Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #432081

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

2020 Annual Report

Objective 1: Develop and evaluate new methodologies and tools for characterizing spatiotemporal variability in land-surface water balance components from plot to global scales, integrating multi-sensor remote and in-situ measurement sources. Sub-objective 1.1: Improve representations of water and energy exchanges in structured agricultural environments, developed using in-situ measurements. Sub-objective 1.2: Improve multi-sensor tools for mapping water use over irrigated and rainfed crops, forests and rangelands. Sub-objective 1.3 Improve remote sensing tools for mapping regional and global soil moisture. Sub-objective 1.4: Develop new techniques for measuring soil moisture variability in situ and upscaling for validation of satellite retrievals. Sub-objective 1.5: Evaluate the terrestrial water budget at basin scale via the integration of remote sensing with ground observations. Objective 2: Develop remote sensing and modeling approaches for determining the timing and magnitude of agricultural drought and its impact on agroecosystems and onhe regional hydrology. Sub-objective 2.1: Improve early warning tools for identifying agricultural drought onset, severity and recovery at local to regional scales. Sub-objective 2.2: Improve techniques for assessing crop and rangeland phenology and condition and for forecasting yields. Sub-objective 2.3: Enhance understanding and monitoring of drought impacts on regional hydrologic components. Objective 3 (short): Assess the hydrologic status and trends within the Lower Chesapeake Bay Long-Term Agroecosystem Research site through measurements, remote sensing, and modeling. Sub-objective 3.1: Establish long-term data streams for the LCB LTAR project to examine agroecosystem status and trends. Sub-objective 3.2: Examine the effects of irrigation intensification within the LCB LTAR on trends in regional hydrology and nitrogen dynamics. Sub-objective 3.3: Improve prediction capability of SWAT in evaluating the effects of both natural riparian and restored wetlands on water quality. Sub-objective 3.4: Investigate sources and fate of nitrate in the LCB LTAR.

This project seeks to develop new tools for agricultural monitoring and management that integrate ground observations, remote sensing data and modeling frameworks. In specific, these multiscale tools will be used to address characterization of water supply (soil moisture), water demand (evapotranspiration), water quality drivers and drought impacts over agricultural landscapes.

Progress Report
This report documents progress for the 8042-13610-029-00D “Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems” which started in 2017. Substantial progress was made in all three objectives outlined in the project plan, all of which fall under NP 211. Under Objective 1, research activities focused on refining and publishing results from ongoing data collection and modeling efforts. The Grape Remote sensing Atmospheric Profile & Evapotranspiration eXperiment (GRAPEX) project produced a Special Issue publication in Irrigation Science covering a broad range of topics as well as a full range in spatial and temporal scales. This includes a set of papers dealing, at micrometeorological scales, with analysis of turbulent intermittency above and below the vine/interrow system, surface energy balance (SEB) above and below the vine canopy, modeling and measurement of radiation divergence through the vine canopy, the micro-scale distribution of soil heat flux, and investigation of the temporal behavior of canopy/leaf level crop water stress index (Sub-Objective 1.1). Findings that horizontal advection of heat is commonplace over California vineyards and results in a significant enhancement of evaporative water loss led to a study aimed at quantifying the effects of advection and the ability of remote sensing models to account for these effects. Also included in the GRAPEX special issue are analyses of the land surface temperature (LST)-based two-source energy balance (TSEB) model and its utility in partitioning actual evapotranspiration (ETa) between vine and interrow (cover crop) water use, including: validation of TSEB at the micrometeorological scale, the impact of modifications to TSEB wind extinction algorithms through the vine canopy layer to account for unique vine row and canopy architecture/structure; and sensitivity of TSEB model SEB output to uncertainty in aerodynamic roughness parameters (Sub-Objective 1.2). At the canopy to field scale, the utility of very-high-resolution imagery is examined: use of very-high-resolution UAV imagery for mapping vineyard ETa and partitioning of ET between vine and interrow and detection and impact of shadows in UAV imagery on SEB modeling with validation of the satellite-based data fusion ETa modeling system that generates daily 30-m ETa year-round, beginning to address Sub-Objective 1.2’s 48 and 60 month milestones. National and international soil moisture monitoring capabilities were advanced in FY20 using both in situ sensor networks and spaceborne imagery. A draft strategy for the development of a National Soil Moisture Network was delivered to the National Drought Monitor for adoption. Soil