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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

2018 Annual Report


1a. Objectives (from AD-416):
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.


1b. Approach (from AD-416):
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.


3. Progress Report:
This report documents progress for the first year of Project 8042-13610-029-00D “Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems” which continues research from Project 1265-13610-028-00D “Leveraging remote sensing, land surface modeling and ground-based observations for the integrative assessment of water quantity and quality variables within heterogeneous agricultural landscapes”. Substantial progress was made in all three objectives outlined in the new project plan, all of which fall under NP 211. Under Objective 1, research activities were focused on the collection of in situ datasets that will be used to evaluate remote sensing products spatially depicting crop water use (evapotranspiration; ET), soil moisture, and the overall water balance at basin scales. ET datasets over diverse agricultural landscapes and climates including commodity crops (corn, soybean and rice), perennial specialty crops (wine grapes), as well as neighboring forests and wetlands, have been collected and processed for use in evaluating daily remote sensing estimates. Under the GRAPEX (Grape Remote sensing and Atmospheric Profiling of Evapotranspiration eXperiment) component of the plan, additional vineyard flux sites were instrumented, sampling different grape wine varieties, trellis designs and climate conditions in the California Central Valley. ET measurements from international collaborators in Brazil, Spain and the Czech Republic have been assembled for testing of global remote sensing products. New soil moisture measurement technologies have been incorporated into the Marena Oklahoma In Situ Sensor Testbed as they have become available and relationships have been developed with commercial manufacturers to include new sensors under development. For example, collaboration with the World Meteorological Organization has led to the deployment of a South African based soil moisture sensor within the Testbed. These datasets are being used to improve the accuracy and robustness of the soil moisture products provided by the NASA Soil Moisture Active Passive satellite. New algorithm and spatial resolution enhancement techniques utilizing advanced radiometer data processing and ancillary information from models and satellites were also evaluated this year. Project personnel collected water balance components (i.e., precipitation, outlet streamflow, change in soil moisture and evapotranspiration) within several-hundred lightly managed, medium-scale (2,000 to 10,000 km2) basins in the United States for integrated evaluation of remote sensing products at basin scale. These datasets form the background for activities under Objective 2, utilizing remote sensing to more effectively identify incipient crop stress, hydrologic and atmospheric feedback processes, and yield impacts resulting from agricultural drought. Response of remotely sensed ET products to the 2016 U.S. northern Plains flash drought and the extended 2012-2017 drought in California was evaluated, demonstrating capabilities for capturing stress and management information at fine to moderate spatial scales. Crop yields reported by the USDA National Agricultural Statistics Service (NASS) were used to assess the impacts of improved temporal and spatial sampling afforded by vegetation index image time series derived at 30-m resolution from multiple satellite sources. Dataset performance was evaluated in a rain-fed agricultural area in central Iowa using data from Landsat, the Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Space Agency’s Sentinel-2 platforms, demonstrating improved performance when using all systems in combination. In addition, multi-sensor data fusion was implemented to map leaf area index at high spatiotemporal resolution over pasture and rangeland under different management approaches (mowed, grazed and fenced) and under varying degrees of drought stress. Data collection in FY18 at the Lower Chesapeake Bay (LCB) LTAR will support focused remote sensing and modeling research on the connections between agricultural water use and water quality under Objective 3. Real-time in situ sensors for water quality are in operation and were maintained at the Tuckahoe and Greensboro gage stations in FY18, extending our existing long-term data record and supporting assessment of water quality parameters in the context of watershed characteristics and storm events. Datasets were also compiled using the surface flux and micrometeorological observations collected at several locations within the LCB LTAR including the OPE3 and the Choptank watershed sites. The micrometeorological tower at the OPE3 site has been fully renovated. Furthermore, two additional in-situ optical sensors were installed at the outlets of the two sub-watersheds within the Choptank watershed, facilitating collection of more detailed stream flow and nitrate information for calibration and assessment of Soil Water Assessment Tool (SWAT) model implementations at these two watersheds. Also in support of SWAT, a remote sensing methodology was developed for mapping temporal dynamics of the inundation pattern in forested wetlands, a major landcover constituent in the LCB landscape. This method combines the long-term Landsat record (1985 to present) with the highly accurate detection of inundation by lidar for calibration. To evaluate and improve SWAT predictions of nitrate fate in the agricultural watersheds, an integrative passive sampling technology has been validated for use in collection of chiral MESA data. This technology will simplify collection of chiral MESA data for age dating waters in watersheds nationally. Streams are being sampled for chiral MESA to age date waters in the sub-watersheds in the Choptank watershed, and age dates are being correlated to watershed characteristics to gain better understanding of parameters affecting the mean residence time and the related mean flow path length of groundwater sources in watersheds.


