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

2025 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 third 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. Under Objective 1, research activities in FY25 focused on transforming ground-based observations into an improved understanding of physical processes impacting water use and availability in agroecosystems, with the goal of improving representation of these processes in modeling frameworks. In collaboration with the ARS Davis lab, we evaluated the impact of vine row orientation and canopy structure on remotely sensed vineyard water use estimates, including effects of shadows and time-varying radiation transfer to the soil surface. Progress was made toward characterizing airflow within and above the vine canopy and its influence on evaporative water loss and other exchange processes (Sub-objective 1.1.1). An intensive field campaign was conducted at the Fresno, California GRAPEX vineyard site, focused on strongly advective conditions that can lead to enhanced evapotranspiration (ET). The campaign included drone imaging and flux measurements collected along a transect extending from an upwind dryland site to the irrigated vineyards. Analyses of these data show that advection enhancement accounts for as 25% of the water loss from the vineyards and begins much earlier in the day than expected (Sub-objective 1.1.2). Based on findings from Sub-objective 1.1.1, we developed a more robust radiation partitioning algorithm for vineyard and orchard cropping systems that improves flux partitioning between vine transpiration and interrow evaporation. Working with scientists at the University of Milan, we are developing a water-energy balance modeling scheme that employs an innovative time-continuous formulation which merges an energy balance model using periodic satellite LST with time-continuous water-energy balance modeling to better estimate daily ET and ET partitioning (Sub-objective 1.2). Under Sub-objective 1.3, we evaluated and improved the ability of remote sensing to capture rapid, field-scale changes in evaporative fluxes. Land surface parameters that influenced processes included routine alfalfa harvest, grazing in pasture lands, controlled burning in prairie systems, and rapid dry-down events. We found that incorporating imagery from multiple satellites, spatially sharpened to a consistent spatial resolution, was critical to obtaining the temporal resolution needed to reproduce water use signals associated with rapid vegetation loss and subsequent regrowth. This work is being documented in several manuscripts in preparation. In situ soil moisture monitoring in support of validation of remote sensing products continued in FY25 (Sub-objective 1.4). Quality control and quality assurance documents have been developed to improve overall community and mesonet engagement on agricultural monitoring. There have been losses however on network maintenance and operations because of vacancies and loss of resources. Alternative methods of monitoring are being sought. Under Sub-objective 1.5, we completed the evaluation of a system to monitor root-zone soil moisture (RZSM) within Central Valley vineyards based on the assimilation of synthetic aperture radar (SAR) and passive microwave surface soil moisture retrievals alongside thermal-infrared (TIR) estimates of ET into a soil-water balance model. Results indicate that, while the system can accurately capture inter-annual variations in spring RZSM, it struggles during the summer. Based on this assessment, we determined that our RZSM product is best suited for timing the start springtime irrigation. In response, operational RZSM data deliveries were moved up to February 1st to provide better early spring coverage. Activities in FY25 under Objective 2 advanced remote sensing tools for monitoring crop health and progress, as well as calibrating watershed models. The Evaporative Stress Index (ESI) is an agricultural drought indicator developed by ARS scientists in Beltsville, Maryland based on seasonal anomalies in remotely sensed actual ET. In collaboration with scientists from the University of Wisconsin-Madison, machine learning applied to a microwave version of ESI covering the U.S. is being used to develop improved forecasts of flash drought. The all-sky capabilities of microwave-ESI, retrievable during both clear and cloudy conditions, enables accurate time-tracking of the rapid decline in surface moisture conditions that define flash drought. In an application over the Central Mississippi River Basin Long-Term Agroecosystem Research (LTAR) site, multi-year ESI timeseries were developed at spatial resolutions of 4-km to 30-m using multi-sensor datasets to investigate relationships of vegetation stress signals with crop and ecosystem functioning down to sub-field sales (Sub-objective 2.1). A near real-time processing system using Harmonized Landsat-Sentinel data to track crop phenology was developed within the SCINet Ceres platform (Sub-objective 2.2). Daily 30-m vegetation index time series for 2018 to 2024 covering the contiguous United States (CONUS) were generated to detect phenological metrics (e.g., emergence, harvest) and monitor crop conditions. Beta versions of crop emergence maps for CONUS have been produced and shared with collaborators. These data are being utilized to enhance the summer crop condition monitoring system for our NP 212 project. A manuscript describing a phenology-aligned crop condition monitoring approach has been published. In FY25 ET, ESI, leaf area index, and phenology products were used in combination to assess forage conditions across experimental rangeland sites in Michigan, Wyoming, and Oklahoma, studying the effects of environmental factors and grazing management on vegetation health. Ongoing development focuses on creating toolkits based on these remote sensing indicators to inform real-time grazing rotation decision making at rangeland research sites. Papers detailing our findings and results are in preparation (Sub-objective 2.3). At regional scales, we derived a new approach for calibrating hydrologic models in ungauged watersheds using a combination of antecedent soil moisture estimates acquired from passive microwave remote sensing and temporally sparse discharge observations available from satellite altimetry measurements (Sub-objective 2.4). Results demonstrate that the combined uses of both passive microwave and altimetry measurements mutually compensate for each measurement type’s individual shortcomings and, when combined, enables the robust validation of hydrologic models in agricultural basins lacking in-channel stream-gauge observations. The third objective of this project is to assess the Lower Chesapeake Bay (LCB) agroecosystem via measurements and modeling. To achieve this objective, meteorological, surface fluxes, crop phenology, and other environmental measurements were collected at the LCB-LTAR locations at the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental watershed located 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 continued evaluation of a prototype phosphorus probe was conducted at several USGS gage stations in the CRW and elsewhere in the LTAR Network (Sub-objective 3.2). Under Sub-objectives 3.3 and 3.4, we further advanced watershed modeling as an important tool for assessing impacts of agricultural practices on soil and water quality and carbon storage. Human-made small, channelized ditches connect agricultural landscapes/wetlands with river networks, prevalent in the LCB-LTAR, can have important impacts on water and nutrient cycling. In FY25 we employed a deep convolutional neural network to classify ditches from combinations of aerial optical and lidar derived features, yielding higher accuracy than other flowline products. We communicated these results with the NRCS CEAP wetland teams and made plans to test the new ditch maps in future assessments of wetland ecosystem services. We also demonstrated the significant impact of river-routing time steps on watershed modeling, with results published in Journal of Environmental Modelling and Software (Sub-objective 3.3). To improve availability of data for model calibration and validation, we tested the use of remotely sensed evapotranspiration products and vegetation parameters to reduce prediction uncertainty (Sub-objective 3.4.1). Our findings indicated that the use of multiple remotely sensed products as model constraints enabled model evaluations at finer scales, thereby increasing accuracy of calibrated models and improved ability to represent the spatial characteristics of hydrological variables. Incorporation of remotely sensed cover crop information, an important conservation practice in the LCB, is underway (Sub-objective 3.4.3). Finally, we quantified the amount of carbon in wetlands and riparian habitats and the rate and time of carbon being stored for private conservation easements. We found that the wetlands and riparian habitats store significant amounts of carbon, and that restored wetlands can recover significant carbon storage capacity within the typical length of private conservation easement contracts (Sub-objective 3.4.2).

