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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

2021 Annual Report


Accomplishments
1. A 30-m leaf area index product in Google Earth Engine for monitoring crop condition and water use. Leaf area index (LAI) is a key biophysical parameter used for monitoring vegetation health and water use. Current LAI data products derived from satellite remote sensing data are typically generated at low spatial resolution (0.25 to 1 km), which is often too coarse for many agricultural applications at field scales. ARS scientists in Beltsville, Maryland, developed an operational approach to map LAI at 30-m resolution in Google Earth Engine (GEE). By leveraging the cloud computing power of GEE, long-term records of 30-m LAI can be generated with Landsat starting from the 1980s and covering the United States. Results show good agreement with ground measurements of LAI over various landscapes. The approach provides a feasible method for producing sub-field-scale LAI products for routine monitoring and retrospective analysis of crop condition and water use in the United States.

2. Improving numerical weather prediction in the central United States. Accurate short-term (< 48 hours) air temperature forecasts are valuable for a range of important agricultural management decisions; however, many weather forecast centers routinely overestimate summertime daily air temperature maximums in the central United States. Moreover, counter to expectations, the magnitude of this warm bias increases when these centers assimilate remotely sensed soil moisture retrievals into their land-surface models to improve their representation of surface soil water availability. ARS researchers in Beltsville, Maryland, have recently explained this (counterintuitive) tendency by showing that land-surface models used in numerical weather prediction tend to over-couple soil moisture and surface evapotranspiration in the summertime – in such a way that improving the representation of soil moisture in the model (via the assimilation of remotely sensed soil moisture products) can exacerbate pre-existing air temperature/evapotranspiration biases. Through this insight, ARS researchers have identified a valuable path forward for improving short-term numerical weather prediction within the central United States.

3. Development of a high-resolution soil moisture product from satellites. Soil moisture (SM) is a key indicator of crop health and developing agricultural drought. The SMAP (Soil Moisture Active Passive) satellite has proven to be an effective method of monitoring soil moisture content at fairly coarse resolution (36-km grid). With advanced downscaling techniques, it has been possible to reduce this gridding to 9 km, but this is still too coarse for many agricultural applications. ARS scientists in Beltsville, Marylad have developed a new technique that uses information about spatial variability land surface temperature and vegetation to produce 1-km soil moisture products of comparable accuracy to the original SMAP 36-km product. With these new high-resolution products, 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.

4. A Good Practices document for soil moisture product validation. Satellites can be used to generate highly accurate maps of soil moisture at near-daily timesteps. While these products are very beneficial for agricultural applications, no consistent methods have been developed for determining the accuracy of different soil moisture products. ARS scientists in Beltsville, Maryland, worked with a team of remote sensing experts to develop a Soil Moisture Product Validation Good Practices Protocol document for the Committee on Earth Observing Satellites, bringing soil moisture to a Validation Stage of 3 (out of 4) by the standards established for this international body. The preparation of this document included over 50 scientists and academics working together toward formulating a common basis for soil moisture remote sensing product evaluation. This document provides a valuable resource for developing a standard platform for remote sensing evaluation for soil moisture, which is recognized as a critical parameter for agriculture.

5. Improved detection of wetland inundation below forest canopy. To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use variation, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which are difficult to map under forest vegetation. Commonly used lidar (Light Detection and Ranging) instrumentation uses reflections from a scanning laser to map topography and, as a byproduct, also collects maps of the intensity of laser light reflection. While these intensity data provide accurate information on forested wetland inundation when trees have lost their leaves, the presence of evergreen vegetation can interfere with the collection of inundation information. ARS scientists in Beltsville, Maryland, demonstrated a data processing approach to correct for the influence of evergreens on inundation mapping. Improved inundation maps will permit more accurate mapping of forested wetlands and allow better training of artificial intelligence procedures for assessing wetland ecosystem services provision in agricultural landscapes.

6. Modeling sediment diagenesis processes on riverbeds. Despite the widely recognized importance of aquatic processes for bridging gaps in the global carbon cycle, there is still a lack of understanding of riverbed processes' role in carbon flows and stocks in aquatic environments. ARS scientists in Beltsville, Marylad modified the USDA Soil Water Assessment Tool (SWAT) model to include two new modules that capture sediment dynamics for particulate and dissolved organic carbon and tested the revised model using a four-year observational dataset in a U.S. mid-Atlantic watershed. The new modules showed good agreement with observations and emphasize the importance of modeling these dynamics so that carbon fluxes and stocks are properly understood at the watershed scale. Findings from the revised SWAT model are useful to inform ecosystem services for watershed assessment and planning.