moisture calibration and validation for National Aeronautics and Space Administration (NASA) products continued, for the Soil Moisture Active Passive and CYGNSS missions, as well as the future NASA-ISRO (Indian Space Research Organization) Synthetic Aperture Radar (NISAR) mission, which will monitor agricultural landscapes among other domains (Sub-Objective 1.3 and 1.4). Progress was made on collecting synthetic aperture radar validation data over multiple LTAR locations during the summer and fall of 2019 in cooperation with NASA and University of Massachusetts-Amherst as a part of a larger project to improve our understanding of radar remote sensing of agriculture. Continued progress is being made on the monitoring of small field agriculture in the Eastern United States with the SMAPVEX19-21 campaign which is focusing on small field/forest interfaces. In addition, an intercomparison study was enacted to evaluate the utility of leading portable soil moisture probes for irrigation management applications, and to provide an assessment of their accuracy and limitations. Under Objective 2, significant progress was made in advancing remote sensing methods for characterizing drought, phenology, and regional hydrology from field to watershed scale. Multi-year time series of sub-field scale (30-m resolution) vegetation stress metrics were constructed over several LTAR target sites (Sub-objective 2.1). These metrics represent the ratio of actual-to-reference evapotranspiration (fRET), a scaled indicator of crop water use/availability. Preliminary investigations of field-scale fRET and crop yield relationships are promising and point to the need for accurate geospatial definition of critical crop growth stages. A within-season crop emergence (WISE) algorithm has been developed to map crop emergence dates at the field to sub-field scales in near real-time (Sub-objective). The WISE approach was first optimized using VENµS data (5 m, 2-day revisit) and assessed using ground observations collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, Maryland, during the 2019 growing season. Results show that early crop growth stages can be reliably detected at the sub-field scale about two weeks after crop emergence. The application of the WISE approach to large regions is in progress. At the project mid-point, our primary focus under Sub-objectives 1.5 and 2.3 has been on the integration of existing tools for an improved ability to monitor and predict variations in the terrestrial water balance. Key advances include: the near-real-time production of soil moisture products from the Vineyard Data Assimilation system, the combined use of microwave and thermal remote sensing to diagnosis soil moisture/evapotranspiration coupling bias in a numerical weather forecasting model, and the first application of remotely sensed soil moisture products to falsify existing models linking pre-storm soil moisture to storm-scale runoff efficiency. Taken as a whole, these advances significantly improve our ability to evaluate components of the terrestrial water budget and monitor agricultural drought at multiple scales. Data collection and analyses at the Lower Chesapeake Bay (LCB) LTAR continued into FY2020 under Objective 3 in support of remote sensing and modeling research on the connections between agricultural water use, land management and water quality. Real-time in situ water quality data were collected at the Tuckahoe and Greensboro United States Geological Survey (USGS) gage stations to extend our long-term water quality record (Sub-objective 3.1). Maps of irrigation intensity were developed for the Delmarva peninsula which can inform watershed models to better reflect water usage in the LCB LTAR region. These maps are being used to generate water budgets for irrigated and unirrigated crops based on remotely sensed ET map timeseries (30-m pixels/daily timeseries) generated over the Choptank watershed (Sub-objective 3.2). Maps of poultry house intensities were also developed which will be used later for estimates of local ammonia emissions with potential deposition on surface water bodies. Wetland hydrology has continued to be monitored at various wetland sites in the Choptank watershed to assess the connection of isolated wetlands to downstream discharges. A Soil and Water Assessment Tool (SWAT) assessment of impacts of riparian buffers on nitrogen export to streamflow was published (Sub-objective 3.3). In addition, monthly water samples continued at the gage stations and were collected and processed for metachlor ethane sulfonic acid (MESA) analyses to determine groundwater lag time (Sub-objective 3.4). Watershed sampling for groundwater lag time using the MESA as transit tracer, was continued at 15 watersheds in the LTAR and CEAP Watersheds networks. A new passive integrative sampler was introduced that reduces short-term measurement variability, enabling routine age dating to be performed on stream water samples rather than requiring groundwater sampling with its associated limitations. Archived samples were analyzed, and results will be combined with current samples to discern temporal variability. This effort is part of a nationwide network to address a key question concerning influences of land use, soil type, management on water quality as influenced by watershed lag times.

