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

2019 Annual Report

Accomplishments
1. Toolkit for daily water use monitoring in California Central Valley vineyards. Persistent and extreme drought has plagued California in the last decade with enormous implications on surface and groundwater water resources for agriculture. Achieving long-term water use sustainability in an economically viable way will require more efficient irrigation management to successfully address future water shortages. ARS scientists in Beltsville, Maryland, have led the Grape Remote Sensing Atmospheric profile & Evapotranspiration eXperiment (GRAPEX) project with the goal of developing a new remote sensing-based data fusion technique that allows, for the first time, accurate estimation of daily water use and stress information from field to regional scales for high-valued perennial crops. Operational application of this technique has begun and is expected to facilitate substantial reductions in irrigation water usage for these crops.

2. Global mapping of soil moisture/evapotranspiration coupling. Accurately estimating the flux of water and energy between the land surface and the lower atmosphere is important for forecasting the onset and evolution of an agricultural drought event. ARS scientists in Beltsville, Maryland, have developed a new mathematical technique for correcting sampled estimates of the correlation between surface soil moisture and surface evaporation for the impact of random measurement error. This technique enables the application of existing remotely sensed estimates of soil moisture and surface evaporation. By improving our understanding of land/atmosphere feedbacks, this research enhances our ability to forecast precipitation and air temperature extremes within the central United States.

3. Variability of corn and soybean yields explained by high resolution imagery. Accurate estimation of crop yield is critical for sustaining agricultural markets and ensuring food security. Remote sensing data have been used to estimate crop yield for decades. However, the value of high spatial and temporal resolution remote sensing data for yield estimation has not yet been thoroughly investigated. Using a remote sensing data fusion system, ARS scientists in Beltsville, Maryland, evaluated the added value of high temporal and spatial resolution data for yield estimation in the Corn Belt using Landsat, Sentinel-2, MODIS and multi-sensor data fusion. Results demonstrate the improvement in yield estimate accuracy achieved when more frequent satellite imagery with sub-field spatial resolution is incorporated into the modeling process. Once implemented operationally, this research will improve the ability of the National Agricultural Statistics Service to track and predict interannual variations in domestic commodity crop production.

4. Determining the connection of geographically isolated wetlands with downstream waters. The nexus of wetlands to downstream waters is critical to wetland regulatory status. Numerous studies have been reported on the impacts of riparian wetlands on downstream water flow and quality, but little is known about the impact of geographically isolated wetlands. To demonstrate the hydrological impacts of isolated wetlands on the downstream flow, we compared two model scenarios of Soil and Water Assessment Tool (or SWAT) model; one including all wetlands that connect to the stream network during the seasonal inundation and the other excluding all the connected wetlands. Our model simulation results indicated that geographically isolated wetlands served as important landscape features to control watershed hydrology. Further, based on our findings, we conclude that isolated wetlands exert significant impacts on maintenance of upstream hydrology and downstream flow for this region. This insight improves our ability to manage watersheds in a way that minimizes agricultural impacts on downstream water quality and quantity.

Review Publications
Otkin, J., Svoboda, M., Hunt, E., Anderson, M.C., Hain, C., Basara, J. 2018. Flash droughts: a review and assessment of the challenges imposed by rapid onset droughts in the United States. Bulletin of the American Meterological Society. 99: 911-919. https://doi.org/10.1175/BAMS-D-17-0149.1.
Enenkel, M., Anderson, M.C., Osgood, D., Powell, B., Brown, M., McCarty, J., Neigh, C., Carroll, M., Hain, C., Husak, G., Wooten, M. 2019. Exploiting the convergence of evidence in satellite data for advanced weather index insurance design. Weather, Climate, and Society. 11:65-93. https://doi.org/10.1175/WCAS-D-17-0111.1.
Joiner, J., Yoshida, Y., Anderson, M.C., Holmes, T., Hain, C., Riechle, R., Koster, R., Middleton, E., Zeng, F. 2018. Global relationships between satellite-derived solar-induced fluorescence (SIF), traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture anomalies. Remote Sensing of Environment. 219:339-352. https://doi.org/10.1016/j.rse.2018.10.020.
Mishra, V., Cruise, J., Hain, C., Mecikalski, J., Anderson, M.C. 2018. Development of soil moisture profiles through coupled microwave-thermal infrared observations in the southeastern United States. Hydrology and Earth System Sciences. 22:4935-4957. https://doi.org/10.5194/hess-22-4935-2018.
