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

2024 Annual Report

Objectives
Objective 1: Develop and evaluate enhanced methods for quantifying spatiotemporal variability in hydrologic states and fluxes, from soil-plant systems to regional scales. Subobjective 1.1: Characterize the influence of micro-, local- and regional-scale meteorological conditions on turbulent exchange processes within and above crops with a highly structured canopy. Subobjective 1.2: Improve modeling capability for estimating evapotranspiration (ET), partitioning ET between soil evaporation and plant transpiration, and tracking soil water stress in irrigated crops. Subobjective 1.3: Assess impacts of land use, land management, and climate variability on water use over agricultural landscapes. Subobjective 1.4: Improve soil moisture monitoring for agricultural landscapes via remote sensing and in situ technologies. Subobjective 1.5: Assessment of regional water balance using modeling and remote sensing retrievals. Objective 2: Advance remote sensing and modeling approaches for assessing hydrologic extremes and impacts on agroecosystem health, phenology, and productivity. Subobjective 2.1: Advance remote sensing capabilities for monitoring agricultural drought. Subobjective 2.2: Develop techniques for operational field-scale phenology mapping for crop and vegetation monitoring. Subobjective 2.3: Develop multi-scale remote sensing metrics of agroecosystem health and productivity. Subobjective 2.4: Improve monitoring and forecasting of extremes in streamflow and ET. Objective 3: Characterize spatiotemporal effects of conservation practices on water quality through modeling using continuous in situ monitoring, periodic measurements, and remote sensing. Subobjective 3.1: Maintain existing and establish new long-term data streams for the LCB-LTAR watershed site to assess agroecosystem status and trends and for use in modeling efforts. Subobjective 3.2: Explore the use of multiple tracer methods to discern agricultural versus urban nutrient sources and dynamics at the sub-watershed and watershed scales for use in modeling the effectiveness of conservation practices. Subobjective 3.3: Integrate remote sensing data and hydrologic modeling to better represent watershed physical processes and effects on ecosystem function. Subobjective 3.4: Assess the effectiveness and ecosystem service provisioning of wetlands and other conservation practices in agricultural landscapes.

Approach
This project seeks to provide basic research on linkages in the agricultural water cycle, from field to watershed to global scales, and to deliver useful modeling and remote sensing tools for monitoring and decision making. Under Objective 1, we will integrate in situ observations with imagery from unmanned aerial systems and satellites to quantify the water balance over a range of scales, supporting decision making for precision irrigation to regional water management. The work proposed under Objective 2 will use these mapping technologies to improve multi-scale drought and flood monitoring and predictive capacity, to operationally monitor crop and grazing-land conditions, and to create new satellite-based metrics of ecosystem health and productivity. Remote sensing advancements and ground measurements are brought together under Objective 3 to characterize the spatiotemporal effects of conservation practices and land management strategies on water quality at the watershed scale, assessing their impacts on contaminant transport across agricultural landscapes. Throughout this project, we will work closely with stakeholders in grower and commodity groups, state and local water and land-management agencies, and federal partner agencies to ensure delivery of useful and actionable information.