Accomplishments
1. Remote sensing satellites improve soil moisture monitoring in vegetatively dense regions.. Microwave remote sensing of soil moisture has limited validation for forested or dense vegetation regions. The Soil Moisture Active Passive Validation Experiment 2019-2022 was conducted to determine how to improve soil moisture monitoring from remote sensing satellites for landscapes with high vegetation cover. ARS researchers in Beltsville, Maryland, conducted a study from 2019-2022 with coordinated satellite overpasses and aircraft flights to capture moderate resolution microwave map products for soil moisture and vegetation density. It was determined that soil moisture signals can be detected for mixed landscapes of row crop agriculture and forest, as well as dominantly forested regions. As a result of this study, the extent to which microwave remote sensing products can produce soil moisture data has been dramatically increased. This research has broad impacts for farmers whose production fields are intermixed with forests and shrublands, which bias the remote sensing signals for soil moisture. This will improve soil moisture estimates for these regions, thus improving management decisions and weather forecasting for regions with mixed agriculture and forests, specifically the eastern U.S. This effort increases the coverage and accuracy of satellite estimates for soil moisture across a much larger domain than previously possible.

2. Assessment of environmental drivers for grazing land across Continental United States. Effective management of grazing lands requires precise biomass assessment. In recent decades, advancements in remote sensing technology have provided cost-effective and efficient alternatives to traditional field-based methods for monitoring and estimating biomass in grazing lands. However, estimating herbaceous biomass remains challenging due to the complexity of environmental factors, including precipitation, elevation, land surface temperature, vegetation cover, and soil texture. ARS scientists in Beltsville, Maryland, assessed these environmental influences on grazing lands and identified key factors for developing reliable models to estimate herbaceous biomass across the Conterminous United States . By utilizing Earth observation data from Google Earth Engine and machine learning techniques, environmental factors were evaluated and compared to the performance of various ML models over the U.S. The findings offer valuable insights into the development of comprehensive biomass estimation models, which can improve grazing land management under diverse environmental conditions. This will directly improve planning and management decisions for ranchers who must maintain herds across expansive landscapes, with forage limited by moisture availability and drought.