7. Using NASA earth observations and Google Earth Engine to map winter cover crop performance. The Maryland Cover Crop Program managed by the Maryland Department of Agriculture (MDA) incentivizes farmers to grow winter cover crops to reduce nutrient and sediment loss from farmland. ARS has collaborated with the MDA since 2006, developing remote sensing techniques to assess winter cover crop performance in Maryland. ARS scientists in Beltsville, Maryland developed Google Earth Engine (GEE) scripts to create composite seasonal satellite reflectance indices from Landsat and Sentinel 2 images covering the State of Maryland. They combined this information with MDA cost-share enrollment field boundary data to produce a winter and springtime evaluation of winter cover crop performance for all enrolled fields falling within three test counties on the Eastern Shore, and one test county in western Maryland. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit. It is expected that this tool will now be used operationally by the MDA in the implementation of their ongoing winter cover crop program.


Review Publications
Sun, L., Gao, F.N., Xie, D., Anderson, M.C., Chen, R., Yang, Y., Yang, Y., Chen, Z. 2020. Reconstructing daily 30 m vegetation index over complex agricultural landscapes using crop reference curves approach. Remote Sensing of Environment. 253:112156. https://doi.org/10.1016/j.rse.2020.112156.
Knipper, K.R., Kustas, W.P., Anderson, M.C., Nieto, H., Alfieri, J.G., Prueger, J.H., Hain, C.R., Gao, F.N., McKee, L.G., Mar Alsina, M., Sanchez, L. 2020. Using high-spatiotemporal thermal satellite ET retrievals to monitor water use over California vineyards of different climate, vine variety and trellis design. Agricultural Water Management. 241. Article 106361. https://doi.org/10.1016/j.agwat.2020.106361.
Whitcomb, J., Clewley, D., Colliander, A., Cosh, M.H., Powers, J., Friesen, M., McNairn, H., Berg, A., Bosch, D.D., Coffin, A.W., Holifield Collins, C.D., Prueger, J.H., Entekhabi, D., Moghaddam, M. 2020. Evaluation of SMAP core validation site representativeness errors using dense networks of in situ sensors and random forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13:6457-6472. https://doi.org/10.1109/JSTARS.2020.3033591.
Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M.H., Crow, W.T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M., De Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y., Lovergine, F., Marzahn, P., Mattia, F., Musial, J., Preuschmann, S., Reichle, R., Satalino, G., Silgram, M., Van Bodegom, P. 2020. A roadmap for high resolution satellite soil moisture applications - confronting product characteristics with user requirements. Nature Reviews Earth & Environment. 252:112162. https://doi.org/10.1016/j.rse.2020.112162.
Kim, H., Wigneron, J., Kumar, S., Dong, J., Wagner, W., Cosh, M.H., Bosch, D.D., Holifield Collins, C.D., Starks, P.J., Seyfried, M.S., Lakshmi, V. 2020. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions. Remote Sensing of Environment. 251:112052. https://doi.org/10.1016/j.rse.2020.112052.
Liu, P., Bindlish, R., Fang, B., Lakshmi, V., O'Neill, P., Yang, Z., Cosh, M.H., Bongiovqnni, T., Bosch, D.D., Holifield Collins, C.D., Starks, P.J., Prueger, J.H., Seyfried, M.S., Livingston, S.J. 2021. Assessing disaggregated SMAP soil moisture products in the United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14:2577-2592. https://doi.org/10.1109/JSTARS.2021.3056001.
Kang, Y., Ozdogan, M., Gao, F.N., Anderson, M.C., White, W.A., Yang, Y., Yang, Y., Erickson, T. 2021. Estimation of the leaf area index from Landsat over the contiguous US. Remote Sensing of Environment. 258:112383. https://doi.org/10.1016/j.rse.2021.112383.
Lee, S., Qi, J., Kim, H., McCarty, G.W., Moglen, G.E., Anderson, M.C., Zhang, X., Du, L. 2021. Utility of remotely sensed evapotranspiration products on assessing an improved model structure. Sustainability. 13(4):2375 .https://doi.org/10.3390/su13042375.