Progress Report
This report documents progress for the 24-month milestones of Project 8042-13610-030-000D “From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling”. Substantial progress was made in all three objectives outlined in the project plan. Under Objective 1, research activities in FY24 focused on better understanding the drivers of water use and water budget partitioning at in situ to basin scales. Eddy covariance (EC) data collection has continued at multiple vineyard and almond orchard field sites in California as a part of the GRAPEX and T-REX field projects and has been expanded into olive orchards and a new site in Israel under a BARD-funded project (Sub-objective 1.1.1). A pair of studies of the impacts of advection on evapotranspiration (ET) were conducted at almond and vineyard sites during the 2023 growing season. These involved EC measurements along a transect from a large fallow (hot and dry) area upwind from the irrigated fields and included atmospheric profile measurements using drones and atmospheric Lidar to capture large scale advection effects on ET. For the 2024 growing season, a similar advection study is planned for July over irrigated vineyards near Fresno, California (Sub-objective 1.1.2). Under Sub-objective 1.2, new ET partitioning techniques using high frequency EC data were used to evaluate model ET partitioning into soil/interrow evaporation (E) and plant transpiration (T) over vineyards and almond orchards in California. This study used the remote sensing-based Two-Source Energy Balance model (TSEB) applied spatially to satellite data using the multiscale ALEXI-DisALEXI framework developed by ARS scientists. In this Mediterranean climate, a transpiration method more sensitive to the effects of advection was shown to produce more reliable E and T values than the traditional TSEB method. DisALEXI ET partitioning is also being investigated in a more humid setting within the Central Mississippi River Basin Long-Term Agroecosystem Research (LTAR) region. Under Sub-objective 1.3, a new machine-learning approach was developed to obtain a pseudo-atmospherically corrected Landsat land-surface temperature (LST) product in near real-time, almost two weeks before the official product is distributed by the U.S. Geological Survey. Ingested into DisALEXI, this LST product enables field-scale ET mapping in near real-time, improving utility for daily water management and irrigation scheduling. In addition, an approach fusing LST data from multiple satellites with Landsat-like resolution has been used to improve the temporal frequency of satellite-based ET observations over GRAPEX and T-REX sites and thus better capture ET dynamics from irrigation and stress events. Research related to ground-based soil moisture monitoring has advanced significantly in the past year, with efforts to improve quality assurance and quality control through community-developed documentation. Training sessions were conducted in coordination with the American Association of State Climatologists and at the National Soil Moisture Workshop, held in Beltsville, Maryland,(Sub-objective 1.4). HRSL scientists participated in the first publication describing use of unmanned aerial vehicles to monitor soil moisture with an L-band radiometer. Field campaigns aimed at increasing educational outreach for remote sensing of soil moisture and vegetation were also conducted this year. Methods for quantifying regional water balance using modeling and remote sensing retrievals were improved in FY24 (Sub-objective 1.5). A technique has been developed and tested for extrapolating soil moisture-runoff coupling strength (SRCS) information from limited gauged basins to all neighboring ungauged basins using remote sensing data acquired from the NASA Soil Moisture Active/Passive (SMAP) and Surface Water Ocean Topography (SWOT) missions. This approach will substantially increase the fraction of U.S. basins where satellite-based calibration can be applied to improve operational hydrologic forecasting. Activities in FY24 under Objective 2 advanced use of high-resolution remote sensing for monitoring productivity in crop and pasturelands and their response to climate extremes. Phenological adjustments to the Evaporative Stress Index (ESI), reflecting anomalies in remotely sensed ET, were evaluated in comparison with National Agricultural Statistics Service (NASS) yield and crop condition metrics (Sub-objective 2.1). These adjustments enable the comparison of crop water stress signals aligned by growth stage rather than by calendar day, providing more relevant information for forecasting drought impact on yields. Remote sensing methods for mapping these critical growth stages also progressed. For validation, phenological stages of corn and soybean fields at the Beltsville Agricultural Research Center (BARC) were monitored intensively in 2023 and 2024. These observations were used to refine the Within-Season Emergence (WISE) algorithm for mapping crop emergence (peer-reviewed journal article submitted). The refined WISE approach was then applied to the Corn Belt states and beyond. Crop emergence maps at 30-m resolution over the conterminous United States (CONUS) were produced for 2018 to 2023 using the USDA SCINet high-performance computing system and are under assessment. An initial comparison shows good agreement between the WISE-generated remote sensing green-up dates and