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





- Anderson, Martha
- Alfieri, Joseph
- Cosh, Michael
- Crow, Wade
- Gao, Feng
- Hapeman, Cathleen
- Kustas, William - Bill
- McCarty, Gregory
- Zhang, Xuesong
- 2023
- 2022
- Publications
- Water Availability and Watershed Management (211)
- Sustainable Agricultural Systems Research (216)

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** 2023 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 first year 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, all of which fall under NP 211.
 
Under Objective 1, research activities in FY23 focused on collecting data toward a better understanding of soil water and evaporative fluxes in agricultural systems. Ongoing micrometeorological data collection activities continued in multiple vineyard and almond orchard field sites as a part of the GRAPEX and TREX field projects, with the addition of a new vineyard site in Israel (Sub-objective 1.1.1). Significant progress was made toward developing observational methods to characterize land-atmosphere exchange in structured canopies that will lead to new insights into the surface fluxes, including evapotranspiration (ET), from vineyards and orchards. This includes the refinement and evaluation of new spectral analysis methods to describe turbulent structure within and above vineyards and the investigation of a novel wavelet-based method for characterize intermittency. Data were also collected at these sites to investigate the effects of advection on ET (Sub-objective 1.1.2), including flux and meteorological data collected at multiple heights, and horizontal profiles of temperature and humidity collected via UAV. These data are being analyzed to assess new techniques for quantifying the influence of advective processes.

These micrometeorological observations were also used to support multi-scale remote sensing algorithm development. For example, partitioning methods for determining evaporation (E) and transpiration (T) contributions to the total ET were applied over vineyard sites in California using eddy covariance measurements (Sub-objective 1.2). The partitioned E and T values were then compared to estimates from the Two-Source Energy Balance Model (TSEB) model, generated using improved leaf area index (LAI) remote sensing retrieval algorithms and new transpiration algorithms that include a stomatal conductance term to account for the influence of vapor pressure deficits that enhance transpiration. Field-scale results showed reasonable agreement with the E and T estimates, with improved partitioning using the new transpiration algorithms. 

At a larger scale, a regional implementation of the TSEB, known as DisALEXI, was used to generate multi-year ET mapping timeseries (30 m resolution, daily timesteps) over several key experiment sites within the U.S, including the GRAPEX and TREX flux sites (Sub-objective 1.3). ET timeseries were also generated for intensive field sites in an FFAR-funded regenerative grazing experiment, comparing time changes in soil carbon in pastures with grazing managed adaptively and prescriptively. In this study, ET will serve as a metric of ecosystem health response to management. In addition, multi-year ET timeseries were constructed for two LTAR Common Experiment sites to contrast seasonal water use patterns in business-as-usual versus aspirational cropping systems.

Paralleling ground-based work with evaporative fluxes, a multi-state soil moisture sensor testbed was established in collaboration with universities in the Central Plains to examine how sensor performance differs across a diverse set of soil textures (Sub-objective 1.4) and examining how changing sensor types impacts long-term soil moisture data series. In addition, the 2022 Soil Moisture Active Passive Validation Experiment was completed in FY23. This experiment investigated the impact of high biomass vegetation such as forests on soil moisture remote sensing products and supports the extension of existing soil moisture remote sensing techniques into forested regions. 

As soil moisture remote sensing techniques mature, it is also critical to identify and address key obstacles preventing their wider utilization in operational forecasting and monitoring activities. Over the past decade, substantial improvements have been made in the precision of land surface models via the assimilation of satellite-based soil moisture information. However, to date, these improvements have not yet been extended into water flux estimates like runoff and ET that are critical to weather and hydrologic forecasting applications. During FY23, project scientists demonstrated that this shortcoming is linked to the inability of existing land surface models to accurately describe the impact of water state anomalies on water fluxes. In addition, progress was made in the development of remote sensing approaches capable of detecting, and mitigating, such systematic modelling errors. These advances provide a foundation for future planned research aimed at improving the ability of land surface models to accurately characterize extremes in the hydrologic cycle (Sub-objectives 1.5 and 2.4).