Li, Y., Huang, C., Kustas, W.P., Nieto, H., Sun, L., Hou, J. 2020. Evapotranspiration partitioning at field scales using TSEB and multi-satellite data fusion in the middle reaches of Heihe river basin, northwest China . Remote Sensing. 12(9):3223. https://doi.org/10.3390/rs12193223.
Gao, F.N., Anderson, M.C., Hively, W.D. 2020. Detecting cover crop end-of-season using VENS and Sentinel-2 satellite imagery. Remote Sensing. 12(21):3524. https://doi.org/10.3390/rs12213524.
Togliatti, K., Lewis-Beck, C., Walker, V.A., Hartman, T., Van Loocke, A., Cosh, M.H., Hornbuckle, B. 2020. Quantitative assessment of satellite L-band vegetation optical depth in the U.S. corn belt. Geoscience and Remote Sensing Letters. 1-5. https://doi.org/10.1109/LGRS.2020.3034174.
Gao, F.N., Zhang, X. 2021. Mapping crop phenology in near real-time using satellite remote sensing: challenges and opportunities. Journal of Remote Sensing. 2021:14. https://doi.org/10.34133/2021/8379391.
Huang, X., Ziniti, B., Cosh, M.H., Reba, M.L., Wang, J., Torbick, N. 2020. Field scale soil moisture retrieval using PALSAR-2 polarimetric decomposition and machine learning . Agronomy. 11(1):35. https://doi.org/10.3390/agronomy11010035.
Kraatz, S., Rose, S., Cosh, M.H., Torbick, N., Huang, X., Siqueira, P. 2020. Performance evaluation of UAVSAR and simulated NISAR data for crop/non-crop classification over Stoneville, MS . Earth and Space Science. 8, e2020EA00136. https://doi.org/10.1029/2020EA001363.
Anderson, M.C., Yang, Y., Xue, J., Knipper, K.R., Yang, Y., Gao, F.N., Hain, C., Kustas, W.P., Cawse-Nicholson, K., Hulley, G., Fisher, J., Alfieri, J.G., Meyers, T., Prueger, J.H., Baldocchi, D., Sanchez, C. 2020. Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales. Remote Sensing of Environment. 252. Article 112189. https://doi.org/10.1016/j.rse.2020.112189.
Xue, J., Anderson, M.C., Gao, F.N., Hain, C., Sun, L., Yang, Y., Knipper, K.R., Kustas, W.P., Torres, A., Schull, M. 2020. Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances. Remote Sensing of Environment. 251. Article 112055. https://doi.org/10.1016/j.rse.2020.112055.
Yang, Y., Anderson, M.C., Gao, F.N., Johnson, D., Yang, Y., Sun, L., Dulaney, W.P., Hain, C., Otkin, J., Prueger, J.H., Meyers, T., Bernacchi, C.J., Moore, C. 2021. Phenological corrections to a field-scale, ET-based crop stress indicator: an application to yield forecasting across the U.S. Corn Belt. Remote Sensing of Environment. 257:112337. https://doi.org/10.1016/j.rse.2021.112337.
Zhong, Y., Otkin, J., Anderson, M.C., Hain, C. 2020. Observational assessment of the relationship between the Evaporative Stress Index and soil moisture and temperature across the United States. Journal of Hydrometeorology. 21(7):1469–1484. https://doi.org/10.1175/JHM-D-19-0205.1.
Mourad, R., Jaafar, H., Anderson, M.C., Gao, F.N. 2020. Assessment of leaf area index derived from the harmonized Landsat and Sentinel-2 surface reflectance-based vegetation indices and crop height in semi-arid irrigated landscapes. Remote Sensing. 12(19):3121. https://doi.org/10.3390/rs12193121.
Kang, Y., Ozdogan, M., Zhu, X., Ye, Z., Hain, C., Anderson, M.C. 2020. Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest. Environmental Research Letters. 15:064005. https://doi.org/10.1088/1748-9326/ab7df9.
Crocetti, L., Forkel, M., Fischer, M., Jurecka, F., Grlj, A., Salentinig, A., Trnka, M., Anderson, M.C., Ng, W., Kokalj, Ž., Bucur, A., Dorigo, W. 2020. Earth observation for agricultural drought monitoring in the Pannonian Basin: current state and future directions. Regional Environmental Change. 20:123. https://doi.org/10.1007/s10113-020-01710-w.