NASS crop emergence dates at the state level (Sub-objective 2.2). These remote sensing data, describing phenology, water stress, and leaf area, are being integrated into geospatial yield modeling frameworks with potential to run at sub-field scales. Expansion of a validation yield map data archive for BARC production fields continued in FY24, now covering most of the period 2014 to 2023 (methods described in a published journal article). A process-based light-use efficiency model and the Decision Support System for Agro-Technology (DSSAT) model have been used to estimate BARC crop yields, while machine learning approaches for yield estimation are under assessment (Sub-objective 2.2). At larger spatial scales (CONUS-wide), we have constructed a climatological data set of SMAP soil moisture, ALEXI ET, and GRACE terrestrial storage anomalies for drought events since 2015. These anomalies are being analyzed for insight into how soil moisture and terrestrial water storage anomalies are related to concurrent ET anomalies over agricultural regions of the central U.S. (Sub-objective 2.3). The third Objective of this project is to assess the Lower Chesapeake Bay (LCB) agroecosystem via measurements and modeling at the LCB-LTAR sites. Collection of meteorological, surface flux, crop phenology, and other environmental measurements continued at LCB-LTAR locations at the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental watershed in Beltsville, Maryland, and the Choptank River watershed (CRW) located on Maryland’s Delmarva Peninsula (Sub-objective 3.1). Real-time water quality data were collected at several USGS gage stations in the CRW and elsewhere in the LTAR Network. Collection and analysis of point-in-time water samples associated with the USDA Watershed Lag Time Project (WLTP) and the USGS National Water Quality Assessment (NAWQA) network also continued in FY24, and two manuscripts are in preparation (Sub-objective 3.2). Improvements in hydrologic modeling of agricultural landscapes using the Soil and Water Assessment Tool (SWAT) were made this year. Model representation of drainage networks on the Delmarva peninsula was refined, improving connectivity ditches with natural drainage systems through a minimum-cost approach and incorporation of a flow accumulation algorithm. The connected drainage networks were evaluated against flowlines derived from a typical flow routing method (D8), an open-source channel network extraction tool (GeoNet), and the U.S. Geological Survey National Hydrography Dataset High Resolution data at 1:24,000 scale (Sub-objective 3.3.1). The SWAT river-routing code was modified to study the effects of river-routing time steps (from 1 minute to 1 day) on the model simulations of streamflow, water depth, and water storage in river networks in the CRW (Sub-objective 3.3.2). It was found that 1-day time steps can lead to unrealistically low stream water depth and water storage and cause large bias in the assessment of hydrologic connectivity and aquatic ecosystem health. Therefore, time steps shorter than 1 hour are recommended, while recognizing that this may depend on actual watershed size and local conditions. The utility of integrating remote sensing and yield survey data into the SWAT-Carbon model was evaluated. While calibration using ET and soil moisture data did not significantly improved performance in comparison with using streamflow alone, constraints based on remotely sensed LAI and surveyed crop yield reduced uncertainties in riverine carbon export, particularly for particulate organic carbon (Sub-objective 3.4.1). Procedures were designed to link tillage and cover crop maps with other geospatial inputs (such as land use and soil maps) for use in the SWAT model, with initial testing in the Tuckahoe Creek Watershed (Sub-objective 3.4.3). Methane flux measurements collected over wetlands in the CRW were analyzed, revealing daily patterns that are strongly linked to vegetation class. Activities of methane-generating microorganisms in natural and restored wetland soils were evaluated using advanced stable isotope labeling and microbial population characterization, showing greater activity in restored wetlands (Sub-objective 3.4.2). This work suggests that wetland restoration approaches may need modification to reduce methane emissions from agroecosystems.

Accomplishments
1. Evaluation of satellite leaf area index in California vineyards for improving water use estimation. Remote sensing estimation of evapotranspiration (ET) directly indicates plant water stress and availability and is helpful for irrigation scheduling. Energy balance ET models use surface temperature and Leaf Area Index (LAI) to partition evaporative fluxes between soil and plant. However, LAI estimation is subject to errors due to uncertainties from remote sensing signals and model assumptions. ARS scientists in Beltsville, Maryland, evaluated six remotely sensed approaches for LAI estimation from Landsat and Sentinel-2 data and assessed the sensitivity of ET modeling due to the uncertainty of LAI in four vineyard sites as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. Results show that the ET estimates are more sensitive to positive LAI biases than negative ones. The findings are valuable in understanding uncertainties of LAI and ET estimations from satellite remote sensing and will help to improve water use monitoring and irrigation scheduling.

