWN%2B20050101%2B20180612','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20100031193&hterms=soil&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsoil%26Nf%3DPublication-Date%257CBTWN%2B20050101%2B20180612"><span>Australian <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Field Experiments in Support of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Satellite Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kim, Edward; Walker, Jeff; Rudiger, Christopher; Panciera, Rocco</p> <p>2010-01-01</p> <p>Large-scale field campaigns provide the critical fink between our understanding retrieval algorithms developed at the point scale, and algorithms suitable for satellite applications at vastly larger pixel scales. Retrievals of land parameters must deal with the substantial sub-pixel heterogeneity that is present in most regions. This is particularly the case for <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing, because of the long microwave wavelengths (L-band) that are optimal. Yet, airborne L-band imagers have generally been large, heavy, and required heavy-lift aircraft resources that are expensive and difficult to schedule. Indeed, US <span class="hlt">soil</span> <span class="hlt">moisture</span> campaigns, have been constrained by these factors, and European campaigns have used non-imagers due to instrument and aircraft size constraints. Despite these factors, these campaigns established that large-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing was possible, laying the groundwork for satellite missions. Starting in 2005, a series of airborne field campaigns have been conducted in Australia: to improve our understanding of <span class="hlt">soil</span> <span class="hlt">moisture</span> remote sensing at large scales over heterogeneous areas. These field data have been used to test and refine retrieval algorithms for <span class="hlt">soil</span> <span class="hlt">moisture</span> satellite missions, and most recently with the launch of the European Space Agency's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Ocean Salinity (SMOS) mission, to provide validation measurements over a multi-pixel area. The campaigns to date have included a preparatory campaign in 2005, two National Airborne Field Experiments (NAFE), (2005 and 2006), two campaigns to the Simpson Desert (2008 and 2009), and one Australian Airborne Cal/val Experiment for SMOS (AACES), just concluded in the austral spring of 2010. The primary airborne sensor for each campaign has been the Polarimetric L-band Microwave Radiometer (PLMR), a 6-beam pushbroom imager that is small enough to be compatible with light aircraft, greatly facilitating the execution of the series of campaigns, and a key to their success. An</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/31968','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/31968"><span><span class="hlt">Moisture</span>-strength-constructability guidelines for subgrade foundation <span class="hlt">soils</span> found in Indiana.</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2016-09-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important indicator of constructability in the field. Construction activities become difficult when the <span class="hlt">soil</span> <span class="hlt">moisture</span> content is excessive, especially in fine-grained <span class="hlt">soils</span>. Change orders caused by excessive <span class="hlt">soil</span> <span class="hlt">moisture</span> during c...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H31C1516D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H31C1516D"><span>Stochastic Analysis and Probabilistic Downscaling of <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deshon, J. P.; Niemann, J. D.; Green, T. R.; Jones, A. S.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable for rainfall-runoff response estimation, ecological and biogeochemical flux estimation, and biodiversity characterization, each of which is useful for watershed condition assessment. These applications require not only accurate, fine-resolution <span class="hlt">soil-moisture</span> estimates but also confidence limits on those estimates and <span class="hlt">soil-moisture</span> patterns that exhibit realistic statistical properties (e.g., variance and spatial correlation structure). The Equilibrium <span class="hlt">Moisture</span> from Topography, Vegetation, and <span class="hlt">Soil</span> (EMT+VS) model downscales coarse-resolution (9-40 km) <span class="hlt">soil</span> <span class="hlt">moisture</span> from satellite remote sensing or land-surface models to produce fine-resolution (10-30 m) estimates. The model was designed to produce accurate deterministic <span class="hlt">soil-moisture</span> estimates at multiple points, but the resulting patterns do not reproduce the variance or spatial correlation of observed <span class="hlt">soil-moisture</span> patterns. The primary objective of this research is to generalize the EMT+VS model to produce a probability density function (pdf) for <span class="hlt">soil</span> <span class="hlt">moisture</span> at each fine-resolution location and time. Each pdf has a mean that is equal to the deterministic <span class="hlt">soil-moisture</span> estimate, and the pdf can be used to quantify the uncertainty in the <span class="hlt">soil-moisture</span> estimates and to simulate <span class="hlt">soil-moisture</span> patterns. Different versions of the generalized model are hypothesized based on how uncertainty enters the model, whether the uncertainty is additive or multiplicative, and which distributions describe the uncertainty. These versions are then tested by application to four catchments with detailed <span class="hlt">soil-moisture</span> observations (Tarrawarra, Satellite Station, Cache la Poudre, and Nerrigundah). The performance of the generalized models is evaluated by comparing the statistical properties of the simulated <span class="hlt">soil-moisture</span> patterns to those of the observations and the deterministic EMT+VS model. The versions of the generalized EMT+VS model with normally distributed stochastic components produce <span class="hlt">soil-moisture</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000117691','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000117691"><span>BOREAS HYD-1 Volumetric <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cuenca, Richard H.; Kelly, Shaun F.; Stangel, David E.; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Smith, David E. (Technical Monitor)</p> <p>2000-01-01</p> <p>The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-1 team made measurements of volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> at the Southern Study Area (SSA) and Northern Study Area (NSA) tower flux sites in 1994 and at selected tower flux sites in 1995-97. Different methods were used to collect these measurements, including neutron probe and manual and automated Time Domain Reflectometry (TDR). In 1994, the measurements were made every other day at the NSA-OJP (Old Jack Pine), NSA-YJP (Young Jack Pine), NSA-OBS (Old Black Spruce), NSA-Fen, SSA-OJP, SSA-YJP, SSA-Fen, SSA-YA (Young Aspen), and SSA-OBS sites. In 1995-97, when automated equipment was deployed at NSA-OJP, NSA-YJP, NSA-OBS, SSA-OBS, and SSA-OA (Old Aspen), the measurements were made as often as every hour. The data are stored in tabular ASCII files. The volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005PhDT.......214B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005PhDT.......214B"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> observations using L-, C-, and X-band microwave radiometers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bolten, John Dennis</p> <p></p> <p>The purpose of this thesis is to further the current understanding of <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals, and examine future satellite-based technologies for <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing. The cycling of Earth's water, energy and carbon is vital to understanding <span class="hlt">global</span> climate. Over land, these processes are largely dependent on the amount of <span class="hlt">moisture</span> within the top few centimeters of the <span class="hlt">soil</span>. However, there are currently no methods available that can accurately characterize Earth's <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> during the three field experiments is achieved through sensor calibration and algorithm validation. Comparisons of satellite observations and resampled aircraft observations are made using <span class="hlt">soil</span> <span class="hlt">moisture</span> from a Numerical Weather Prediction (NWP) model in order to further demonstrate a <span class="hlt">soil</span> <span class="hlt">moisture</span> correlation where point data was unavailable. The influence of vegetation, spatial</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H31G1467L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H31G1467L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> under Different Vegetation cover in response to Precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liang, Z.; Zhang, J.; Guo, B.; Ma, J.; Wu, Y.</p> <p>2016-12-01</p> <p>The response study of <span class="hlt">soil</span> <span class="hlt">moisture</span> to different precipitation and landcover is significant in the field of Hydropedology. The influence of precipitation to <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics in the root zone (10-50 cm) of those vegetation. The <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> under dry period were better. Precipitation was more in June, 2003, the <span class="hlt">soil</span> <span class="hlt">moisture</span> change of alluvial wetland forest in 10-30 cm <span class="hlt">soil</span> layer and Bahia grass in 10 cm <span class="hlt">soil</span> layer were consistent with the precipitation change conspicuously. The alluvial wetland forest <span class="hlt">soil</span> <span class="hlt">moisture</span> 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; <span class="hlt">soil</span> <span class="hlt">moisture</span>; HYDRUS-3D; precipitation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMIN21A1724P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMIN21A1724P"><span>A Citizen Science <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensor to Support SMAP Calibration/Validation</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.</p> <p>2016-12-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite mission was launched in Jan. 2015 and is currently acquiring <span class="hlt">global</span> measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the top 5 cm of the <span class="hlt">soil</span> every 3 days. SMAP 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-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 <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://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 <span class="hlt">global</span> 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('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> <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, <span class="hlt">globally</span> 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 <span class="hlt">global</span> 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('http://adsabs.harvard.edu/abs/2014SPIE.9299E..0JW','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014SPIE.9299E..0JW"><span>An integrated GIS application system for <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang</p> <p>2014-11-01</p> <p>The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive <span class="hlt">soil</span> <span class="hlt">moisture</span> data visually and mine the data for further research.This article introduces a comprehensive <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and <span class="hlt">soil</span> <span class="hlt">moisture</span> products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of <span class="hlt">soil</span> moiture will be plotted. By analyzing the <span class="hlt">soil</span> <span class="hlt">moisture</span> impact factors, it is possible to acquire the correlation coefficients between <span class="hlt">soil</span> <span class="hlt">moisture</span> value and its every single impact factor. Daily and monthly comparative analysis of <span class="hlt">soil</span> <span class="hlt">moisture</span> products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, <span class="hlt">soil</span> <span class="hlt">moisture</span> map production function is realized for business application.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JHyd..535..637Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..535..637Z"><span>Misrepresentation and amendment of <span class="hlt">soil</span> <span class="hlt">moisture</span> in conceptual hydrological modelling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhuo, Lu; Han, Dawei</p> <p>2016-04-01</p> <p>Although many conceptual models are very effective in simulating river runoff, their <span class="hlt">soil</span> <span class="hlt">moisture</span> schemes are generally not realistic in comparison with the reality (i.e., getting the right answers for the wrong reasons). This study reveals two significant misrepresentations in those models through a case study using the Xinanjiang model which is representative of many well-known conceptual hydrological models. The first is the setting of the upper limit of its <span class="hlt">soil</span> <span class="hlt">moisture</span> at the field capacity, due to the 'holding excess runoff' concept (i.e., runoff begins on repletion of its storage to the field capacity). The second is neglect of capillary rise of water movement. A new scheme is therefore proposed to overcome those two issues. The amended model is as effective as its original form in flow modelling, but represents more logically realistic <span class="hlt">soil</span> water processes. The purpose of the study is to enable the hydrological model to get the right answers for the right reasons. Therefore, the new model structure has a better capability in potentially assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> observations to enhance its real-time flood forecasting accuracy. The new scheme is evaluated in the Pontiac catchment of the USA through a comparison with satellite observed <span class="hlt">soil</span> <span class="hlt">moisture</span>. The correlation between the XAJ and the observed <span class="hlt">soil</span> <span class="hlt">moisture</span> is enhanced significantly from 0.64 to 0.70. In addition, a new <span class="hlt">soil</span> <span class="hlt">moisture</span> term called SMDS (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit to Saturation) is proposed to complement the conventional SMD (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19840014939','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19840014939"><span>A microwave systems approach to measuring root zone <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Newton, R. W.; Paris, J. F.; Clark, B. V.</p> <p>1983-01-01</p> <p>Computer microwave satellite simulation models were developed and the program was used to test the ability of a coarse resolution passive microwave sensor to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> over large areas, and to evaluate the effect of heterogeneous ground covers with the resolution cell on the accuracy of the <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate. The use of realistic scenes containing only 10% to 15% bare <span class="hlt">soil</span> and significant vegetation made it possible to observe a 60% K decrease in brightness temperature from a 5% <span class="hlt">soil</span> <span class="hlt">moisture</span> to a 35% <span class="hlt">soil</span> <span class="hlt">moisture</span> at a 21 cm microwave wavelength, providing a 1.5 K to 2 K per percent <span class="hlt">soil</span> <span class="hlt">moisture</span> sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span>. It was shown that resolution does not affect the basic ability to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> with a microwave radiometer system. Experimental microwave and ground field data were acquired for developing and testing a root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> prediction algorithm. The experimental measurements demonstrated that the depth of penetration at a 21 cm microwave wavelength is not greater than 5 cm.</p> </li> </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://rosap.ntl.bts.gov/view/dot/22075','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/22075"><span>Typical <span class="hlt">moisture</span>-density curves : part II : lime treated <span class="hlt">soils</span>.</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>1966-05-01</p> <p>The objective of the study covered by this report was to determine whether the family of curves developed for untreated <span class="hlt">soils</span>, could be used for determining the optimum <span class="hlt">moisture</span> and maximum density of lime treated <span class="hlt">soils</span>. This investigation was initia...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19790008160&hterms=soil+runoff&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dsoil%2Brunoff','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19790008160&hterms=soil+runoff&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dsoil%2Brunoff"><span>Remote sensing as a tool in assessing <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carlson, C. W.</p> <p>1978-01-01</p> <p>The effects of <span class="hlt">soil</span> <span class="hlt">moisture</span> as it relates to agriculture is briefly discussed. The use of remote sensing to predict scheduling of irrigation, runoff and <span class="hlt">soil</span> erosion which contributes to the prediction of crop yield is also discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/10464','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/10464"><span><span class="hlt">Soil</span> <span class="hlt">moisture-soil</span> temperature interrelationships on a sandy-loam <span class="hlt">soil</span> exposed to full sunlight</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>David A. Marquis</p> <p>1967-01-01</p> <p>In a study of birch regeneration in New Hampshire, <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature were found to be intimately related. Not only does low <span class="hlt">moisture</span> lead to high temperature, but high temperature undoubtedly accelerates <span class="hlt">soil</span> drying, setting up a vicious cycle of heating and drying that may prevent seed germination or kill seedlings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810004909','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810004909"><span>Joint microwave and infrared studies for <span class="hlt">soil</span> <span class="hlt">moisture</span> determination</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Njoku, E. G.; Schieldge, J. P.; Kahle, A. B. (Principal Investigator)</p> <p>1980-01-01</p> <p>The feasibility of using a combined microwave-thermal infrared system to determine <span class="hlt">soil</span> <span class="hlt">moisture</span> content is addressed. Of particular concern are bare <span class="hlt">soils</span>. The theoretical basis for microwave emission from <span class="hlt">soils</span> and the transport of heat and <span class="hlt">moisture</span> in <span class="hlt">soils</span> is presented. Also, a description is given of the results of two field experiments held during vernal months in the San Joaquin Valley of California.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913136S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913136S"><span>Assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> into an Earth System Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stacke, Tobias; Hagemann, Stefan</p> <p>2017-04-01</p> <p>Several modelling studies reported potential impacts of <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies on regional climate. In particular for short prediction periods, perturbations of the <span class="hlt">soil</span> <span class="hlt">moisture</span> state may result in significant alteration of surface temperature in the following season. However, it is not clear yet whether or not <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies affect climate also on larger temporal and spatial scales. In an earlier study, we showed that <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies can persist for several seasons in the deeper <span class="hlt">soil</span> layers of a land surface model. Additionally, those anomalies can influence root zone <span class="hlt">moisture</span>, in particular during explicitly dry or wet periods. Thus, one prerequisite for predictability, namely the existence of long term memory, is evident for simulated <span class="hlt">soil</span> <span class="hlt">moisture</span> and might be exploited to improve climate predictions. The second prerequisite is the sensitivity of the climate system to <span class="hlt">soil</span> <span class="hlt">moisture</span>. In order to investigate this sensitivity for decadal simulations, we implemented a <span class="hlt">soil</span> <span class="hlt">moisture</span> assimilation scheme into the Max-Planck Institute for Meteorology's Earth System Model (MPI-ESM). The assimilation scheme is based on a simple nudging algorithm and updates the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> state once per day. In our experiments, the MPI-ESM is used which includes model components for the interactive simulation of atmosphere, land and ocean. Artificial assimilation data is created from a control simulation to nudge the MPI-ESM towards predominantly dry and wet states. First analyses are focused on the impact of the assimilation on land surface variables and reveal distinct differences in the long-term mean values between wet and dry state simulations. Precipitation, evapotranspiration and runoff are larger in the wet state compared to the dry state, resulting in an increased <span class="hlt">moisture</span> transport from the land to atmosphere and ocean. Consequently, surface temperatures are lower in the wet state simulations by more than one Kelvin. In terms of spatial pattern</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050192452','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050192452"><span>Estimating Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Simulated AVIRIS Spectra</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Whiting, Michael L.; Li, Lin; Ustin, Susan L.</p> <p>2004-01-01</p> <p><span class="hlt">Soil</span> albedo is influenced by many physical and chemical constituents, with <span class="hlt">moisture</span> being the most influential on the spectra general shape and albedo (Stoner and Baumgardner, 1981). Without <span class="hlt">moisture</span>, the intrinsic or matrix reflectance of dissimilar <span class="hlt">soils</span> varies widely due to differences in surface roughness, particle and aggregate sizes, mineral types, including salts, and organic matter contents. The influence of <span class="hlt">moisture</span> on <span class="hlt">soil</span> reflectance can be isolated by comparing similar <span class="hlt">soils</span> in a study of the effects that small differences in <span class="hlt">moisture</span> content have on reflectance. However, without prior knowledge of the <span class="hlt">soil</span> physical and chemical constituents within every pixel, it is nearly impossible to accurately attribute the reflectance variability in an image to <span class="hlt">moisture</span> or to differences in the physical and chemical constituents in the <span class="hlt">soil</span>. The effect of <span class="hlt">moisture</span> on the spectra must be eliminated to use hyperspectral imagery for determining minerals and organic matter abundances of bare agricultural <span class="hlt">soils</span>. Accurate <span class="hlt">soil</span> mineral and organic matter abundance maps from air- and space-borne imagery can improve GIS models for precision farming prescription, and managing irrigation and salinity. Better models of <span class="hlt">soil</span> <span class="hlt">moisture</span> and reflectance will also improve the selection of <span class="hlt">soil</span> endmembers for spectral mixture analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H31A1391G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H31A1391G"><span>Evaluating Land-Atmosphere Interactions with the North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giles, S. M.; Quiring, S. M.; Ford, T.; Chavez, N.; Galvan, J.</p> <p>2015-12-01</p> <p>The North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database (NASMD) is a high-quality observational <span class="hlt">soil</span> <span class="hlt">moisture</span> database that was developed to study land-atmosphere interactions. It includes over 1,800 monitoring stations the United States, Canada and Mexico. <span class="hlt">Soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> observations have been quality controlled using the North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database QAQC algorithm. The database is designed to facilitate observationally-driven investigations of land-atmosphere interactions, validation of the accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations in <span class="hlt">global</span> land surface models, satellite calibration/validation for SMOS and SMAP, and an improved understanding of how <span class="hlt">soil</span> <span class="hlt">moisture</span> influences climate on seasonal to interannual timescales. This paper provides some examples of how the NASMD has been utilized to enhance understanding of land-atmosphere interactions in the U.S. Great Plains.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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 <span class="hlt">global</span>, 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://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 <span class="hlt">global</span> 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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4363572','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4363572"><span>Temporal Variations in <span class="hlt">Soil</span> <span class="hlt">Moisture</span> for Three Typical Vegetation Types in Inner Mongolia, Northern China</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zheng, Hao; Gao, Jixi; Teng, Yanguo; Feng, Chaoyang; Tian, Meirong</p> <p>2015-01-01</p> <p>Drought and shortages of <span class="hlt">soil</span> water are becoming extremely severe due to <span class="hlt">global</span> climate change. A better understanding of the relationship between vegetation type and <span class="hlt">soil-moisture</span> conditions is crucial for conserving <span class="hlt">soil</span> water in forests and for maintaining a favorable hydrological balance in semiarid areas, such as the Saihanwula National Nature Reserve in Inner Mongolia, China. We investigated the temporal dynamics of <span class="hlt">soil</span> <span class="hlt">moisture</span> in this reserve to a depth of 40 cm under three types of vegetation during a period of rainwater recharge. Rainwater from most rainfalls recharged the <span class="hlt">soil</span> water poorly below 40 cm, and the rainfall threshold for increasing the <span class="hlt">moisture</span> content of surface <span class="hlt">soil</span> for the three vegetations was in the order: artificial Larix spp. (AL) > Quercus mongolica (QM) > unused grassland (UG). QM had the highest mean <span class="hlt">soil</span> <span class="hlt">moisture</span> content (21.13%) during the monitoring period, followed by UG (16.52%) and AL (14.55%); and the lowest coefficient of variation (CV 9.6-12.5%), followed by UG (CV 10.9-18.7%) and AL (CV 13.9-21.0%). QM <span class="hlt">soil</span> had a higher nutrient content and higher <span class="hlt">soil</span> porosities, which were likely responsible for the higher ability of this cover to retain <span class="hlt">soil</span> water. The relatively smaller QM trees were able to maintain <span class="hlt">soil</span> <span class="hlt">moisture</span> better in the study area. PMID:25781333</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=344643','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=344643"><span>Spatially enhanced passive microwave derived <span class="hlt">soil</span> <span class="hlt">moisture</span>: capabilities and opportunities</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Low frequency passive microwave remote sensing is a proven technique for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval, but its coarse resolution restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution <span class="hlt">soil</span> <span class="hlt">moistur</span>...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=336401','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=336401"><span>Use of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors for irrigation scheduling</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Various types of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing devices have been developed and are commercially available for water management applications. Each type of <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors has its advantages and shortcomings in terms of accuracy, reliability, and cost. Resistive and capacitive based sensors, and time-d...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/8694','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/8694"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and groundwater recharge under a mixed conifer forest</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Robert R. Ziemer</p> <p>1978-01-01</p> <p>The depletion of <span class="hlt">soil</span> <span class="hlt">moisture</span> within the surface 7 m by a mixed conifer forest in the Sierra Nevada was measured by the neutron method every 2 weeks during 5 consecutive summers. <span class="hlt">Soil</span> <span class="hlt">moisture</span> recharge was measured periodically during the intervening winters. Groundwater fluctuations within the surface 17 m were continuously recorded during the same period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=336447','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=336447"><span>A review of the applications of ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wid...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170002508','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170002508"><span>Evaluating ESA CCI <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in East Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.</p> <p>2016-01-01</p> <p>To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from missions like the European Space Agency (ESA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) and NASAs <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R greater than 0.5) with modeled, seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span>, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8087G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8087G"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> retrival from Sentinel-1 and Modis synergy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Qi; Zribi, Mehrez; Escorihuela, Maria Jose; Baghdadi, Nicolas</p> <p>2017-04-01</p> <p>This study presents two methodologies retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span> from SAR remote sensing data. The study is based on Sentinel-1 data in the VV polarization, over a site in Urgell, Catalunya (Spain). In the two methodologies using change detection techniques, preprocessed radar data are combined with normalized difference vegetation index (NDVI) auxiliary data to estimate the mean <span class="hlt">soil</span> <span class="hlt">moisture</span> with a resolution of 1km. By modeling the relationship between the backscatter difference and NDVI, the <span class="hlt">soil</span> <span class="hlt">moisture</span> at a specific NDVI value is retrieved. The first algorithm is already developed on West Africa(Zribi et al., 2014) from ERS scatterometer data to estimate <span class="hlt">soil</span> water status. In this study, it is adapted to Sentinel-1 data and take into account the high repetitiveness of data in optimizing the inversion approach. Another new method is developed based on the backscatter difference between two adjacent days of Sentinel-1 data w.r.t. NDVI, with smaller vegetation change, the backscatter difference is more sensitive to <span class="hlt">soil</span> <span class="hlt">moisture</span>. The proposed methodologies have been validated with the ground measurement in two demonstrative fields with RMS error about 0.05 (in volumetric <span class="hlt">moisture</span>), and the coherence between <span class="hlt">soil</span> <span class="hlt">moisture</span> variations and rainfall events is observed. <span class="hlt">Soil</span> <span class="hlt">moisture</span> maps at 1km resolution are generated for the study area. The results demonstrate the potential of Sentinel-1 data for the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> at 1km or even better resolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29599664','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29599664"><span>Evaluating ESA CCI <span class="hlt">soil</span> <span class="hlt">moisture</span> in East Africa.