Enenkel, M., Brown, M., Vogt, J., Mccarty, J., Bell, A., Guha-Sapir, D., Dorigo, W., Vasilaky, K., Svoboda, M., Bonifacio, R., Anderson, M.C., Funk, C., Osgood, D., Hain, C., Vinck, P. 2020. Why predict climate hazards if we need to understand impacts? Mobile technologies could put humans back into the equation. Climatic Change. 162:1161–1176. https://doi.org/10.1007/s10584-020-02878-0.
Cristobal, J., Prakash, A., Anderson, M.C., Kustas, W.P., Alfieri, J.G., Gens, R. 2020. Surface energy flux estimation in two boreal settings in Alaska using a thermal-based remote sensing mode. Remote Sensing. 12(24):4108. https://doi.org/10.3390/rs12244108.
Colliander, A., Cosh, M.H., Misra, S., Jackson, T.J., Crow, W.T., Powers, J., Mccain, H., Bullock, P., Berg, A., Magagi, R., Bindlish, R., Williamson, R., Ramos, I., Latham, B., Oneil, P., Yueh, S. 2019. Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture during the SMAP validation experiment 2016 (SMAPVEX16). Remote Sensing of Environment. 227:137-150. https://doi.org/10.1016/j.rse.2019.04.004.
Caldwell, T., Bongiovanni, T., Cosh, M.H., Jackson, T.J., Colliander, A., Abolt, C., Casteel, R., Larson, T., Scanlon, B., Young, M. 2019. The Texas Soil Observation Network: A comprehensive soil moisture dataset for remote sensing and land surface model validation. Vadose Zone Journal. 18:1. https://doi.org/10.2136/vzj2019.04.0034.
Rodrigues, J., Cosh, M.H., Hunt Jr, E.R., De Moraes, G., Barroso, G., White, W.A., Ochoa, R. 2020. Tracking red palm mite damage in the Western Hemisphere invasion with Landsat remote sensing data. Insects. 11(9):627. https://doi.org/10.3390/insects11090627.
Colliander, A., Cosh, M.H., Kelly, V., Kraatz, S., Bourgeau-Chavez, L., Siqueira, P., Roy, A., Konings, A., Holtzman, N., Misra, S., Entekhabi, D., O'Neill, P.E., Yueh, S. 2020. SMAP detects soil moisture under temperate forest canopies. Geophysical Research Letters. 47(19):e2020GL089697. https://doi.org/10.1029/2020GL089697.
Dong, L., Tang, S., Cosh, M.H., Zhao, P., Lu, P., Zhao, K., Han, S., Min, M., Xu, N., Chen, L., Wang, F. 2020. Studying soil moisture and temperature on the Tibetan Plateau: Initial results of an integrated, multiscale observatory. IEEE Geoscience and Remote Sensing Magazine. 8(3):18-36. https://doi.org/10.1109/MGRS.2019.2924678.
Qi, J., Zhang, X., Cosh, M.H. 2019. Modeling soil temperature in a temperate region: A comparison between empirical and physically based methods. Ecological Engineering. 129(4):134-143. https://doi.org/10.1016/j.ecoleng.2019.01.017.
Yao, P., Shi, J., Cosh, M.H., Bindish, R., Lu, H. 2019. An L-band brightness temperature disaggregation method using S-band radiometer data for the Water Cycle Observation Mission (WCOM). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(9):3184-3193. https://doi.org/10.1109/JSTARS.2019.2922780.
Kim, H., Lee, S., Cosh, M.H., Lakshmi, V., Kwon, Y., McCarty, G.W. 2020. Assessment and combination of SMAP and Sentinel-1A/B derived soil moisture estimates with land surfacemodel outputs in the Mid-Atlantic coastal plain, U.S.A.. IEEE Transactions on Geoscience and Remote Sensing. 59(2):991-1011. https://doi.org/doi:10.1109/TGRS.2020.2991665.
Jadidoleslam, N., Mantilla, R., Krajewski, W., Cosh, M.H. 2019. Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture. Journal of Hydrology. 576(9):85–97. https://doi.org/10.1016/J.JHYDROL.2019.06.026.
Neelam, M., Colliander, A., Mohanty, B., Cosh, M.H., Misra, S., Jackson, T. 2020. Multi-scale surface roughness for improved soil moisture estimation. IEEE Transactions on Geoscience and Remote Sensing. 58(8):5264-5276. https://doi.org/10.1109/TGRS.2019.2961008.
