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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #441524

Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Publications (Clicking on the reprint icon Reprint Icon will take you to the publication reprint.)

Soil moisture profiles of ecosystem water use revealed with ECOSTRESS Reprint Icon - (Peer Reviewed Journal)
Feldman, A., Koster, R., Cawse-Nicholson, K., Crow, W.T., Holmes, T., Poutler, B. 2024. Soil moisture profiles of ecosystem water use revealed with ECOSTRESS. Geophysical Research Letters. 51. https://doi.org/10.1029/2024GL108326.

Neglect of potential seasonal streamflow forecasting skill in the United States national water model Reprint Icon - (Peer Reviewed Journal)
Crow, W.T., Koster, R., Reichle, R., Chen, F., Liu, Q. 2024. Neglect of potential seasonal streamflow forecasting skill in the United States national water model. Geophysical Research Letters. 51. https://doi.org/10.1029/2023GL105649.

Forming the future of agrohydrology research Reprint Icon - (Peer Reviewed Journal)
Smidt, S., Haaker, E., Bai, X., Cherkauer, K., Choat, B., Crompton, O.V., Deines, J., Groh, J., Guzman, S., Hartman, K., Kenall, A., Khan, S., Kustas, W.P., Mcgill, B.M., Nocco, M.A., Pensky, J., Rapp, J., Schreiner-Mcgraw, A.P., Simmons, T., Sprenger, M., Wan, L., Weldegebriel, L., Zipper, S., Zoccatelli, D. 2023. Forming the future of agrohydrology research. Earth's Future. https://doi.org/10.1029/2022EF003410.

Interpreting effective hydrologic depth estimates derived from soil moisture remote sensing: A Bayesian non-linear modelling approach Reprint Icon - (Peer Reviewed Journal)
Hyunglok, K., Crow, W.T. 2023. Interpreting effective hydrologic depth estimates derived from soil moisture remote sensing: A Bayesian non-linear modelling approach. Remote Sensing of Environment. 908. https://doi.org/10.1016/j.scitotenv.2023.168067.

Systematic modelling errors undermine the application of land data assimilation systems for hydrological and weather forecasting Reprint Icon - (Peer Reviewed Journal)
Crow, W.T., Kim, H., Kumar, S. 2023. Systematic modelling errors undermine the application of land data assimilation systems for hydrological and weather forecasting. Journal of Hydrometeorology. 25, 3-26. https://doi.org/10.1175/JHM-D-23-0069.1.

Large-scale urban building function mapping by integrating multi-source web-based geospatial data - (Peer Reviewed Journal)

Nitrous oxide emissions from multiple agroecosystems in the U.S. Corn Belt simulated using the modified SWAT-C model Reprint Icon - (Peer Reviewed Journal)
Liang, K., Zhang, X., Qi, J., Emmett, B.D., Johnson, J.M., Malone, R.W., Moglen, G.E., Venterea, R.T. 2023. Nitrous oxide emissions from multiple agroecosystems in the U.S. Corn Belt simulated using the modified SWAT-C model . Environmental Pollution. 337 (2023). https://doi.org/10.1016/j.envpol.2023.122537.

A basic and applied remote sensing research project (GRAPEX) for actual evapotranspiration monitoring to improve vineyard water management - (Peer Reviewed Journal)

True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis - (Peer Reviewed Journal)

The Two-Source Energy Balance (TSEB) model formulation using thermal-infrared remote sensing for evapotranspiration estimation: Applications from field to global scales - (Abstract Only)

Watershed scale modeling of dissolved organic carbon export from variable source areas - (Peer Reviewed Journal)

An introduction to Bayesian Machine Learning with an application in global-scale active and passive satellite-based soil moisture error pattern analysis Reprint Icon - (Peer Reviewed Journal)
Kim, H., Wagner, W., Crow, W.T., Li, X., Lakshmi, V. 2023. An introduction to Bayesian Machine Learning with an application in global-scale active and passive satellite-based soil moisture error pattern analysis. Remote Sensing of Environment. 296. https://doi.org/10.1016/j.rse.2023.113718.

IMERG precipitation improves the SMAP level-4 soil moisture product Reprint Icon - (Peer Reviewed Journal)
Reichle, R., Liu, Q., Ardizzone, J., Crow, W.T., De Lannoy, G., Kimball, J., Koster, R. 2023. IMERG precipitation improves the SMAP level-4 soil moisture product. Journal of Hydrometeorology. 24, 1699-1723. https://doi.org/10.1175/JHM-D-23-0063.1.

Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow Reprint Icon - (Peer Reviewed Journal)
Koster, R.D., Liu, Q., Crow, W.T., Reichle, R.H. 2023. Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow. Nature Communications. 14:35-45. https://doi.org/10.1038/s41467-023-39318-3.

Attributing the drivers of runoff decline in the Thaya River basin Reprint Icon - (Peer Reviewed Journal)

Multivariate calibration of the SWAT model using remotely sensed datasets Reprint Icon - (Peer Reviewed Journal)
Dangol, S., Zhang, X., Liang, X., Anderson, M.C., Crow, W.T., Lee, S., Moglen, G.E., McCarty, G.W. 2023. Multivariate calibration of the SWAT model using remotely sensed datasets. Remote Sensing. 15(9):2417. https://doi.org/10.3390/rs15092417.

SWAT-3PG: Improving forest growth simulation with a process-based forest model in SWAT Reprint Icon - (Peer Reviewed Journal)
Karki, R., Qi, J., Gonzales-Benecke, C., Zhang, X., Martin, T., Arnold, J.G. 2023. SWAT-3PG: Improving forest growth simulation with a process-based forest model in SWAT. Journal of Environmental Modeling and Software. 164. Article 105705. https://doi.org/10.1016/j.envsoft.2023.105705.

Potential of remote sensing surface temperature- and evapotranspiration-based land-atmosphere coupling metrics for land surface model calibration Reprint Icon - (Peer Reviewed Journal)
Zhou, J., Yang, K., Crow, W.T., Ding, J., Zhao, L., Fenng, H., Zou, M., Lu, H., Tang, R. 2023. Potential of remote sensing surface temperature- and evapotranspiration-based land-atmosphere coupling metrics for land surface model calibration. Remote Sensing of Environment. 291. Article 113557. https://doi.org/10.1016/j.rse.2023.113557.

Form field observations to temporally dynamic roughness retrievals in the corn belt Reprint Icon - (Peer Reviewed Journal)
Walker, V., Yildrim, E., Wallace, V., Eichinger, W., Cosh, M.H., Hornbuckle, B. 2023. Form field observations to temporally dynamic roughness retrievals in the corn belt. Remote Sensing of Environment. 287. Article e113458. https://doi.org/10.1016/j.rse.2023.113458.

ET partitioning assessment using the TSEB model and sUAS information across California Central Valley vineyards Reprint Icon - (Peer Reviewed Journal)
Gao, R., Torres-Rua, A., Nieto, H., Zahn, E., Hipps, L., Kustas, W.P., Alsina, M., Ortiz, N., Castro, S., Prueger, J., Alfieri, J.G., McKee, L.G., White, W.A., Gao, F.N., McElrone, A.J., Anderson, M.C., Knipper, K.R., Coopmans, C., Gowing, I., Agam, N., Sanchez, L., Dokoozlian, N. 2023. ET partitioning assessment using the TSEB model and sUAS information across California Central Valley vineyards. Remote Sensing. 15(3). Article 756. https://doi.org/10.3390/rs15030756.

Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment Reprint Icon - (Peer Reviewed Journal)
Gao, F.N., Jennewein, J.S., Hively, W.D., Soroka, A., Thieme, A., Bradley, D., Keppler, J., Mirsky, S.B., Akumaga, U. 2022. Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment. Science of Remote Sensing. 7. Article 100073. https://doi.org/10.1016/j.srs.2022.100073.

Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland Reprint Icon - (Peer Reviewed Journal)
Feng, S., Qui, J., Crow, W.T., Mo, X., Wang, S., Gao, L. 2022. Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland. Journal of Photogrammetry and Remote Sensing. 617. Article 129015. https://doi.org/10.1016/j.jhydrol.2022.129015.

Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay Reprint Icon - (Peer Reviewed Journal)
Lee, J., Abbas, A., McCarty, G.W., Zhang, X., Lee, S., Cho, K. 2022. Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay. Journal of Hydrology. 617. Article 128916. https://doi.org/10.1016/j.jhydrol.2022.128916.

Integrating vegetation phenology and SWAT model for improved modeling of ecohydrological processes Reprint Icon - (Peer Reviewed Journal)
Chen, S., Fu, Y., Wu, Z., Hao, F., Hao, Z., Guo, Y., Geng, X., Li, X., Zhang, X., Tang, J., Singh, V.P., Zhang, X. 2022. Integrating vegetation phenology and SWAT model for improved modeling of ecohydrological processes. Remote Sensing. 616:128817. https://doi.org/10.1016/j.jhydrol.2022.128817.