Activities in FY23 under Objective 2 advanced high-resolution remote sensing approaches for monitoring productivity in crop and pasturelands and their response to climate extremes. Evaporative Stress Index (ESI) timeseries developed from ET data at resolutions of 30 m to 5 km were compared to drought indicators such as the U.S. Drought Monitor and showed good spatiotemporal agreement. These comparisons suggest an effective means to downscale agricultural drought impacts to the field scale (Sub-objective 2.1).
 
These monitoring efforts also included phenological indicators. For example, throughout the 2022 growing season, the growth of corn and soybeans in Beltsville Agricultural Research Center (BARC) fields was monitored intensively. The observations include physiological growth stages from emergence to harvest. These observations were used to refine the Within-Season Emergence (WISE) algorithm and develop a new remote sensing phenology algorithm to track all stages of crop growth in near real-time (Sub-objective 2.2). Planet Fusion data with a resolution of 3 meters and daily coverage were utilized to facilitate the development of this advanced algorithm. 

Newly developed remote sensing estimates of crop phenology, LAI, and ET were then used to assess crop yields at field scales in two modeling approaches (Sub-objective 2.3). A process-based Light Use Efficiency (LUE) model was assessed for corn, soybean, and wheat. A good relationship between crop yields and remote sensing primary productivity was found. Remote sensing phenology and LAI were also employed to calibrate the Decision Support System for Agro-Technology (DSSAT) model for yield estimation in BARC fields from 2017 to 2020. The calibrated model was subsequently evaluated against an independent dataset comprising phenology and yield data from BARC fields. The calibrated model successfully simulated the days from emergence to flowering and maturity, providing reasonably accurate yield estimates for most fields and years. 

The third objective of this project is to assess the Lower Chesapeake Bay agroecosystem via measurements and modeling and support the establishment of the Lower Chesapeake Bay Long-Term Agroecosystem Research (LCB-LTAR) sites. To achieve this objective, meteorological, surface fluxes, crop phenology, and other environmental measurements were collected at the LCB-LTAR locations at BARC 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, and evaluation of a prototype phosphorus probe continued at several USGS gage stations in the CRW and elsewhere in the LTAR Network.
 
An analysis of water samples from the USDA Watershed Lag Time Project (WLTP) and the USGS National Water Quality Assessment (NAWQA) network revealed that smaller watersheds are more predictable, and that complexity increases with scale of observation leading to new research questions about watershed processes. Point-in-time sampling was initiated to determine the concentrations of nitrate-N and metolachlor degradation product (MESA – metolachlor ethane sulfonic acid) in the Choptank and Monocacy River watersheds and at several exceptional LTAR Network sites (Sub-objective 3.2). The USDA WLTP was used as the basis for the new national CEAP Legacy N project led by ARS-Beltsville, Maryland.

In support of hydrologic modeling, deep convolutional neural network models were used to map and characterize wetland connectivity with ditch networks in low relief landscapes of the Delmarva Peninsula (Sub-objective 3.3.1). Physical processes in terrestrial and aquatic ecosystems have been incorporated within the Soil and Water Assessment Tool (SWAT) to determine stream water temperature, enabling the SWAT model to assess the impact of agricultural activities (such as tillage, residue management, irrigation, and cover crops) on stream temperature and other related water quality indicators in the Choptank River watershed and multiple watersheds in the U.S. Midwest (Sub-objective 3.3.2). 