Montzkka, C., Bogena, H., Herbst, M., Cosh, M.H., Jaghuber, T., Vereecken, H. 2020. Estimating the number of reference sites necessary for the validation of global soil moisture products. Geoscience and Remote Sensing Letters. 99:1-5. https://doi.org/10.1109/LGRS.2020.3005730.
Bayat, B., Camacho, F., Nickeson, J., Cosh, M.H., Bolten, J., Vereecken, H., Montzka, C. 2020. Towards operational validation systems for global satellite-derived terrestrial essential climate variables. International Journal of Applied Earth Observation and Geoinformation. 95:102240. https://doi.org/10.1016/j.jag.2020.102240.
Kang, C.S., Zhao, T., Shi, J.C., Cosh, M.H., Chen, Y., Starks, P.J., Holifield Collins, C.D., Wu, S., Sun, R., Zheng, J. 2020. Global soil moisture retrievals from the Chinese FY-3D microwave radiation imager. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2020.3019408.
Quets, J., Delannoy, G., Al Yaari, A., Chan, S., Cosh, M.H., Gruber, A., Reichle, R., Van Der Schalie, R., Wigneron, J. 2019. Uncertainty in soil moisture retrievals: an ensemble approach using SMOS L-band microwave data. Remote Sensing of Environment. 229:133-147. https://doi.org/10.1016/j.rse.2019.05.008.
Ma, H., Zeng, J., Chen, N., Zhang, X., Cosh, M.H., Wang, W. 2019. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: a comprehensive assessment using global ground-based soil moisture observations. Remote Sensing of Environment. 231:111215. https://doi.org/10.1016/j.rse.2019.111215.
Dingle Robertson, L., Davidson, A., McNairn, H., Hosseini, M., Mitchell, S., De Abelleyra, D., Veron, S., Cosh, M.H. 2020. Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping. International Journal of Remote Sensing. 41(18):7112-7144. https://doi.org/10.1080/01431161.2020.1754494.
Tabatabaeenejad, A., Chen, R., Burgin, M., Duan, X., Cuenca, R., Cosh, M.H., Scott, R.L., Moghaddam, M. 2020. Assessment and validation of AirMOSS P-band root zone soil moisture products. IEEE Transactions on Geoscience and Remote Sensing. 58(9):6181-6196. https://doi.org/10.1109/TGRS.2020.2974976.
Bulut, B., Tugrul, Y., Afshar, M., Unal, S., Yucel, I., Cosh, M.H., Simek, O. 2019. Evaluation of remotely-sensed and model-based soil moisture products according to different soil type, vegetation cover and climate regime using station-based observations over Turkey. Remote Sensing. 11(16):1875. https://doi.org/10.3390/rs11161875.
Shelito, P., Kumar, S., Santanello, J., Lawston, P., Bolton, J., Cosh, M.H., Bosch, D.D., Holifield Collins, C.D., Livingston, S.J., Prueger, J.H., Seyfried, M.S., Starks, P.J. 2020. Assessing the impact of soil layer specification on the observability of modeled soil moisture and brightness temperature. Journal of Hydrometeorology. 21(9):2041-2060. https://doi.org/10.1175/JHM-D-19-0280.1.
Zhou, Y., Sharma, A., Kurum, M., Lang, R., O'Neil, P., Cosh, M.H. 2020. The backscattering contribution of soybean pods at L-band. Remote Sensing of Environment. 248:111977. https://doi.org/10.1016/j.rse.2020.111977.
Park, C., Jagdhuber, T., Colliander, A., Lee, J., Berg, A., Cosh, M.H., Kim, S., Kim, Y., Wulfmeyer, V. 2020. Parameterization of vegetation scattering albedo in the tau-omega model for soil moisture retrieval on croplands. Remote Sensing. 12(18):2939. https://doi.org/10.3390/rs12182939.
Fang, L., Zhan, X., Yin, J., Schull, M., Walker, J., Wen, J., Cosh, M.H., Lakankar, T., Holifield Collins, C.D., Bosch, D.D., Starks, P.J., Caldwell, T. 2020. An intercomparison study of algorithms for downscaling SMAP radiometer soil moisture retrievals. Journal of Hydrometeorology. 21(8):1761-1775. https://doi.org/10.1175/JHM-D-19-0034.1.
Schroeder, R., Jacobs, J., Cho, E., Olheiser, C., Deweese, M., Cosh, M.H., Jia, X., Vuyovich, C., Tuttle, S. 2019. Comparison of satellite passive microwave with modeled snow water equivalent estimates in the Red River of the North Basin. IEEE Journal of Selected Topics in Applied Remote Sensing. 12(9):3233-3246. https://doi.org/10.1109/JSTARS.2019.2926058.