1. High-resolution, near-real-time water use and root-zone soil moisture products for optimizing irrigation within Californian vineyards. Near-real-time monitoring of vine water use and root-zone soil water availability is key to improving irrigation management in vineyards and meeting the dual goals of maximizing grape quality and conserving water resources. However, existing soil moisture monitoring techniques do not meet the accuracy and spatial resolution thresholds required to effectively maintain target vine stress levels across large vineyards over the growing season. To address this need, ARS scientists in Beltsville, Maryland, have developed the Vineyard Irrigation Data Assimilation (VIDA) system to merge soil moisture information derived from radar remote sensing, thermal-infrared remote sensing of evapotranspiration (ET) and high-resolution soil water modelling. As part of the joint USDA ARS-E&J Gallo Winery GRAPEX project, operational delivery of high-resolution ET and root-zone soil moisture products began in 2020 for target California vineyards in the Central Valley and was used for the first time to schedule precision irrigation applications. This work was conducted in cooperation with the National Grape Research Alliance and the California Almond Board with the goal of developing open-source irrigation management tools that significantly reduce water use and management costs while improving yield and quality.

2. New approach for mapping crop emergence in near real-time. Accurate maps of crop emergence date, produced at the subfield-scale in near real-time (within the growing season) would significantly benefit USDA crop monitoring and yield forecasting efforts. Detection of emerging crops using satellites is very challenging because the signal on the land-surface is small and difficult to identify quickly enough for operational decision making. ARS scientists in Beltsville, Maryland, have developed a new WIthin-Season crop Emergence (WISE) mapping approach to detect crop green-up dates using satellite observations during early crop growth stages. Results show that crop emergence dates measured in-field can be reliably detected within 6 days and can be mapped with WISE as soon as two weeks after emergence. WISE also provides information about spatial variability in crop development within the field, which will be useful for precision crop management.

3. Winter cover crop programs reduced nitrogen loadings to surface waters. Winter cover crops are one of the most cost-effective conservation practices for reducing agricultural nitrogen pollution of surface waters in the Chesapeake Bay Watershed. The State of Maryland, through its cost-share programs, has placed a major focus on the use of cover crops to achieve water quality objectives. ARS scientists in Beltsville, Maryland, combined winter cover crop cost-share enrollment data in Maryland, with satellite remote sensing of wintertime vegetation, and the results of Soil and Water Assessment Tool (SWAT) to estimate the environmental performance of winter cover crops at the watershed scale from 2008 through 2017. Enrollment data documented a strong increase in the use of cover crops during the study period, from 27 percent of corn fields to 89 percent in 2017, and nine percent of soybean fields in 2008 to 46 percent of soybean fields in 2016. SWAT modeling indicated a 25 percent reduction in nitrate leaching to groundwater due to cover crop implementation during the 10-year period.

Review Publications
Mao, Y., Crow, W.T., Nijssen, B. 2020. Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data. Hydrology and Earth System Sciences. 24/615-631.
Quan, Y., Yang, Z., Di, L., Rahman, M., Xue, L., Tran, Z., Gao, F.N., Yu, E., Zhang, X. 2019. Crop growth condition assessment at county scale based on heat-aligned growth stages. Remote Sensing. 11:2439.
Song, L., Bian, Z., Kustas, W.P., Liu, S., Xiao, Q., Nieto, H., Xu, Z., Yang, Y., Xu, T. 2020. Estimation of surface heat fluxes using multi-angular observations of radiative surface temperature. Remote Sensing of Environment. 239:111674.