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R; Wang, Shugong; Peters-Lidard, Christa D; Verdin, James P</p> <p>2016-06-01</p> <p>To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from missions like the European Space Agency (ESA) <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) and NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal <span class="hlt">soil</span> <span class="hlt">moisture</span>, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=338693','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=338693"><span>Combined radar-radiometer surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and roughness estimation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>A robust physics-based combined radar-radiometer, or Active-Passive, surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and roughness estimation methodology is presented. <span class="hlt">Soil</span> <span class="hlt">moisture</span> and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution rad...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=271413','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=271413"><span>Long term observation and validation of windsat <span class="hlt">soil</span> <span class="hlt">moisture</span> data</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The surface <span class="hlt">soil</span> <span class="hlt">moisture</span> controls surface energy budget. It is a key environmental variable in the coupled atmospheric and hydrological processes that are related to drought, heat waves and monsoon formation. Satellite remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> provides information that can contribute to unde...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA555920','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA555920"><span>Remote Sensing of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Using Airborne Hyperspectral Data</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2011-01-01</p> <p>the relationship between reflec- tance and <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span>. Alternatively, they could</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://hdl.handle.net/2060/20070030242','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070030242"><span>Muiti-Sensor Historical Climatology of Satellite-Derived <span class="hlt">Global</span> Land Surface <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>Owe, Manfred; deJeu, Richard; Holmes, Thomas</p> <p>2007-01-01</p> <p>A historical climatology of continuous satellite derived <span class="hlt">global</span> land surface <span class="hlt">soil</span> <span class="hlt">moisture</span> is being developed. The data set consists of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from observations of both historical and currently active satellite microwave sensors, including Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and AQUA AMSR-E. The data sets span the period from November 1978 through the end of 2006. The <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals are made with the Land Parameter Retrieval Model, a physically-based model which was developed jointly by researchers from the above institutions. These data are significant in that they are the longest continuous data record of observational surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at a <span class="hlt">global</span> scale. Furthermore, while previous reports have intimated that higher frequency sensors such as on SSM/I are unable to provide meaningful information on <span class="hlt">soil</span> <span class="hlt">moisture</span>, our results indicate that these sensors do provide highly useful <span class="hlt">soil</span> <span class="hlt">moisture</span> data over significant parts of the globe, and especially in critical areas located within the Earth's many arid and semi-arid regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810012915','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810012915"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> determination study. [Guymon, Oklahoma</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blanchard, B. J.</p> <p>1979-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> data collected in conjunction with aircraft sensor and SEASAT SAR data taken near Guymon, Oklahoma are summarized. In order to minimize the effects of vegetation and roughness three bare and uniformly smooth fields were sampled 6 times at three day intervals on the flight days from August 2 through 17. Two fields remained unirrigated and dry. A similar pair of fields was irrigated at different times during the sample period. In addition, eighteen other fields were sampled on the nonflight days with no field being sampled more than 24 hours from a flight time. The aircraft sensors used included either black and white or color infrared photography, L and C band passive microwave radiometers, the 13.3, 4.75, 1.6 and .4 GHz scatterometers, the 11 channel modular microwave scanner, and the PRT5.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19800010884','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19800010884"><span>A high resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> radiometer</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dod, L. R.</p> <p>1980-01-01</p> <p>The design of an L-band high resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> radiometer is described. The selected system is a planar slotted waveguide array at L-band frequencies. The square aperture is 74.75 m by 74.75 m subdivided into 8 tilted subarrays. The system has a 290 km circular orbit and provides a spatial resolution of 1 km. The aperture forms 230 simultaneous beams in a cross-track pattern which covers a swath 420 km wide. A revisit time of 6 days is provided for an orbit inclination of 50 deg. The 1 km resolution cell allows an integration time of 1/7 second and sharing this time period sequentially between two orthogonal polarization modes can provide a temperature resolution of 0.7 K.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1915197R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1915197R"><span>Assimilation of neural network <span class="hlt">soil</span> <span class="hlt">moisture</span> in land surface models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rodriguez-Fernandez, Nemesio; de Rosnay, Patricia; Albergel, Clement; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Richaume, Philippe; Muñoz-Sabater, Joaquin; Drusch, Matthias</p> <p>2017-04-01</p> <p>In this study a set of land surface data assimilation (DA) experiments making use of satellite derived <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) are presented. These experiments have two objectives: (1) to test the information content of satellite remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> for numerical weather prediction (NWP) models, and (2) to test a simplified assimilation of these data through the use of a Neural Network (NN) retrieval. Advanced Scatterometer (ASCAT) and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) data were used. The SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> dataset was obtained specifically for this project training a NN using SMOS brightness temperatures as input and using as reference for the training European Centre for Medium-Range Weather Forecasts (ECMWF) H-TESSEL SM fields. In this way, the SMOS NN SM dataset has a similar climatology to that of the model and it does not present a <span class="hlt">global</span> bias with respect to the model. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. This system is very computationally efficient and allows to perform long surface assimilation experiments (one whole year, 2012). SMOS NN SM DA experiments are compared to ASCAT SM DA experiments. In both cases, experiments with and without 2 m air temperature and relative humidity DA are discussed using different observation errors for the ASCAT and SMOS datasets. Seasonal, geographical and <span class="hlt">soil</span>-depth-related differences between the results of those experiments are presented and discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives in general similar results to the model open loop with no assimilation even if significant differences can be seen for specific sites with in situ measurements. The sensitivity to observation errors to the SM dataset slightly differs depending on the networks of in situ measurements, however it is relatively low for the tests</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMIN43B0078S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMIN43B0078S"><span>Drive by <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Measurement: A Citizen Science Project</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Senanayake, I. P.; Willgoose, G. R.; Yeo, I. Y.; Hancock, G. R.</p> <p>2017-12-01</p> <p>Two of the common attributes of <span class="hlt">soil</span> <span class="hlt">moisture</span> are that at any given time it varies quite markedly from point to point, and that there is a significant deterministic pattern that underlies this spatial variation and which is typically 50% of the spatial variability. The spatial variation makes it difficult to determine the time varying catchment average <span class="hlt">soil</span> <span class="hlt">moisture</span> using field measurements because any individual measurement is unlikely to be equal to the average for the catchment. The traditional solution to this is to make many measurements (e.g. with <span class="hlt">soil</span> <span class="hlt">moisture</span> probes) spread over the catchment, which is very costly and manpower intensive, particularly if we need a time series of <span class="hlt">soil</span> <span class="hlt">moisture</span> variation across a catchment. An alternative approach, explored in this poster is to use the deterministic spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> to calibrate one site (e.g. a permanent <span class="hlt">soil</span> <span class="hlt">moisture</span> probe at a weather station) to the spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> over the study area. The challenge is then to determine the spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span>. This poster will present results from a proof of concept project, where data was collected by a number of undergraduate engineering students, to estimate the spatial pattern. The approach was to drive along a series of roads in a catchment and collect <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements at the roadside using field portable <span class="hlt">soil</span> <span class="hlt">moisture</span> probes. This drive was repeated a number of times over the semester, and the time variation and spatial persistence of the <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern were examined. Provided that the students could return to exactly the same location on each collection day there was a strong persistent pattern in the <span class="hlt">soil</span> <span class="hlt">moisture</span>, even while the average <span class="hlt">soil</span> <span class="hlt">moisture</span> varied temporally as a result of preceding rainfall. The poster will present results and analysis of the student data, and compare these results with several field sites where we have spatially distributed permanently installed <span class="hlt">soil</span> <span class="hlt">moisture</span> probes. The</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/33766','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/33766"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> dynamics and smoldering combustion limits of pocosin <span class="hlt">soils</span> in North Carolina, USA</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>James Reardon; Gary Curcio; Roberta Bartlette</p> <p>2009-01-01</p> <p>Smoldering combustion of wetland organic <span class="hlt">soils</span> in the south-eastern USA is a serious management concern. Previous studies have reported smoldering was sensitive to a wide range of <span class="hlt">moisture</span> contents, but studies of <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics and changing smoldering combustion potential in wetland communities are limited. Linking <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements with estimates of...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000PhDT........38W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000PhDT........38W"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> profile variability in land-vegetation- atmosphere continuum</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Wanru</p> <p></p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> 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, <span class="hlt">soil</span> <span class="hlt">moisture</span> controls the response of the land surface to atmospheric forcing and determines the partitioning of precipitation into infiltration and runoff. Meanwhile, the <span class="hlt">soil</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> variability with <span class="hlt">soil</span> depth may be important in affecting the atmosphere. The natural variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> profile is demonstrated using observations. The 16-year field observational data of <span class="hlt">soil</span> <span class="hlt">moisture</span> with 11-layer (top 2.0 meters) measured <span class="hlt">soil</span> depths over Illinois are analyzed and used to identify and quantify the <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> profile variability characteristics and investigating the underlying mechanisms. Numerical</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H41C0899C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H41C0899C"><span>What is the philosophy of modelling <span class="hlt">soil</span> <span class="hlt">moisture</span> movement?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, J.; Wu, Y.</p> <p>2009-12-01</p> <p>In laboratory, the <span class="hlt">soil</span> <span class="hlt">moisture</span> movement in the different <span class="hlt">soil</span> textures has been analysed. From field investigation, at a spot, the <span class="hlt">soil</span> <span class="hlt">moisture</span> movement in the root zone, vadose zone and shallow aquifer has been explored. In addition, on ground slopes, the interflow in the near surface <span class="hlt">soil</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> movement, numerical models to represent this hydrologic process have been developed. However, generally, due to high heterogeneity and stratification of <span class="hlt">soil</span> in a basin, modelling <span class="hlt">soil</span> <span class="hlt">moisture</span> movement is rather challenging. Normally, some empirical equations or artificial manipulation are employed to adjust the <span class="hlt">soil</span> <span class="hlt">moisture</span> movement in various numerical models. In this study, we inspect the <span class="hlt">soil</span> <span class="hlt">moisture</span> movement equations used in a watershed model, SWAT (<span class="hlt">Soil</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> movement in SWAT. Basically, the results of the study reveal, to some extent, the philosophy of modelling <span class="hlt">soil</span> <span class="hlt">moisture</span> 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. <span class="hlt">Soil</span> and Water Assessment Tool Theoretical Documentation, Grassland, <span class="hlt">soil</span> and research service, Temple, TX.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011691','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011691"><span>NASA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active and Passive (SMAP) Mission</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kellogg, Kent; Njoku, Eni; Thurman, Sam; Edelstein, Wendy; Jai, Ben; Spencer, Mike; Chen, Gun-Shing; Entekhabi, Dara; O'Neill, Peggy; Piepmeier, Jeffrey; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20110011691'); toggleEditAbsImage('author_20110011691_show'); toggleEditAbsImage('author_20110011691_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20110011691_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20110011691_hide"></p> <p>2010-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active-Passive (SMAP) Mission is one of the first Earth observation satellites being formulated by NASA in response to the 2007 National Research Council s Decadal Survey. SMAP will make <span class="hlt">global</span> measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the Earth's land surface and its freeze-thaw state. These measurements will allow significantly improved estimates of water, energy and carbon transfers between the land and atmosphere. <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurements are also of great importance in assessing flooding and monitoring drought. Knowledge gained from SMAP observations can help mitigate these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw timing over the boreal latitudes will also reduce a major uncertainty in quantifying the <span class="hlt">global</span> carbon balance and help to resolve an apparent missing carbon sink over land. The SMAP mission concept will utilize an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna flying in a 680 km polar orbit with an 8-day exact ground track repeat aboard a 3-axis stabilized spacecraft to provide high-resolution and high-accuracy <span class="hlt">global</span> maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state every two to three days. In addition, the SMAP project will use these surface observations with advanced modeling and data assimilation to provide estimates of deeper root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span> and net ecosystem exchange of carbon. SMAP recently completed its Phase A Mission Concept Study Phase for NASA and transitioned into Phase B (Formulation and Detailed Design). A number of significant accomplishments occurred during this initial phase of mission development. The SMAP project held several open meetings to solicit community feedback on possible science algorithms, prepared preliminary draft Algorithm Theoretical Basis Documents (ATBDs) for each mission science product, and established a prototype algorithm testbed to enable testing and evaluation of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=302366','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=302366"><span>Pre-Launch phase 2 rehearsal of the calibration and validation of <span class="hlt">soil</span> <span class="hlt">moisture</span> active passive (SMAP) geophysical data products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>NASA’s <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission is scheduled for launch in early November 2014. The objective of the mission is <span class="hlt">global</span> mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> and landscape freeze/thaw state. SMAP utilizes L-band radar and radiometer measurements sharing a rotating 6-meter mesh reflector antenna...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=335159','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=335159"><span>The SMAP mission combined active-passive <span class="hlt">soil</span> <span class="hlt">moisture</span> product at 9 km and 3km spatial resolutions</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission with onboard L-band radiometer and radar was launched on January 31st, 2015. The spacecraft provided high-resolution (3 km and 9 km) <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates at regular intervals by combining radiometer and radar observations for ~2.5 months...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=349648','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=349648"><span>An initial assessment of SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> disaggregation scheme using TIR surface evaporation data over the continental United States</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission is dedicated toward <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> mapping. Typically, an L-band microwave radiometer has a spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of...</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> better results than the USLE. Specifically, the SM4E model has proven to be particularly effective at Masse, providing the best <span class="hlt">soil</span> loss estimations, especially when the modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> is used. In this case, the RSR index (ratio between the Root Mean Square Error and the Observed Standard deviation) is equal to 0.94. Instead, the SCRRM is able to better estimate the event runoff at Sparacia than at Masse, thus resulting in good performances of the USLE-derived models using the estimated runoff; however, even at Sparacia the SM4E with modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> gives the better <span class="hlt">soil</span> loss estimates, with RSR = 0.54. These results open an interesting scenario in the use of empirical models to determine <span class="hlt">soil</span> loss at a large scale, since <span class="hlt">soil</span> <span class="hlt">moisture</span> is a not only a simple in situ measurement, but only a widely available information on a <span class="hlt">global</span> scale from remote sensing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.9394U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.9394U"><span>The SWEX at the area of Eastern Poland: Comparison of <span class="hlt">soil</span> <span class="hlt">moisture</span> obtained from ground measurements and SMOS satellite data*</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Usowicz, J. B.; Marczewski, W.; Usowicz, B.; Lukowski, M. I.; Lipiec, J.; Slominski, J.</p> <p>2012-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span>, together with <span class="hlt">soil</span> and vegetation characteristics, plays an important role in exchange of water and energy between the land surface and the atmospheric boundary layer. Accurate knowledge of current and future spatial and temporal variation in <span class="hlt">soil</span> <span class="hlt">moisture</span> is not well known, nor easy to measure or predict. Knowledge of <span class="hlt">soil</span> <span class="hlt">moisture</span> in surface and root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> is critical for achieving sustainable land and water management. The importance of SM is so high that this ECV is recommended by GCOS (<span class="hlt">Global</span> Climate Observing System) to any attempts of evaluating of effects the climate change, and therefore it is one of the goals for observing the Earth by the ESA SMOS Mission (<span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity), <span class="hlt">globally</span>. SMOS provides its observations by means of the interferometric radiometry method (1.4 GHz) from the orbit. In parallel, ten ground based stations are kept by IA PAN, in area of the Eastern Wall in Poland, in order to validate SMOS data and for other ground based agrophysical purposes. <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurements obtained from ground and satellite measurements from SMOS were compared using Bland-Altman method of agreement, concordance correlation coefficient (CCC) and total deviation index (TDI). Observed similar changes in <span class="hlt">soil</span> <span class="hlt">moisture</span>, but the values obtained from satellite measurements were lower. Minor differences between the compared data are at higher <span class="hlt">moisture</span> contents of <span class="hlt">soil</span> and they grow with decreasing <span class="hlt">soil</span> <span class="hlt">moisture</span>. <span class="hlt">Soil</span> <span class="hlt">moisture</span> trends are maintained in the individual stations. Such distributions of <span class="hlt">soil</span> <span class="hlt">moisture</span> were mainly related to <span class="hlt">soil</span> type. * The work was financially supported in part by the ESA Programme for European Cooperating States (PECS), No.98084 "SWEX-R, <span class="hlt">Soil</span> Water and Energy Exchange/Research", AO3275.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=272272','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=272272"><span>Upscaling sparse ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> observations for the validation of satellite surface <span class="hlt">soil</span> <span class="hlt">moisture</span> products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The contrast between the point-scale nature of current ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> instrumentation and the footprint resolution (typically >100 square kilometers) of satellites used to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> poses a significant challenge for the validation of data products from satellite missions suc...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=228413','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=228413"><span>Evaluation of Ku-Band Sensitivity To <span class="hlt">Soil</span> <span class="hlt">Moisture</span>: <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Change Detection Over the NAFE06 Study 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>A very promising technique for spatial disaggregation of <span class="hlt">soil</span> <span class="hlt">moisture</span> is on the combination of radiometer and radar observations. Despite their demonstrated potential for long term large scale monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span>, passive and active have their disadvantages in terms of temporal and spatial ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70036019','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70036019"><span>Effects of climate change on <span class="hlt">soil</span> <span class="hlt">moisture</span> over China from 1960-2006</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Zhu, Q.; Jiang, H.; Liu, J.</p> <p>2009-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is an important variable in the climate system and it has sensitive impact on the <span class="hlt">global</span> climate. Obviously it is one of essential components in the climate change study. The Integrated Biosphere Simulator (IBIS) is used to evaluate the spatial and temporal patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> across China under the climate change conditions for the period 1960-2006. Results show that the model performed better in warm season than in cold season. Mean errors (ME) are within 10% for all the months and root mean squared errors (RMSE) are within 10% except winter season. The model captured the spatial variability higher than 50% in warm seasons. Trend analysis based on the Mann-Kendall method indicated that <span class="hlt">soil</span> <span class="hlt">moisture</span> in most area of China is decreased especially in the northern China. The areas with significant increasing trends in <span class="hlt">soil</span> <span class="hlt">moisture</span> mainly locate at northwestern China and small areas in southeastern China and eastern Tibet plateau. ?? 2009 IEEE.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESASP.740E.142C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESASP.740E.142C"><span>Validation/Calibration of SMOS L2 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Crop Area, Eastern China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cui, Huizhen; Jiang, Lingmei; Yang, Na; Lu, Zheng</p> <p>2016-08-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) is the worldwide satellite dedicated to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> information at the <span class="hlt">global</span> scale, with a high temporal resolution, and from space borne L-band 2-D interferometry radiometer. Product validation for the accuracy of data and utilization is a crucial step. At present, the validation work carried out in China was mainly concentrated in the Tibetan Plateau. The study of this paper mainly focused on the validation of SMOS L2 <span class="hlt">soil</span> <span class="hlt">moisture</span> data products in the north of Henan province plain region. This region is in front of Taihang Mountains. Results from the average-average and node- site comparison show that the correlation coefficients (R) between 0.20 and 0.40, also the existence of dry bias mainly concentrated in the 0.07 0.11m3m-3. Finally, this paper analysed the influence factors on the quality of SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> products.</p> </li> <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 <span class="hlt">global</span> scale has still to be carried out. In this study, we carried out a crash test for SM2RAIN algorithm on a <span class="hlt">global</span> 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('https://ntrs.nasa.gov/search.jsp?R=19910031252&hterms=soil+maps&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dsoil%2Bmaps','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910031252&hterms=soil+maps&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dsoil%2Bmaps"><span>Remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> input to a hydrologic model</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Engman, E. T.; Kustas, W. P.; Wang, J. R.</p> <p>1989-01-01</p> <p>The possibility of using detailed spatial <span class="hlt">soil</span> <span class="hlt">moisture</span> maps as input to a runoff model was investigated. The water balance of a small drainage basin was simulated using a simple storage model. Aircraft microwave measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> were used to construct two-dimensional maps of the spatial distribution of the <span class="hlt">soil</span> <span class="hlt">moisture</span>. Data from overflights on different dates provided the temporal changes resulting from <span class="hlt">soil</span> drainage and evapotranspiration. The study site and data collection are described, and the <span class="hlt">soil</span> measurement data are given. The model selection is discussed, and the simulation results are summarized. It is concluded that a time series of <span class="hlt">soil</span> <span class="hlt">moisture</span> is a valuable new type of data for verifying model performance and for updating and correcting simulated streamflow.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14..175C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14..175C"><span>A simple nudging scheme to assimilate ASCAT <span class="hlt">soil</span> <span class="hlt">moisture</span> data in the WRF model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Capecchi, V.; Gozzini, B.