2. Development of model for functional assessment of wetlands using remote metrics. With loss of wetlands and their associated ecosystem services within landscapes, it is imperative to be able to understand the change in ecological functions underlying these services. To address this need, ARS scientists in Beltsville, Maryland, developed wetland ecological functional assessment metrics that can be applied remotely across broad geographic areas. These remote metrics were statistically screened and tested against field-based results to validate their use as remote proxies for field-based assessment methods with minimal loss of accuracy. The resulting model provides a means of desktop functional assessment across broad scales with the diversity and specificity of field-based assessments but with the reduced costs associated with remote assessments. Its basis in a commonly used wetland classification scheme allows this methodology to be adopted regionally as well as used for national wetland functional assessment.

3. Open access water-use information for western U.S. water management. Fresh water availability is a major challenge facing agriculture today, one which will only intensify as climate patterns continue to change and as competing water demands continue to grow. In the western U.S., ongoing drought has led to significant fluctuations in reservoir and groundwater storage, irrigation capacity, hydroelectric power production, as well as provision of ecosystem services. Finding sustainable methods for managing our freshwater resources into the future means that we need reliable ways to measure how water is being used today, from field to basin scales, and to get this information effectively into hands of the decision makers. Under the OpenET project, ARS scientists in Beltsville, Maryland, implemented a satellite-based model of evapotranspiration (ET) on Google Earth Engine, contributing to an ensemble of 6 models estimating daily ET at 30-meter resolution in near-real time over the 17 western states. Data from the ensemble average and individual models can be accessed through a web-based interface (openetdata.org), or through an automated programming interface for direct ingestion into existing water management toolkits. Current use cases include irrigation scheduling, groundwater planning, water accounting and allocation, and evaluation of water conservation measures. This platform provides shared and open access to a trusted water use dataset at field scale that is spatially consistent across state boundaries, addressing a major data gap in water resource management.

4. SWAT-Carbon model released. Agricultural practices, such as conservation tillage, nutrient management, and cover crops, hold great potential to sequester and store carbon in agricultural soils to mitigate greenhouse gas emissions and improve soil health. Notably, these agricultural practices also have significant implications for water quality and quantity. ARS scientists in Beltsville, Maryland, have made modifications to the soil organic carbon (SOC) algorithms within the Soil and Water Assessment Tool - Carbon (SWAT-Carbon) model and applied it to simulate SOC dynamics across diverse cropping systems in the U.S. Corn Belt. These sites include locations supported by USDA GRACEnet (Greenhouse gas Reduction through Agricultural Carbon Enhancement network) and REAP (Renewable Energy Assessment Project). Results demonstrate that the modified SWAT-Carbon model effectively captured SOC dynamics at various sites, soil depths, and under different tillage intensities. Such capabilities allow SWAT-Carbon to be a first-of-its-kind watershed model that can simultaneously assess multidimensional indicators of agroecosystem sustainability, such as soil carbon sequestration, agricultural water use, and water quality. As an open-source model, SWAT-Carbon can be freely shared to support future carbon assessment and management in climate-smart agroecosystems.

5. Identification of a key bias in operational hydrologic forecasts. Hydrologic forecasting systems estimate future streamflow amounts based on the consideration of current conditions. For example, knowledge of current (i.e., initial) soil moisture conditions can guide our expectations regarding the conversion of future precipitation into streamflow. However, it is currently unclear whether existing hydrologic forecasting models accurately capture the value of soil moisture as an initial condition. Using new soil moisture data from the NASA Soil Moisture Active/Passive (SMAP) satellite mission, ARS researchers in Beltsville, Maryland, compared the correlation between soil moisture and future streamflow estimates provided by the National Water Model (NWM) to the same correlation sampled between observations. Results demonstrate that the NWM systematically underestimates the forecast value of soil moisture. By identifying this bias, this research points the way towards critical changes that will improve the ability of the NWM to operationally forecast streamflow, and thus water resource availability, in agricultural regions of the Central and Eastern United States.