Huryan, H., Cohen, Y., Karneli, A., Panov, N., Kustas, W.P., Agam, N. 2019. Evaluation of TsHARP utility for thermal sharpening of Sentinel-3 satellite images using Sentinel-2 visual imagery. Remote Sensing.
Gao, F.N., Anderson, M.C., Daughtry, C.S., Karnieli, A., Hively, W., Kustas, W.P. 2020. A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery. Remote Sensing of Environment.
Aboutalebi, M., Torres, A., McKee, M., Kustas, W.P., Nieto, H., Alsana, M., White, W.A., Prueger, J.H., McKee, L.G., Alfieri, J.G., Hipps, L., Coopmans, C., Dokoozlian, N. 2019. Incorporation of unmanned aerial vehicle (UAV) point cloud product into remote sensing evapotranspiration models. Remote Sensing. 12(1):50.
Lee, S., Yen, H., Yeo, I.Y., Moglen, G.E., Rabenhorst, M., McCarty, G.W. 2020. Use of multiple modules and Bayesian model averaging to assess structural uncertainty of catchment-scale wetland modeling in a coastal plain landscape. Journal of Hydrology.
Walker, V., Hornbuckle, B., Cosh, M.H., Prueger, J.H. 2019. Seasonal evaluation of SMAP soil moisture in the U.S. corn belt. Remote Sensing. 11(21):248.
Xu, C., Qu, J., Hao, X., Cosh, M.H., Zhu, Z., Gutenberg, L. 2020. Monitoring crop water content for corn and soybean fields through data fusion of MODIS and Landsat measurements in Iowa. Agricultural Water Management. 227:105844.
Nassar, A., Torres, A., Kustas, W.P., Nieto, H., McKee, M., Hipps, L., Stevens, D., Alfieri, J.G., Prueger, J.H., Alsina, M., McKee, L.G., Coopmans, C., Sanchez, L., Dokoozlian, N. 2020. Influence of model grid size on the estimation of surface fluxes using the two source energy balance model and sUAS imagery in vineyards. Remote Sensing.
Lee, J., Cosh, M.H., Starks, P.J., Toth, Z. 2019. Self-correction of Soil Moisture Ocean Salinity (SMOS) soil moisture dry bias. Canadian Journal of Remote Sensing. 45(6):814-828.
Zhang, X., Wang, J., Henebry, G., Gao, F.N. 2020. Development and evaluation of a new algorithm for detecting 30m land surface phenology from VIIRS and HLS time series. Journal of Photogrammetry and Remote Sensing. 161:37-51.
Yang, Y., Anderson, M.C., Gao, F.N., Hain, C., Noormets, A., Sun, G., Wynne, R., Thomas, V. 2020. Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data. Remote Sensing of Environment.
Trnka, M., Hayes, M., Jurecka, F., Bartosova, L., Anderson, M.C., Brazdil, R., Brown, J., Camarero, J., Cudlin, P., Dobrovolny, P., Eitzinger, J., Feng, S., Finnessey, T., Gregoric, G., Havlik, P., Hain, C., Holman, I., Johnson, D., Kersebaum, K., Ljunggvist, F., Luterbacher, J., Micale, F., Hartl-Meier, C., Mozny, M., Nejedlik, P. 2018. Priority questions in multidisciplinary drought research. Climate Research. 75:241-260.
Zhang, B., Xia, Y., Long, B., Hobbins, M., Hain, C., Li, Y., Anderson, M.C. 2020. Evaluation and inter-comparison of multi-source evapotranspiration products over the continental United States: Implications for the next phase of NLDAS development. Agricultural and Forest Meteorology.
Wulder, M., Loveland, T., Roy, D., Crawford, C., Masek, J., Woodcock, C., Allen, R., Anderson, M.C., Belward, A., Cohen, W., Dwyer, J., Erd, A., Gao, F.N., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J., Hostert, P., Hughes, M., Huntington, J., Johnson, D., Kennedy, R., Kilic, A., Li, Z., Lymburner, L. 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment. 225:127-147.