Modifications to the soil organic carbon (SOC) algorithms were made within SWAT-Carbon model (Sub-objective 3.4). SWAT-C was applied to simulate SOC dynamics across seven sites in the U.S. Corn Belt and to identify optimal methods for estimating effects of soil temperature, soil water, and tillage on SOC decomposition. CH4 and CO2 flux measurements from natural, restored, and drained wetlands continued; observations will be used to analyze differences in SOC stocks on the Delmarva Peninsula in wetlands versus agricultural systems and in modeling efforts. High-resolution remote sensing timeseries of ET and LAI have been developed over LCB LTAR sites for integration into SWAT-C, as a diagnostic control on soil moisture status and plant growth. As an open-source model, SWAT-C is freely shared to contribute to future carbon assessment and agroecosystem management. **Accomplishments**
                                

1. Estimating maize and soybean growth and yield using DSSAT and remote sensing. Crop models can be used to predict yield by simulating the growth and development of a crop. However, these models are limited by the availability and quality of input data. Remote sensing provides a means of constraining these uncertainties. ARS scientists in Beltsville, Maryland, integrated remote sensing and field observations into the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. Results based on the fields in the USDA-ARS Beltsville Agricultural Research Center (BARC) show that the calibrated model accurately simulated days to flowering and maturity and produced reasonable yield estimates for most fields and years. This study demonstrates that remotely sensed crop information can augment field observations for crop modeling, especially for data-poor regions where no field records are available for modeling purposes. 2. 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., an ongoing megadrought has caused major reservoirs to drop to historically low levels, resulting in emergency shortages affecting water rights, 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-m 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 (e.g., fallowing). 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. 3. Development of a water research vision for agriculture. Water supplies are facing increasing pressure globally from both climate change and growing populations. However, water sustainability can only be realized by balancing the needs of agriculture, society, and ecosystems. To address these competing demands, scientists from USDA Southwest Climate Hub and ARS-Beltsville, Maryland, scientists co-led a team of more than 40 leading U.S. agricultural and water scientists to develop a multi-decadal, national water research vision with a broad group of partners. The goals represent the role of water in agriculture, the importance of water security, and water’s interconnection with other socio-cultural values. Key elements include a focus on societal-level outcomes that build global food, fiber, and water security; innovation of cutting-edge, systems-level science, decisions support tools, and solutions; engagement of partnerships and collaboration with a wide range of communities including producers, stakeholders, Federal, State, and local agencies, and the public; lasting, sustainable change in water use, management, productivity, and quality; and effective water research outreach. The vision also describes the need for partnership and collaboration, science-management tools and technological advances, and a range of discipline-specific and transdisciplinary science approaches toward solving water challenges of today and the future. This strategic vision serves as a framework U.S. agriculture and will facilitate collaborative and transdisciplinary water research within and outside ARS to 2050. 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 researchers in Beltsville, Maryland, 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). Our results demonstrated 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 is freely shared to contribute to future carbon assessment and management in climate-smart agroecosystems. 