A global implementation of single- and dual-source surface energy balance models for estimating actual evapotranspiration at 30-m resolution using Google Earth Engine Reprint Icon - (Peer Reviewed Journal)
Jafaar, H., Mourad, R., Kustas, W.P., Anderson, M.C. 2022. A global implementation of single- and dual-source surface energy balance models for estimating actual evapotranspiration at 30-m resolution using Google Earth Engine. Water Resources Research. 58. Article e2022WR032800. https://doi.org/10.1029/2022WR032800.

An agenda for land data assimilation priorities: Realizing the promise of terrestrial water, energy, and vegetation observations from space Reprint Icon - (Peer Reviewed Journal)
Kumar, S., Kolassa, J., Reichle, R., De Lannoy, G., De Rosnay, P., Macbean, N., Girotto, M., Fox, A., Quaife, T., Draper, C., Forman, B., Balsamo, G., Steele-Dunne, S., Albergel, C., Bonan, B., Calvet, J.C., Dong, J., Liddy, H., Ruston, B., Crow, W.T. 2022. An agenda for land data assimilation priorities: Realizing the promise of terrestrial water, energy, and vegetation observations from space. Journal of Advances in Modeling Earth Systems. 14(11). https://doi.org/10.1029/2022MS003259.

Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations Reprint Icon - (Peer Reviewed Journal)
Crow, W.T., Chen, F., Colliander, A. 2022. Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations. Remote Sensing of Environment. 283. Article 113300. https://doi.org/10.1016/j.rse.2022.113300.

From vine to vineyard: The GRAPEX multi-scale remote sensing experiment for improving vineyard irrigation management - (Review Article)

High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States Reprint Icon - (Peer Reviewed Journal)
Vergopolan, N., Sheffield, J., Chaney, N., Pan, M., Beck, H., Ferguson, C., Torres-Rojas, L., Eigenbrod, F., Crow, W.T., Wood, E. 2022. High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States. Geophysical Research Letters. 49(15):e2022GL098586. https://doi.org/10.1029/2022GL098586.

Fifty years of Landsat science and impacts Reprint Icon - (Peer Reviewed Journal)
Wulder, M., Roy, D., Radeloff, V., Loveland, T., Anderson, M.C., Johnson, D., Healey, S., Zhu, Z., Scambos, T., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C., Masek, J., Hermosilla, T., White, J., Belward, A., Schaaf, C., Woodcock, C., Huntington, J., Lymburner, L., Hostert, P., Gao, F.N., Lyapustin, A., Pekel, J., Strobl, P., Cook, B. 2022. Fifty years of Landsat science and impacts. Remote Sensing of Environment. 280. Article 113195. https://doi.org/10.1016/j.rse.2022.113195.

Assessing the spatiotemporal variability of SMAP soil moisture accuracy in a deciduous forest region Reprint Icon - (Peer Reviewed Journal)
Abdelkader, M., Temimi, M., Colliander, A., Cosh, M.H., Kelly, V., Lakhankar, T., Fares, A. 2022. Assessing the spatiotemporal variability of SMAP soil moisture accuracy in a deciduous forest region. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14(14). https://doi.org/10.3390/rs14143329.

NRCS curve number method: A comparison of methods for estimating the curve number from rainfall-runoff data Reprint Icon - (Peer Reviewed Journal)
Moglen, G.E., Sadeq, H., Hughes, L., Meadows, M., Miller, J.J., Ramirez-Avila, J., Tollner, E. 2022. NRCS curve number method: A comparison of methods for estimating the curve number from rainfall-runoff data. Journal Hydrologic Engineering. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002210.

Seeing our planet anew: fifty years of Landsat Reprint Icon - (Peer Reviewed Journal)
Loveland, T., Anderson, M.C., Huntington, J., Irons, J., Johnson, D., Rocchio, L., Woodcock, C., Wulder, M. 2022. Seeing our planet anew: fifty years of Landsat. Photogrammetric Engineering and Remote Sensing. 88:7. https://doi.org/10.14358/PERS.88.7.429.

LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning Reprint Icon - (Peer Reviewed Journal)
Gao, R., Torres, A., Aboutalebi, M., White, W.A., Anderson, M.C., Kustas, W.P., Agam, N., Alsina, N., Alfieri, J.G., Hipps, L., Dokoozlian, N., Nieto, H., Gao, F.N., McKee, L.G., Prueger, J.H., Sanchez, L., McElrone, A.J., Bambach, N., Coopmans, C., Gowing, I. 2022. LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning. Irrigation Science. 40:731-759. https://doi.org/10.1007/s00271-022-00776-0.