Otkin, J., Zhong, Y., Lorenz, D., Anderson, M.C., Hain, C. 2018. Exploring seasonal and regional relationships between the Evaporative Stress Index and surface weather and soil moisture anomalies across the United States. Journal of Hydrometeorology. 22:5373-5386. https://doi.org/10.5194/hess-22-5373-2018.
Anderson, M.C., Gao, F.N., Knipper, K.R., Hain, C., Dulaney, W.P., Baldocchi, D., Eichelmann, E., Hemes, K., Yang, Y., Medellin, A., Kustas, W.P. 2018. Field-scale assessment of land and water use change over the California Delta using remote sensing. Remote Sensing. 10(6):889. https://doi.org/10.3390/rs10060889.
Mariano, D., Santos, C., Wardlow, B., Anderson, M.C., Schiltmeyer, A., Tadesse, T., Svoboda, M. 2018. Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil. Remote Sensing of Environment. 213:129-143. https://doi.org/10.1016/j.rse.2018.04.048.
Oktin, J., Haigh, T., Mucia, A., Anderson, M.C., Hain, C. 2018. Comparison of agricultural stakeholder survey results and drought monitoring datasets during the 2016 U.S. northern Plains flash drought. Weather, Climate, and Society. 10:867-883. https://doi.org/10.1175/WCAS-D-18-0051.1.
Otkin, J., Zhong, Y., Hunt, E., Basara, J., Svoboda, M., Anderson, M.C., Hain, C. 2019. Assessing the evolution of soil moisture and vegetation conditions during a flash drought - flash recovery sequence over the south-central United States. Journal of Hydrometeorology. 20(3):549-562. https://doi.org/10.1175/JHM-D-18-0171.1.
Uz, S., Ruane, A., Duncan, B., Tucker, C., Huffman, G., Mladenova, I., Osmanoglu, B., Holmes, T., Mcnally, A., Peter-Lidard, C., Bolten, J., Das, N., Rodell, M., McCartney, S., Anderson, M.C., Doorn, B. 2018. Earth observations and integrative models in support of food security. Remote Sensing in Earth Systems Sciences. 2(1):18-38. https://doi.org/10.1007/s41976-019-0008-6.
Anderson, M.C., Diak, G., Gao, F.N., Knipper, K.R., Hain, C., Eichelmann, E., Hemes, K., Baldocchi, D., Kustas, W.P., Alfieri, J.G. 2019. Impact of insolation data source on remote sensing retrievals of evapotranspiration over the California Delta. Remote Sensing. 11:216. https://doi.org/10.3390/rs11030216.
Kustas, W.P., Anderson, M.C., Alfieri, J.G., Knipper, K.R., Torres, A., Parry, C.K., Nieto, H., Agam, N., White, W.A., Gao, F.N., McKee, L.G., Prueger, J.H., McElrone, A.J., Los, S., Alsina, M., Sanchez, L., Sam, B., Dokoozlian, N., McKee, M., Jones, S., Hipps, L., Heitman, J., Howard, A., Post, K., Melton, F. 2018. An overview of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Bulletin of the American Meterological Society. 99(9):1791-1812. https://doi.org/10.1175/BAMS-D-16-0244.1.
Crow, W.T., Chen, F., Reiche, R., Xia, Liu, Q. 2018. SMAP Level 4 soil moisture estimates reveal possible bias in the runoff response of land surface models. Geophysical Research Letters. 45(10):4869-4878. https://doi.org/10.1016/j.rse.2018.05.008.
Chen, F., Crow, W.T., Bindlish, R., Colliander, A., Burgins, M., Asanuma, J., Aida, K. 2018. Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation. Remote Sensing of Environment. 214:1-13. https://doi.org/10.1016/j.rse.2018.05.008.
Koster, R., Crow, W.T., Reichle, R., Mahanama, S. 2018. Estimating basin-scale water budgets with SMAP soil moisture data. Water Resources Research. 54(7):4228-4244. https://doi.org/10.1029/2018WR022669.
Kumar, S., Moglen, G.E., Godrej, A., Grizzard, T., Post, H. 2018. Trends in water yield under climate change and urbanization in the U.S. mid-atlantic region. Journal of Water Resources Planning and Management. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000937.
Bhatkoti, R., Triantis, K., Moglen, G.E., Sabounchi, N. 2018. Performance assessment of a water supply system under the impact of climate change and droughts: a case study of the Washington metropolitan area. Journal of Infrastructure Systems. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000435.
Zwieback, S., Colliander, A., Cosh, M.H., Martinez, F., McNairn, H., Starks, P.J., Thibeault, M., Berg, A. 2018. Estimating time-dependent vegetation biases in the SMAP soil moisture product. Hydrology and Earth System Sciences. 22(8):4473-4489. https://doi.org/10.5194/hess-22-4473-2018.