Harrower, M., Smiti, N., Zerue, K., Dumitri, I., Meresa, Y., Bongers, J., Gebreegziabher, G., Zaitchik, B., Anderson, M.C. 2020. Water, geography, and Aksumite civilization: The Southern Red Sea Archaeological Histories (SRSAH) project survey (2009-2016). African Archaeological Review.
Cawse-Nicholson, Braverman, A., Kang, E., Li, M., Johnson, M., Halverson, G., Anderson, M.C., Hain, C., Gunson, M., Hooks, S. 2020. Sensitivity and uncertainty quantification for the ECOSTRESS evapotranspiration algorithm - DisALEXI. International Journal of Applied Earth Observation and Geoinformation.
Fang, L., Zhang, X., Schull, M., Kulluri, S., Laszlo, I., Yu, P., Carter, C., Hain, C., Anderson, M.C. 2020. Evapotranspiration data product from NESDIS GET-D system upgraded for GOES-16 ABI observations. Remote Sensing.
White, W.A., Alsina, M., Nieto, H., McKee, L.G., Gao, F.N., Kustas, W.P. 2018. Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals. Irrigation Science.
Knipper, K.R., Kustas, W.P., Anderson, M.C., Alsina, M., Hain, C., Alfieri, J.G., Prueger, J.H., Gao, F.N., McKee, L.G., Sanchez, L. 2019. Using high-spatiotemporal thermal satellite ET retrievals for near-real time water use and stress monitoring in a California vineyard. Remote Sensing. 11(18):2124.
Sharma, A., Lang, R., Kurum, M., O'Neill, P., Cosh, M.H. 2020. L-band radar experiment and modeling of a corn canopy over a full growing season. IEEE Transactions on Geoscience and Remote Sensing. 1-15.
Gruber, A., De Lannoy, G., Albergel, C., Al-Yarri, A., Brocca, L., Calvet, J., Colliander, A., Cosh, M.H., Crow, W.T., Dorigo, W., Draper, C., Hirschi, M., Kerr, Y., Konings, A., Lahoz, W., Mccoll, K., Montzkka, C., Munoz-Sabater, J., Peng, J., Reichle, R., Richaume, P., Rudiger, C., Scanlon, T., Van Der Schalie, R., Wigneron, J. 2020. Validation practices for satellite soil moisture retrievals: What are (the) errors? Remote Sensing of Environment. 244:111806.
Kim, H., Cosh, M.H., Bindlish, R., Lakshmi, V. 2020. Field evaluation of portable soil moisture sensors in a sandy loam. Vadose Zone Journal. 19:e20033.
Lee, S., Sadeghi, A.M., Yeo, I., McCarty, G.W., Hively, W.D. 2017. Assessing the impacts of future climate conditions on the effectiveness of winter cover crops in reducing nitrate loads into the Chesapeake Bay Watersheds using SWAT model. Transactions of the ASABE. 60(6):1939-1955.
Rice, C., Hively, W.D., McCarty, G.W., Hapeman, C.J. 2020. Fluxes of agricultural nitrogen and metolachlor metabolites are highly correlated in a first order stream in Maryland, USA. Science of the Total Environment.
Crow, W.T., Chen, F., Reichele, R., Xia, Y. 2019. Diagnosing bias in modeled soil moisture/runoff coefficient correlation using the SMAP Level 4 soil moisture product. Water Resources Research. 55:7010-7026.
Mao, Y., Crow, W.T., Nijssen, B. 2020. A unified data-driven method to derive hydrologic dynamics from global SMAP surface soil moisture and GPM precipitation data. Water Resources Research. 56:E2019WR024949.
Chen, F., Crow, W.T., Cosh, M.H., Colliander, A., Asanuma, J., Berg, A., Bosch, D.D., Caldwell, T., Holifield Collins, C.D., Martinez-Fernandez, J., Mcnairn, H., Starks, P.J., Su, Z., Walker, J. 2019. Uncertainty of reference pixel soil moisture averages sampled at SMAP core validation sites. Journal of Hydrometeorology.