Review Publications
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.
Boyd, A., Luo, Y., Lunney, J.K., Kustas, B., Fukagawa, N.K., Mattoo, A.K., Crow, W.T., Pachepsky, Y.A., Kim, M.S., Lillehoj, H.S., Van Tassell, C.P., Zhang, H.Q., Blomberg, L., Dubey, J.P. 2023. Cross-cutting concepts to transform agricultural research. Frontiers in Sustainable Food Systems. 7. Article e1242665. https://doi.org/10.3389/fsufs.2023.1242665.
Song, L., Ding, Z., Kustas, W.P., Hua, W., Liu, X., Liu, L., Liu, S., Ma, M., Bai, Y., Xu, Z. 2023. Applications of a thermal-based two-source energy balance model coupling the sun-induced chlorophyll fluorescence data. IEEE Geoscience and Remote Sensing Magazine. 20:2500705. https://doi.org/10.1109/LGRS.2023.3240996.
Xu, Y., Song, L., Kustas, W.P., Xue, K., Liu, S., Ma, M., Xu, T., Jiang, H. 2022. Application of the two-source energy balance model with microwave-derived soil moisture in a semi-arid agricultural region. International Journal of Applied Earth Observation and Geoinformation. 112. Article e102879. https://doi.org/10.1016/j.jag.2022.102879.
Burchard-Levine, V., Nieto, H., Kustas, W.P., Guerra, J., Borra, I., Dorado, J., Mesias-Ruiz, G., McKee, L.G., Pena, J. 2024. Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics. Irrigation Science. https://doi.org/10.1007/s00271-024-00931-9.
Rouze, G., Neely, H., Morgan, C., Kustas, W.P., Wiethorn, M. 2021. Evaluating unoccupied aerial systems (UAS) imagery as an alternative tool towards cotton-based management zones. Precision Agriculture. https://doi.org/10.1007/s11119-021-09816-9.
Meza, K., Torres, A., Hipps, L.E., Kustas, W.P., Gao, R., Christianse, L., Kopp, K., Nieto, H., Burchard-Levine, V., Martin, M., Coopmans, C., Gowing, I. 2023. Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model. Irrigation Science. https://doi.org/10.1007/s00271-023-00899-y.
Bhattarai, N., Lobell, D., Balwinder, S., Fisher, R., Kustas, W.P., Jain, M. 2023. Warming temperatures exacerbate groundwater depletion rates in India. Science Advances. 9(35). https://www.science.org/doi/10.1126/sciadv.adi1401.
Safre, A.L., Nassar, A., Torres-Rua, A., Aboutalebi, M., Saad, J.C., Manzione, R.L., Teixeira, A.H., Prueger, J.H., McKee, L.G., Alfieri, J.G., Hipps, L.E., Nieto, H., White, W.A., Alsina, M., Sanchez, L., Kustas, W.P., Dokoozlian, N., Feng, G.G., Anderson, M.C. 2022. Performance of Sentinel-2 SAFER ET model for daily and seasonal estimation of grapevine water consumption. Irrigation Science. 40:635-654. https://doi.org/10.1007/s00271-022-00810-1.
Mallick, K., Boegh, E., Trebs, I., Alfieri, J.G., Kustas, W.P., Prueger, J.H., Niyogi, D., Hoffman, L., Jarvis, A. 2015. Reintroducing radiometric surface temperature into the Penman-Monteith equation. Water Resources Research. 51(8):6214-6243. https://doi.org/10.1002/2014WR016106.
Starkenburg, D., Metzger, S., Fochesatto, G., Alfieri, J.G., Gens, R., Prakash, A., Cristobal, J. 2016. Assessment of de-spiking methods for turbulence data in micrometeorology. Journal of Geophysical Research Atmospheres. 33(9):2001-2013. https://doi.org/10.1175/JTECH-D-15-0154.1.