Xu, C., Qu, J., Hao, Cosh, M.H., Prueger, J.H., Zhu, Z., Gutenberg, L. 2018. Downscaling of surface soil moisture retrieval by combining MODIS/Landsat and in situ measurements. Remote Sensing. 10(2):210. https://doi.org/10.3390/rs10020210.
Caldwell, T., Bongiovanni, T., Cosh, M.H., Halley, C., Young, M. 2018. Field and laboratory evaluation of the CS655 soil water content sensor. Vadose Zone Journal. 17:170214. https://doi.org/10.2136/vzj2017.12.0214.
Walker, V., Hornbuckle, B., Cosh, M.H. 2018. Investigating the SMOS dry bias in the corn belt of the United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(12):4664-4675. https://doi.org/10.1109/JSTARS.2018.2864897.
Kraatz, S., Jacobs, J., Schroeder, R., Cho, E., Cosh, M.H., Seyfried, M.S., Prueger, J.H., Livingston, S.J. 2019. Evaluation of SMAP freeze/thaw retrieval accuracy at core validation sites in the contiguous United States. Remote Sensing. 10(9):1483. https://doi.org/10.3390/rs10091483.
Gao, F.N., Anderson, M.C., Daughtry, C.S., Johnson, D. 2018. Assessing variability of corn and soybean yields in central Iowa using high spatiotemporal resolution multi-satellite imagery. Remote Sensing of Environment. 10:1489. https://doi.org/10.3390/rs10091489.
Tao, H., Gao, F.N., Liang, S., Peng, Y. 2019. Mapping climatological bare soil albedos over the contiguous United States using MODIS data. Remote Sensing. 11:666. https://doi.org/10.3390/rs11060666.
Li, Z., Huag, C., Zhu, Z., Gao, F.N., Tang, H., Xin, X., Ding, L., Shen, B., Liu, J., Chen, B., Wang, X., Yan, R. 2018. Mapping daily leaf area index at 30m resolution over a meadow steppe area by fusing Landsat, Sentinel-2A and MODIS data. International Journal of Remote Sensing. 39(23):9025-9053. https://doi.org/10.1080/01431161.2018.1504342.
Lee, S., Yeo, I., Lang, M., Sadeghi, A.M., McCarty, G.W., Moglen, G.E., Evenson, G. 2018. Assessing the cumulative impacts of geographically isolated wetlands on watershed hydrology using the SWAT model coupled with improved wetland modules. Journal of Environmental Management. 223:37-48. https://doi.org/10.1016/j.jenvman.2018.06.006.
Knipper, K.R., Kustas, W.P., Anderson, M.C., Alfieri, J.G., Prueger, J.H., Hain, C., Gao, F.N., Yang, Y., McKee, L.G., Nieto, H., Hipps, L., Aisha, M., Sanchez, L. 2018. Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrigation Science. https://doi.org/10.1007/s00271-018-0591-y.
Nieto, H., Kustas, W.P., Torres, A., Alfieri, J.G., Gao, F.N., Anderson, M.C., White, W.A., Song, L., Alsina, M., Prueger, J.H., McKee, L.G. 2018. Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery. Irrigation Science. https://doi.org/10.1007/s00271-018-0585-9.
Song, L., Liu, S., Kustas, W.P., Nieto, H., Sun, L., Xu, Z., Skaggs, T.H., Ma, M., Xu, T., Tang, X., Li, Q. 2018. Monitoring and validating spatio-temporal continuously daily evapotranspiration and its components at river the basin scale. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.10.002.
Kustas, W.P., Agam, N., Alfieri, J.G., McKee, L.G., Prueger, J.H., Hipps, L., Howard, A., Heitman, J. 2018. Below canopy radiation divergence in a vineyard – implications on inter-row surface energy balance. Irrigation Science. https://doi.org/10.1007/s00271-018-0601-0.
Kustas, W.P., Alfieri, J.G., Nieto, H., Wilson, T.G., Gao, F.N., Anderson, M.C. 2018. Utility of the Two-Source Energy Balance (TSEB) model in vine and interrow flux partitioning over the growing season. Irrigation Science. https://doi.org/10.1007/s00271-018-0586-8.
Nieto, H., Kustas, W.P., Alfieri, J.G., Feng, M., Hipps, L., Los, S., Prueger, J.H., McKee, L.G., Anderson, M.C. 2018. Impact of different within-canopy wind attenuation formulations on modelling evapotranspiration using TSEBm. Irrigation Science. https://doi.org/10.1007/s00271-018-0611-y.