Dong, J., Crow, W.T., Reichle, R., Liu, Q., Lei, F., Cosh, M.H. 2019. A global assessment of added value in the SMAP Level-4 soil moisture product relative to its baseline land surface model. Geophysical Research Letters.
Qui, J., Crow, W.T., Dong, J., Nearing, G. 2020. Land surface model representation of the mutual information context between multi-layer soil moisture and evapotranspiration. Hydrology and Earth System Sciences. 24:581–594.
Lei, F., Crow, W.T., Kustas, W.P., Dong, J., Yang, Y., Knipper, K.R., Anderson, M.C., Gao, F.N., Notarnicola, C., Greifeneder, F., Mckee, L.G., Alfieri, J.G., Hain, C., Dokoozlian, N. 2020. Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard. Remote Sensing of Environment.
Das, N., Entekhabi, D., Dunbar, R., Caubell, M., Colliander, A., Yueh, S., Jaghuber, T., Chen, F., Crow, W.T., O'Neil, P., Walker, J., Berg, A., Bosch, D.D., Caldwell, T., Cosh, M.H., Holifield Collins, C.D., Lopez-Baeza, E., Thibeault, M. 2019. The SMAP and Copernicus Sentinel 1A/B microwave active-passive, high-resolution surface soil moisture product. Remote Sensing of Environment.
Andreu, A., Kustas, W.P., Polo, M., Carrara, A., Gonzalez-Dugo, M. 2018. Modelling surface energy fluxes over a dehesa (oak savanna) ecosystem using a thermal based two source energy balance model (TSEB) II – integration of remote sensing medium and low spatial resolution satellite images. Agricultural Water Management.
Wilson, T.G., Kustas, W.P., Alfieri, J.G., Anderson, M.C., Prueger, J.H., McKee, L.G., Alsina, M., Sanchez, L., Alsted, K. 2020. Relationships between soil water content, evapotranspiration, and irrigation measurements in a California drip-irrigated Pinot noir vineyard. Agricultural Water Management.
Parry, C.K., Kustas, W.P., Knipper, K.R., Anderson, M.C., Alfieri, J.G., Prueger, J.H., Mcelrone, A.J. 2019. Comparison of vineyard evapotranspiration estimates from surface renewal using measured and modeled energy balance components in the GRAPEX project. Irrigation Science. 37:333-343.
Shuai, Y., Tuerhanjiang, L., Shao, C., Gao, F.N., Zhou, Y., Xie, D., Liu, T., Liang, J., Chu, N. 2020. Re-understanding of land surface albedo and related terms in satellite-based retrievals. Big Earth Data.
Plummer, R.E., Hapeman, C.J., Rice, C., McCarty, G.W., Schmidt, W.F., Downey, P.M., Moorman, T.B., Douglass, E.A., Strickland, T.C., Pisani, O., Bosch, D.D., Elkin, K.R., Buda, A.R. 2020. Method to evaluate the age of groundwater inputs to surface waters by determining the chirality change of metolachlor ethanesulfonic acid (MESA) captured on a polar organic chemical integrative sampler (POCIS). Journal of Agricultural and Food Chemistry. 68(8):2297-2305.
Tobin, K., Crow, W.T., Dong, J., Bennett, M. 2019. Validation of a new soil moisture product: Soil MERGE (SMERGE). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(9):3351-3365.
Baran, A., Moglen, G.E., Godrej, A. 2019. Quantifying the hydrological impacts of climate change on a watershed in northern Virginia. Journal Hydrologic Engineering.
Hively, W., Lee, S., Sadeghi, A.M., McCarty, G.W., Lamb, B., Soroka, A., Keppler, J., Yeo, I., Moglen, G.E. 2020. Estimating the environmental effects of winter cover crops in the Tuckahoe Creek watershed using cost-share enrollment data, satellite remote sensing, and SWAT modeling. Soil and Water Conservation Society. 75(3):362-375.