4. Accomplishments
1. Development of a new data assimilation technique for operational drought monitoring. Agricultural drought has enormous implications for domestic economic interests and international food security concerns. However, the negative impacts of drought can be reduced through early detection and rapid adoption of mitigating management and/or response techniques. Land data assimilation systems have been designed to merge multiple sources of soil moisture information (acquired from, for example, hydrologic modeling, ground-based observations and satellite-based remote sensing) into an optimized tool for detecting the early onset of drought. To function optimally, these systems require accurate statistical information concerning errors in each source of soil moisture information so that they can be properly weighted in the merging process. ARS scientists in Beltsville, Maryland, have developed a new mathematical technique that allows us, for the first time, to obtain this type of error information. With this information in hand, operational drought monitors can now design systems to optimally merge soil moisture information acquired from multiple sources (including ground-based observations) and maximize the probability of early drought detection.

2. Increased the spatial resolution of the Soil moisture Active Passive (SMAP) satellite products. The Soil Moisture Active Passive (SMAP) satellite launched in 2015 provides near-daily global information about surface soil moisture under all-sky conditions. However, the 36-km resolution of the standard SMAP soil moisture product is too coarse for many agricultural applications. ARS scientists in Beltsville, Maryland, developed a new high spatial resolution soil moisture product providing 9 km resolution - four times better than the standard radiometer product. This was achieved by exploiting the oversampling of the instrument in an optimal interpolation technique. Results of validation tests using ground-based observation have demonstrated that the approach is accurate and reliable. This new product supports a wider range of hydrologic and agricultural applications, including improved global drought monitoring and yield assessment.

3. Improved wetland modeling by use of remotely sensed data. Wetlands are important features of the landscape, enabling a suite of hydrologic, biogeochemical, and biological functions including water storage and buffering connectivity between land and waterways. Modeling wetland function has been hindered by our inability to accurately map inundated areas within small, seasonal-forested wetlands, which are densely distributed in the coastal plain of the mid-Atlantic Region and difficult to detect through the forest canopy. To address this, ARS scientists in Beltsville, Maryland, collaboratively developed a methodology using Light Detection and Ranging (LiDAR) and time series Landsat data to improve maps of inundated area and wetland storage capacity. These maps were then used in hydrologic simulations using the Soil Water Assessment Tool (SWAT). Resulting improvements in SWAT modeling performance demonstrated how intra-watershed processes can be better captured by utilizing spatialized wetland parameters developed from remotely sensed data. This enhanced modeling framework permits improved estimates of wetland ecosystem service provision in agricultural landscapes, and will facilitate developing state and federal regulations for wetland protections.


Review Publications
Herman, M., Najadhashemi, P., Abouali, M., Hernandez-Suarez, J., Daneshvar, F., Zhen, Z., Anderson, M.C., Sadeghi, A.M., Hain, C., Sharifi, A. 2017. Evaluate the role of evapotranspiration remote sensing data in improving hydrological modeling predictability. Journal of Hydrology. 556:39-49.

Bindlish, R., Cosh, M.H., Jackson, T.J., Koike, T., Fuiji, X., De Jeu,, R., Chan, S., Asanuma, J., Berg, A., Bosch, D.D., Caldwell, T., Holifield Collins, C.D., McNairn, H., Martinez-Fernandez, J., Prueger, J.H., Rowlandson, T., Seyfried, M.S., Starks, P.J., Su, Z., Thibeault, M., van der Velde, R., Walker, J., Coopersmith, E. 2018. GCOM-W AMSR2 soil moisture product validation using core validation sites. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(1):209-219. https://doi.org/10.1109/JSTARS.2017.2754293.

Kim, S., Arii, M., Jackson, T.J. 2017. Modeling L-band synthetic aperture radar observations through dielectric changes in soil moisture and vegetation over shrublands. IEEE Journal of Selected Topics in Applied Remote Sensing. 10:4753-62.

Colliander, A., Fisher, J., Halverson, G., Merlin, O., Misra, S., Bindlish, R., Jackson, T.J., Yeuh, S. 2017. Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15. Geoscience and Remote Sensing Letters. 14:2107-2111.