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) I. Remote Sensing. https://doi.org/10.3390/rs10040567.
Li, Y., Kustas, W.P., Huang, C., Nieto, H., Haghighi, E., Anderson, M.C., Domingo, F., Garcia, M. 2018. Evaluating soil resistance formulations in thermal-based two source energy balance (TSEB) model: Implications for heterogeneous semiarid and arid regions. Water Resources Research. https://doi.org/10.1029/2018WR022981.
Li, Y., Kustas, W.P., Huang, C., Kool, D., Haghighi, E. 2018. Evaluation of soil resistance formulations for estimates of sensible heat flux in a desert vineyard. Agricultural and Forest Meteorology. https://doi.org/10.1O6/j.agrformet.2018.06.019.
Agam, N., Kustas, W.P., Alfieri, J.G., Gao, F.N., Mckee, L.G., Prueger, J.H., Hipps, L. 2019. Grass intercrop and soil water content have a secondary effect on soil heat flux (SHF) in a wine vineyard – implications on SHF measurements. Irrigation Science. https://doi.org/10.1007/s00271-019-00634-6.
Cheng, J., Kustas, W.P. 2019. Using very high resolution thermal infrared imagery for more accurate determination of the impact of land cover differences on evapotranspiration in an irrigated agricultural area. Remote Sensing. 11(6):613. https://doi.org/10.3390/rs11060613.
Brocca, L., Crow, W.T., Ciabatta, L., Massari, C., De Rosnay, P., Enenkel, M., Hahn, S., Amarnath, G., Camici, S., Tarpanelli, A., Wagner, W. 2017. A review of the applications of ASCAT soil moisture products. International Journal of Applied Earth Observation and Geoinformation. 10(5):2285-2306. https://doi.org/10.1109/JSTARS.2017.2651140.
Dong, J., Crow, W.T. 2018. The added value of assimilating remotely sensed soil moisture for analysis of soil moisture-air temperature interactions. Water Resources Research. 54:6072-6084. https://doi.org/10.1029/2018WR022619.
Crow, W.T., Malik, S., Moghaddam, M., Tabatabaeenejad, A., Jaruwatanadilok, S., Yu, X., Shi, Y., Riechle, R., Hagimoto, Y., Cuenca, R. 2018. Spatial and temporal variability of root-zone soil moisture acquired from hydrologic modeling and AirMOSS P-band radar. IEEE Journal of Selected Topics in Applied Remote Sensing. 11(12):4578-4590. https://doi.org/10.1109/JSTARS.2018.2865251.
Lei, F., Crow, W.T., Holmes, T., Hain, C., Anderson, M.C. 2018. Global investigation of soil moisture and latent heat flux coupling strength. Water Resources Research. 54:8196-8215. https://doi.org/10.1029/2018WR023469.
Wei, G., Lu, H., Crow, W.T., Zhu, Y., Wang, J., Su, J. 2018. Comprehensive evaluation of GPM-IMERG, CMORPH and TMPA precipitation products with gauged rainfall over mainland China. Advances in Meteorology. 2018:3024190. https://doi.org/10.1155/2018/3024190.
Dong, J., Crow, W.T. 2018. Use of satellite soil moisture to diagnosis climate model representations of European air temperature-soil moisture coupling strengths. Geophysical Research Letters. 45:12884–12891. https://doi.org/10.1029/2018GL080547.
Dong, J., Crow, W.T. 2018. L-band remote sensing increases sampled levels of global soil moisture - air temperature coupling strength. Remote Sensing of Environment. 220:51-58. https://doi.org/10.1016/j.rse.2018.10.024.
Mayo, Y., Crow, W.T., Nijsseen, B. 2019. A 3-step framework for understanding the efficiency of surface soil moisture data assimilation for improving large-scale runoff prediction. Journal of Hydrometeorology. 20:79–97. https://doi.org/10.1175/JHM-D-18-0115.1.
Yeo, I., Lee, S., Lang, M., Yetemen, O., McCarty, G.W., Sadeghi, A.M., Evenson, G. 2018. Mapping landscape-scale hydrological connectivity of headwater wetlands to downstream water: a catchment modelling approach - Part 2. Science of the Total Environment. 12/15/2018. https://doi.org/10.1016/j.scitotenv.2018.11.237.
Yeo, I., Lang, M., Lee, S., Haung, C., McCarty, G.W., Sadeghi, A.M., Yetemen, O. 2018. Mapping the landscape-level hydrological connectivity of headwater wetlands to downstream waters: a geospatial modeling approach - Part I. Science of the Total Environment. 653:1546-1556. https://doi.org/10.1016/j.scitotenv.2018.11.238.