</p> <p>2012-04-01</p> <p>The present work shows results obtained in a numerical experiment using the WRF (Weather and Research Forecasting, www.wrf-model.org) model. A control run where <span class="hlt">soil</span> <span class="hlt">moisture</span> is constrained by GFS <span class="hlt">global</span> analysis is compared with a test run where <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis is obtained via a simple nudging scheme using ASCAT data. The basic idea of the assimilation scheme is to "nudge" the first level (0-10 cm below ground in NOAH model) of volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> of the first-guess (say θ(b,1) derived from <span class="hlt">global</span> model) towards the ASCAT derived value (say ^θ A). The <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis θ(a,1) is given by: { θ + K (^θA - θ ) l = 1 θ(a,1) = θ(b,l) (b,l) l > 1 (b,l) (1) where l is the model <span class="hlt">soil</span> level. K is a constant scalar value that is user specified and in this study it is equal to 0.2 (same value as in similar studies). <span class="hlt">Soil</span> <span class="hlt">moisture</span> is critical for estimating latent and sensible heat fluxes as well as boundary layer structure. This parameter is, however, poorly assimilated in current <span class="hlt">global</span> and regional numerical models since no extensive <span class="hlt">soil</span> <span class="hlt">moisture</span> observation network exists. Remote sensing technologies offer a synoptic view of the dynamics and spatial distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> with a frequent temporal coverage and with a horizontal resolution similar to mesoscale NWP model. Several studies have shown that measurements of normalized backscatter (surface <span class="hlt">soil</span> wetness) from the Advanced Scatterometer (ASCAT) operating at microwave frequencies and boarded on the meteorological operational (Metop) satellite, offer quality information about surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. Recently several studies deal with the implementation of simple assimilation procedures (nudging, Extended Kalman Filter, etc...) to integrate ASCAT data in NWP models. They found improvements in screen temperature predictions, particularly in areas such as North-America and in the Tropics, where it is strong the land-atmosphere coupling. The ECMWF (Newsletter No. 127) is currently</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..547...10L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..547...10L"><span>Evaluation of different approaches for identifying optimal sites to predict mean hillslope <span class="hlt">soil</span> <span class="hlt">moisture</span> content</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liao, Kaihua; Zhou, Zhiwen; Lai, Xiaoming; Zhu, Qing; Feng, Huihui</p> <p>2017-04-01</p> <p>The identification of representative <span class="hlt">soil</span> <span class="hlt">moisture</span> sampling sites is important for the validation of remotely sensed mean <span class="hlt">soil</span> <span class="hlt">moisture</span> in a certain area and ground-based <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements in catchment or hillslope hydrological studies. Numerous approaches have been developed to identify optimal sites for predicting mean <span class="hlt">soil</span> <span class="hlt">moisture</span>. Each method has certain advantages and disadvantages, but they have rarely been evaluated and compared. In our study, surface (0-20 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> data from January 2013 to March 2016 (a total of 43 sampling days) were collected at 77 sampling sites on a mixed land-use (tea and bamboo) hillslope in the hilly area of Taihu Lake Basin, China. A total of 10 methods (temporal stability (TS) analyses based on 2 indices, K-means clustering based on 6 kinds of inputs and 2 random sampling strategies) were evaluated for determining optimal sampling sites for mean <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. They were TS analyses based on the smallest index of temporal stability (ITS, a combination of the mean relative difference and standard deviation of relative difference (SDRD)) and based on the smallest SDRD, K-means clustering based on <span class="hlt">soil</span> properties and terrain indices (EFs), repeated <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements (Theta), EFs plus one-time <span class="hlt">soil</span> <span class="hlt">moisture</span> data (EFsTheta), and the principal components derived from EFs (EFs-PCA), Theta (Theta-PCA), and EFsTheta (EFsTheta-PCA), and <span class="hlt">global</span> and stratified random sampling strategies. Results showed that the TS based on the smallest ITS was better (RMSE = 0.023 m3 m-3) than that based on the smallest SDRD (RMSE = 0.034 m3 m-3). The K-means clustering based on EFsTheta (-PCA) was better (RMSE <0.020 m3 m-3) than these based on EFs (-PCA) and Theta (-PCA). The sampling design stratified by the land use was more efficient than the <span class="hlt">global</span> random method. Forty and 60 sampling sites are needed for stratified sampling and <span class="hlt">global</span> sampling respectively to make their performances comparable to the best K</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830006286','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830006286"><span>Investigation of remote sensing techniques of measuring <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Newton, R. W. (Principal Investigator); Blanchard, A. J.; Nieber, J. L.; Lascano, R.; Tsang, L.; Vanbavel, C. H. M.</p> <p>1981-01-01</p> <p>Major activities described include development and evaluation of theoretical models that describe both active and passive microwave sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>, the evaluation of these models for their applicability, the execution of a controlled field experiment during which passive microwave measurements were acquired to validate these models, and evaluation of previously acquired aircraft microwave measurements. The development of a root zone <span class="hlt">soil</span> water and <span class="hlt">soil</span> temperature profile model and the calibration and evaluation of gamma ray attenuation probes for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> profiles are considered. The analysis of spatial variability of <span class="hlt">soil</span> information as related to remote sensing is discussed as well as the implementation of an instrumented field site for acquisition of <span class="hlt">soil</span> <span class="hlt">moisture</span> and meteorologic information for use in validating the <span class="hlt">soil</span> water profile and <span class="hlt">soil</span> temperature profile models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=252439','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=252439"><span>Microwave <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval Under Trees Using a Modified Tau-Omega Model</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>IPAD is to provide timely and accurate estimates of <span class="hlt">global</span> crop conditions for use in up-to-date commodity intelligence reports. A crucial requirement of these <span class="hlt">global</span> crop yield forecasts is the regional characterization of surface and sub-surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. However, due to the spatial heterogen...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=232049','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=232049"><span>Continental-Scale Evaluation of Assimilated <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrievals From the Advanced Microwave Scanning Radiometer</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a fundamental data source used in crop growth stage and crop stress models developed by the USDA Foreign Agriculture Service for <span class="hlt">global</span> crop estimation. USDA’s International Production Assessment Division (IPAD) of the Office of <span class="hlt">Global</span> Analysis (OGA). Currently, the PECAD DSS utiliz...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1917967C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1917967C"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring using Surface Electrical Resistivity measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Calamita, Giuseppe; Perrone, Angela; Brocca, Luca; Straface, Salvatore</p> <p>2017-04-01</p> <p>The relevant role played by the <span class="hlt">soil</span> <span class="hlt">moisture</span> (SM) for <span class="hlt">global</span> and local natural processes results in an explicit interest for its spatial and temporal estimation in the vadose zone coming from different scientific areas - i.e. eco-hydrology, hydrogeology, atmospheric research, <span class="hlt">soil</span> and plant sciences, etc... A deeper understanding of natural processes requires the collection of data on a higher number of points at increasingly higher spatial scales in order to validate hydrological numerical simulations. In order to take the best advantage of the Electrical Resistivity (ER) data with their non-invasive and cost-effective properties, sequential Gaussian geostatistical simulations (sGs) can be applied to monitor the SM distribution into the <span class="hlt">soil</span> by means of a few SM measurements and a densely regular ER grid of monitoring. With this aim, co-located SM measurements using mobile TDR probes (MiniTrase), and ER measurements, obtained by using a four-electrode device coupled with a geo-resistivimeter (Syscal Junior), were collected during two surveys carried out on a 200 × 60 m2 area. Two time surveys were carried out during which Data were collected at a depth of around 20 cm for more than 800 points adopting a regular grid sampling scheme with steps (5 m) varying according to logistic and <span class="hlt">soil</span> compaction constrains. The results of this study are robust due to the high number of measurements available for either variables which strengthen the confidence in the covariance function estimated. Moreover, the findings obtained using sGs show that it is possible to estimate <span class="hlt">soil</span> <span class="hlt">moisture</span> variations in the pedological zone by means of time-lapse electrical resistivity and a few SM measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19780009499','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19780009499"><span>Microwave remote sensing and its application to <span class="hlt">soil</span> <span class="hlt">moisture</span> detection</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Newton, R. W. (Principal Investigator)</p> <p>1977-01-01</p> <p>The author has identified the following significant results. Experimental measurements were utilized to demonstrate a procedure for estimating <span class="hlt">soil</span> <span class="hlt">moisture</span>, using a passive microwave sensor. The investigation showed that 1.4 GHz and 10.6 GHz can be used to estimate the average <span class="hlt">soil</span> <span class="hlt">moisture</span> within two depths; however, it appeared that a frequency less than 10.6 GHz would be preferable for the surface measurement. Average <span class="hlt">soil</span> <span class="hlt">moisture</span> within two depths would provide information on the slope of the <span class="hlt">soil</span> <span class="hlt">moisture</span> gradient near the surface. Measurements showed that a uniform surface roughness similar to flat tilled fields reduced the sensitivity of the microwave emission to <span class="hlt">soil</span> <span class="hlt">moisture</span> changes. Assuming that the surface roughness was known, the approximate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation accuracy at 1.4 GHz calculated for a 25% average <span class="hlt">soil</span> <span class="hlt">moisture</span> and an 80% degree of confidence, was +3% and -6% for a smooth bare surface, +4% and -5% for a medium rough surface, and +5.5% and -6% for a rough surface.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H43G1553S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H43G1553S"><span>Retrieving pace in vegetation growth using precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohoulande Djebou, D. C.; Singh, V. P.</p> <p>2013-12-01</p> <p>The complexity of interactions between the biophysical components of the watershed increases the challenge of understanding water budget. Hence, the perspicacity of the continuum <span class="hlt">soil</span>-vegetation-atmosphere's functionality still remains crucial for science. This study targeted the Texas Gulf watershed and evaluated the behavior of vegetation covers by coupling precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns. Growing season's Normalized Differential Vegetation Index NDVI for deciduous forest and grassland were used over a 23 year period as well as precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> data. The role of time scales on vegetation dynamics analysis was appraised using both entropy rescaling and correlation analysis. This resulted in that <span class="hlt">soil</span> <span class="hlt">moisture</span> at 5 cm and 25cm are potentially more efficient to use for vegetation dynamics monitoring at finer time scale compared to precipitation. Albeit <span class="hlt">soil</span> <span class="hlt">moisture</span> at 5 cm and 25 cm series are highly correlated (R2>0.64), it appeared that 5 cm <span class="hlt">soil</span> <span class="hlt">moisture</span> series can better explain the variability of vegetation growth. A logarithmic transformation of <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation data increased correlation with NDVI for the different time scales considered. Based on a monthly time scale we came out with a relationship between vegetation index and the couple <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation [NDVI=a*Log(% <span class="hlt">soil</span> <span class="hlt">moisture</span>)+b*Log(Precipitation)+c] with R2>0.25 for each vegetation type. Further, we proposed to assess vegetation green-up using logistic regression model and transinformation entropy using the couple <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation as independent variables and vegetation growth metrics (NDVI, NDVI ratio, NDVI slope) as the dependent variable. The study is still ongoing and the results will surely contribute to the knowledge in large scale vegetation monitoring. Keywords: Precipitation, <span class="hlt">soil</span> <span class="hlt">moisture</span>, vegetation growth, entropy Time scale, Logarithmic transformation and correlation between <span class="hlt">soil</span> <span class="hlt">moisture</span> and NDVI, precipitation and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GeoRL..42.6662K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GeoRL..42.6662K"><span>A framework for combining multiple <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals based on maximizing temporal correlation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Seokhyeon; Parinussa, Robert M.; Liu, Yi. Y.; Johnson, Fiona M.; Sharma, Ashish</p> <p>2015-08-01</p> <p>A method for combining two microwave satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> products by maximizing the temporal correlation with a reference data set has been developed. The method was applied to two <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets, Japan Aerospace Exploration Agency (JAXA) and Land Parameter Retrieval Model (LPRM), retrieved from the Advanced Microwave Scanning Radiometer 2 observations for the period 2012-2014. A <span class="hlt">global</span> comparison revealed superior results of the combined product compared to the individual products against the reference data set of ERA-Interim volumetric water content. The <span class="hlt">global</span> mean temporal correlation coefficient of the combined product with this reference was 0.52 which outperforms the individual JAXA (0.35) as well as the LPRM (0.45) product. Additionally, the performance was evaluated against in situ observations from the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network. The combined data set showed a significant improvement in temporal correlation coefficients in the validation compared to JAXA and minor improvements for the LPRM product.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.6111M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.6111M"><span>A Round Robin evaluation of AMSR-E <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>Mittelbach, Heidi; Hirschi, Martin; Nicolai-Shaw, Nadine; Gruber, Alexander; Dorigo, Wouter; de Jeu, Richard; Parinussa, Robert; Jones, Lucas A.; Wagner, Wolfgang; Seneviratne, Sonia I.</p> <p>2014-05-01</p> <p>Large-scale and long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> develops and evaluates a consistent <span class="hlt">global</span> long-term <span class="hlt">soil</span> <span class="hlt">moisture</span> data set, which is based on merging passive and active remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span>. 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> are considered and evaluated separately in daily and monthly resolution over the 2007-2011 time period. Absolute values of <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> of 75 quality controlled <span class="hlt">soil</span> <span class="hlt">moisture</span> sites from the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network (ISMN) are used, which cover a wide range of vegetation density and climate conditions. For the application of the triple collocation method, surface <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates from the <span class="hlt">Global</span> Land Data Assimilation System are used as third independent data set. We find that the participating algorithms generally display a better</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110013309','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110013309"><span>SMOS/SMAP Synergy for SMAP Level 2 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Algorithm Evaluation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann</p> <p>2011-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) satellite has been proposed to provide <span class="hlt">global</span> measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> products. In this investigation we will be using SMOS brightness temperature, ancillary data, and <span class="hlt">soil</span> <span class="hlt">moisture</span> products to develop and evaluate a candidate SMAP L2 passive <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">global</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and <span class="hlt">soil</span> texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of <span class="hlt">soil</span> temperature for the top layer (as provided as part of the SMOS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20180002215','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20180002215"><span>Using Data Assimilation Diagnostics to Assess 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; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe</p> <p>2018-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission Level-4 <span class="hlt">Soil</span> <span class="hlt">Moisture</span> (L4_SM) product provides 3-hourly, 9-km resolution, <span class="hlt">global</span> estimates of surface (0-5 cm) and root-zone (0-100 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with <span class="hlt">global</span> mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the <span class="hlt">soil</span> <span class="hlt">moisture</span> increments (under approx. 0.01 cu m/cu m), and the surface <span class="hlt">soil</span> 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) <span class="hlt">soil</span> <span class="hlt">moisture</span> increments, and approx. 0.6 K for surface <span class="hlt">soil</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26748720','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26748720"><span>Historical precipitation predictably alters the shape and magnitude of microbial functional response to <span class="hlt">soil</span> <span class="hlt">moisture</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Averill, Colin; Waring, Bonnie G; Hawkes, Christine V</p> <p>2016-05-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> constrains the activity of decomposer <span class="hlt">soil</span> microorganisms, and in turn the rate at which <span class="hlt">soil</span> carbon returns to the atmosphere. While increases in <span class="hlt">soil</span> <span class="hlt">moisture</span> are generally associated with increased microbial activity, historical climate may constrain current microbial responses to <span class="hlt">moisture</span>. However, it is not known if variation in the shape and magnitude of microbial functional responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> can be predicted from historical climate at regional scales. To address this problem, we measured <span class="hlt">soil</span> enzyme activity at 12 sites across a broad climate gradient spanning 442-887 mm mean annual precipitation. Measurements were made eight times over 21 months to maximize sampling during different <span class="hlt">moisture</span> conditions. We then fit saturating functions of enzyme activity to <span class="hlt">soil</span> <span class="hlt">moisture</span> and extracted half saturation and maximum activity parameter values from model fits. We found that 50% of the variation in maximum activity parameters across sites could be predicted by 30-year mean annual precipitation, an indicator of historical climate, and that the effect is independent of variation in temperature, <span class="hlt">soil</span> texture, or <span class="hlt">soil</span> carbon concentration. Based on this finding, we suggest that variation in the shape and magnitude of <span class="hlt">soil</span> microbial response to <span class="hlt">soil</span> <span class="hlt">moisture</span> due to historical climate may be remarkably predictable at regional scales, and this approach may extend to other systems. If historical contingencies on microbial activities prove to be persistent in the face of environmental change, this approach also provides a framework for incorporating historical climate effects into biogeochemical models simulating future <span class="hlt">global</span> change scenarios. © 2016 John Wiley & Sons Ltd.</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 <span class="hlt">global</span> <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/2015E%26ES...25a2001M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015E%26ES...25a2001M"><span>The <span class="hlt">Global</span> <span class="hlt">Soil</span> Partnership</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Montanarella, Luca</p> <p>2015-07-01</p> <p>The <span class="hlt">Global</span> <span class="hlt">Soil</span> Partnership (GSP) has been established, following an intensive preparatory work of the Food and Agriculture Organization of the United Nations (FAO) in collaboration with the European Commission (EC), as a voluntary partnership coordinated by the FAO in September 2011 [1]. The GSP is open to all interested stakeholders: Governments (FAO Member States), Universities, Research Organizations, Civil Society Organizations, Industry and private companies. It is a voluntary partnership aiming towards providing a platform for active engagement in sustainable <span class="hlt">soil</span> management and <span class="hlt">soil</span> protection at all scales: local, national, regional and <span class="hlt">global</span>. As a “coalition of the willing” towards <span class="hlt">soil</span> protection, it attempts to make progress in reversing <span class="hlt">soil</span> degradation with those partners that have a genuine will of protecting <span class="hlt">soils</span> for our future generations. It openly aims towards creating an enabling environment, despite the resistance of a minority of national governments, for effective <span class="hlt">soil</span> protection in the large majority of the countries that are genuinely concerned about the rapid depletion of their limited <span class="hlt">soil</span> resources.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160003392','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160003392"><span>Technical Report Series on <span class="hlt">Global</span> Modeling and Data Assimilation. Volume 40; <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Project Assessment Report for the Beta-Release L4_SM Data Product</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.; Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Liu, Qing; Colliander, Andreas; Conaty, Austin; Jackson, Thomas; Kimball, John</p> <p>2015-01-01</p> <p>During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Passive (L4_SM) product specifically for the product's public beta release scheduled for 30 October 2015. The primary objective of the beta release is to allow users to familiarize themselves with the data product before the validated product becomes available. The beta release also allows users to conduct their own assessment of the data and to provide feedback to the L4_SM science data product team. The assessment of the L4_SM data product includes comparisons of SMAP L4_SM <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates with in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations from core validation sites and sparse networks. The assessment further includes a <span class="hlt">global</span> evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 1 validation and supports the beta release of the data. The validation against</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730017711','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730017711"><span>Remote monitoring of <span class="hlt">soil</span> <span class="hlt">moisture</span> using airborne microwave radiometers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kroll, C. L.</p> <p>1973-01-01</p> <p>The current status of microwave radiometry is provided. The fundamentals of the microwave radiometer are reviewed with particular reference to airborne operations, and the interpretative procedures normally used for the modeling of the apparent temperature are presented. Airborne microwave radiometer measurements were made over selected flight lines in Chickasha, Oklahoma and Weslaco, Texas. Extensive ground measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> were made in support of the aircraft mission over the two locations. In addition, laboratory determination of the complex permittivities of <span class="hlt">soil</span> samples taken from the flight lines were made with varying <span class="hlt">moisture</span> contents. The data were analyzed to determine the degree of correlation between measured apparent temperatures and <span class="hlt">soil</span> <span class="hlt">moisture</span> content.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H33I..05B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.H33I..05B"><span>Evaluation of a <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data Assimilation System Over the Conterminous United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bolten, J. D.; Crow, W. T.; Zhan, X.; Reynolds, C. A.; Jackson, T. J.</p> <p>2008-12-01</p> <p>A data assimilation system has been designed to integrate surface <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) with an online <span class="hlt">soil</span> <span class="hlt">moisture</span> model used by the USDA Foreign Agriculture Service for <span class="hlt">global</span> crop estimation. USDA's International Production Assessment Division (IPAD) of the Office of <span class="hlt">Global</span> Analysis (OGA) ingests <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> within a Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS) to provide nowcasts of crop conditions and agricultural-drought. This information is primarily used to derive mid-season crop yield estimates for the improvement of foreign market access for U.S. agricultural products. The CADRE is forced by daily meteorological observations (precipitation and temperature) provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO). The integration of AMSR-E observations into the two-layer <span class="hlt">soil</span> <span class="hlt">moisture</span> model employed by IPAD can potentially enhance the reliability of the CADRE <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates due to AMSR-E's improved repeat time and greater spatial coverage. Assimilation of the AMSR-E <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates is accomplished using a 1-D Ensemble Kalman filter (EnKF) at daily time steps. A diagnostic calibration of the filter is performed using innovation statistics by accurately weighting the filter observation and modeling errors for three ranges of vegetation biomass density estimated using historical data from the Advanced Very High Resolution Radiometer (AVHRR). Assessment of the AMSR-E assimilation has been completed for a five year duration over the conterminous United States. To evaluate the ability of the filter to compensate for incorrect precipitation forcing into the model, a data denial approach is employed by comparing <span class="hlt">soil</span> <span class="hlt">moisture</span> results obtained from separate model simulations forced with precipitation products of varying uncertainty. An analysis of surface and root-zone anomalies is presented for each</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA254702','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA254702"><span>Vegetation Response to Rainfall and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Variability in Botswana</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1991-01-01</p> <p>Effects of Varying <span class="hlt">Soil</span> Type on the NDVI /Rainfall and NDVI /<span class="hlt">Soil</span> <span class="hlt">Moisture</span>...examine the effects of different <span class="hlt">soil</span> types on the vegetation growth/rainfall relationship. The goals are to determine whether differences in the water-use...34first step" in removing the <span class="hlt">soil</span> effect (Huete et al., 1985). Indeed, no large-scale <span class="hlt">soil</span> corrections have been attempted as yet on NDVI data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20120010398&hterms=Moran&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D10%26Ntt%3DMoran','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20120010398&hterms=Moran&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D10%26Ntt%3DMoran"><span>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Applications Activity</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Brown, Molly E.; Moran, Susan; Escobar, Vanessa; Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni</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 satellite missions recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space. The SMAP mission 1 is under development by NASA and is scheduled for launch late in 2014. The SMAP measurements will allow <span class="hlt">global</span> and high-resolution mapping of <span class="hlt">soil</span> <span class="hlt">moisture</span> and its freeze/thaw state at resolutions from 3-40 km. These measurements will have high value for a wide range of environmental applications that underpin many weather-related decisions including drought and flood guidance, agricultural productivity estimation, weather forecasting, climate predictions, and human health risk. In 2007, NASA was tasked by The National Academies to ensure that emerging scientific knowledge is actively applied to obtain societal benefits by broadening community participation and improving means for use of information. SMAP is one of the first missions to come out of this new charge, and its Applications Plan forms the basis for ensuring its commitment to its users. The purpose of this paper is to outline the methods and approaches of the SMAP applications activity, which is designed to increase and sustain the interaction between users and scientists involved in mission development.</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('https://www.fs.usda.gov/treesearch/pubs/43095','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/43095"><span>Core vs. Bulk Samples in <span class="hlt">Soil-Moisture</span> Tension Analyses</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Walter M. Broadfoot</p> <p>1954-01-01</p> <p>The usual laboratory procedure in determining <span class="hlt">soil-moisture</span> tension values is to use "undisturbed" <span class="hlt">soil</span> cores for tensions up to 60 cm. of water and bulk <span class="hlt">soil</span> samples for higher tensions. Low tensions are usually obtained with a tension table and the higher tensions by use of pressure plate apparatus. In tension analysis at the Vicksburg Infiltration Project...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://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 <span class="hlt">global</span> maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> content and surface freeze/thaw state. <span class="hlt">Global</span> 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 <span class="hlt">global</span> 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/2060/20120013516','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 <span class="hlt">global</span> 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://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 <span class="hlt">global</span>-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/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 <span class="hlt">global</span> 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://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 <span class="hlt">global</span> 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('http://adsabs.harvard.edu/abs/2018GeoRL..45.4869C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.4869C"><span>Exploiting <span class="hlt">Soil</span> <span class="hlt">Moisture</span>, Precipitation, and Streamflow Observations to Evaluate <span class="hlt">Soil</span> <span class="hlt">Moisture</span>/Runoff Coupling in Land Surface Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crow, W. T.; Chen, F.; Reichle, R. H.; Xia, Y.; Liu, Q.