Sabaghty, S., Walker, J., Renzuillo, L., Jackson, T.J. 2018. Spatially enhanced passive microwave derived soil moisture: capabilities and opportunities. Remote Sensing of Environment. 209:551-580.

Gao, Y., Walker, J., Ye, N., Panciera, R., Monerris, A., Ryu, D., Rudiger, C., Jackson, T.J. 2018. Evaluation of the tau-omega model for passive microwave soil moisture retrieval using SMAPEx data sets. IEEE Journal of Selected Topics in Applied Remote Sensing. 11(3):1-10.

Su, J., Lu, H., Zhu, Y., Sadeghi, A.M. 2017. Evaluating the applicability of four recent satellite–gauge combined precipitation estimates for extreme precipitation and streamflow predictions over the upper Yellow river basin in China. Remote Sensing. https://doi.org/10.3390/rs9111176.

Akbar, R., Cosh, M.H., O'Neill, P., Entekhabi, D., Moghaddam, M. 2017. Combined radar-radiometer surface soil moisture and roughness estimation. IEEE Transactions on Geoscience and Remote Sensing. 55(7):4098-4110. https://doi.org/10.1109/TGRS.2017.2688403.

Tao, H., Liang, S., Wang, D., Cao, Y., Gao, F.N., Yu, Y., Feng, M. 2017. Deriving a global land surface albedo product from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach. Remote Sensing of Environment. 24:181-196. https://doi.org/10.1016/j.rse.2017.10.031.

Renkenberger, J., Montas, H., Leisnham, P., Chanse, V., Shirmohammadi, A., Sadeghi, A.M., Brubaker, K., Rockler, A., Hutson, T., Lansing, D. 2017. Effectiveness of best management practices (BMPs) with changing climate in a Maryland watershed. Transactions of the ASABE. 60(3):769-782. https://doi.org/10.13031/trans.11.

Simmons, G., Bastiaanssen, W., Ngo, L., Hain, C., Anderson, M.C., Senay, G. 2016. Integrating global satellite-derived data products as a pre-analysis for hydrological modelling studies: a case study for the Red River Basin. Remote Sensing. https://doi.org/10.3390/rs8040279.

McEvoy, D., Huntington, J., Hobbins, M., Wood, A., Morton, C., Verdin, J., Anderson, M.C., Hain, C. 2016. The Evaporative Demand Drought Index: Part II – CONUS-wide assessment against common drought indicators. Journal of Hydrometeorology, 17: 1763-1779.

Narvekar, P., Tomer, S., Sekhar, M., Mohan, S., Bandyopadhyay, S., Jackson, T.J., Entekhabi, D. 2017. High resolution land surface geophysical parameters estimation from ALOS PALSAR data. Journal of Remote Sensing Society of Japan. 37(2):105-111.

Lee, S., Yeo, I., Sadeghi, A.M., McCarty, G.W., Hively, D., Lang, M., Sharifi, A. 2018. Comparative analysis of hydrological responses of two adjacent watersheds to climate variability and change scenarios using SWAT model. Hydrology and Earth System Sciences. 22(1):689-708.

Yang, Y., Anderson, M.C., Gao, F.N., Hain, C., Kustas, W.P., Meyers, T., Crow, W.T., Finocchiaro, R., Otkin, J., Sun, L., Yang, Y. 2017. Impact of tile drainage on evapotranspiration in South Dakota, USA based on high spatiotemporal resolution ET timeseries from a multi-satellite data fusion system. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2017.2680411.

Al-Yaari, A., Wigneron, J., Kerr, Y., Rodriguez-Fernandez, N., O'Neill, P., Jackson, T.J. 2017. Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets. Remote Sensing of Environment. 193:257-273.

Huang, H., Liao, T., Tsang, L., Njoku, E., Colliander, A., Jackson, T.J., Burgin, M., Yueh, S. 2017. Combined active and passive microwave remote sensing of vegetated surfaces at l-band. Progress in Electromagnetics Research. 78:91-124.

Hain, C., Anderson, M.C. 2017. Estimating morning changes in land surface temperature from MODIS day/night land surface temperature: Applications for surface energy balance modeling. Geophysical Research Letters. https://doi.org/10.1002/2017GL074952.

Guan, K., Wu, J., Kimball, J., Anderson, M.C., Frolking, S., Li, B., Hain, C., Lobell, D. 2017. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. Remote Sensing. 199:333-349.