Anderson, M.C., Hain, C., Otkin, J., Zhan, X., Mo, K., Svoboda, M., Wardlow, B., Pimstein, A. 2013. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. drought monitor classifications. Journal of Hydrometeorology.
Guzinski, R., Anderson, M.C., Kustas, W.P., Nieto, H., Sandholt, I. 2013. Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day-night MODIS observations. Hydrology and Earth System Sciences. 17:2809-2825.
Cammalleri, C., Anderson, M.C., Gao, F.N., Hain, C., Kustas, W.P. 2014. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agricultural and Forest Meteorology. 186:1-11.
Cammalleri, C.N., Anderson, M.C., Kustas, W.P. 2014. Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications. Hydrology and Earth System Sciences. 18(5):1885-1894. https://doi.org/10.5194/hess-18-1885-2014.
Fang, L., Hain, C., Zhan, X., Anderson, M.C. 2016. An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model. International Journal of Applied Earth Observation and Geoinformation. 48:37-50. https://doi.org/10.1016/j.jag.2015.10.006.
Hobbins, M., Wood, A., McEvoy, D., Huntington, J., Morton, C., Verdin, J., Anderson, M.C., Hain, C. 2016. The evaporative demand drought index: Part I 1 – Linking drought evolution to variations in evaporative demand. Journal of Hydrometeorology. 17(6):1745-1761. https://doi.org/10.1175/JHM-D-15-0121.1.
Fisher, J.B., Middleton, E., Melton, F., Anderson, M.C., Hook, S., Hain, C., Allen, R., McCabe, M., Lagouarde, J., Tu, K., Baldocchi, D., Townsend, P.A., Kilic, A., Perret, J., Miralles, D., Waliser, D., French, A.N., Schimel, D., Famiglietti, J., Stephens, G., 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(4):2618-2626. https://doi.org/10.1002/2016WR020175.
Wang, M., Xu, C., Johnson, D., Allen, C., Hain, C., Anderson, M.C., Wang, G., Qie, G., McDowell, N. 2022. Multi-scale quantification of anthropogenic, fire, and drought-associated forest disturbances across the continental U.S., 2000–2014. Frontiers in Forests and Global Change. 5. Article e693418. https://doi.org/10.3389/ffgc.2022.693418.
Isaacson, B., Yang, Y., Clark, K., Anderson, M.C., Grabosky, J. 2023. The effects of forest composition and management on evapotranspiration in the New Jersey Pinelands. Agricultural and Forest Meteorology. 339. Article e109588. https://doi.org/10.1016/j.agrformet.2023.109588.
Fischer, M., Pavik, P., Vizina, A., Bernsteinova, J., Parajka, J., Anderson, M.C., Rehor, J., Ivancicova, J., Stepanek, P., Balek, J., Hain, C., Tacheci, P., Hanel, M., Lukes, P., Blahova, M., Zahradnicek, P., Maca, P., Rapantova, N., Feng, S., Janal, P., Zeman, E., Zalud, Z., Trnka, M. 2023. Attributing the drivers of runoff decline in the Thaya River basin. Journal of Hydrology. 48. Article e101436. https://doi.org/10.1016/j.ejrh.2023.101436.
Duethmann, D., Anderson, M.C., Maneta, M., Tetzlaff, D. 2023. Multivariate calibration of an ecohydrological model using spatial patterns of remote sensing-derived land surface temperature. Hydrological Processes. 628. Article e130433. https://doi.org/10.1016/j.jhydrol.2023.130433.
Ma Lu, S., Yang, D., Anderson, M.C., Zainali, S., Stridh, B., Avelin, A., Campana, P. 2024. Photosynthetically active radiation separation model for high-latitude regions in agrivoltaic systems modeling. Solar Energy. 16(1). Article e0181311. https://doi.org/10.1063/5.0181311.