</p> <p>2018-05-01</p> <p>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 <span class="hlt">soil</span> <span class="hlt">moisture</span>, which determine <span class="hlt">soil</span> infiltration capacity and unsaturated storage. Utilizing the National Aeronautics and Space Administration <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Level-4 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H53D1489C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H53D1489C"><span>An Approach to Flooding Inundation Combining the Streamflow Prediction Tool (SPT) and Downscaled <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>Cotterman, K. A.; Follum, M. L.; Pradhan, N. R.; Niemann, J. D.</p> <p>2017-12-01</p> <p>Flooding impacts numerous aspects of society, from localized flash floods to continental-scale flood events. Many numerical flood models focus solely on riverine flooding, with some capable of capturing both localized and continental-scale flood events. However, these models neglect flooding away from channels that are related to excessive ponding, typically found in areas with flat terrain and poorly draining <span class="hlt">soils</span>. In order to obtain a holistic view of flooding, we combine flood results from the Streamflow Prediction Tool (SPT), a riverine flood model, with <span class="hlt">soil</span> <span class="hlt">moisture</span> downscaling techniques to determine if a better representation of flooding is obtained. This allows for a more holistic understanding of potential flood prone areas, increasing the opportunity for more accurate warnings and evacuations during flooding conditions. Thirty-five years of near-<span class="hlt">global</span> historical streamflow is reconstructed with continental-scale flow routing of runoff from <span class="hlt">global</span> land surface models. Elevation data was also obtained worldwide, to establish a relationship between topographic attributes and <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns. Derived <span class="hlt">soil</span> <span class="hlt">moisture</span> data is validated against observed <span class="hlt">soil</span> <span class="hlt">moisture</span>, increasing confidence in the ability to accurately capture <span class="hlt">soil</span> <span class="hlt">moisture</span> patterns. Potential flooding situations can be examined worldwide, with this study focusing on the United States, Central America, and the Philippines.</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 <span class="hlt">global</span> 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('https://www.ncbi.nlm.nih.gov/pubmed/29604221','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29604221"><span>Quantifying <span class="hlt">soil</span> <span class="hlt">moisture</span> impacts on light use efficiency across biomes.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stocker, Benjamin D; Zscheischler, Jakob; Keenan, Trevor F; Prentice, I Colin; Peñuelas, Josep; Seneviratne, Sonia I</p> <p>2018-06-01</p> <p>Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for <span class="hlt">soil</span> <span class="hlt">moisture</span>. However, <span class="hlt">soil</span> <span class="hlt">moisture</span> limitation is known to strongly 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple <span class="hlt">soil</span> <span class="hlt">moisture</span> datasets and remotely sensed greenness. This reveals substantial impacts of <span class="hlt">soil</span> <span class="hlt">moisture</span> alone that reduce GPP by up to 40% at sites located in sub-humid, semi-arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying <span class="hlt">soil</span>, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly-based drought indices. Counter to common assumptions, fLUE reductions are largest in drought-deciduous vegetation, including grasslands. Our results highlight the necessity to account for <span class="hlt">soil</span> <span class="hlt">moisture</span> limitation in terrestrial primary productivity data products, especially for drought-related assessments. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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://hdl.handle.net/2060/19820016659','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820016659"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> variation patterns observed in Hand County, South Dakota</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jones, E. B.; Owe, M.; Schmugge, T. J. (Principal Investigator)</p> <p>1981-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> data were taken during 1976 (April, June, October), 1977 (April, May, June), and 1978 (May, June, July) Hand County, South Dakota as part of the ground truth used in NASA's aircraft experiments to study the use of microwave radiometers for the remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The spatial variability observed on the ground during each of the sampling events was studied. The data reported are the mean gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> contained in three surface horizon depths: 0 to 2.5, 0 to 5 and 0 to 10 cm. The overall <span class="hlt">moisture</span> levels ranged from extremely dry conditions in June 1976 to very wet in May 1978, with a relatively even distribution of values within that range. It is indicated that well drained sites have to be partitioned from imperfectly drained areas when attempting to characterize the general <span class="hlt">moisture</span> profile throughout an area of varying <span class="hlt">soil</span> and cover type conditions. It is also found that the variability in <span class="hlt">moisture</span> content is greatest in the 0 to 2.5 cm measurements and decreases as the measurements are integrated over a greater depth. It is also determined that the sampling intensity of 10 measurements per km is adequate to estimate the mean <span class="hlt">moisture</span> with an uncertainty of + or - 3 percent under average <span class="hlt">moisture</span> conditions in areas of moderate to good drainage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/10610','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/10610"><span>Light, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and tree reproduction in hardwood forest openings.</span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Leon S. Minckler; John D. Woerheide; Richard C. Schlesinger</p> <p>1973-01-01</p> <p>Light, <span class="hlt">soil</span> <span class="hlt">moisture</span>, and tree reproduction were measured at five positions in six openings on each of three aspects in southern Illinois. Amount of light received was clearly related to position in the light openings, opening size, and aspect. More <span class="hlt">moisture</span> was available in the centers of the openings, although 4 years after openings were made the differences...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H51I1508E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H51I1508E"><span>Automated Quality Control of in Situ <span class="hlt">Soil</span> <span class="hlt">Moisture</span> from the North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database Using NLDAS-2 Products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ek, M. B.; Xia, Y.; Ford, T.; Wu, Y.; Quiring, S. M.</p> <p>2015-12-01</p> <p>The North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models and validating satellite-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms. The NASMD has collected data from over 30 <span class="hlt">soil</span> <span class="hlt">moisture</span> observation networks providing millions of in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations in all 50 states as well as Canada and Mexico. It is recognized that the quality of measured <span class="hlt">soil</span> <span class="hlt">moisture</span> in NASMD is highly variable due to the diversity of climatological conditions, land cover, <span class="hlt">soil</span> texture, and topographies of the stations and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy and imprecision in the data set can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure the data is of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System phase 2 (NLDAS-2) Noah <span class="hlt">soil</span> porosity, <span class="hlt">soil</span> temperature, and fraction of liquid and total <span class="hlt">soil</span> <span class="hlt">moisture</span> to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the <span class="hlt">soil</span> is partially frozen. A validation example using NLDAS-2 multiple model <span class="hlt">soil</span> <span class="hlt">moisture</span> products at the 20 cm <span class="hlt">soil</span> layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and West Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1,200 NASMD stations in the near future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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('https://ntrs.nasa.gov/search.jsp?R=19890058149&hterms=LOSS+SOIL&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DLOSS%2BSOIL','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890058149&hterms=LOSS+SOIL&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DLOSS%2BSOIL"><span>Mapping surface <span class="hlt">soil</span> <span class="hlt">moisture</span> with L-band radiometric measurements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, James R.; Shiue, James C.; Schmugge, Thomas J.; Engman, Edwin T.</p> <p>1989-01-01</p> <p>A NASA C-130 airborne remote sensing aircraft was used to obtain four-beam pushbroom microwave radiometric measurements over two small Kansas tall-grass prairie region watersheds, during a dry-down period after heavy rainfall in May and June, 1987. While one of the watersheds had been burned 2 months before these measurements, the other had not been burned for over a year. Surface <span class="hlt">soil-moisture</span> data were collected at the time of the aircraft measurements and correlated with the corresponding radiometric measurements, establishing a relationship for surface <span class="hlt">soil-moisture</span> mapping. Radiometric sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span> variation is higher in the burned than in the unburned watershed; surface <span class="hlt">soil</span> <span class="hlt">moisture</span> loss is also faster in the burned watershed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19760020542','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760020542"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and evapotranspiration predictions using Skylab data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Myers, V. I. (Principal Investigator); Moore, D. G.; Horton, M. L.; Russell, M. J.</p> <p>1975-01-01</p> <p>The author has identified the following significant results. Multispectral reflectance and emittance data from the Skylab workshop were evaluated for prediction of evapotranspiration and <span class="hlt">soil</span> <span class="hlt">moisture</span> for an irrigated region of southern Texas. Wavelengths greater than 2.1 microns were required to spectrally distinguish between wet and dry fallow surfaces. Thermal data provided a better estimate of <span class="hlt">soil</span> <span class="hlt">moisture</span> than did data from the reflective bands. Thermal data were dependent on <span class="hlt">soil</span> <span class="hlt">moisture</span> but not on the type of agricultural land use. The emittance map, when used in conjunction with existing models, did provide an estimate of evapotranspiration rates. Surveys of areas of high <span class="hlt">soil</span> <span class="hlt">moisture</span> can be accomplished with space altitude thermal data. Thermal data will provide a reliable input into irrigation scheduling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060040337&hterms=inversion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dinversion','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060040337&hterms=inversion&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dinversion"><span>A quantitative comparison of <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion algorithms</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zyl, J. J. van; Kim, Y.</p> <p>2001-01-01</p> <p>This paper compares the performance of four bare surface radar <span class="hlt">soil</span> <span class="hlt">moisture</span> inversion algorithms in the presence of measurement errors. The particular errors considered include calibration errors, system thermal noise, local topography and vegetation cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19810059143&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=19810059143&hterms=801&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3D801"><span>Aircraft active microwave measurements for estimating <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>Jackson, T. J.; Chang, A.; Schmugge, T. J.</p> <p>1981-01-01</p> <p>Both active and passive microwave sensors are sensitive to variations in near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span>. The principal advantage of active microwave systems for <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span>. 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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).</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..516....6R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..516....6R"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> at local scale: Measurements and simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Romano, Nunzio</p> <p>2014-08-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> refers to the water present in the uppermost part of a field <span class="hlt">soil</span> and is a state variable controlling a wide array of ecological, hydrological, geotechnical, and meteorological processes. The literature on <span class="hlt">soil</span> <span class="hlt">moisture</span> is very extensive and is developing so rapidly that it might be considered ambitious to seek to present the state of the art concerning research into this key variable. Even when covering investigations about only one aspect of the problem, there is a risk of some inevitable omission. A specific feature of the present essay, which may make this overview if not comprehensive at least of particular interest, is that the reader is guided through the various traditional and more up-to-date methods by the central thread of techniques developed to measure <span class="hlt">soil</span> <span class="hlt">moisture</span> interwoven with applications of modeling tools that exploit the observed datasets. This paper restricts its analysis to the evolution of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the local (spatial) scale. Though a somewhat loosely defined term, it is linked here to a characteristic length of the <span class="hlt">soil</span> volume investigated by the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensing probe. After presenting the most common concepts and definitions about the amount of water stored in a certain volume of <span class="hlt">soil</span> close to the land surface, this paper proceeds to review ground-based methods for monitoring <span class="hlt">soil</span> <span class="hlt">moisture</span> and evaluates modeling tools for the analysis of the gathered information in various applications. Concluding remarks address questions of monitoring and modeling of <span class="hlt">soil</span> <span class="hlt">moisture</span> at scales larger than the local scale with the related issue of data aggregation. An extensive, but not exhaustive, list of references is provided, enabling the reader to gain further insights into this subject.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H21O..05Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H21O..05Z"><span>Development of an Objective High Spatial Resolution <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Index</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zavodsky, B.; Case, J.; White, K.; Bell, J. R.</p> <p>2015-12-01</p> <p>Drought detection, analysis, and mitigation has become a key challenge for a diverse set of decision makers, including but not limited to operational weather forecasters, climatologists, agricultural interests, and water resource management. One tool that is heavily used is the United States Drought Monitor (USDM), which is derived from a complex blend of objective data and subjective analysis on a state-by-state basis using a variety of modeled and observed precipitation, <span class="hlt">soil</span> <span class="hlt">moisture</span>, hydrologic, and vegetation and crop health data. The NASA Short-term Prediction Research and Transition (SPoRT) Center currently runs a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework. The LIS-Noah is run at 3-km resolution for local numerical weather prediction (NWP) and situational awareness applications at select NOAA/National Weather Service (NWS) forecast offices over the Continental U.S. (CONUS). To enhance the practicality of the LIS-Noah output for drought monitoring and assessing flood potential, a 30+-year <span class="hlt">soil</span> <span class="hlt">moisture</span> climatology has been developed in an attempt to place near real-time <span class="hlt">soil</span> <span class="hlt">moisture</span> values in historical context at county- and/or watershed-scale resolutions. This LIS-Noah <span class="hlt">soil</span> <span class="hlt">moisture</span> climatology and accompanying anomalies is intended to complement the current suite of operational products, such as the North American Land Data Assimilation System phase 2 (NLDAS-2), which are generated on a coarser-resolution grid that may not capture localized, yet important <span class="hlt">soil</span> <span class="hlt">moisture</span> features. Daily <span class="hlt">soil</span> <span class="hlt">moisture</span> histograms are used to identify the real-time <span class="hlt">soil</span> <span class="hlt">moisture</span> percentiles at each grid point according to the county or watershed in which the grid point resides. Spatial plots are then produced that map the percentiles as proxies to the different USDM categories. This presentation will highlight recent developments of this gridded, objective <span class="hlt">soil</span> <span class="hlt">moisture</span> index, comparison to subjective</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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 <span class="hlt">global</span> <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 <span class="hlt">global</span> trends (1988-2010) in harmonized multi-satellite surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, Geophysical Research Letters, 39, L18405, 1-7. Liu, Y.Y., W.A. Dorigo, R.M. Parinussa, R.A.M. de Jeu, W. Wagner, M.F. McCabe, J.P. Evans, A.I.J.M. van Dijk (2012) Trend-preserving blending of passive and active microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals, Remote Sensing of Environment</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.5338W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.5338W"><span>Using high-resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> modelling to assess the uncertainty of microwave remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> products at the correct spatial and temporal support</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.</p> <p>2012-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating <span class="hlt">soil</span> <span class="hlt">moisture</span> into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> content throughout the river basin. Space-borne remote sensing may provide this information with a high temporal and spatial resolution and with a <span class="hlt">global</span> coverage. Currently three microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span>. 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> products at the correct spatial support. To overcome the difference in support size between in-situ <span class="hlt">soil</span> <span class="hlt">moisture</span> observations and remote sensed <span class="hlt">soil</span> <span class="hlt">moisture</span>, 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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</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 <span class="hlt">global</span> 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 <span class="hlt">Global</span> 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 <span class="hlt">global</span> 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 <span class="hlt">global</span> <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://hdl.handle.net/2060/20120009042','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120009042"><span>The NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission - Science and Data Product Development Status</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nloku, E.; Entekhabi, D.; O'Neill, P.</p> <p>2012-01-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission, planned for launch in late 2014, has the objective of frequent, <span class="hlt">global</span> mapping of near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and its freeze-thaw state. The SMAP measurement system utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The instruments will operate on a spacecraft in a 685 km polar orbit with 6am/6pm nodal crossings, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day <span class="hlt">global</span> coverage. Data from the instruments will yield <span class="hlt">global</span> maps of <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw state at 10 km and 3 km resolutions, respectively, every two to three days. The 10-km <span class="hlt">soil</span> <span class="hlt">moisture</span> product will be generated using a combined radar and radiometer retrieval algorithm. SMAP will also provide a radiometer-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product at 40-km spatial resolution and a radar-only <span class="hlt">soil</span> <span class="hlt">moisture</span> product at 3-km resolution. The relative accuracies of these products will vary regionally and will depend on surface characteristics such as vegetation water content, vegetation type, surface roughness, and landscape heterogeneity. The SMAP <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw measurements will enable significantly improved estimates of the fluxes of water, energy and carbon between the land and atmosphere. <span class="hlt">Soil</span> <span class="hlt">moisture</span> and freeze/thaw controls of these fluxes are key factors in the performance of models used for weather and climate predictions and for quantifYing the <span class="hlt">global</span> carbon balance. <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurements are also of importance in modeling and predicting extreme events such as floods and droughts. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. In the Testbed algorithms are developed and evaluated using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors including data from the SMOS and Aquarius missions. We report here on the development status</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H13K1738P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H13K1738P"><span>A Methodology for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval from Land Surface Temperature, Vegetation Index, Topography and <span class="hlt">Soil</span> Type</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pradhan, N. R.</p> <p>2015-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> conditions have an impact upon hydrological processes, biological and biogeochemical processes, eco-hydrology, floods and droughts due to changing climate, near-surface atmospheric conditions and the partition of incoming solar and long-wave radiation between sensible and latent heat fluxes. Hence, <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions virtually effect on all aspects of engineering / military engineering activities such as operational mobility, detection of landmines and unexploded ordinance, natural material penetration/excavation, peaking factor analysis in dam design etc. Like other natural systems, <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern can vary from completely disorganized (disordered, random) to highly organized. To understand this varying <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern, this research utilized topographic wetness index from digital elevation models (DEM) along with vegetation index from remotely sensed measurements in red and near-infrared bands, as well as land surface temperature (LST) in the thermal infrared bands. This research developed a methodology to relate a combined index from DEM, LST and vegetation index with the physical <span class="hlt">soil</span> <span class="hlt">moisture</span> properties of <span class="hlt">soil</span> types and the degree of saturation. The advantage in using this relationship is twofold: first it retrieves <span class="hlt">soil</span> <span class="hlt">moisture</span> content at the scale of <span class="hlt">soil</span> data resolution even though the derived indexes are in a coarse resolution, and secondly the derived <span class="hlt">soil</span> <span class="hlt">moisture</span> distribution represents both organized and disorganized patterns of actual <span class="hlt">soil</span> <span class="hlt">moisture</span>. The derived <span class="hlt">soil</span> <span class="hlt">moisture</span> is used in driving the hydrological model simulations of runoff, sediment and nutrients.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B13H1840B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B13H1840B"><span>Ecosystem-scale plant hydraulic strategies inferred from remotely-sensed <span class="hlt">soil</span> <span class="hlt">moisture</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bassiouni, M.; Good, S. P.; Higgins, C. W.</p> <p>2017-12-01</p> <p>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 <span class="hlt">soil</span> <span class="hlt">moisture</span> observations provides key information about the <span class="hlt">soil</span> <span class="hlt">moisture</span> states at which evapotranspiration is reduced by water stress. Here, an inverse Bayesian approach is applied to a standard bucket model of <span class="hlt">soil</span> column hydrology forced with stochastic precipitation inputs. Through this approach, we are able to determine the <span class="hlt">soil</span> <span class="hlt">moisture</span> thresholds at which stomata are open or closed that are most consistent with observed <span class="hlt">soil</span> <span class="hlt">moisture</span> probability density functions. This research utilizes remotely-sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> data to explore <span class="hlt">global</span> 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 <span class="hlt">soil</span>-water dynamics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H43H1638S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H43H1638S"><span>Prediction of Root Zone <span class="hlt">Soil</span> <span class="hlt">Moisture</span> using Remote Sensing Products and In-Situ Observation under Climate Change Scenario</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, G.; Panda, R. K.; Mohanty, B.</p> <p>2015-12-01</p> <p>Prediction of root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> status at field level is vital for developing efficient agricultural water management schemes. In this study, root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> was estimated across the Rana watershed in Eastern India, by assimilation of near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> estimate from SMOS satellite into a physically-based <span class="hlt">Soil</span>-Water-Atmosphere-Plant (SWAP) model. An ensemble Kalman filter (EnKF) technique coupled with SWAP model was used for assimilating the satellite <span class="hlt">soil</span> <span class="hlt">moisture</span> observation at different spatial scales. The universal triangle concept and artificial intelligence techniques were applied to disaggregate the SMOS satellite monitored near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at a 40 km resolution to finer scale (1 km resolution), using higher spatial resolution of MODIS derived vegetation indices (NDVI) and land surface temperature (Ts). The disaggregated surface <span class="hlt">soil</span> <span class="hlt">moisture</span> were compared to ground-based measurements in diverse landscape using portable impedance probe and gravimetric samples. Simulated root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> were compared with continuous <span class="hlt">soil</span> <span class="hlt">moisture</span> profile measurements at three monitoring stations. In addition, the impact of projected climate change on root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> were also evaluated. The climate change projections of rainfall were analyzed for the Rana watershed from statistically downscaled <span class="hlt">Global</span> Circulation Models (GCMs). The long-term root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamics were estimated by including a rainfall generator of likely scenarios. The predicted long term root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> status at finer scale can help in developing efficient agricultural water management schemes to increase crop production, which lead to enhance the water use efficiency.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913342B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913342B"><span>Towards an improved <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval for organic-rich <span class="hlt">soils</span> from SMOS passive microwave L-band observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>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.</p> <p>2017-04-01</p> <p>From the passive L-band microwave radiometer onboard the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) space mission <span class="hlt">global</span> surface <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval algorithm is exclusively calibrated over test sites in dry and temperate climate zones. Furthermore, the included dielectric mixing model relating <span class="hlt">soil</span> <span class="hlt">moisture</span> to relative permittivity accounts only for mineral <span class="hlt">soils</span>. However, <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soils</span> 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 <span class="hlt">soils</span> 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 <span class="hlt">soils</span> was derived and implemented in the SMOS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Level 2 Prototype Processor (SML2PP). Unfortunately, the current SMOS retrieved <span class="hlt">soil</span> <span class="hlt">moisture</span> product seems to show unrealistically low values compared to in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data collected from organic surface layers in North America, Europe and the Tibetan Plateau so that the impact of the dielectric model for organic <span class="hlt">soils</span> cannot really be tested. A simplified SMOS processing scheme yielding higher <span class="hlt">soil</span> <span class="hlt">moisture</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26098202','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26098202"><span>Galvanic Cell Type Sensor for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Analysis.