Coopersmith, E., Bell, J., Benedict, K., Schriber, J., Mccotter, O., Cosh, M.H. 2017. Relating coccidioidomycosis (Valley Fever) incidence via to soil moisture conditions. American Geophysical Union. https://doi.org/10.1002/2016GH000033.

Stagge, J., Moglen, G.E. 2017. Water resources adaptation to climate and demand change in the Potomac river. Journal Hydrologic Engineering. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001579.

Peng, J., Misra, S., Piepmeier, J., Dinnat, E., Hudson, D., Levine, D., De Amici, G., Mohammad, P., Bindlish, R., Yueh, S., Meissner, T., Jackson, T.J. 2017. Soil Moisture Active/Passive (SMAP) L-band microwave radiometer post-launch calibration. IEEE Transactions on Geoscience and Remote Sensing. 55:1897-1914.

Chan, S., Bindlish, R., O'Neill, P., Jackson, T.J., Njoku, E., Dunbar, R., Chaubell, J., Peipmeier, J., Yueh, S., Entekhabi, D., Colliander, A., Chen, F., Cosh, M.H., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Udall, F., Seyfried, M.S., Bosch, D.D., Starks, P.J., Holifield Collins, C.D., Prueger, J.H., Crow, W.T. 2018. Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of Environment. 204:931-941. https://doi.org/10.1016/j.rse.2017.08.025.

Lievens, H., Reichle, R., Liu, Q., De Lannoy, Dunbar, R., Kim, S., Das, N., Cosh, M.H., Walker, J., Wagner, W. 2018. Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters. 44(12):6145-6153. https://doi.org/10.1002/2017GL073904.

Yang, Y., Anderson, M.C., Gao, F.N., Wardlow, B., Hain, C., Otkin, J., Alfieri, J.G., Yang, Y., Sun, L., Dulaney, W.P. 2018. Field-scale mapping of evaporative stress indicators of crop yield: an application over Mead, Nebraska. Remote Sensing of Environment. 210:387-402.

Zhou, Y., Xiao, X., Zhang, G., Wagle, P., Bajgain, R., Dong, J., Jin, C., Basara, J., Anderson, M.C., Hain, C., Otkin 2017. Quantifying agricultural drought in tallgrass prairie region in the U.S. Southern Great Plains through analysis of a water-related vegetation index from MODIS images. Agricultural and Forest Meteorology. 246:111-122.

Sun, L., Anderson, M.C., Gao, F.N., Hain, C., Alfieri, J.G., Sharifi, A., Mccarty, G.W., Yang, Y., Yang, Y., Kustas, W.P., Mckee, L.G. 2017. Investigating water use over the Choptank River Watershed using a multi-satellite data fusion approach. Water Resources Research. 53:5298-5319.

Spiegal, S.A., Bestelmeyer, B.T., Archer, D.W., Augustine, D.J., Boughton, E., Boughton, R., Clark, P., Derner, J.D., Duncan, E.W., Cavigelli, M.A., Hapeman, C.J., Harmel, R.D., Heilman, P., Holly, M.A., Huggins, D.R., King, K.W., Kleinman, P.J., Liebig, M.A., Locke, M.A., McCarty, G.W., Millar, N., Mirsky, S.B., Moorman, T.B., Pierson Jr, F.B., Rigby Jr, J.R., Robertson, G., Steiner, J.L., Strickland, T.C., Swain, H., Wienhold, B.J., Wulfhorts, J., Yost, M., Walthall, C.L. 2018. Evaluating strategies for sustainable intensification of U.S. agriculture through the Long-Term Agroecosystem Research network. Environmental Research Letters. 13(3):034031. https://doi.org/10.1088/1748-9326/aaa779.

Kim, H., Parinussa, R., Konings, A., Wagner, W., Cosh, M.H., Lakshmi, V., Zohaib, M., Choi, M. 2018. Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products. Remote Sensing of Environment. 204:260-275. https://doi.org/10.1016/j.rse.2017.10.026.

Molero, B., Leroux, D., Richaume, P., Kerr, Y., Merlin, O., Cosh, M.H., Bindlish, R. 2017. Multi-time scale analysis of the spatial representativeness of in situ soil moisture data within satellite footprints. Journal of Geophysical Research. 123(1):3-21. https://doi.org/10.1002/2017JD027478.