5. A new remote tool for improved water resource forecasting. Hydrologic models track the flow of water through agricultural landscapes. Such tracking is critical for forecasting changes in agricultural water-resource availability and understanding the impact of climate change on the hydrologic cycle. Unfortunately, hydrologic models generally require access to long-term streamflow data in order to be adequately calibrated – a requirement that is not met in many agricultural basins. In response to this shortcoming, ARS scientists in Beltsville, Maryland, have developed a new calibration strategy for hydrologic models that utilizes satellite-based soil moisture estimates in place of ground-based streamflow observations. By substituting widely available remote sensing retrievals for sparse ground-based streamflow observations, this approach greatly increases the number and extent of agricultural basins where hydrologic models can be adequately calibrated. Having access to improved hydrologic models will, in turn, enhance our ability to predict and track future extremes in water-resource availability. **Review Publications**
                            *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.*
                                *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.*
                                *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.*
                                *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.*
                                *Chen, F., Lei, F., Knipper, K.R., Gao, F.N., McKee, L.G., Alsina, M., Alfieri, J.G., Anderson, M.C., Bambach, N., Castro, S.J., McElrone, A.J., Alstad, K., Dokoozlian, N., Greifender, F., Kustas, W.P., Notarnicola, C., Agam, N., Prueger, J.H., Hipps, L., Crow, W.T. 2022. Application of the vineyard data assimilation (VIDA) system to vineyard root-zone soil moisture monitoring in the California Central Valley. Irrigation Science. https://doi.org/10.1007/s00271-022-00789-9.*
                                *Yang, Z., Diao, C., Gao, F.N. 2023. Towards scalable within-season crop mapping with phenology normalization and deep learning. Geoscience and Remote Sensing Letters. 16:1390-1402. https://doi.org/10.1109/JSTARS.2023.3237500.*
                                *Akumaga, U., Gao, F.N., Anderson, M.C., Dulaney, W.P., Houborg, R., Russ, A.L., Hively, W. 2023. Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA. Agronomy. 13:1540. https://doi.org/10.3390/agronomy13061540.*
                                *Doherty, C.T., Johnson, L.F., Volk, J., Mauter, M.S., Bambach, N.E., McElrone, A.J., Alfieri, J.G., Hipps, L.E., Prueger, J.H., Castro, S.J., Alsina, M., Kustas, W.P., Melton, F.S. 2022. Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS. Irrigation Science. 40:515-530. https://doi.org/10.1007/s00271-022-00808-9.*
                                *Fang, L., Zhan, X., Kalluri, S., Yu, P., Hain, C., Anderson, M.C., Laszlo, I. 2022. Application of a machine learning algorithm in generating evapotranspiration data product from thermal infrared and microwave coupled satellite observations. Frontiers in Big Data. https://doi.org/10.3389/fdata.2022.768676.*
                                *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.*
                                *Elias, E.H., Tsegaye, T.D., Hapeman, C.J., Mankin, K.R., Kleinman, P.J., Cosh, M.H., Peck, D.E., Coffin, A.W., Archer, D.W., Alfieri, J.G., Anderson, M.C., Baffaut, C., Baker, J.M., Bingner, R.L., Bjorneberg, D.L., Bryant, R.B., Gao, F.N., Gao, S., Heilman, P., Knipper, K.R., Kustas, W.P., Leytem, A.B., Locke, M.A., McCarty, G.W., McElrone, A.J., Moglen, G.E., Moriasi, D.N., O'Shaughnessy, S.A., Reba, M.L., Rice, P.J., Silber-Coats, N., Wang, D., White, M.J., Dobrowolski, J.P. 2023. A vision for integrated, collaborative solutions to critical water and food challenges. Journal of Soil and Water Conservation. 78(3):63A-68A. https://doi.org/10.2489/jswc.2023.1220A.*
                                *Qi, Y., Lee, S., Du, X., Ficklin, D., Wang, Q., Myers, D., Singh, D., Moglen, G.E., Mccarty, G.W., Zhou, Y., Zhang, X. 2021. Coupling terrestrial and aquatic thermal processes for improving stream temperature modeling at watershed scale. Journal of Hydrology. 603. Article 126983. https://doi.org/10.1016/j.jhydrol.2021.126983.*
                                *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.*
                                *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.*
                                *Dangol, S., Zhang, X., Lang, X., Miralles-Wilhelm, F. 2022. Agricultural irrigation effects on hydrological processes in the northern high plains simulated by the coupled SWAT-MODFLOW system. Water. 14(12):1938. https://doi.org/10.3390/w14121938.*