Dulaney, W.P., Anderson, M.C., Gao, F.N., Stern, A.J., Meyers, G.E., Daughtry, C.S., White, W.A., Akumaga, U., Showalter, J.J., Moglen, G.E. 2024. Development of a gridded yield data archive for farm management and research at the USDA Beltsville Agricultural Research Center. Agrosystems, Geosciences & Environment. 7(1). Article e20474. https://doi.org/10.1002/agg2.20474.
Meitner, J., Balek, J., Bláhová, M., Semerádová, D., Hlavinka, P., Lukas, V., Jurecka, F., Žalud, Z., Klem, K., Anderson, M.C., Dorigo, W.A., Fischer, M., Trnka, M. 2023. Estimating drought-induced crop yield losses in near-real time. Agronomy. 13(7):1669. https://doi.org/10.3390/agronomy13071669.
Volk, J., Huntington, J., Melton, F., Allen, R.G., Anderson, M.C., Fisher, J., Kilic, A., Ruhoff, A., Senay, G.B., Minor, B., Morton, C., Ott, T., Carrara, W., Doherty, C., Dunkerly, C., Friedrichs, M., Guzman, A., Hain, C., Halverson, G., Johnson, L., Kang, Y., Knipper, K.R., Ortega-Salazar, S., Pearson, C., Parrish, G.E., Purdy, A.J., Revelle, P.M., Wang, T., Yang, Y., Laipelt, L., Comini De Andrade, B. 2024. Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications. Nature Water. 2:193-205. https://doi.org/10.1038/s44221-023-00181-7.
Crawford, C., Roy, D., Arab, S., Barnes, C., Vermote, E., Hulley, G., Gerace, A., Choate, M., Engebretson, C., Schmidt, G., Anderson, C., Anderson, M.C., Bouchard, M., Skakun, S., Yan, L., Zhang, H., Zhu, Z., Zahn, S. 2023. The 50-year Landsat collection 2 archive. Science of Remote Sensing. 8. Article e100103. https://doi.org/10.1016/j.srs.2023.100103.
Koster, R., Feldman, A., Holmes, T., Anderson, M.C., Crow, W.T., Hain, C. 2024. Estimating hydrological regimes from observational soil moisture, evapotranspiration, and air temperature data. Journal of Hydrometeorology. 25(3):495-513. https://doi.org/10.1175/JHM-D-23-0140.1.
Anderson, M.C., Kustas, W.P., Norman, J., Diak, G., Hain, C., Gao, F.N., Yang, Y., Knipper, K.R., Xue, J., Yang, Y., Crow, W.T., Holmes, T., Nieto, H., Guzinski, R., Otkin, J., Mecikalski, J., Cammalleri, C., Torres-Rua, A., Zhan, X., Fang, L., Colaizzi, P.D., Agam, N. 2024. A brief history of the thermal IR-based Two-Source Energy Balance (TSEB) model – diagnosing water and energy fluxes from plant to global scales. Agricultural and Forest Meteorology. 350. Article e109951. https://doi.org/10.1016/j.agrformet.2024.109951.
Kang, Y., Gao, F.N., Anderson, M.C., Kustas, W.P., Nieto, H., Knipper, K.R., Yang, Y., White, W.A., Torres-Rua, A., Alsina, M., Karnell, A. 2022. Evaluation of satellite leaf area index in California vineyards for improving water use estimation. Irrigation Science. https://doi.org/10.1007/s00271-022-00798-8.
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.
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.
Gorden, B.L., Crow, W.T., Konings, A.G., Dralle, D.N., Harpold, A.A. 2022. Can we use the water budget to infer upland catchment behavior? The role of dataset error estimation and interbasin groundwater flow. Water Resources Research. 58(9):e2021WR030966. https://doi.org/10.1029/2021WR030966.
Kim, H., Crow, W.T., Li, X., Wagner, W., Hahn, S., Lakshmi, V. 2023. True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis. Remote Sensing of Environment. 298: Article e113776. https://doi.org/10.1016/j.rse.2023.113776.
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