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gaikwad, Pramod; Devendrachari, Mruthyunjayachari Chattanahalli; Thimmappa, Ravikumar; Paswan, Bhuneshwar; Raja Kottaichamy, Alagar; Makri Nimbegondi Kotresh, Harish; Thotiyl, Musthafa Ottakam</p> <p>2015-07-21</p> <p>Here we report the first potentiometric sensor for <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis by bringing in the concept of Galvanic cells wherein the redox energies of Al and conducting polyaniline are exploited to design a battery type sensor. The sensor consists of only simple architectural components, and as such they are inexpensive and lightweight, making it suitable for on-site analysis. The sensing mechanism is proved to be identical to a battery type discharge reaction wherein polyaniline redox energy changes from the conducting to the nonconducting state with a resulting voltage shift in the presence of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Unlike the state of the art <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors, a signal derived from the proposed <span class="hlt">moisture</span> sensor is probe size independent, as it is potentiometric in nature and, hence, can be fabricated in any shape or size and can provide a consistent output signal under the strong aberration conditions often encountered in <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis. The sensor is regenerable by treating with 1 M HCl and can be used for multiple analysis with little read out hysteresis. Further, a portable sensor is fabricated which can provide warning signals to the end user when the <span class="hlt">moisture</span> levels in the <span class="hlt">soil</span> go below critically low levels, thereby functioning as a smart device. As the sensor is inexpensive, portable, and potentiometric, it opens up avenues for developing effective and energy efficient irrigation strategies, understanding the heat and water transfer at the atmosphere-land interface, understanding <span class="hlt">soil</span> mechanics, forecasting the risk of natural calamities, and so on.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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><span class="hlt">Global</span> 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('https://www.ncbi.nlm.nih.gov/pubmed/27600157','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27600157"><span>Individual contributions of climate and vegetation change to <span class="hlt">soil</span> <span class="hlt">moisture</span> trends across multiple spatial scales.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Feng, Huihui</p> <p>2016-09-07</p> <p>Climate and vegetation change are two dominating factors for <span class="hlt">soil</span> <span class="hlt">moisture</span> trend. However, their individual contributions remain unknown due to their complex interaction. Here, I separated their contributions through a trajectory-based method across the <span class="hlt">global</span>, regional and local scales. Our results demonstrated that climate change accounted for 98.78% and 114.64% of the <span class="hlt">global</span> drying and wetting trend. Vegetation change exhibited a relatively weak influence (contributing 1.22% and -14.64% of the <span class="hlt">global</span> drying and wetting) because it occurred in a limited area on land. Regionally, the impact of vegetation change cannot be neglected, which contributed -40.21% of the <span class="hlt">soil</span> <span class="hlt">moisture</span> change in the wetting zone. Locally, the contributions strongly correlated to the local environmental characteristics. Vegetation negatively affected <span class="hlt">soil</span> <span class="hlt">moisture</span> trends in the dry and sparsely vegetated regions and positively in the wet and densely vegetated regions. I conclude that individual contributions of climate and vegetation change vary at the <span class="hlt">global</span>, regional and local scales. Climate change dominates the <span class="hlt">soil</span> <span class="hlt">moisture</span> trends, while vegetation change acts as a regulator to drying or wetting the <span class="hlt">soil</span> under the changing climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMEP41C0925W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMEP41C0925W"><span>Modeling the Impact of <span class="hlt">Soil</span> Conditions on <span class="hlt">Global</span> Water Balance</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, P. L.; Feddema, J. J.</p> <p>2016-12-01</p> <p>The amount of water the <span class="hlt">soil</span> can hold for plant use, defined as <span class="hlt">soil</span> water-holding capacity (WHC), has a large influence on the water cycle and climatic variables. Although <span class="hlt">soil</span> properties vary widely worldwide, many climate modeling applications assume WHC to be spatially invariant. This study explores how a more realistic <span class="hlt">soil</span> WHC estimate affects the <span class="hlt">global</span> water balance relative to commonly assumed <span class="hlt">soil</span> properties. We use a modified Thornthwaite water balance model combined with a newly developed <span class="hlt">soil</span> WHC and <span class="hlt">soil</span> thickness data at a 30 arc second resolution. The <span class="hlt">soil</span> WHC data was obtained by integrating WHCs to a depth of 2 m and modified by the <span class="hlt">soil</span> thickness data on a grid-by-grid basis, and then resampling to the 0.5 degree climatology data. We observed that down scaling <span class="hlt">soils</span> data before modifying <span class="hlt">soil</span> depths greatly increases <span class="hlt">global</span> <span class="hlt">soil</span> WHCs. This new dataset is compared to WHC information with a fixed 2-m <span class="hlt">soil</span> depth, and a constant 150-mm <span class="hlt">soil</span> WHC. Results indicate higher <span class="hlt">soil</span> WHC results in increased <span class="hlt">soil</span> <span class="hlt">moisture</span>, decreased <span class="hlt">moisture</span> surplus and deficits, and increased actual evapotranspiration (AE), and vice-versa. However, due to high variability in <span class="hlt">soil</span> characteristics across climate gradients, this generalization does not hold true for regionally averaged outcomes. Compared to using a constant 150-mm WHC, more realistic <span class="hlt">soil</span> WHC increases <span class="hlt">global</span> averaged AE 1%, and decreases deficit 2% and surplus 3%. Most change is observed in areas with pronounced wet and dry seasons; using a constant 2-m <span class="hlt">soil</span> depth doubles the differences. Regionally, Europe was most affected: AE increases 4%, and the deficit and surplus decrease 20% and 12%. Australia shows that regionally averaged results are not equivocal for <span class="hlt">moisture</span> surplus and deficit; deficit decreases 0.4%, while surplus decreases 9%. This research highlights the importance of <span class="hlt">soil</span> condition for climate modeling and how a better representation of <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions affects <span class="hlt">global</span> water balance</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28632172','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28632172"><span>Fiber Optic Thermo-Hygrometers for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Leone, Marco; Principe, Sofia; Consales, Marco; Parente, Roberto; Laudati, Armando; Caliro, Stefano; Cutolo, Antonello; Cusano, Andrea</p> <p>2017-06-20</p> <p>This work deals with the fabrication, prototyping, and experimental validation of a fiber optic thermo-hygrometer-based <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor, useful for rainfall-induced landslide prevention applications. In particular, we recently proposed a new generation of fiber Bragg grating (FBGs)-based <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors for irrigation purposes. This device was realized by integrating, inside a customized aluminum protection package, a FBG thermo-hygrometer with a polymer micro-porous membrane. Here, we first verify the limitations, in terms of the volumetric water content (VWC) measuring range, of this first version of the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor for its exploitation in landslide prevention applications. Successively, we present the development, prototyping, and experimental validation of a novel, optimized version of a <span class="hlt">soil</span> VWC sensor, still based on a FBG thermo-hygrometer, but able to reliably monitor, continuously and in real-time, VWC values up to 37% when buried in the <span class="hlt">soil</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5492425','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5492425"><span>Fiber Optic Thermo-Hygrometers for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Leone, Marco; Principe, Sofia; Consales, Marco; Parente, Roberto; Laudati, Armando; Caliro, Stefano; Cutolo, Antonello; Cusano, Andrea</p> <p>2017-01-01</p> <p>This work deals with the fabrication, prototyping, and experimental validation of a fiber optic thermo-hygrometer-based <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor, useful for rainfall-induced landslide prevention applications. In particular, we recently proposed a new generation of fiber Bragg grating (FBGs)-based <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors for irrigation purposes. This device was realized by integrating, inside a customized aluminum protection package, a FBG thermo-hygrometer with a polymer micro-porous membrane. Here, we first verify the limitations, in terms of the volumetric water content (VWC) measuring range, of this first version of the <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor for its exploitation in landslide prevention applications. Successively, we present the development, prototyping, and experimental validation of a novel, optimized version of a <span class="hlt">soil</span> VWC sensor, still based on a FBG thermo-hygrometer, but able to reliably monitor, continuously and in real-time, VWC values up to 37% when buried in the <span class="hlt">soil</span>. PMID:28632172</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23579833','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23579833"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> dynamics modeling considering multi-layer root zone.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kumar, R; Shankar, V; Jat, M K</p> <p>2013-01-01</p> <p>The <span class="hlt">moisture</span> uptake by plant from <span class="hlt">soil</span> is a key process for plant growth and movement of water in the <span class="hlt">soil</span>-plant system. A non-linear root water uptake (RWU) model was developed for a multi-layer crop root zone. The model comprised two parts: (1) model formulation and (2) <span class="hlt">moisture</span> flow prediction. The developed model was tested for its efficiency in predicting <span class="hlt">moisture</span> depletion in a non-uniform root zone. A field experiment on wheat (Triticum aestivum) was conducted in the sub-temperate sub-humid agro-climate of Solan, Himachal Pradesh, India. Model-predicted <span class="hlt">soil</span> <span class="hlt">moisture</span> parameters, i.e., <span class="hlt">moisture</span> status at various depths, <span class="hlt">moisture</span> depletion and <span class="hlt">soil</span> <span class="hlt">moisture</span> profile in the root zone, are in good agreement with experiment results. The results of simulation emphasize the utility of the RWU model across different agro-climatic regions. The model can be used for sound irrigation management especially in water-scarce humid, temperate, arid and semi-arid regions and can also be integrated with a water transport equation to predict the solute uptake by plant biomass.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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> wheat (Pforzheim, 2013) and maize (Braunschweig, 2014) and differ in <span class="hlt">soil</span> type and management. The results confirm a general good agreement between <span class="hlt">soil</span> <span class="hlt">moisture</span> estimated by CRNS and the <span class="hlt">soil</span> <span class="hlt">moisture</span> network. However, several sources of uncertainty were identified i.e., overestimation of dry conditions, strong effects of the additional hydrogen pools and an influence of the vertical <span class="hlt">soil</span> <span class="hlt">moisture</span> profile. Based on that, a <span class="hlt">global</span> sensitivity analysis based on Monte Carlo sampling can be performed and evaluated in terms of <span class="hlt">soil</span> <span class="hlt">moisture</span> and footprint characteristics. The results allow quantifying the role of the different factors and identifying further improvements in the method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170002444','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170002444"><span>A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal D.; Reichle, Rolf H.; Mahanama, Sarith P. P.</p> <p>2017-01-01</p> <p>NASAs <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission provides <span class="hlt">global</span> surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals with a revisit time of 2-3 days and a latency of 24 hours. Here, to enhance the utility of the SMAP data, we present an approach for improving real-time <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates (nowcasts) and for forecasting <span class="hlt">soil</span> <span class="hlt">moisture</span> several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States (CONUS) is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of <span class="hlt">soil</span> <span class="hlt">moisture</span> forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000073234&hterms=How+soil+form&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DHow%2Bsoil%2Bform','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000073234&hterms=How+soil+form&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DHow%2Bsoil%2Bform"><span>Estimating Long Term Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in the GCIP Area From Satellite Microwave Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Owe, Manfred; deJeu, Vrije; VandeGriend, Adriaan A.</p> <p>2000-01-01</p> <p> surface layers in order to interpolate daily surface <span class="hlt">moisture</span> values. Such a climate-based approach is often more appropriate for estimating large-area spatially averaged <span class="hlt">soil</span> <span class="hlt">moisture</span> because meteorological data are generally more spatially representative than isolated point measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface <span class="hlt">soil</span> <span class="hlt">moisture</span> at <span class="hlt">global</span> scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and <span class="hlt">global</span> change studies.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.B43F0621F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.B43F0621F"><span>Study Variability of Seasonal <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Ensemble of CMIP5 Models Over South Asia During 1950-2005</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fahim, A. M.; Shen, R.; Yue, Z.; Di, W.; Mushtaq Shah, S.</p> <p>2015-12-01</p> <p><span class="hlt">Moisture</span> in the upper most layer of <span class="hlt">soil</span> column from 14 different models under Coupled Model Intercomparison Project Phase-5 (CMIP5) project were analyzed for four seasons of the year. Aim of this study was to explore variability in <span class="hlt">soil</span> <span class="hlt">moisture</span> over south Asia using multi model ensemble and relationship between summer rainfall and <span class="hlt">soil</span> <span class="hlt">moisture</span> for spring and summer season. GLDAS (<span class="hlt">Global</span> Land Data Assimilation System) dataset set was used for comparing CMIP5 ensemble mean <span class="hlt">soil</span> <span class="hlt">moisture</span> in different season. Ensemble mean represents <span class="hlt">soil</span> <span class="hlt">moisture</span> well in accordance with the geographical features; prominent arid regions are indicated profoundly. Empirical Orthogonal Function (EOF) analysis was applied to study the variability. First component of EOF explains 17%, 16%, 11% and 11% variability for spring, summer, autumn and winter season respectively. Analysis reveal increasing trend in <span class="hlt">soil</span> <span class="hlt">moisture</span> over most parts of Afghanistan, Central and north western parts of Pakistan, northern India and eastern to south eastern parts of China, in spring season. During summer, south western part of India exhibits highest negative trend while rest of the study area show minute trend (increasing or decreasing). In autumn, south west of India is under highest negative loadings. During winter season, north western parts of study area show decreasing trend. Summer rainfall has very week (negative or positive) spatial correlation, with spring <span class="hlt">soil</span> <span class="hlt">moisture</span>, while possess higher correlation with summer <span class="hlt">soil</span> <span class="hlt">moisture</span>. Our studies have significant contribution to understand complex nature of land - atmosphere interactions, as <span class="hlt">soil</span> <span class="hlt">moisture</span> prediction plays an important role in the cycle of sink and source of many air pollutants. Next level of research should be on filling the gaps between accurately measuring the <span class="hlt">soil</span> <span class="hlt">moisture</span> using satellite remote sensing and land surface modelling. Impact of <span class="hlt">soil</span> <span class="hlt">moisture</span> in tracking down different types of pollutant will also be studied.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4366536','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4366536"><span>Reconciling spatial and temporal <span class="hlt">soil</span> <span class="hlt">moisture</span> effects on afternoon rainfall</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Guillod, Benoit P.; Orlowsky, Boris; Miralles, Diego G.; Teuling, Adriaan J.; Seneviratne, Sonia I.</p> <p>2015-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> impacts on precipitation have been strongly debated. Recent observational evidence of afternoon rain falling preferentially over land parcels that are drier than the surrounding areas (negative spatial effect), contrasts with previous reports of a predominant positive temporal effect. However, whether spatial effects relating to <span class="hlt">soil</span> <span class="hlt">moisture</span> heterogeneity translate into similar temporal effects remains unknown. Here we show that afternoon precipitation events tend to occur during wet and heterogeneous <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions, while being located over comparatively drier patches. Using remote-sensing data and a common analysis framework, spatial and temporal correlations with opposite signs are shown to coexist within the same region and data set. Positive temporal coupling might enhance precipitation persistence, while negative spatial coupling tends to regionally homogenize land surface conditions. Although the apparent positive temporal coupling does not necessarily imply a causal relationship, these results reconcile the notions of <span class="hlt">moisture</span> recycling with local, spatially negative feedbacks. PMID:25740589</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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/2017AGUFM.B41I2077M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B41I2077M"><span>Effect of <span class="hlt">soil</span> <span class="hlt">moisture</span> on the temperature sensitivity of Northern <span class="hlt">soils</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Minions, C.; Natali, S.; Ludwig, S.; Risk, D.; Macintyre, C. M.</p> <p>2017-12-01</p> <p>Arctic and boreal ecosystems are vast reservoirs of carbon and are particularly sensitive to climate warming. Changes in the temperature and precipitation regimes of these regions could significantly alter <span class="hlt">soil</span> respiration rates, impacting atmospheric concentrations and affecting climate change feedbacks. Many incubation studies have shown that both temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> are important environmental drivers of <span class="hlt">soil</span> respiration; this relationship, however, has rarely been demonstrated with in situ data. Here we present the results of a study at six field sites in Alaska from 2016 to 2017. Low-power automated <span class="hlt">soil</span> gas systems were used to measure <span class="hlt">soil</span> surface CO2 flux from three forced diffusion chambers and <span class="hlt">soil</span> profile concentrations from three <span class="hlt">soil</span> depth chambers at hourly intervals at each site. HOBO Onset dataloggers were used to monitor <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature profiles. Temperature sensitivity (Q10) was determined at each site using inversion analysis applied over different time periods. With highly resolved data sets, we were able to observe the changes in <span class="hlt">soil</span> respiration in response to changes in temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span>. Through regression analysis we confirmed that temperature is the primary driver in <span class="hlt">soil</span> respiration, but <span class="hlt">soil</span> <span class="hlt">moisture</span> becomes dominant beyond a certain threshold, suppressing CO2 flux in <span class="hlt">soils</span> with high <span class="hlt">moisture</span> content. This field study supports the conclusions made from previous <span class="hlt">soil</span> incubation studies and provides valuable insights into the impact of both temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> changes on <span class="hlt">soil</span> respiration.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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://adsabs.harvard.edu/abs/2013EGUGA..15.2932K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.2932K"><span>Inter-Comparison of Retrieved and Modelled <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Coherency of Remotely Sensed Hydrology Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kolassa, Jana; Aires, Filipe</p> <p>2013-04-01</p> <p>A neural network algorithm has been developed for the retrieval of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> (SM) from <span class="hlt">global</span> satellite observations. The algorithm estimates <span class="hlt">soil</span> <span class="hlt">moisture</span> from a synergy of passive and active microwave, infrared and visible satellite observations in order to capture the different SM variabilities that the individual sensors are sensitive to. The advantages and drawbacks of each satellite observation have been analysed and the information type and content carried by each observation have been determined. A <span class="hlt">global</span> data set of monthly mean <span class="hlt">soil</span> <span class="hlt">moisture</span> for the 1993-2000 period has been computed with the neural network algorithm (Kolassa et al., in press, 2012). The resulting <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval product has then been used in an inter-comparison study including <span class="hlt">soil</span> <span class="hlt">moisture</span> from (1) the HTESSEL model (Balsamo et al., 2009), (2) the WACMOS satellite product (Liu et al., 2011), and (3) in situ measurements from the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network (Dorigo et al., 2011). The analysis showed that the satellite remote sensing products are well-suited to capture the spatial variability of the in situ data and even show the potential to improve the modelled <span class="hlt">soil</span> <span class="hlt">moisture</span>. Both satellite retrievals also display a good agreement with the temporal structures of the in situ data, however, HTESSEL appears to be more suitable for capturing the temporal variability (Kolassa et al., in press, 2012). The use of this type of neural network approach is currently being investigated as a retrieval option for the SMOS mission. Our <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval product has also been used in a coherence study with precipitation data from GPCP (Adler et al., 2003) and inundation estimates from GIEMS (Prigent et al., 2007). It was investigated on a <span class="hlt">global</span> scale whether the three observation-based datasets are coherent with each other and show the expected behaviour. For most regions of the Earth, the datasets were consistent and the behaviour observed could be explained with the known</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SPIE10426E..0JH','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SPIE10426E..0JH"><span>Creating <span class="hlt">soil</span> <span class="hlt">moisture</span> maps based on radar satellite imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hnatushenko, Volodymyr; Garkusha, Igor; Vasyliev, Volodymyr</p> <p>2017-10-01</p> <p>The presented work is related to a study of mapping <span class="hlt">soil</span> <span class="hlt">moisture</span> basing on radar data from Sentinel-1 and a test of adequacy of the models constructed on the basis of data obtained from alternative sources. Radar signals are reflected from the ground differently, depending on its properties. In radar images obtained, for example, in the C band of the electromagnetic spectrum, <span class="hlt">soils</span> saturated with <span class="hlt">moisture</span> usually appear in dark tones. Although, at first glance, the problem of constructing <span class="hlt">moisture</span> maps basing on radar data seems intuitively clear, its implementation on the basis of the Sentinel-1 data on an industrial scale and in the public domain is not yet available. In the process of mapping, for verification of the results, measurements of <span class="hlt">soil</span> <span class="hlt">moisture</span> obtained from logs of the network of climate stations NOAA US Climate Reference Network (USCRN) were used. This network covers almost the entire territory of the United States. The passive microwave radiometers of Aqua and SMAP satellites data are used for comparing processing. In addition, other supplementary cartographic materials were used, such as maps of <span class="hlt">soil</span> types and ready <span class="hlt">moisture</span> maps. The paper presents a comparison of the effect of the use of certain methods of roughening the quality of radar data on the result of mapping <span class="hlt">moisture</span>. Regression models were constructed showing dependence of backscatter coefficient values Sigma0 for calibrated radar data of different spatial resolution obtained at different times on <span class="hlt">soil</span> <span class="hlt">moisture</span> values. The obtained <span class="hlt">soil</span> <span class="hlt">moisture</span> maps of the territories of research, as well as the conceptual solutions about automation of operations of constructing such digital maps, are presented. The comparative assessment of the time required for processing a given set of radar scenes with the developed tools and with the ESA SNAP product was carried out.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC53D0923D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC53D0923D"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature variability among three plant communities in a High Arctic Lake Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Davis, M. L.; Konkel, J.; Welker, J. M.; Schaeffer, S. M.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature are critical to plant community distribution and <span class="hlt">soil</span> carbon cycle processes in High Arctic tundra. As environmental drivers of <span class="hlt">soil</span> biochemical processes, the predictability of <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature by vegetation zone in High Arctic landscapes has significant implications for the use of satellite imagery and vegetation distribution maps to estimate of <span class="hlt">soil</span> gas flux rates. During the 2017 growing season, we monitored <span class="hlt">soil</span> <span class="hlt">moisture</span> and <span class="hlt">soil</span> temperature weekly at 48 sites in dry tundra, moist tundra, and wet grassland vegetation zones in a High Arctic lake basin. <span class="hlt">Soil</span> temperature in all three communities reflected fluctuations in air temperature throughout the season. Mean <span class="hlt">soil</span> temperature was highest in the dry tundra community at 10.5±0.6ºC, however, did not differ between moist tundra and wet grassland communities (2.7±0.6 and 3.1±0.5ºC, respectively). Mean volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> differed significantly among all three plant communities with the lowest and highest <span class="hlt">soil</span> <span class="hlt">moisture</span> measured in the dry tundra and wet grassland (30±1.2 and 65±2.7%), respectively. For all three communities, <span class="hlt">soil</span> <span class="hlt">moisture</span> was highest during the early season snow melt. <span class="hlt">Soil</span> <span class="hlt">moisture</span> in wet grassland remained high with no significant change throughout the season, while significant drying occurred in dry tundra. The most significant change in <span class="hlt">soil</span> <span class="hlt">moisture</span> was measured in moist tundra, ranging from 61 to 35%. Our results show different gradients in <span class="hlt">soil</span> <span class="hlt">moisture</span> variability within each plant community where: 1) <span class="hlt">soil</span> <span class="hlt">moisture</span> was lowest in dry tundra with little change, 2) highest in wet grassland with negligible change, and 3) variable in moist tundra which slowly dried but remained moist. Consistently high <span class="hlt">soil</span> <span class="hlt">moisture</span> in wet grassland restricts this plant community to areas with no significant drying during summer. The moist tundra occupies the intermediary areas between wet grassland and dry tundra and experiences the widest range</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.5810Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.5810Y"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Temperature Measuring Networks in the Tibetan Plateau and Their Hydrological Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Kun; Chen, Yingying; Qin, Jun; Lu, Hui</p> <p>2017-04-01</p> <p>Multi-sphere interactions over the Tibetan Plateau directly impact its surrounding climate and environment at a variety of spatiotemporal scales. Remote sensing and modeling are expected to provide hydro-meteorological data needed for these process studies, but in situ observations are required to support their calibration and validation. For this purpose, we have established two networks on the Tibetan Plateau to measure densely two state variables (<span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature) and four <span class="hlt">soil</span> depths (0 5, 10, 20, and 40 cm). The experimental area is characterized by low biomass, high <span class="hlt">soil</span> <span class="hlt">moisture</span> dynamic range, and typical freeze-thaw cycle. As auxiliary parameters of these networks, <span class="hlt">soil</span> texture and <span class="hlt">soil</span> organic carbon content are measured at each station to support further studies. In order to guarantee continuous and high-quality data, tremendous efforts have been made to protect the data logger from <span class="hlt">soil</span> water intrusion, to calibrate <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors, and to upscale the point measurements. One <span class="hlt">soil</span> <span class="hlt">moisture</span> network is located in a semi-humid area in central Tibetan Plateau (Naqu), which consists of 56 stations with their elevation varying over 4470 4950 m and covers three spatial scales (1.0, 0.3, 0.1 degree). The other is located in a semi-arid area in southern Tibetan Plateau (Pali), which consists of 25 stations and covers an area of 0.25 degree. The spatiotemporal characteristics of the former network were analyzed, and a new spatial upscaling method was developed to obtain the regional mean <span class="hlt">soil</span> <span class="hlt">moisture</span> truth from the point measurements. Our networks meet the requirement for evaluating a variety of <span class="hlt">soil</span> <span class="hlt">moisture</span> products, developing new algorithms, and analyzing <span class="hlt">soil</span> <span class="hlt">moisture</span> scaling. Three applications with the network data are presented in this paper. 1. Evaluation of Current remote sensing and LSM products. The in situ data have been used to evaluate AMSR-E, AMSR2, SMOS and SMAP products and four modeled outputs by the <span class="hlt">Global</span> Land Data</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 <span class="hlt">global</span> precipitation estimation.