Herman, M., Nejadhashemi, A., Hernandez-Suarez, J., Sadeghi, A.M. 2020. Analyzing the variability of remote sensing and hydrologic model evapotranspiration products in a watershed in Michigan. Journal of the American Water Resources Association.
Baffaut, C., Baker, J.M., Biederman, J.A., Bosch, D.D., Brooks, E.S., Buda, A.R., Demaria, E.M., Elias, E.H., Flerchinger, G.N., Goodrich, D.C., Hamilton, S.K., Hardegree, S.P., Harmel, R.D., Hoover, D.L., King, K.W., Kleinman, P.J., Liebig, M.A., McCarty, G.W., Moglen, G.E., Moorman, T.B., Moriasi, D.N., Okalebo, J., Pierson Jr, F.B., Russell, E.S., Saliendra, N.Z., Saha, A.K., Smith, D.R., Yasarer, L.M. 2020. Comparative analysis of water budgets across the U.S. long-term agroecosystem research network. Journal of Hydrology. 588.
Sadeghi, M., Ebtenaj, A., Crow, W.T., Gao, L., Purdy, A., Fisher, J., Jones, S., Tuller, M. 2020. Global estimates of land surface net water flux from SMOS and SMAP satellite soil moisture data. Journal of Hydrometeorology. 21:241–253.
Qi, J., Du, X., Zhang, X., Lee, S., Wu, Y., Moglen, G.E., Sadeghi, A.M., McCarty, G.W. 2019. Modeling riverine dissolved and particulate organic carbon fluxes from two small watersheds in the northeastern United States. Environmental Modelling & Software.
Zhang, K., Ali, A., Moghaddam, M., Saatchi, S., Cuenca, R., Crow, W.T., Moorcroft, P. 0209. The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: An analysis using radar-derived estimates of root zone soil moisture. Journal of Geophysical Research. 124:3208-3231.
Mladenova, I., Bolten, J., Crow, W.T., Sazib, N., Cosh, M.H., Tucker, C., Reynolds, C. 2019. Evaluating the operational application of SMAP for global agricultural drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(9):3387-3397.
Ashfar, M., Yilmaz, M., Crow, W.T. 2019. Impact of rescaling approaches on the simple fusion of soil moisture products. Water Resources Research. 55:7804-7825. 2019.
Reichle, R., Liu, Q., Koster, R., Crow, W.T., Delannoy, G., Kimball, J., Ardizzone, J., Bosch, D.D., Colliander, A., Cosh, M.H., Kolassa, Mahanama, S., McNairn, H., Prueger, J.H., Starks, P.J., Walker, J. 2020. Version 4 of the SMAP level-4 soil moisture algorithm and data product. Journal of Advances in Modeling Earth Systems. 11:3106-3130.
Colliander, A., Yang, Z., Mueller, R., Sandborn, A., Reichle, R., Crow, W.T., Entekhabi, D., Yeuh, S. 2019. Consistency between NASS surveyed soil moisture conditions and SMAP soil moisture observations. Water Resources Research.
Su, J., Lu, H., Zhu, Y., Crow, W.T., Cui, Q. 2020. The effect of spatiotemporal resolution degradation on the accuracy of IMERG products over the Huai River Basin. Journal of Hydrometeorology. 21(5):1073-1088.
Sadeghi, M., Gao, L., Ebtenaj, A., Wigneron, J., Reager, J., Crow, W.T. 2020. Retrieving surface soil moisture from GRACE satellite gravity data. Journal of Hydrology.
Mladenova, I., Bolten, J., Crow, W.T., Sazib, N., Reynolds, C. 2020. Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model. Frontiers in Big Data.
Lee, S., McCarty, G.W., Moglen, G.E., Lang, M., Jones, C., Palmer, M., Yeo, I., Anderson, M.C., Sadeghi, A.M., Rabenhorst, M. 2020. Seasonal drivers of geographically isolated wetland hydrology in a low-gradient, Coastal Plain landscape. Journal of Hydrology.