Kolassa, J., Reichle, R., Liu, Q., Cosh, M.H., Bosch, D.D., Caldwell, T., Colliander, A., Holifield Collins, C.D., Jackson, T.J., Livingston, S.J., Moghaddam, M., Starks, P.J. 2017. Data assimilation to extract soil moisture information from SMAP observations. Remote Sensing. 9(11):1179. https://doi.org/10.3390/rs9111179.

Rowlandson, T., Berg, A., Bullock, P., Hanis-Gervais, K., Ojo, E., Cosh, M.H., Powers, J., McNairn, H. 2018. Temporal transferability of soil moisture calibration equations. Journal of Hydrology. 556:349-358. https://doi.org/10.1016/j.jhydrol.2017.11.023.

Nie, W., Zaitchik, B., Rodell, M., Kumar, S., Anderson, M.C., Hain, C. 2018. Groundwater withdrawals under drought: reconciling GRACE and land surface models in the United States High Plains Aquifer. Water Resources Research. https://doi.org/10.1029/2017WR022178.

Carter, E., Hain, C., Anderson, M.C., Steinscheider, S. 2018. A water balance based, spatiotemporal evaluation of terrestrial evapotranspiration products across the contiguous United States. Journal of Hydrometeorology. https://doi.org/10.1175/JHM-D-17-0186.1.

Mishra, V., Ellenburg, W., Griffin, R., Mecikalski, J., Hain, C., Anderson, M.C. 2018. An initial assessment of SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States. International Journal of Applied Earth Observation and Geoinformation. 68:92-104.

Colliander, A., Jackson, T.J., Chan, S., O'Neill, P., Bindlish, R., Cosh, M.H., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Jensen, K., Asanuma, J., Seyfried, M.S., Bosch, D.D., Starks, P., Holifield Collins, C.D., Prueger, J.H., Su, Z., Lopez-Beeza, E., Yeuh, S. 2018. An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites. Remote Sensing of Environment. 207:65-70.

Lorenz, D., Otkin, J., Svoboda, M., Hain, C., Anderson, M.C., Zhong, Y. 2017. Predicting the US Drought Monitor (USDM) using precipitation, soil noisture, and evapotranspiration anomalies, Part II: Intraseasonal drought intensification forecasts. Journal of Hydrometeorology. 18:1943-1962. https://doi.org/10.1175/JHM-D-16-0067.1.

Lorenz, D., Otkin, J., Svoboda, M., Hain, C., Anderson, M.C., Zhong, Y. 2017. Predicting US Drought Monitor (USDM) states using precipitation, soil moisture, and evapotranspiration anomalies, Part I: Development of a non-discrete USDM index. Journal of Hydrometeorology. 18:1963-1982. https://doi.org/10.1175/JHM-D-16-0066.1.

Grippa, M., Kergoat, L., Boone, A., Peugot, C., Demarty, J., Cappelaere, B., Gal, L., Hiernaux, P., Mougin, E., Anderson, M.C., Hain, C. 2017. Modelling surface runoff and water fluxes over contrasted soils in pastoral Sahel: evaluation of the ALMIP2 land surface models over the Gourma region in Mali. Journal of Hydrometeorology. 18:1847-1866. https://doi.org/10.1175/JHM-D-16-0170.1.

Lei, F., Crow, W.T., Shen, H., Su, C., Holmes, T. 2018. Assessment of the spatial heterogeneity on microwave satellite soil moisture periodic error. Remote Sensing of Environment. 205:85-99. https://doi.org/10.106/j.rse.2017.11.002.

Castelli, M., Anderson, M.C., Yang, Y., Wohlfart, G., Bertoldi, G., Hammerle, A., Zhao, P., Niedrist, G., Zebisch, M., Notarnicola, C. 2018. Two source energy balance modeling of evapotranspiration in Alpine grasslands. Remote Sensing of Environment. 209:327-342. https://doi.org/10.1016/j.rse.2018.02.062.

Das, N., Entekhabi, D., Dunbar, R., Colliander, A., Chen, F., Crow, W.T., Jackson, T.J., Berg, A., Bosch, D.D., Caldwell, T., Cosh, M.H., Holifield Collins, C.D., Lopez-Baeza, E. 2018. The SMAP mission combined active-passive soil moisture product at 9 km and 3km spatial resolutions. Remote Sensing of Environment. 211:204-217. https://doi.org/10.106/j.rse.2018.04.11.