                                *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.*
                                *Zhou, J., Crow, W.T., Wu, Z., Dong, J., He, H., Feng, H. 2022. Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information. Remote Sensing of Environment. 280. Article 113161. https://doi.org/10.1016/j.rse.2022.113161.*
                                *Crow, W.T., Dong, J., Reichle, R. 2022. Leveraging pre-storm soil moisture estimates for enhanced land surface model calibration in ungauged hydrologic basins. Water Resources Research. 58. Article e2021WR031565. https://doi.org/10.1029/2021WR031565.*
                                *Dong, J., Crow, W.T., Xi, C., Tangdamrongsub, N., Gao, M., Sun, S., Qiu, J., Wei, L., Gao, H., Duan, Z. 2022. Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation. Remote Sensing of Environment. 283. Article 113299. https://doi.org/10.1016/j.rse.2022.113299.*
                                *Wang, X., Lu, H., Crow, W.T., Zhu, Y., Su, J., Zheng, J., Gou, Q. 2022. A reduced latency regional gap-filling method for SMAP using random forest regression. iScience. 20. Article 105853. https://doi.org/10.1016/j.isci.2022.105853.*
                                *Qui, J., Crow, W.T., Dong, J., Li, Y., Garcia, M., Shangguan, W. 2022. Microwave-based soil moisture improves estimates of vegetation response to drought in China. Remote Sensing of Environment. 849:157535. https://doi.org/10.1016/j.scitotenv.2022.157535.*
                                *Li, H., Chai, L., Crow, W.T., Dong, J., Liu, S., Zhao, S. 2022. The reliability of categorical triple collocation for evaluating soil freeze/thaw datasets. Remote Sensing of Environment. 281. Arcicle 113240. https://doi.org/10.1016/j.rse.2022.113240.*
                                *Hu, Z., Chai, L., Crow, W.T., Liu, S., Zhu, Z., Zhou, J., Qu, Y., Yang, S., Lu, Z. 2022. Applying a wavelet transform technique to optimize general fitting models for SM analysis: A case study in downscaling over the Qinghai-Tibet Plateau. Remote Sensing. 14(13):3063. https://doi.org/10.3390/rs14133063.*
                                *Zhang, C., Yang, Z., Zhao, H., Sun, Z., Di, L., Bindlish, R., Liu, P.W., Colliander, A., Mueller, R., Crow, W.T., Reichle, R., Bolten, J., Yueh, S. 2022. Crop-CASMA: A web geoprocessing and map service based architecture and implementation for serving soil moisture and crop vegetation condition data over United States cropland. International Journal of Applied Earth Observation and Geoinformation. 112. Article 102902. https://doi.org/10.1016/j.jag.2022.102902.*
                                *Feldman, A., Short Gianotti, D., Dong, J., Akbar, R., Crow, W.T., McColl, K., Konings, S., Nippert, J., Tumber-Dávila, S., Holbrook, N., Rockwell, F., Scott, R.L., Reichle, R.H., Chatterjee, A., Joiner, J., Poulter, B., Entekhabi, D. 2023. Remotely sensed soil moisture can capture dynamics relevant to plant water uptake. Water Resources Research. 59(2). Article e2022WR033814. https://doi.org/10.1029/2022WR033814.*
                                *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.*
                                *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.*
                                *Aboutalebi, M., Torres, A., McKee, M., Kustas, W.P., Nieto, H., Alsina, M., White, W.A., Prueger, J.H., McKee, L.G., Alfieri, J.G., Hipps, L., Coopmans, C., Sanchez, L., Dokoozlian, N. 2022. Downscaling UAV land surface temperature using a coupled wavelet-machine learning-optimization algorithm and its impact on evapotranspiration. Irrigation Science. 40:553-574. https://doi.org/10.1007/s00271-022-00801-2.*
                                *Knipper, K.R., Yang, Y., Anderson, M.C., Bambach, N., Kustas, W.P., McElrone, A.J., Gao, F.N., Alsina, M. 2023. Decreased latency in landsat-derived land surface temperature products: A case for near-real-time evapotranspiration estimation in California. Agricultural Water Management. 283. Article 108316. https://doi.org/10.1016/j.agwat.2023.108316.*
                                *Kraatz, S.G., Bourgeau-Chavez, L., Battaglia, M., Pooley, A., Siqueira, P. 2022. Mapping and scaling of in situ above ground biomass to regional extent with SAR in the Great Slave region. Earth and Space Science. 9:Article e2022EA002431. https://doi.org/10.1029/2022EA002431.*
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