</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/2017AGUFM.H51R..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H51R..02L"><span>Four Decades of Microwave Satellite <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Observations: Product validation and inter-satellite comparisons</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lanka, K.; Pan, M.; Wanders, N.; Kumar, D. N.; Wood, E. F.</p> <p>2017-12-01</p> <p>The satellite based passive and active microwave sensors enhanced our ability to retrieve <span class="hlt">soil</span> <span class="hlt">moisture</span> at <span class="hlt">global</span> scales. It has been almost four decades since the first passive microwave satellite sensor was launched in 1978. Since then <span class="hlt">soil</span> <span class="hlt">moisture</span> has gained considerable attention in hydro-meteorological, climate, and agricultural research resulting in the deployment of two dedicated missions in the last decade, SMOS and SMAP. Signifying the four decades of microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>, this work aims to present an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy of retrieving <span class="hlt">soil</span> <span class="hlt">moisture</span>. We considered daily coverage, temporal performance, and spatial performance to assess the accuracy of products corresponding to eight passive sensors (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS and SMAP), two active sensors (ERS-Scatterometer, MetOp-ASCAT), and one active/passive merged <span class="hlt">soil</span> <span class="hlt">moisture</span> product (ESA-CCI combined product), using 1058 ISMN in-situ stations and the VIC LSM <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations (VICSM) over the CONUS. Our analysis indicated that the daily coverage has increased from 30 % during 1980s to 85 % (during non-winter months) with the launch of dedicated <span class="hlt">soil</span> <span class="hlt">moisture</span> missions SMOS and SMAP. The temporal validation of passive and active <span class="hlt">soil</span> <span class="hlt">moisture</span> products with the ISMN data place the range of median RMSE as 0.06-0.10 m3/m3 and median correlation as 0.20-0.68. When TMI, AMSR-E and WindSAT are evaluated, the AMSR-E sensor is found to have produced the brightness temperatures with better quality, given that these sensors are paired with same retrieval algorithm (LPRM). The ASCAT product shows a significant improvement during the temporal validation of retrievals compared to its predecessor ERS, thanks to enhanced sensor configuration. The SMAP mission, through its improved sensor design and RFI handling, shows a high retrieval accuracy under all-topography conditions</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 <span class="hlt">Global</span> 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('https://pubs.er.usgs.gov/publication/70030715','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70030715"><span>Dependence of <span class="hlt">soil</span> respiration on <span class="hlt">soil</span> temperature and <span class="hlt">soil</span> <span class="hlt">moisture</span> in successional forests in Southern China</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tang, X.-L.; Zhou, G.-Y.; Liu, S.-G.; Zhang, D.-Q.; Liu, S.-Z.; Li, Ji; Zhou, C.-Y.</p> <p>2006-01-01</p> <p>The spatial and temporal variations in <span class="hlt">soil</span> respiration and its relationship with biophysical factors in forests near the Tropic of Cancer remain highly uncertain. To contribute towards an improvement of actual estimates, <span class="hlt">soil</span> respiration rates, <span class="hlt">soil</span> temperature, and <span class="hlt">soil</span> <span class="hlt">moisture</span> were measured in three successional subtropical forests at the Dinghushan Nature Reserve (DNR) in southern China from March 2003 to February 2005. The overall objective of the present study was to analyze the temporal variations of <span class="hlt">soil</span> respiration and its biophysical dependence in these forests. The relationships between biophysical factors and <span class="hlt">soil</span> respiration rates were compared in successional forests to test the hypothesis that these forests responded similarly to biophysical factors. The seasonality of <span class="hlt">soil</span> respiration coincided with the seasonal climate pattern, with high respiration rates in the hot humid season (April-September) and with low rates in the cool dry season (October-March). <span class="hlt">Soil</span> respiration measured at these forests showed a clear increasing trend with the progressive succession. Annual mean (±SD) <span class="hlt">soil</span> respiration rate in the DNR forests was (9.0 ± 4.6) Mg CO2-C/hm2per year, ranging from (6.1 ± 3.2) Mg CO2-C/hm2per year in early successional forests to (10.7 ± 4.9) Mg CO2-C/hm2 per year in advanced successional forests. <span class="hlt">Soil</span> respiration was correlated with both <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span>. The T/M model, where the two biophysical variables are driving factors, accounted for 74%-82% of <span class="hlt">soil</span> respiration variation in DNR forests. Temperature sensitivity decreased along progressive succession stages, suggesting that advanced-successional forests have a good ability to adjust to temperature. In contrast, <span class="hlt">moisture</span> increased with progressive succession processes. This increase is caused, in part, by abundant respirators in advanced-successional forest, where more <span class="hlt">soil</span> <span class="hlt">moisture</span> is needed to maintain their activities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=316894','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=316894"><span>Using a <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation network for satellite validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>A long term in situ network for the study of <span class="hlt">soil</span> <span class="hlt">moisture</span> and precipitation was deployed in north central Iowa, in cooperation between USDA and NASA. A total of 20 dual precipitation gages were established across a watershed landscape with an area of approximately 600 km2. In addition, four <span class="hlt">soil</span> mo...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=331453','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=331453"><span>Validation of the GCOM-W SCA and JAXA <span class="hlt">soil</span> <span class="hlt">moisture</span> algorithms</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Satellite-based remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> has matured over the past decade as a result of the <span class="hlt">Global</span> Climate Observing Mission-Water (GCOM-W) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H21G1512Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H21G1512Y"><span>Evaluation of Long-term <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Proxies in the U.S. Great Plains</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yuan, S.; Quiring, S. M.</p> <p>2016-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> for investigating long-term spatial and temporal variations of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Hence, it is necessary to find suitable approximations of <span class="hlt">soil</span> <span class="hlt">moisture</span> observations. 5 drought indices will be compared with simulated and observed <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">Moisture</span> Index (CMI) will be calculated by PRISM data. The <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations will be derived from NLDAS. In situ <span class="hlt">soil</span> <span class="hlt">moisture</span> will be obtained from North American <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Database. The evaluation will focus on three main aspects: trends, variations and persistence. The results will support further research investigating long-term variations in <span class="hlt">soil</span> <span class="hlt">moisture</span>-climate interactions.</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('http://hdl.handle.net/2060/20150014256','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150014256"><span>Assessment of SMOS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Retrieval Parameters Using Tau-Omega Algorithms for <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit Estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika</p> <p>2014-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution <span class="hlt">soil</span> <span class="hlt">moisture</span> data for land applications. However, the efficient retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> for hydrological applications depends on optimally choosing the <span class="hlt">soil</span> and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval from SMOS brightness temperature are performed to estimate <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval. In tau-omega, the <span class="hlt">soil</span> <span class="hlt">moisture</span> is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018WRR....54.2199D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018WRR....54.2199D"><span><span class="hlt">Soil</span> Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Patterns</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dong, Jingnuo; Ochsner, Tyson E.</p> <p>2018-03-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> patterns are commonly thought to be dominated by land surface characteristics, such as <span class="hlt">soil</span> 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) <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> along the transect 18 times over 13 months. Spatial structures of <span class="hlt">soil</span> <span class="hlt">moisture</span>, <span class="hlt">soil</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> and sand content or API. The correlation lengths of <span class="hlt">soil</span> <span class="hlt">moisture</span>, sand content, and API ranged from 12-32 km, 13-20 km, and 14-45 km, respectively. <span class="hlt">Soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_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/2015AGUFM.H52E..04M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H52E..04M"><span>ESA's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> dnd Ocean Salinity Mission - Contributing to Water Resource Management</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mecklenburg, S.; Kerr, Y. H.</p> <p>2015-12-01</p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission, launched in November 2009, is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. The scientific objectives of the SMOS mission directly respond to the need for <span class="hlt">global</span> observations of <span class="hlt">soil</span> <span class="hlt">moisture</span> and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on the characterisation of ice and snow covered surfaces and the sea ice effect on ocean-atmosphere heat fluxes and dynamics, which affects large-scale processes of the Earth's climate system. The focus of this paper will be on SMOS's contribution to support water resource management: SMOS surface <span class="hlt">soil</span> <span class="hlt">moisture</span> provides the input to derive root-zone <span class="hlt">soil</span> <span class="hlt">moisture</span>, which in turn provides the input for the drought index, an important monitoring prediction tool for plant available water. In addition to surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, SMOS also provides observations on vegetation optical depth. Both parameters aid agricultural applications such as crop growth, yield forecasting and drought monitoring, and provide input for carbon and land surface modelling. SMOS data products are used in data assimilation and forecasting systems. Over land, assimilating SMOS derived information has shown to have a positive impact on applications such as NWP, stream flow forecasting and the analysis of net ecosystem exchange. Over ocean, both sea surface salinity and severe wind speed have the potential to increase the predictive skill on the seasonal and short- to medium-range forecast range. Operational users in particular in Numerical Weather Prediction and operational hydrology have put forward a requirement for <span class="hlt">soil</span> <span class="hlt">moisture</span> data to be available in near-real time (NRT). This has been addressed by developing a fast retrieval for a NRT level 2 <span class="hlt">soil</span> <span class="hlt">moisture</span> product based on Neural Networks, which will be available by autumn 2015. This paper will focus on presenting the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=330382','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=330382"><span>Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval using the L-band synthetic aperture radar onboard the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive satellite and evaluation at core validation sites</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>This paper evaluates the retrieval of <span class="hlt">soil</span> <span class="hlt">moisture</span> in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface <span class="hlt">soil</span> <span class="hlt">moisture</span> ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160008073','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160008073"><span>Analyzing and Visualizing Precipitation and <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in ArcGIS</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Yang, Wenli; Pham, Long; Zhao, Peisheng; Kempler, Steve; Wei, Jennifer</p> <p>2016-01-01</p> <p>Precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> are among the most important parameters in many land GIS (Geographic Information System) research and applications. These data are available <span class="hlt">globally</span> from NASA GES DISC (Goddard Earth Science Data and Information Services Center) in GIS-ready format at 10-kilometer spatial resolution and 24-hour or less temporal resolutions. In this presentation, well demonstrate how rainfall and <span class="hlt">soil</span> <span class="hlt">moisture</span> data are used in ArcGIS to analyze and visualize spatiotemporal patterns of droughts and their impacts on natural vegetation and agriculture in different parts of the world.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.2702L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.2702L"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Sensing Using Reflected GPS Signals: Description of the GPS <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Product.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Larson, Kristine; Small, Eric; Chew, Clara</p> <p>2015-04-01</p> <p>As first demonstrated by the GPS reflections group in 2008, data from GPS networks can be used to monitor multiple parameters of the terrestrial water cycle. The GPS L-band signals take two paths: (1) the "direct" signal travels from the satellite to the antenna, which is typically located 2-3 meters above the ground; (2) the reflected signal interacts with the Earth's surface before traveling to the antenna. The direct signal is used by geophysicists and surveyors to measure the position of the antenna, while the effects of reflected signals are a source of error. If one focuses on the reflected signal rather than the positioning observables, one has a method that is sensitive to surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (top 5 cm), vegetation water content, and snow depth. This method - known as GPS Interferometric Reflectometry (GPS-IR) - has a footprint of ~1000 m2 for most GPS sites. This is intermediate in scale to most in situ and satellite observations. A significant advantage of GPS-IR is that data from existing GPS networks can be used without any changes to the instrumentation. This means that there is a new source of cost-effective instrumentation for satellite validation and climate studies. This presentation will provide an overview of the GPS-IR methodology with an emphasis on the <span class="hlt">soil</span> <span class="hlt">moisture</span> product. GPS water cycle products are currently produced on a daily basis for a network of ~500 sites in the western United States; results are freely available at http://xenon.colorado.edu/portal. Plans to expand the GPS-IR method to the network of international GPS sites will also be discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1711428H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1711428H"><span>Assessing seasonal backscatter variations with respect to uncertainties in <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval in Siberian tundra regions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Högström, Elin; Trofaier, Anna Maria; Gouttevin, Isabella; Bartsch, Annett</p> <p>2015-04-01</p> <p>Data from the Advanced Scatterometer (ASCAT) instrument provide the basis of a near real-time, coarse scale, <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> product. Numerous studies have shown the applicability of this product, including recent operational use for numerical weather forecasts. <span class="hlt">Soil</span> <span class="hlt">moisture</span> is a key element in the <span class="hlt">global</span> cycles of water, energy and carbon. Among many application areas, it is essential for the understanding of permafrost development in a future climate change scenario. Dramatic climate changes are expected in the Arctic, where ca 25% of the land is underlain by permafrost, and it is to a large extent remote and inaccessible. The availability and applicability of satellite derived land-surface data relevant for permafrost studies, such as surface <span class="hlt">soil</span> <span class="hlt">moisture</span>, is thus crucial to landscape-scale analyses of climate-induced change. However, there are challenges in the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval that are specific to the Arctic. This study investigates backscatter variability unrelated to <span class="hlt">soil</span> <span class="hlt">moisture</span> variations in order to understand the possible impact on the <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval. The focus is on tundra lakes, which are a common feature in the Arctic and are expected to affect the retrieval. ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (120 m) data are used to resolve lakes and later understand and quantify their impacts on Metop ASCAT (25 km) <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval during the snow free period. Sites of interest are chosen according to high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. The results show that in most cases low model agreement is related to high water fraction. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.91-0.97). Backscatter deviations of up to 5 dB can occur in areas with less than 50% water fraction and an assumed <span class="hlt">soil</span> <span class="hlt">moisture</span> related range (sensitivity) of 7 dB in the ASCAT</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.B33E2116A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B33E2116A"><span>2015-16 ENSO Drove Tropical <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Dynamics and Methane Fluxes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aronson, E. L.; Dierick, D.; Botthoff, J.; Swanson, A. C.; Johnson, R. F.; Allen, M. F.</p> <p>2017-12-01</p> <p>The El Niño/Southern Oscillation Event (ENSO) cycle drives large-scale climatic trends <span class="hlt">globally</span>. Within the new world tropics, El Niño brings dryer weather than the counterpart La Niña. Atmospheric methane growth rates have shown extreme variability over the past three decades. One proposed driver is the proportion of tropical land surface saturated, affecting methane production or consumption. We measured methane flux bimonthly through the transition of 2015-16 ENSO. The date of measurement, across El Niño and La Niña within the typical "rainy" and "dry" seasons, to be the most significant driver of methane flux. <span class="hlt">Soil</span> <span class="hlt">moisture</span> varied across this time period, and regulated methane flux. During the strong El Niño, extreme dry <span class="hlt">soil</span> conditions occurred in a typical "rainy" season month reducing <span class="hlt">soil</span> <span class="hlt">moisture</span>. Wetter than usual <span class="hlt">soil</span> conditions appeared during the "rainy" season month of the moderate La Niña. The dry El Niño <span class="hlt">soils</span> corresponded to greater methane consumption by tropical forest <span class="hlt">soils</span>, and a reduced local atmospheric column methane concentration. Conversely, the wet La Niña <span class="hlt">soils</span> had lower methane consumption and higher local atmospheric column methane concentrations. The ENSO cycle is a strong driver of tropical terrestrial and wetland <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions, and can regulate <span class="hlt">global</span> atmospheric methane dynamics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H21F1205J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H21F1205J"><span>Sensitivity of Active and Passive Microwave Observations to <span class="hlt">Soil</span> <span class="hlt">Moisture</span> during Growing Corn</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Judge, J.; Monsivais-Huertero, A.; Liu, P.; De Roo, R. D.; England, A. W.; Nagarajan, K.</p> <p>2011-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> (SM) in the root zone is a key factor governing water and energy fluxes at the land surface and its accurate knowledge is critical to predictions of weather and near-term climate, nutrient cycles, crop-yield, and ecosystem productivity. Microwave observations, such as those at L-band, are highly sensitive to <span class="hlt">soil</span> <span class="hlt">moisture</span> in the upper few centimeters (near-surface). The two satellite-based missions dedicated to <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation include, the European Space Agency's <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Ocean Salinity (SMOS) mission and the planned NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active/Passive (SMAP) [4] mission. The SMAP mission will include active and passive sensors at L-band to provide <span class="hlt">global</span> observations of SM, with a repeat coverage of every 2-3 days. These observations can significantly improve root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates through data assimilation into land surface models (LSMs). Both the active (radar) and passive (radiometer) microwave sensors measure radiation quantities that are functions of <span class="hlt">soil</span> dielectric constant and exhibit similar sensitivities to SM. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of <span class="hlt">soil</span> surface and scattering within the vegetation. These effects may produce a much larger dynamic range in backscatter than that produced due to SM changes alone. In this study, we discuss the field observations of active and passive signatures of growing corn at L-band from several seasons during the tenth Microwave, Water and Energy Balance Experiment (MicroWEX-10) conducted in North Central Florida, and to understand the sensitivity of these signatures to <span class="hlt">soil</span> <span class="hlt">moisture</span> under dynamic vegetation conditions. The MicroWEXs are a series of season-long field experiments conducted during the growing seasons of sweet corn, cotton, and energy cane over the past six years (for example, [22]). The corn was planted on July 5 and harvested on September 23, 2011 during MicroWEX-10. The size of the field was 0.04 km2 and the <span class="hlt">soils</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1513286A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1513286A"><span><span class="hlt">Soil</span> <span class="hlt">moisture</span> changes in two experimental sites in Eastern Spain. Irrigation versus rainfed orchards under organic farming</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Azorin-Molina, Cesar; Vicente-Serrano, Sergio M.; Cerdà, Artemi</p> <p>2013-04-01</p> <p>Within the <span class="hlt">Soil</span> Erosion and Degradation Research Group Experimental Stations, <span class="hlt">soil</span> <span class="hlt">moisture</span> is being researched as a key factor of the <span class="hlt">soil</span> hydrology and <span class="hlt">soil</span> erosion (Cerdà, 1995; Cerda, 1997; Cerdà 1998). This because under semiarid conditions <span class="hlt">soil</span> <span class="hlt">moisture</span> content plays a crucial role for agriculture, forest, groundwater recharge and <span class="hlt">soil</span> chemistry and scientific improvement is of great interest in agriculture, hydrology and <span class="hlt">soil</span> sciences. <span class="hlt">Soil</span> <span class="hlt">moisture</span> has been seeing as the key factor for plant photosynthesis, respiration and transpiration in orchards (Schneider and Childers, 1941) and plant growth (Veihmeyer and Hendrickson, 1950). Moreover, <span class="hlt">soil</span> <span class="hlt">moisture</span> determine the root growth and distribution (Levin et al., 1979) and the <span class="hlt">soil</span> respiration ( Velerie and Orchard, 1983). Water content is expressed as a ratio, ranging from 0 (dry) to the value of <span class="hlt">soil</span> porosity at saturation (wet). In this study we present 1-year of <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements at two experimental sites in the Valencia region, Eastern Spain: 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). The EC-5 <span class="hlt">soil</span> <span class="hlt">moisture</span> smart sensor S-SMC-M005 integrated with the field-proven ECH2O™ Sensor and a 12-bit A/D has been choosen for measuring <span class="hlt">soil</span> water content providing ±3% accuracy in typical <span class="hlt">soil</span> conditions. <span class="hlt">Soil</span> <span class="hlt">moisture</span> measurements were carried out at 5-minute intervals from January till December 2012. In addition, <span class="hlt">soil</span> <span class="hlt">moisture</span> was measured at two depths in each landscape: 2 and 20 cm depth - in order to retrieve a representative vertical cross-section of <span class="hlt">soil</span> <span class="hlt">moisture</span>. Readings are provided directly from 0 (dry) to 0.450 m3/m3 (wet) volumetric water content. The <span class="hlt">soil</span> <span class="hlt">moisture</span> smart sensor is conected to a HOBO U30 Station - GSM-TCP which also stored 5-minute temperature, relative humidity, dew point, <span class="hlt">global</span> solar radiation, precipitation, wind speed and wind direction</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.H51J..08W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.H51J..08W"><span>Using the Spatial Persistence of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Patterns to Estimate Catchment <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in Semi-arid Areas</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Willgoose, G. R.</p> <p>2006-12-01</p> <p>In humid catchments the spatial distribution of <span class="hlt">soil</span> water is dominated by subsurface lateral fluxes, which leads to a persistent spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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, <span class="hlt">soils</span>), or (2) long term memory in the <span class="hlt">soil</span> <span class="hlt">moisture</span> store. We argue that it is permanent in which case it is possible to monitor the <span class="hlt">soil</span> <span class="hlt">moisture</span> status of a catchment using a single location measurement (continuous in time) of <span class="hlt">soil</span> <span class="hlt">moisture</span> using a permanently installed reflectometry instrument. This instrument will need to be calibrated to the catchment averaged <span class="hlt">soil</span> <span class="hlt">moisture</span> but the temporal persistence of the spatial pattern of <span class="hlt">soil</span> <span class="hlt">moisture</span> will mean that this calibration will be deterministically</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950048080&hterms=Soil+use&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Buse','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950048080&hterms=Soil+use&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DSoil%2Buse"><span>Use of midlatitude <span class="hlt">soil</span> <span class="hlt">moisture</span> and meteorological observations to validate <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations with biosphere and bucket models</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Robock, Alan; Vinnikov, Konstantin YA.; Schlosser, C. Adam; Speranskaya, Nina A.; Xue, Yongkang</p> <p>1995-01-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> observations in sites with natural vegetation were made for several decades in the former Soviet Union at hundreds of stations. In this paper, the authors use data from six of these stations from different climatic regimes, along with ancillary meteorological and actinometric data, to demonstrate a method to validate <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations with biosphere and bucket models. Some early and current general circulation models (GCMs) use bucket models for <span class="hlt">soil</span> hydrology calculations. More recently, the Simple Biosphere Model (SiB) was developed to incorporate the effects of vegetation on fluxes of <span class="hlt">moisture</span>, momentum, and energy at the earth's surface into <span class="hlt">soil</span> hydrology models. Until now, the bucket and SiB have been verified by comparison with actual <span class="hlt">soil</span> <span class="hlt">moisture</span> data only on a limited basis. In this study, a Simplified SiB (SSiB) <span class="hlt">soil</span> hydrology model and a 15-cm bucket model are forced by observed meteorological and actinometric data every 3 h for 6-yr simulations at the six stations. The model calculations of <span class="hlt">soil</span> <span class="hlt">moisture</span> are compared to observations of <span class="hlt">soil</span> <span class="hlt">moisture</span>, literally 'ground truth,' snow cover, surface albedo, and net radiation, and with each other. For three of the stations, the SSiB and 15-cm bucket models produce good simulations of seasonal cycles and interannual variations of <span class="hlt">soil</span> <span class="hlt">moisture</span>. For the other three stations, there are large errors in the simulations by both models. Inconsistencies in specification of field capacity may be partly responsible. There is no evidence that the SSiB simulations are superior in simulating <span class="hlt">soil</span> <span class="hlt">moisture</span> variations. In fact, the models are quite similar since SSiB implicitly has a bucket embedded in it. One of the main differences between the models is in the treatment of runoff due to melting snow in the spring -- SSiB incorrectly puts all the snowmelt into runoff. While producing similar <span class="hlt">soil</span> <span class="hlt">moisture</span> simulations, the models produce very different surface latent and sensible heat fluxes, which</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://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 <span class="hlt">global</span> 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 <span class="hlt">Global</span> 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://hdl.handle.net/2060/19760025537','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760025537"><span>Remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> with microwave radiometers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schmugge, T.; Wilheit, T.; Webster, W., Jr.; Gloerson, P.