Lee, S., Yeo, I., Lang, M., McCarty, G.W., Sadeghi, A.M., Sharifi, A., Jin, H., Liu, Y. 2017. Improving the catchment scale wetland modeling using remotely sensed data. Journal of Environmental Modeling and Software. https://doi.org/10.1016/j.envsoft.2017.11.001.

Nearing, G., Yatheendrades, S., Crow, W.T., Bosch, D.D., Cosh, M.H., Goodrich, D.C., Seyfried, M.S., Starks, P.J. 2017. Nonparametric triple collocation. Water Research. 53(7):5516-5530. https://doi.org/10.1002/2017WR020359.

Sharifi, A., Wallace, C., Mccarty, G.W., Crow, W.T., Momen, B., Lang, M., Sadeghi, A.M., Hawe, Y., Sangchul, L., Denver, J., Rabenhorst, M. 2017. Effect of water quality sampling time and frequency on storm load predictions of a prominent regression-based model. Water Resources Research. https://doi.org/10.3390/w9110895. 2017.

Fisher, J., Melton, F., Middleton, E., Hain, C., Anderson, M.C., Allen, R., Mccabe, M., Hook, S., Baldocchi, D., Towsend, P., Kilic, A., Tu, R., Mirales, D., Perret, J., Lagouarde, J., Waliser, D., Purdy, A., French, A.N., Schimel, D., Famiglietti, J., Turner, R., Wood, E. 2017. The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resources Research. 53:2618-2626. https://doi.org/10.1002/2016WR020175.

Guan, K., Li, Z., Rao, N., Gao, F.N., Xie, D., Zeng, Z. 2018. Mapping paddy rice area and yields over Thai Binh province in Viet Nam from MODIS, Landsat and ALOS-2/PALSAR-2. Remote Sensing of Environment. 1-15. https://doi.org/10.1109/JSTARS.2018.2834383.

Crow, W.T., Chen, F., Reichle, R., Liu, Q. 2017. L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting. Geophysical Research Letters. 44(11). https://doi.org/10.1002/2017GL073642.

Tobin, K., Torres, R., Crow, W.T., Bennett, M. 2017. Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS. Hydrology and Earth System Sciences. 21:4403-4417. https://doi.org/10.5194/hess-21-4403-2017.

Holmes, T., Hain, C., Crow, W.T., Anderson, M.C., Kustas, W.P. 2018. Microwave implementation of two-source energy balance approach for estimating evapotranspiration. Hydrology and Earth System Sciences. 22:1351-1369. https://doi.org/10.5194/hess-22-1351-2018.

Roman-Cascon, C., Pellarin, T., Gibon, F., Cosme, E., Kerr, Y., Brocca, L., Massari, C., Crow, W.T., Fernanez, D. 2017. Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX. Remote Sensing of Environment. 200:295-310. https//doi.org/10.1016/j.rse.2017.08.022.

Riechle, R., De Lannoy, G., Liu, Q., Koster, R., Kimball, J., Crow, W.T., Ardizzone, J., Chakraborty, P., Collins, D., Conasty, A., Girotti, M., Jones, L., Kolassa, J., Lievens, H., Lucchesi, R., Smith, E. 2017. Global assessment of the SMAP level-4 surface and root-zone soil moisture product using assimilation diagnostics. Journal of Hydrometeorology. 18(12):3217-3237. https://doi.org/10.1175/JHM-D-17-0130.1.

Gruber, A., Crow, W.T., Dorigo, W. 2018. Assimilation of spatially sparse in situ soil moisture networks into a continuous model domain. Water Resources Research. 54:1353-1367. https://doi.org/10.1002/2017WR021277.

Ghatak, D., Zaitchik, B., Hains, C., Anderson, M.C. 2017. The role of local heating in the 2015 Indian heat wave. Scientific Reports. 7:7707. https://doi.org/10.1038/s41598-017-07956-5.

Dong, J., Crow, W.T. 2017. An improved triple collocation algorithm for decomposing autocorrelated and white soil moisture retrieval errors. Journal of Geophysical Research. 122(24):13,081-13,094. https://doi.org/10.1002/2017JD027397.

Gruber, A., Dorigo, W., Crow, W.T., Wagner, W. 2017. Triple collocation based merging of satellite soil moisture retrievals. IEEE Transactions on Geoscience and Remote Sensing. 55(12):6780-6792. https://doi.org/10.1109/TGRS.2017.2734070.

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