Kool, D., Kustas, W.P., Ben-Gal, A., Agam, N. 2021. Energy partitioning between plant canopy and soil, performance of the two-source energy balance model in a vineyard. Agricultural and Forest Meteorology. 300:108328. https://doi.org/10.1016/j.agrformet.2021.108328.
Ohana-Levi, N., Knipper, K.R., Kustas, W.P., Anderson, M.C., Netzer, Y., Gao, F.N., del Mar Alsina, M., Sanchez, L., Karneli, A. 2020. Using satellite thermal-based evapotranspiration time series for defining management zones and spatial association to local attributes in a vineyard. Remote Sensing. 12(15):2436. https://doi.org/10.3390/rs12152436.
Dong, J., Crow, W.T., Tobin, K., Cosh, M.H., Bosch, D.D., Starks, P.J., Seyfried, M.S., Holifield Collins, C.D. 2020. Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sensing of Environment. 242:111756 . https://doi.org/10.1016/j.rse.2020.111756.
Dong, J., Crow, W.T., Reichle, R. 2021. Estimating and improving the rain/no-rain detection skill of remotely sensed and reanalyzed precipitation products. Journal of Hydrometeorology. 21:2419:2429. https://doi.org/10.1175/JHM-D-20-0097.1.
De Santis, D., Blondi, D., Crow, W.T., Camici, S., Mondanesi, S., Brocca, L., Massari, C. 2021. Assimilation of satellite soil moisture products for river flow prediction: An extensive experiment in over 700 catchments throughout Europe. Water Resources Research. 57.e2021WR029643. https://doi.org/10.1029/2021WR029643.
Tobin, K., Torres, R., Bennett, M., Dong, J., Crow, W.T. 2020. Long-term trends in root-zone soil moisture across CONUS connected to ENSO. Remote Sensing. 12:2037. https://doi.org/10.3390/rs12122037.
Dong, J., Dirmeyer, P., Lei, F., Anderson, M.C., Holmes, T., Hain, C., Crow, W.T. 2020. Bare soil evaporation stress determines soil moisture - evapotranspiration coupling strength bias in land surface modeling. Geophysical Research Letters. 47. e2020GL090391. https://doi.org/10.1029/2020GL090391.
Yilmaz, M., Crow, W.T., Ryu, D. 2016. Impact of model relative accuracy in framework of rescaling observations in hydrological data assimilation studies. Water Resources Research. 17:2245–2257.
Lee, S., McCarty, G.W., Lang, M., Li, X. 2020. Overview of the USDA Mid-Atlantic regional wetland conservation effects assessment project. Journal of Soil and Water Conservation. 75(6):684-694. https://doi.org/10.2489/jswc.2020.00097.
Du, L., McCarty, G.W., Zhang, X., Lang, M., Vanderhoff,M.K., Li, X., Huang, C., Lee, S. 2020. Mapping forested wetland inundation in the Delmarva peninsula, USA using a deep convolutional neural network. Remote Sensing. 12:644. https://doi.org/doi:10.3390/rs12040644.
Lang, M., Kim, V., McCarty, G.W., Li, X., Yeo, I., Huang, C., Du, L. 2020. Improved detection of inundation below the forest canopy using normalized lidar intensity data. Remote Sensing. 12:707. https://doi.org/doi:10.3390/rs12040707.
Goldman, M., Needleman, B., Rabenhorst, M., Lang, M., McCarty, G.W. 2020. Digital soil disaggregation in a low-relief landscape to support wetland restoration decisions. Geoderma. 373:114420. https://doi.org/10.1016/j.geoderma.2020.114420.
Qi, J., Zhang, X., Lee, S., Wu, Y., Moglen, G.E., McCarty, G.W. 2020. Modeling sediment diagenesis processes on riverbed to better quantify aquatic carbon fluxes and stocks in a small watershed of the mid-Atlantic region. Carbon Balance and Management. 15:13. https://doi.org/10.1186/s13021-020-00148-1.
Thieme, A., Yadav, S., Oddo, P., Fitz, J., McCartney, S., King, L., Keppler, J., McCarty, G.W., Hively, W. 2020. Using NASA earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed. Remote Sensing of Environment. 248:111943. https://doi.org/10.1016/j.rse.2020.111943.
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