</p> <p>1976-01-01</p> <p>Results are presented that were derived from measurements made by microwave radiometers during the March 1972 and February 1973 flights of National Aeronautics and Space Administration (NASA) Convair-9900 aircraft over agricultural test sites in the southwestern part of United States. The purpose of the missions was to study the use of microwave radiometers for the remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. The microwave radiometers covered the 0.8- to 21-cm wavelength range. The results show a good linear correlation between the observed microwave brightness temperature and <span class="hlt">moisture</span> content of the 0- to 1-cm layer of the <span class="hlt">soil</span>. The results at the largest wavelength (21 cm) show the greatest sensitivity to <span class="hlt">soil</span> <span class="hlt">moisture</span> variations and indicate the possibility of sensing these variations through a vegetative canopy. The effect of <span class="hlt">soil</span> texture on the emission from the <span class="hlt">soil</span> was also studied and it was found that this effect can be compensated for by expressing <span class="hlt">soil</span> <span class="hlt">moisture</span> as a percent of field capacity for the <span class="hlt">soil</span>. The results were compared with calculations based on a radiative transfer model for layered dielectrics and the agreement is very good at the longer wavelengths. At the shorter wavelengths, surface roughness effects are larger and the agreement becomes poorer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://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.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/2060/20170006035','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170006035"><span>Combined Radar-Radiometer Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Roughness Estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Akbar, Ruzbeh; Cosh, Michael H.; O'Neill, Peggy E.; Entekhabi, Dara; Moghaddam, Mahta</p> <p>2017-01-01</p> <p>A robust physics-based combined radar-radiometer, or Active-Passive, surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and roughness estimation methodology is presented. <span class="hlt">Soil</span> <span class="hlt">moisture</span> and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval, the importance of consistent forward emission and scattering development is discussed and presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29657350','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29657350"><span>Combined Radar-Radiometer Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Roughness Estimation.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Akbar, Ruzbeh; Cosh, Michael H; O'Neill, Peggy E; Entekhabi, Dara; Moghaddam, Mahta</p> <p>2017-07-01</p> <p>A robust physics-based combined radar-radiometer, or Active-Passive, surface <span class="hlt">soil</span> <span class="hlt">moisture</span> and roughness estimation methodology is presented. <span class="hlt">Soil</span> <span class="hlt">moisture</span> and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of <span class="hlt">soil</span> <span class="hlt">moisture</span> retrieval, the importance of consistent forward emission and scattering development is discussed and presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5672948','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5672948"><span>Relating coccidioidomycosis (valley fever) incidence to <span class="hlt">soil</span> <span class="hlt">moisture</span> conditions</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Coopersmith, E. J.; Bell, J. E.; Benedict, K.; Shriber, J.; McCotter, O.; Cosh, M. H.</p> <p>2017-01-01</p> <p>Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp., in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets, it has not been feasible to perform a direct analysis of the relationships between <span class="hlt">soil</span> <span class="hlt">moisture</span> levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower <span class="hlt">soil</span> <span class="hlt">moisture</span> in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to <span class="hlt">soil</span> <span class="hlt">moisture</span> levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location. PMID:29124249</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820003640','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/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://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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24889286','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24889286"><span>Short-term precipitation exclusion alters microbial responses to <span class="hlt">soil</span> <span class="hlt">moisture</span> in a wet tropical forest.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Waring, Bonnie G; Hawkes, Christine V</p> <p>2015-05-01</p> <p>Many wet tropical forests, which contain a quarter of <span class="hlt">global</span> terrestrial biomass carbon stocks, will experience changes in precipitation regime over the next century. <span class="hlt">Soil</span> microbial responses to altered rainfall are likely to be an important feedback on ecosystem carbon cycling, but the ecological mechanisms underpinning these responses are poorly understood. We examined how reduced rainfall affected <span class="hlt">soil</span> microbial abundance, activity, and community composition using a 6-month precipitation exclusion experiment at La Selva Biological Station, Costa Rica. Thereafter, we addressed the persistent effects of field <span class="hlt">moisture</span> treatments by exposing <span class="hlt">soils</span> to a controlled <span class="hlt">soil</span> <span class="hlt">moisture</span> gradient in the lab for 4 weeks. In the field, compositional and functional responses to reduced rainfall were dependent on initial conditions, consistent with a large degree of spatial heterogeneity in tropical forests. However, the precipitation manipulation significantly altered microbial functional responses to <span class="hlt">soil</span> <span class="hlt">moisture</span>. Communities with prior drought exposure exhibited higher respiration rates per unit microbial biomass under all conditions and respired significantly more CO2 than control <span class="hlt">soils</span> at low <span class="hlt">soil</span> <span class="hlt">moisture</span>. These functional patterns suggest that changes in microbial physiology may drive positive feedbacks to rising atmospheric CO2 concentrations if wet tropical forests experience longer or more intense dry seasons in the future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://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 <span class="hlt">global</span> 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('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 <span class="hlt">global</span> 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://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 <span class="hlt">global</span> 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/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, <span class="hlt">global</span> 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://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 <span class="hlt">global</span> 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/2017JHyd..551..203K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..551..203K"><span>Automated general temperature correction method for dielectric <span class="hlt">soil</span> <span class="hlt">moisture</span> sensors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kapilaratne, R. G. C. Jeewantinie; Lu, Minjiao</p> <p>2017-08-01</p> <p>An effective temperature correction method for dielectric sensors is important to ensure the accuracy of <span class="hlt">soil</span> water content (SWC) measurements of local to regional-scale <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring networks. These networks are extensively using highly temperature sensitive dielectric sensors due to their low cost, ease of use and less power consumption. Yet there is no general temperature correction method for dielectric sensors, instead sensor or site dependent correction algorithms are employed. Such methods become ineffective at <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring networks with different sensor setups and those that cover diverse climatic conditions and <span class="hlt">soil</span> types. This study attempted to develop a general temperature correction method for dielectric sensors which can be commonly used regardless of the differences in sensor type, climatic conditions and <span class="hlt">soil</span> type without rainfall data. In this work an automated general temperature correction method was developed by adopting previously developed temperature correction algorithms using time domain reflectometry (TDR) measurements to ThetaProbe ML2X, Stevens Hydra probe II and Decagon Devices EC-TM sensor measurements. The rainy day effects removal procedure from SWC data was automated by incorporating a statistical inference technique with temperature correction algorithms. The temperature correction method was evaluated using 34 stations from the International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Monitoring Network and another nine stations from a local <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring network in Mongolia. <span class="hlt">Soil</span> <span class="hlt">moisture</span> monitoring networks used in this study cover four major climates and six major <span class="hlt">soil</span> types. Results indicated that the automated temperature correction algorithms developed in this study can eliminate temperature effects from dielectric sensor measurements successfully even without on-site rainfall data. Furthermore, it has been found that actual daily average of SWC has been changed due to temperature effects of dielectric sensors with a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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('http://adsabs.harvard.edu/abs/2012PhDT.......103T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhDT.......103T"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span>-Atmosphere Feedbacks on Atmospheric Tracers: The Effects of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> on Precipitation and Near-Surface Chemistry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tawfik, Ahmed B.</p> <p></p> <p>The atmospheric component is described by rapid fluctuations in typical state variables, such as temperature and water vapor, on timescales of hours to days and the land component evolves on daily to yearly timescales. This dissertation examines the connection between <span class="hlt">soil</span> <span class="hlt">moisture</span> and atmospheric tracers under varying degrees of <span class="hlt">soil</span> <span class="hlt">moisture</span>-atmosphere coupling. Land-atmosphere coupling is defined over the United States using a regional climate model. A newly examined <span class="hlt">soil</span> <span class="hlt">moisture</span>-precipitation feedback is identified for winter months extending the previous summer feedback to colder temperature climates. This feedback is driven by the freezing and thawing of <span class="hlt">soil</span> <span class="hlt">moisture</span>, leading to coupled land-atmosphere conditions near the freezing line. <span class="hlt">Soil</span> <span class="hlt">moisture</span> can also 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> in models. These compounds are important precursors for ozone formation, an air pollutant and a short-lived forcing agent for climate. A mechanistic description of commonly observed relationships between ground-level ozone and meteorology is presented using the concept of <span class="hlt">soil</span> <span class="hlt">moisture</span>-temperature coupling regimes. The extent of surface drying was found to be a better predictor of ozone concentrations than temperature or humidity for the Eastern U.S. This relationship is evaluated in a coupled regional chemistry-climate model under several land-atmosphere coupling and isoprene emissions cases. The coupled chemistry-climate model can reproduce the observed <span class="hlt">soil</span> <span class="hlt">moisture</span>-temperature coupling pattern, yet modeled ozone is insensitive to changes in meteorology due to the balance between isoprene and the primary atmospheric oxidant, the hydroxyl radical (OH). Overall, this work highlights the importance of <span class="hlt">soil</span> <span class="hlt">moisture</span>-atmosphere coupling for previously neglected cold climate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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/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://hdl.handle.net/2060/19760017595','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19760017595"><span>Results of <span class="hlt">soil</span> <span class="hlt">moisture</span> flights during April 1974</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schmugge, T. J.; Blanchard, B. J.; Burke, W. J.; Paris, J. F.; Swang, J. R.</p> <p>1976-01-01</p> <p>The results presented here are derived from measurements made during the April 5 and 6, 1974 flights of the NASA P-3A aircraft over the Phoenix, Arizona agricultural test site. The purpose of the mission was to study the use of microwave techniques for the remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span>. These results include infrared (10-to 12 micrometers) 2.8-cm and 21-cm brightness temperatures for approximately 90 bare fields. These brightness temperatures are compared with surface measurements of the <span class="hlt">soil</span> <span class="hlt">moisture</span> made at the time of the overflights. These data indicate that the combination of the sum and difference of the vertically and the horizontally polarized brightness temperatures yield information on both the <span class="hlt">soil</span> <span class="hlt">moisture</span> and surface roughness conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000116624','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000116624"><span>BOREAS HYD-6 Ground Gravimetric <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carroll, Thomas; Knapp, David E. (Editor); Hall, Forrest G. (Editor); Peck, Eugene L.; Smith, David E. (Technical Monitor)</p> <p>2000-01-01</p> <p>The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-6 team collected several data sets related to the <span class="hlt">moisture</span> content of <span class="hlt">soil</span> and overlying humus layers. This data set contains percent <span class="hlt">soil</span> <span class="hlt">moisture</span> ground measurements. These data were collected on the ground along the various flight lines flown in the Southern Study Area (SSA) and Northern Study Area (NSA) during 1994 by the gamma ray instrument. The data are available in tabular ASCII files. The HYD-06 ground gravimetric <span class="hlt">soil</span> <span class="hlt">moisture</span> data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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://adsabs.harvard.edu/abs/2017AGUFM.B21F2018D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.B21F2018D"><span>Boreal Forest Permafrost Sensitivity Ecotypes to changes in Snow Depth 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>Dabbs, A.; Romanovsky, V. E.; Kholodov, A. L.</p> <p>2017-12-01</p> <p>Changes in the <span class="hlt">global</span> climate, pronounced especially in polar regions due to their accelerated warming, are expected by many <span class="hlt">global</span> climate models to have large impacts on the <span class="hlt">moisture</span> budget throughout the world. Permafrost extent and the <span class="hlt">soil</span> temperature regime are both strongly dependent on <span class="hlt">soil</span> <span class="hlt">moisture</span> and snow depth because of their immense effects on the thermal properties of the <span class="hlt">soil</span> column and surface energy balance respectively. To assess how the ground thermal regime at various ecotypes may react to a change in the <span class="hlt">moisture</span> budget, we performed a sensitivity analysis using the Geophysical Institute Permafrost Laboratory model, which simulates subsurface temperature dynamics by solving a one-dimensional nonlinear heat equation with phase change. We used snow depth and air temperature data from the Fairbanks International Airport meteorological station as forcing for this sensitivity analysis. We looked at five different ecotypes within the boreal forest region of Alaska: mixed, deciduous and black forests, willow shrubs and tundra. As a result of this analysis, we found that ecotypes with higher <span class="hlt">soil</span> <span class="hlt">moisture</span> contents, such as willow shrubs, are most sensitive to changes in snow depth due to the larger amount of latent heat trapped underneath the snow during the freeze up of active layer. In addition, <span class="hlt">soil</span> within these ecotypes has higher thermal conductivity due to high saturation degree allowing for deeper seasonal freezing. Also, we found that permafrost temperatures were most sensitive to changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> in ecotypes that were not completely saturated such as boreal forest. These ecotypes lacked complete saturation because of thick organic layers that have very high porosities or partially drained mineral <span class="hlt">soils</span>. Contrarily, tundra had very little response to changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> due to its thin organic layer and almost completely saturated <span class="hlt">soil</span> column. This difference arises due to the disparity between the frozen and unfrozen thermal</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.6315G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.6315G"><span>Crop yield monitoring in the Sahel using root zone <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies derived from SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> data assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gibon, François; Pellarin, Thierry; Alhassane, Agali; Traoré, Seydou; Baron, Christian</p> <p>2017-04-01</p> <p>West Africa is greatly vulnerable, especially in terms of food sustainability. Mainly based on rainfed agriculture, the high variability of the rainy season strongly impacts the crop production driven by the <span class="hlt">soil</span> water availability in the <span class="hlt">soil</span>. To monitor this water availability, classical methods are based on daily precipitation measurements. However, the raingauge network suffers from the poor network density in Africa (1/10000km2). Alternatively, real-time satellite-derived precipitations can be used, but they are known to suffer from large uncertainties which produce significant error on crop yield estimations. The present study proposes to use root <span class="hlt">soil</span> <span class="hlt">moisture</span> rather than precipitation to evaluate crop yield variations. First, a local analysis of the spatiotemporal impact of water deficit on millet crop production in Niger was done, from in-situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements (AMMA-CATCH/OZCAR (French Critical Zone exploration network)) and in-situ millet yield survey. Crop yield measurements were obtained for 10 villages located in the Niamey region from 2005 to 2012. The mean production (over 8 years) is 690 kg/ha, and ranges from 381 to 872 kg/ha during this period. Various statistical relationships based on <span class="hlt">soil</span> <span class="hlt">moisture</span> estimates were tested, and the most promising one (R>0.9) linked the 30-cm <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies from mid-August to mid-September (grain filling period) to the crop yield anomalies. Based on this local study, it was proposed to derive regional statistical relationships using 30-cm <span class="hlt">soil</span> <span class="hlt">moisture</span> maps over West Africa. The selected approach was to use a simple hydrological model, the Antecedent Precipitation Index (API), forced by real-time satellite-based precipitation (CMORPH, PERSIANN, TRMM3B42). To reduce uncertainties related to the quality of real-time rainfall satellite products, SMOS <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements were assimilated into the API model through a Particular Filter algorithm. Then, obtained <span class="hlt">soil</span> <span class="hlt">moisture</span> anomalies were</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H44E..08W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H44E..08W"><span>The potential of remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> for operational flood forecasting</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S.; Bierkens, M. F.</p> <p>2013-12-01</p> <p>Nowadays, remotely sensed <span class="hlt">soil</span> <span class="hlt">moisture</span> is readily available from multiple space born sensors. The high temporal resolution and <span class="hlt">global</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.3106F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3106F"><span>Integration of <span class="hlt">soil</span> <span class="hlt">moisture</span> and geophysical datasets for improved water resource management in irrigated systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Finkenbiner, Catherine; Franz, Trenton E.; Avery, William Alexander; Heeren, Derek M.</p> <p>2016-04-01</p> <p><span class="hlt">Global</span> trends in consumptive water use indicate a growing and unsustainable reliance on water resources. Approximately 40% of total food production originates from irrigated agriculture. With increasing crop yield demands, water use efficiency must increase to maintain a stable food and water trade. This work aims to increase our understanding of <span class="hlt">soil</span> hydrologic fluxes at intermediate spatial scales. Fixed and roving cosmic-ray neutron probes were combined in order to characterize the spatial and temporal patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> at three study sites across an East-West precipitation gradient in the state of Nebraska, USA. A coarse scale map was generated for the entire domain (122 km2) at each study site. We used a simplistic data merging technique to produce a statistical daily <span class="hlt">soil</span> <span class="hlt">moisture</span> product at a range of key spatial scales in support of current irrigation technologies: the individual sprinkler (˜102m2) for variable rate irrigation, the individual wedge (˜103m2) for variable speed irrigation, and the quarter section (0.82 km2) for uniform rate irrigation. Additionally, we were able to generate a daily <span class="hlt">soil</span> <span class="hlt">moisture</span> product over the entire study area at various key modeling and remote sensing scales 12, 32, and 122 km2. Our <span class="hlt">soil</span> <span class="hlt">moisture</span> products and derived <span class="hlt">soil</span> properties were then compared against spatial datasets (i.e. field capacity and wilting point) from the US Department of Agriculture Web <span class="hlt">Soil</span> Survey. The results show that our "observed" field capacity was higher compared to the Web <span class="hlt">Soil</span> Survey products. We hypothesize that our results, when provided to irrigators, will decrease water losses due to runoff and deep percolation as sprinkler managers can better estimate irrigation application depth and times in relation to <span class="hlt">soil</span> <span class="hlt">moisture</span> depletion below field capacity and above maximum allowable depletion. The incorporation of this non-contact and pragmatic geophysical method into current irrigation practices across the state and globe has the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/9809915','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/9809915"><span>Predicting key malaria transmission factors, biting and entomological inoculation rates, using modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> in Kenya.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Patz, J A; Strzepek, K; Lele, S; Hedden, M; Greene, S; Noden, B; Hay, S I; Kalkstein, L; Beier, J C</p> <p>1998-10-01</p> <p>While malaria transmission varies seasonally, large inter-annual heterogeneity of malaria incidence occurs. Variability in entomological parameters, biting rates and entomological inoculation rates (EIR) have been strongly associated with attack rates in children. The goal of this study was to assess the weather's impact on weekly biting and EIR in the endemic area of Kisian, Kenya. Entomological data collected by the U.S. Army from March 1986 through June 1988 at Kisian, Kenya was analysed with concurrent weather data from nearby Kisumu airport. A <span class="hlt">soil</span> <span class="hlt">moisture</span> model of surface-water availability was used to combine multiple weather parameters with landcover and <span class="hlt">soil</span> features to improve disease prediction. Modelling <span class="hlt">soil</span> <span class="hlt">moisture</span> substantially improved prediction of biting rates compared to rainfall; <span class="hlt">soil</span> <span class="hlt">moisture</span> lagged two weeks explained up to 45% of An. gambiae biting variability, compared to 8% for raw precipitation. For An. funestus, <span class="hlt">soil</span> <span class="hlt">moisture</span> explained 32% variability, peaking after a 4-week lag. The interspecies difference in response to <span class="hlt">soil</span> <span class="hlt">moisture</span> was significant (P < 0.00001). A satellite normalized differential vegetation index (NDVI) of the study site yielded a similar correlation (r = 0.42 An. gambiae). Modelled <span class="hlt">soil</span> <span class="hlt">moisture</span> accounted for up to 56% variability of An. gambiae EIR, peaking at a lag of six weeks. The relationship between temperature and An. gambiae biting rates was less robust; maximum temperature r2 = -0.20, and minimum temperature r2 = 0.12 after lagging one week. Benefits of hydrological modelling are compared to raw weather parameters and to satellite NDVI. These findings can improve both current malaria risk assessments and those based on El Niño forecasts or <span class="hlt">global</span> climate change model projections.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29290638','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29290638"><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.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P</p> <p>2018-01-01</p> <p>A Neural Network (NN) algorithm was developed to estimate <span class="hlt">global</span> surface <span class="hlt">soil</span> <span class="hlt">moisture</span> for April 2015 to March 2017 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 Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 <span class="hlt">soil</span> <span class="hlt">moisture</span> target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.</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://hdl.handle.net/2060/20120015410','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120015410"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active/Passive (SMAP) Forward Brightness Temperature Simulator</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peng, Jinzheng; Peipmeier, Jeffrey; Kim, Edward</p> <p>2012-01-01</p> <p>The SMAP is one of four first-tier missions recommended by the US National Research Council's Committee on Earth Science and Applications from Space (Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, Space Studies Board, National Academies Press, 2007) [1]. It is to measure the <span class="hlt">global</span> <span class="hlt">soil</span> <span class="hlt">moisture</span> and freeze/thaw from space. One of the spaceborne instruments is an L-band radiometer with a shared single feedhorn and parabolic mesh reflector. While the radiometer measures the emission over a footprint of interest, unwanted emissions are also received by the antenna through the antenna sidelobes from the cosmic background and other error sources such as the Sun, the Moon and the galaxy. Their effects need to be considered accurately, and the analysis of the overall performance of the radiometer requires end-to-end performance simulation from Earth emission to antenna brightness temperature, such as the <span class="hlt">global</span> simulation of L-band brightness temperature simulation over land and sea [2]. To assist with the SMAP radiometer level 1B algorithm development, the SMAP forward brightness temperature simulator is developed by adapting the Aquarius simulator [2] with necessary modifications. This poster presents the current status of the SMAP forward brightness simulator s development including incorporating the land microwave emission model and its input datasets, and a simplified atmospheric radiative transfer model. The latest simulation results are also presented to demonstrate the ability of supporting the SMAP L1B algorithm development.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H21I1600T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H21I1600T"><span>Estimating Surface <span class="hlt">Soil</span> <span class="hlt">Moisture</span> in a Mixed-Landscape using SMAP and MODIS/VIIRS Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, J.; Di, L.; Xiao, J.</p> <p>2017-12-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span>, a critical parameter of earth ecosystem linking land surface and atmosphere, has been widely applied in many application (Di, 1991; Njoku et al. 2003; Western 2002; Zhao et al. 2014; McColl et al. 2017) from regional to continental or even <span class="hlt">global</span> scale. The advent of satellite-based remote sensing, particular in the last two decades, has proven successful for mapping the surface <span class="hlt">soil</span> <span class="hlt">moisture</span> (SSM) from space (Petropoulos et al. 2015; Kim et al. 2015; Molero et al. 2016). The current <span class="hlt">soil</span> <span class="hlt">moisture</span> products, however, is not able to fully characterize the spatial and temporal variability of <span class="hlt">soil</span> <span class="hlt">moisture</span> at mixed landscape types (Albergel et al. 2013; Zeng et al. 2015). In this research, we derived the SSM at 1-km spatial resolution by using sensor observation and high-level products from SMAP and MODIS/VIIRS as well as metrorological, landcover, and <span class="hlt">soil</span> data. Specifically, we proposed a practicable method to produce the originally planned SMAP L3_SM_A with comparable quality by downscaling the SMAP L3_SM_P product through a proved method, the geographically weighted regression method at mixed landscape in southern New Hampshire. This estimated SSM was validated using the <span class="hlt">Soil</span> Climate Analysis Network (SCAN) from Natural Resources Conservation Service (NRCS) of United States Department of Agriculture (USDA).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19850035350&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=19850035350&hterms=watershed&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dwatershed"><span>Implications of complete watershed <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements to hydrologic modeling</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Engman, E. T.; Jackson, T. J.; Schmugge, T. J.</p> <p>1983-01-01</p> <p>A series of six microwave data collection flights for measuring <span class="hlt">soil</span> <span class="hlt">moisture</span> were made over a small 7.8 square kilometer watershed in southwestern Minnesota. These flights were made to provide 100 percent coverage of the basin at a 400 m resolution. In addition, three flight lines were flown at preselected areas to provide a sample of data at a higher resolution of 60 m. The low level flights provide considerably more information on <span class="hlt">soil</span> <span class="hlt">moisture</span> variability. The results are discussed in terms of reproducibility, spatial variability and temporal variability, and their implications for hydrologic modeling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUSM.H11C..02T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUSM.H11C..02T"><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, I.; McDonnell, J.</p> <p>2009-05-01</p> <p>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 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements in areas with shallow <span class="hlt">soils</span>?; (2) Can EM represent the temporal and spatial patterns of <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span>? We found that the apparent conductivity measured with the multi-frequency GEM-300 was linearly related to <span class="hlt">soil</span> <span class="hlt">moisture</span> measured with an 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 (7290, 9090, 11250, and 14010 Hz) 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 Aqua</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1332724','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1332724"><span><span class="hlt">Soil</span> Temperature and <span class="hlt">Moisture</span> Profile (STAMP) System Handbook</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Cook, David R.