Lee, S., McCarty, G.W., Moglen, G.E., Lang, M., Sadeghi, A.M., Green, T.R., Yeo, I., Rabenhorst, M. 2018. Effects of subsurface soil characteristics on depressional wetland inundation within the Coastal Plain of the Chesapeake Bay watershed. Hydrological Processes. 33(2):305-315. https://doi.org/10.1002/hyp.13326.
Sharifi, A., Lee, S., McCarty, G.W., Lang, M., Jeogn, J., Sadeghi, A.M., Rabenhorst, M. 2019. Enhancement of APEX model to assess effectiveness of wetland water quality benefits. Water. 11(3):606. https://doi.org/10.3390/w11030606.
Nearing, G., Yatheendradas, S., Crow, W.T., Zhan, X., Liu, J., Chen, F. 2018. The efficiency of data assimilation. Water Resources Research. 54(9):6374-6392. https://doi.org/10.1029/2017WR020991.
Al-Yaari, A., Ducharne, A., Crow, W.T., Cheruy, F., Wigneron, J. 2019. Space-borne microwave surface soil moisture observations provide missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States. Scientific Reports. 9:1657. https://doi.org/10.1038/s41598-018-38309-5.
Dong, J., Crow, W.T. 2019. A double instrumental variable method for geophysical product error estimation. Remote Sensing of Environment. 9:1657. https://doi.org/10.1038/s41598-018-38309-5.
Qui, J., Wagner, W., Zhao, T., Crow, W.T. 2019. Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing. International Journal of Applied Earth Observation and Geoinformation. 80:47-57. https://doi.org/10.1016/j.jag.2019.03.015.
Gruber, A., Delannoy, G., Crow, W.T. 2019. A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation. Remote Sensing of Environment. 228:105-114. https://doi.org/10.1016/j.rse.2019.04.003.
Fang, B., Laskshmi, V., Bindlish, R., Jackson, T.J. 2018. Downscaling of SMAP soil moisture using land surface temperature and vegetation data. Vadose Zone Journal. 17:170198. https://doi.org/10.2136/vzj2017.11.0198.
Fang, B., Lashmi, V., Jackson, T.J., Bindlish, R., Colliander, A. 2019. Passive/active microwave soil moisture disaggregation using SMAPVEX12 data. Journal of Hydrology. 574:1085-1098. https://doi.org/10.1016/j.jhydrol.2019.04.082.
Cosh, M.H., White, W.A., Colliander, A., Jackson, T.J., Prueger, J.H., Hornbudde, B., Hunt Jr, E.R., McNairn, H., Powres, J., Walker, V. 2019. Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment in 2016. Journal of Applied Remote Sensing (JARS). 13(1):014516. https://doi.org/10.1117/1.JRS.13.014516.
Bhuiyan, H., McNairn, H., Powers, J., Friesen, M., Pacheco, A., Jackson, T.J., Cosh, M.H., Colliander, A., Berg, A., Rowlandson, T., Magagi, R. 2018. Assessing SMAP soil moisture scaling and retrieval in the Carman (Canada) study site. Vadose Zone Journal. 17(1). https://doi.org/10.2136/vzj2018.07.0132.
Alfieri, J.G., Kustas, W.P., Nieto, H., Prueger, J., Hipps, L., McKee, L.G. 2018. Influence of wind direction on the surface roughness of vineyards. Irrigation Science. 37(3):359-373. https://doi.org/10.1007/s00271-018-0610-z.
Alfieri, J.G., Kustas, W.P., Prueger, J.H., McKee, L.G., Hipps, L., Gao, F.N. 2018. A multi-year intercomparison of micrometeorological observations at adjacent vineyards in California’s Central Valley during GRAPEX. Irrigation Science. 37(3):345-357. https://doi.org/10.1007/s00271-018-0599-3.
Los, S., Hipps, L., Alfieri, J.G., Kustas, W.P., Prueger, J.H. 2019. Intermittency of water vapor fluxes from vineyards during light wind and convective conditions. Irrigation Science. 37(3):281-295. https://doi.org/10.1007/s00271-018-0617-5.
Aboutalebi, M., Torres-Rua, A., Kustas, W.P., Nieto, H., Coopmans, C., McKee, M. 2018. Assessment of different methods for shadow detection in high-resolution imagery and evaluation of shadows impact on calculation of NDVI, LAI, and evapotranspiration. Irrigation Science. 37(3):407-429. https://doi.org/10.1007/s00271-018-0613-9.