</p> <p></p> <p>The <span class="hlt">soil</span> temperature and <span class="hlt">moisture</span> profile system (STAMP) provides vertical profiles of <span class="hlt">soil</span> temperature, <span class="hlt">soil</span> water content (<span class="hlt">soil</span>-type specific and loam type), plant water availability, <span class="hlt">soil</span> conductivity, and real dielectric permittivity as a function of depth below the ground surface at half-hourly intervals, and precipitation at one-minute intervals. The profiles are measured directly by in situ probes at all extended facilities of the SGP climate research site. The profiles are derived from measurements of <span class="hlt">soil</span> energy conductivity. Atmospheric scientists use the data in climate models to determine boundary conditions and to estimate the surface energy flux. The data are alsomore » useful to hydrologists, <span class="hlt">soil</span> scientists, and agricultural scientists for determining the state of the <span class="hlt">soil</span>. The STAMP system replaced the SWATS system in early 2016.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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('https://www.ncbi.nlm.nih.gov/pubmed/26087288','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26087288"><span>Changes in <span class="hlt">soil</span> <span class="hlt">moisture</span> drive <span class="hlt">soil</span> methane uptake along a fire regeneration chronosequence in a eucalypt forest landscape.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fest, Benedikt; Wardlaw, Tim; Livesley, Stephen J; Duff, Thomas J; Arndt, Stefan K</p> <p>2015-11-01</p> <p>Disturbance associated with severe wildfires (WF) and WF simulating harvest operations can potentially alter <span class="hlt">soil</span> methane (CH4 ) oxidation in well-aerated forest <span class="hlt">soils</span> due to the effect on <span class="hlt">soil</span> properties linked to diffusivity, methanotrophic activity or changes in methanotrophic bacterial community structure. However, changes in <span class="hlt">soil</span> CH4 flux related to such disturbances are still rarely studied even though WF frequency is predicted to increase as a consequence of <span class="hlt">global</span> climate change. We measured in-situ <span class="hlt">soil</span>-atmosphere CH4 exchange along a wet sclerophyll eucalypt forest regeneration chronosequence in Tasmania, Australia, where the time since the last severe fire or harvesting disturbance ranged from 9 to >200 years. On all sampling occasions, mean CH4 uptake increased from most recently disturbed sites (9 year) to sites at stand 'maturity' (44 and 76 years). In stands >76 years since disturbance, we observed a decrease in <span class="hlt">soil</span> CH4 uptake. A similar age dependency of potential CH4 oxidation for three <span class="hlt">soil</span> layers (0.0-0.05, 0.05-0.10, 0.10-0.15 m) could be observed on incubated <span class="hlt">soils</span> under controlled laboratory conditions. The differences in <span class="hlt">soil</span> CH4 uptake between forest stands of different age were predominantly driven by differences in <span class="hlt">soil</span> <span class="hlt">moisture</span> status, which affected the diffusion of atmospheric CH4 into the <span class="hlt">soil</span>. The observed <span class="hlt">soil</span> <span class="hlt">moisture</span> pattern was likely driven by changes in interception or evapotranspiration with forest age, which have been well described for similar eucalypt forest systems in south-eastern Australia. Our results imply that there is a large amount of variability in CH4 uptake at a landscape scale that can be attributed to stand age and <span class="hlt">soil</span> <span class="hlt">moisture</span> differences. An increase in severe WF frequency in response to climate change could potentially increase overall forest <span class="hlt">soil</span> CH4 sinks. © 2015 John Wiley & Sons Ltd.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20180002232','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20180002232"><span>Improving <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Girotto, Manuela</p> <p>2018-01-01</p> <p>Observations from recent <span class="hlt">soil</span> <span class="hlt">moisture</span> dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide <span class="hlt">global</span> high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) <span class="hlt">soil</span> <span class="hlt">moisture</span> profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface <span class="hlt">soil</span> <span class="hlt">moisture</span> 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper <span class="hlt">moisture</span> storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of <span class="hlt">soil</span> <span class="hlt">moisture</span> (i.e., surface and deeper water storages).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JHyd..561..662K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JHyd..561..662K"><span>Can next-generation <span class="hlt">soil</span> data products improve <span class="hlt">soil</span> <span class="hlt">moisture</span> modelling at the continental scale? An assessment using a new microclimate package for the R programming environment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kearney, Michael R.; Maino, James L.</p> <p>2018-06-01</p> <p>Accurate models of <span class="hlt">soil</span> <span class="hlt">moisture</span> are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for <span class="hlt">soil</span> <span class="hlt">moisture</span> modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and <span class="hlt">soil</span> data together with advances in modelling methods. In particular, the <span class="hlt">GlobalSoil</span>Map.net initiative represents next-generation, depth-specific gridded <span class="hlt">soil</span> products that may substantially increase <span class="hlt">soil</span> <span class="hlt">moisture</span> modelling capacity. Here we present an implementation of Campbell's infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the <span class="hlt">GlobalSoil</span>Map.net product <span class="hlt">Soil</span> and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution <span class="hlt">global</span> product <span class="hlt">Soil</span>Grids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3-75 cm) <span class="hlt">soil</span> <span class="hlt">moisture</span> observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0-50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0-2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted <span class="hlt">soil</span> <span class="hlt">moisture</span> to within experimental error for all data sets. Using the SLGA or the SG <span class="hlt">soil</span> database, the OzNet <span class="hlt">soil</span> <span class="hlt">moisture</span> could be predicted with a root mean square error (rmse) of ∼0.075 m3 m-3 and a correlation coefficient (r) of 0.65 consistently through the <span class="hlt">soil</span> profile without any parameter tuning. <span class="hlt">Soil</span> <span class="hlt">moisture</span> predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived <span class="hlt">soil</span> data set (Digital Atlas of Australian <span class="hlt">Soils</span>), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy (r = 0.76 and rmse of ∼0</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25757307','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25757307"><span>[Characteristics of <span class="hlt">soil</span> <span class="hlt">moisture</span> in artificial impermeable layers].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Suo, Gai-Di; Xie, Yong-Sheng; Tian, Fei; Chuai, Jun-Feng; Jing, Min-Xiao</p> <p>2014-09-01</p> <p>For the problem of low water and fertilizer use efficiency caused by nitrate nitrogen lea- ching into deep <span class="hlt">soil</span> layer and <span class="hlt">soil</span> desiccation in dryland apple orchard, characteristics of <span class="hlt">soil</span> <span class="hlt">moisture</span> were investigated by means of hand tamping in order to find a new approach in improving the water and fertilizer use efficiency in the apple orchard. Two artificial impermeable layers of red clay and dark loessial <span class="hlt">soil</span> were built in <span class="hlt">soil</span>, with a thickness of 3 or 5 cm. Results showed that artificial impermeable layers with the two different thicknesses were effective in reducing or blocking water infiltration into <span class="hlt">soil</span> and had higher seepage controlling efficiency. Seepage controlling efficiency for the red clay impermeable layer was better than that for the dark loessial <span class="hlt">soil</span> impermeable layer. Among all the treatments, the red clay impermeable layer of 5 cm thickness had the highest bulk density, the lowest initial infiltration rate (0.033 mm · min(-1)) and stable infiltration rate (0.018 mm · min(-1)) among all treatments. After dry-wet alternation in summer and freezing-thawing cycle in winter, its physiochemical properties changed little. Increase in years did not affect stable infiltration rate of <span class="hlt">soil</span> water. The red clay impermeable layer of 5 cm thickness could effectively increase <span class="hlt">soil</span> <span class="hlt">moisture</span> content in upper <span class="hlt">soil</span> layer which was conducive to raise the water and nutrient use efficiency. The approach could be applied to the apple production of dryland orchard.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/20486','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/20486"><span>Unsaturated <span class="hlt">soil</span> <span class="hlt">moisture</span> drying and wetting diffusion coefficient measurements in the laboratory.</span></a></p> <p><a target="_blank" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a></p> <p></p> <p>2009-09-01</p> <p>ABSTRACTTransient <span class="hlt">moisture</span> flow in an unsaturated <span class="hlt">soil</span> in response to suction changes is controlled by the unsaturated <span class="hlt">moisture</span> diffusion coefficient. The <span class="hlt">moisture</span> diffusion coefficient can be determined by measuring suction profiles over time. The l...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27337651','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27337651"><span><span class="hlt">Moisture</span> effect in prompt gamma measurements from <span class="hlt">soil</span> samples.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Naqvi, A A; Khiari, F Z; Liadi, F A; Khateeb-Ur-Rehman; Raashid, M A; Isab, A H</p> <p>2016-09-01</p> <p>The variation in intensity of 1.78MeV silicon, 6.13MeV oxygen, and 2.22MeV hydrogen prompt gamma rays from <span class="hlt">soil</span> samples due to the addition of 5.1, 7.4, 9.7, 11.9 and 14.0wt% water was studied for 14MeV incident neutron beams utilizing a LaBr3:Ce gamma ray detector. The intensities of 1.78MeV and 6.13MeV gamma rays from silicon and oxygen, respectively, decreased with increasing sample <span class="hlt">moisture</span>. The intensity of 2.22MeV hydrogen gamma rays increases with <span class="hlt">moisture</span>. The decrease in intensity of silicon and oxygen gamma rays with <span class="hlt">moisture</span> concentration indicates a loss of 14MeV neutron flux, while the increase in intensity of 2.22MeV gamma rays with <span class="hlt">moisture</span> indicates an increase in thermal neutron flux due to increasing concentration of <span class="hlt">moisture</span>. The experimental intensities of silicon, oxygen and hydrogen prompt gamma rays, measured as a function of <span class="hlt">moisture</span> concentration in the <span class="hlt">soil</span> samples, are in good agreement with the theoretical results obtained through Monte Carlo calculations. Copyright © 2016 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000038145&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=20000038145&hterms=How+soil+form&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DHow%2Bsoil%2Bform"><span>Potential for Remotely Sensed <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Data in Hydrologic Modeling</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>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> and portray the spatial heterogeneity of hydrologic processes and properties that one encounters in drainage basins. The hydrologic processes that may be detected include ground water recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential ET, and information about the hydrologic properties of <span class="hlt">soils</span> and heterogeneity of hydrologic parameters. Microwave remote sensing has the potential to detect these signatures within a basin in the form of volumetric <span class="hlt">soil</span> <span class="hlt">moisture</span> measurements in the top few cm. These signatures should provide information on how and where to apply <span class="hlt">soil</span> physical parameters in distributed and lumped parameter models and how to subdivide drainage basins into hydrologically similar sub-basins.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19830027190','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19830027190"><span>Advanced microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> studies. [Big Sioux River Basin, Iowa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dalsted, K. J.; Harlan, J. C.</p> <p>1983-01-01</p> <p>Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: <span class="hlt">soil</span> map and land cover data; direct <span class="hlt">soil</span> <span class="hlt">moisture</span> measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (<span class="hlt">soil</span> features and land cover) hold promise for qualitative assessment of <span class="hlt">soil</span> <span class="hlt">moisture</span> and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28886067','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28886067"><span>A wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor powered by solar energy.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Mingliang; Lv, Mouchao; Deng, Zhong; Zhai, Guoliang</p> <p>2017-01-01</p> <p>In a variety of agricultural activities, such as irrigation scheduling and nutrient management, <span class="hlt">soil</span> water content is regarded as an essential parameter. Either power supply or long-distance cable is hardly available within field scale. For the necessity of monitoring <span class="hlt">soil</span> water dynamics at field scale, this study presents a wireless <span class="hlt">soil</span> <span class="hlt">moisture</span> sensor based on the impedance transform of the frequency domain. The sensor system is powered by solar energy, and the data can be instantly transmitted by wireless communication. The sensor electrodes are embedded into the bottom of a supporting rod so that the sensor can measure <span class="hlt">soil</span> water contents at different depths. An optimal design with time executing sequence is considered to reduce the energy consumption. The experimental results showed that the sensor is a promising tool for monitoring <span class="hlt">moisture</span> in large-scale farmland using solar power and wireless communication.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ESASP.729E..15J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ESASP.729E..15J"><span>Estimation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Under Vegetation Cover at Multiple Frequencies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jadghuber, Thomas; Hajnsek, Irena; Weiß, Thomas; Papathanassiou, Konstantinos P.</p> <p>2015-04-01</p> <p><span class="hlt">Soil</span> <span class="hlt">moisture</span> under vegetation cover was estimated by a polarimetric, iterative, generalized, hybrid decomposition and inversion approach at multiple frequencies (X-, C- and L-band). Therefore the algorithm, originally designed for longer wavelength (L-band), was adapted to deal with the short wavelength scattering scenarios of X- and C-band. The Integral Equation Method (IEM) was incorporated together with a pedo-transfer function of Dobson et al. to account for the peculiarities of short wavelength scattering at X- and C-band. DLR's F-SAR system acquired fully polarimetric SAR data in X-, C- and L-band over the Wallerfing test site in Lower Bavaria, Germany in 2014. Simultaneously, <span class="hlt">soil</span> and vegetation measurements were conducted on different agricultural test fields. The results indicate a spatially continuous inversion of <span class="hlt">soil</span> <span class="hlt">moisture</span> in all three frequencies (inversion rates >92%), mainly due to the careful adaption of the vegetation volume removal including a physical constraining of the decomposition algorithm. However, for X- and C-band the inversion results reveal <span class="hlt">moisture</span> pattern inconsistencies and in some cases an incorrectly high inversion of <span class="hlt">soil</span> <span class="hlt">moisture</span> at X-band. The validation with in situ measurements states a stable performance of 2.1- 7.6vol.% at L-band for the entire growing period. At C- and X-band a reliable performance of 3.7-13.4vol.% in RMSE can only be achieved after distinct filtering (X- band) leading to a loss of almost 60% in spatial inversion rate. Hence, a robust inversion for <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation under vegetation cover can only be conducted at L-band due to a constant availability of the <span class="hlt">soil</span> signal in contrast to higher frequencies (X- and C-band).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhDT.......181S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhDT.......181S"><span>Terrestrial precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span>: A case study over southern Arizona and data development</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stillman, Susan</p> <p></p> <p>Quantifying climatological precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> as well as interannual variability and trends requires extensive observation. This work focuses on the analysis of available precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> data and the development of new ways to estimate these quantities. Precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> characteristics are highly dependent on the spatial and temporal scales. We begin at the point scale, examining hourly precipitation and <span class="hlt">soil</span> <span class="hlt">moisture</span> at individual gauges. First, we focus on the Walnut Gulch Experimental Watershed (WGEW), a 150 km2 area in southern Arizona. The watershed has been measuring rainfall since 1956 with a very high density network of approximately 0.6 gauges per km2. Additionally, there are 19 <span class="hlt">soil</span> <span class="hlt">moisture</span> probes at 5 cm depth with data starting in 2002. In order to extend the measurement period, we have developed a water balance model which estimates monsoon season (Jul-Sep) <span class="hlt">soil</span> <span class="hlt">moisture</span> using only precipitation for input, and calibrated so that the modeled <span class="hlt">soil</span> <span class="hlt">moisture</span> fits best with the <span class="hlt">soil</span> <span class="hlt">moisture</span> measured by each of the 19 probes from 2002-2012. This observationally constrained <span class="hlt">soil</span> <span class="hlt">moisture</span> is highly correlated with the collocated probes (R=0.88), and extends the measurement period from 10 to 56 years and the number of gauges from 19 to 88. Then, we focus on the spatiotemporal variability within the watershed and the ability to estimate area averaged quantities. Spatially averaged precipitation and observationally constrained <span class="hlt">soil</span> <span class="hlt">moisture</span> from the 88 gauges is then used to evaluate various gridded datasets. We find that gauge-based precipitation products perform best followed by reanalyses and then satellite-based products. Coupled Model Intercomparison Project Phase 5 (CMIP5) models perform the worst and overestimate cold season precipitation while offsetting the monsoon peak precipitation forward or backward by a month. Satellite-based <span class="hlt">soil</span> <span class="hlt">moisture</span> is the best followed by land data assimilation systems and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007JGRD..112.3102D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007JGRD..112.3102D"><span>Initializing numerical weather prediction models with satellite-derived surface <span class="hlt">soil</span> <span class="hlt">moisture</span>: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI <span class="hlt">soil</span> <span class="hlt">moisture</span> data set</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Drusch, M.</p> <p>2007-02-01</p> <p>Satellite-derived surface <span class="hlt">soil</span> <span class="hlt">moisture</span> data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone <span class="hlt">soil</span> <span class="hlt">moisture</span>. Remotely sensed surface <span class="hlt">soil</span> <span class="hlt">moisture</span> is directly linked to the model's uppermost <span class="hlt">soil</span> layer and therefore is a stronger constraint for the <span class="hlt">soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis, an open loop run with freely evolving <span class="hlt">soil</span> <span class="hlt">moisture</span>, and an experimental run incorporating TMI (TRMM Microwave Imager) derived <span class="hlt">soil</span> <span class="hlt">moisture</span> over the southern United States. In this experimental run the satellite-derived <span class="hlt">soil</span> <span class="hlt">moisture</span> product is introduced through a nudging scheme using 6-hourly increments. Apart from the <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. <span class="hlt">Soil</span> <span class="hlt">moisture</span> 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 <span class="hlt">soil</span> <span class="hlt">moisture</span> analysis influences local weather parameters including the planetary boundary layer height and cloud coverage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.3990H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3990H"><span><span class="hlt">Soil</span> frost-induced <span class="hlt">soil</span> <span class="hlt">moisture</span> precipitation feedback and effects on atmospheric states</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hagemann, Stefan; Blome, Tanja; Ekici, Altug; Beer, Christian</p> <p>2016-04-01</p> <p>Permafrost or perennially frozen ground is an important part of the terrestrial cryosphere; roughly one quarter of Earth's land surface is underlain by permafrost. As it is a thermal phenomenon, its characteristics are highly dependent on climatic factors. The impact of the currently observed warming, which is projected to persist during the coming decades due to anthropogenic CO2 input, certainly has effects for the vast permafrost areas of the high northern latitudes. The quantification of these effects, however, is scientifically still an open question. This is partly due to the complexity of the system, where several feedbacks are interacting between land and atmosphere, sometimes counterbalancing each other. Moreover, until recently, many <span class="hlt">global</span> circulation models (GCMs) and Earth system models (ESMs) lacked the sufficient representation of permafrost physics in their land surface schemes. Within the European Union FP7 project PAGE21, the land surface scheme JSBACH of the Max-Planck-Institute for Meteorology ESM (MPI-ESM) has been equipped with the representation of relevant physical processes for permafrost studies. These processes include the effects of freezing and thawing of <span class="hlt">soil</span> water for both energy and water cycles, thermal properties depending on <span class="hlt">soil</span> water and ice contents, and <span class="hlt">soil</span> <span class="hlt">moisture</span> movement being influenced by the presence of <span class="hlt">soil</span> ice. In the present study, it will be analysed how these permafrost relevant processes impact large-scale hydrology and climate over northern hemisphere high latitude land areas. For this analysis, the atmosphere-land part of MPI-ESM, ECHAM6-JSBACH, is driven by prescribed observed SST and sea ice in an AMIP2-type setup with and without the newly implemented permafrost processes. Results show a large improvement in the simulated discharge. On one hand this is related to an improved snowmelt peak of runoff due to frozen <span class="hlt">soil</span> in spring. On the other hand a subsequent reduction of <span class="hlt">soil</span> <span class="hlt">moisture</span> leads to a positive</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFMSF53A0723T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFMSF53A0723T"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> and Vegetation Effects on GPS Reflectivity From Land</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Torres, O.; Grant, M. S.; Bosch, D.</p> <p>2004-12-01</p> <p>While originally designed as a navigation system, the GPS signal has been used to achieve a number of useful scientific measurements. One of these measurements utilizes the reflection of the GPS signal from land to determine <span class="hlt">soil</span> <span class="hlt">moisture</span>. The study of GPS reflections is based on a bistatic configuration that utilizes forward reflection from the surface. The strength of the GPS signal varies in proportion to surface parameters such as <span class="hlt">soil</span> <span class="hlt">moisture</span>, <span class="hlt">soil</span> type, vegetation cover, and topography. This paper focuses on the effects of <span class="hlt">soil</span> water content and vegetation cover on the surface based around a reflectivity. A two-part method for calibrating the GPS reflectivity was developed that permits the comparison of the data with surface parameters. The first part of the method relieves the direct signal from any multipath effects, the second part is an over-water calibration that yields a reflectivity independent of the transmitting satellite. The sensitivity of the GPS signal to water in the <span class="hlt">soil</span> is shown by presenting the increase in reflectivity after rain as compared to before rain. The effect of vegetation on the reflected signal is also presented by the inclusion of leaf area index as a fading parameter in the reflected signal from corn and soy bean fields. The results are compared to extensive surface measurements made as part of the <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Experiment 2002 (SMEX 2002) in Iowa and SMEX 2003 in Georgia.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA426497','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA426497"><span>Scaling Properties and Spatial Interpolation of <span class="hlt">Soil</span> <span class="hlt">Moisture</span></span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2004-08-24</p> <p>the sensitivities is useful not only for characterizing <span class="hlt">soil</span> <span class="hlt">moisture</span> but also for forecasting the vulnerability of a region’s water cycle to climate...regional water balance was presented that can be used to assess the impact of climatic fluctuations and changes on the water cycle of a region. In</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> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=317084','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=317084"><span><span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive Satellite Status and Recent Validation Results</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission was launched in January, 2015 and began its calibration and validation (cal/val) phase in May, 2015. Cal/Val will begin with a focus on instrument measurements, brightness temperature and backscatter, and evolve to the geophysical products that include...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=315838','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=315838"><span>SMAP Validation and Accuracy Assessment of <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Products</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Introduction: The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission was launched in January, 2015 and will begin its calibration and validation (Cal/Val) phase in May, 2015. This will begin with a focus on instrument measurements, brightness temperature and backscatter, and evolve to the geophysical produ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=323268','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=323268"><span>GCOM-W <span class="hlt">soil</span> <span class="hlt">moisture</span> and temperature algorithms and validation</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Passive microwave remote sensing of <span class="hlt">soil</span> <span class="hlt">moisture</span> has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://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('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('http://www.ars.usda.gov/research/publications/publication/?seqNo115=294399','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=294399"><span>U.S National cropland <span class="hlt">soil</span> <span class="hlt">moisture</span> monitoring using SMAP</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Crop condition information is critical for public and private sector decision making that concerns agricultural policy, food production, food security, and food commodity prices. Crop conditions change quickly due to various growing condition events, such as temperature extremes, <span class="hlt">soil</span> <span class="hlt">moisture</span> defic...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19930053193&hterms=watershed+analysis&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dwatershed%2Banalysis','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930053193&hterms=watershed+analysis&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dwatershed%2Banalysis"><span>Microwave <span class="hlt">soil</span> <span class="hlt">moisture</span> estimation in humid and semiarid watersheds</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. E.; Jackson, T. J.; Chauhan, N. S.; Seyfried, M. S.</p> <p>1993-01-01</p> <p>Land surface hydrologic-atmospheric interactions in humid and semi-arid watersheds were investigated. Active and passive microwave sensors were used to estimate the spatial and temporal distribution of <span class="hlt">soil</span> <span class="hlt">moisture</span> at the catchment scale in four areas. Results are presented and discussed. The eventual use of this information in the analysis and prediction of associated hydrologic processes is examined.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=268159','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=268159"><span>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) applications activity</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) mission is one of the first-tier satellite missions recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space. The SMAP mission 1 is under development by NASA and is scheduled for launch late in 2014. The SMAP mea...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=286116','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=286116"><span>Overview of the NASA <span class="hlt">soil</span> <span class="hlt">moisture</span> active/passive 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 NASA <span class="hlt">Soil</span> <span class="hlt">Moisture</span> Active Passive (SMAP) Mission is currently in design Phase C and scheduled for launch in October 2014. Its mission concept is based on combined L-band radar and radiometry measurements obtained from a shared, rotating 6-meter antennae. These measurements will be used to retrie...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=345327','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=345327"><span>Triple collocation based merging of satellite <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>We propose a method for merging <span class="hlt">soil</span> <span class="hlt">moisture</span> retrievals from space borne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from u...</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 stochasti