3. Using remote sensing to improve the forecasting of agricultural drought impacts. Agricultural drought has enormous economic implications for American farmers and ranchers. A key aspect of forecasting these impacts is understanding how efficiently agricultural landscapes can reduce soil-water losses (i.e., evapotranspiration) in response to reductions in the availability of root-zone soil moisture. In theory, such an assessment is possible using existing remotely sensed soil moisture and evapotranspiration products. However, in practice, it has been greatly complicated by the random error present in both products – which obscures the true relationship between soil moisture and evapotranspiration. In response, ARS scientists in Beltsville, Maryland developed a new mathematical approach that corrects for the presence of such error and provides the best-available description of this relationship. This improved description can, in turn, be leveraged to enhance the ability to forecast, and therefore mitigate, the economic impact of drought on domestic agricultural production. This insight will improve the ability of weather forecasters to anticipate agricultural drought and provide earlier warnings to US farmers and ranchers.

4. 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 the 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 incorporation into existing water management toolkits. This new technology is being used for 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 for the western state water managers and basin managers who need better estimates of water availability.

Review Publications
Kustas, W.P., Knipper, K.R., Alsina, M., Bambach, N., Mcelrone, A.J., Prueger, J.H., Alfieri, J.G., Bhattarai, N., Anderson, M.C., Torres, A., Nieto, H., Gao, F.N., Hipps, L., Mckee, L.G., Castro, S.J., Agam, N., Crow, W.T., Burchard-Levine, V., Jin, Y., Dokoozlian, N. 2024. A basic and applied remote sensing research project (GRAPEX) for actual evapotranspiration monitoring to improve vineyard water management. Acta horticulturae. 1409:151-158. https://doi.org/10.17660/ActaHortic.2024.1409.21.
Adams, M., Miller, C., Mccarty, G.W., Lang, M., Strano, S., Rizzo, A., Page-Dumroese, D., Jurgensen, M. 2025. Wood decomposition in poorly-drained forested wetland soils: How important are termites?. Journal of Soil Biology and Biochemistry. 204. https://doi.org/10.1016/j.soilbio.2025.109754.
Paciolla, N., Corbari, C., Kustas, W.P., Nieto, H., Alfieri, J.G., Gao, F.N., Prueger, J.H., Alsina, M., Hipps, L.E., Mckee, L.G., Mcelrone, A.J., Bambach, N. 2024. Two-source energy balance schemes exploiting land surface temperature and soil moisture for continuous vineyard water use estimation. Irrigation Science. 43:731-753. https://doi.org/10.1007/s00271-024-00991-x.
Cammalleri, C., Anderson, M.C., Corbari, C., Yang, Y., Hain, C.R., Salamon, P., Mancini, M. 2024. Evaluating a multi-step collocation approach for an ensemble climatological dataset of actual evapotranspiration over Italy. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2024.132209.
Otkin, J., Zhong, Y., Ford, T.W., Anderson, M.C., Hain, C., Hoell, A., Svoboda, M., Wang, H. 2024. Multivariate evaluation of flash drought across the United States. Water Resources Research. https://doi.org/10.1029/2024WR037333.
Hamovit, N., Roychowdhury, T., Akob, D., Zhang, X., Mccarty, G.W., Yarwood, S. 2025. Comparative assessment of a restored and natural wetland using 13C-DNA SIP reveals a higher potential for methane production in the restored wetland. Applied and Environmental Microbiology. 91(3). https://doi.org/10.1128/aem.02161-24.
Lahmers, T.M., Kumar, S.V., Ahmad, S., Holmes, T., Getirana, A., Orland, E., Locke, K., Biswas, N., Nie, W., Pflug, J., Whitney, K., Anderson, M.C., Yang, Y. 2025. An observation-driven framework for modeling post-fire hydrologic response: evaluation for two central California case studies. Water Resources Research. 61(2). Article e2023WR036582. https://doi.org/10.1029/2023WR036582.
Cammalleri, C., Anderson, M.C., Bambach, N., Mcelrone, A.J., Knipper, K.R., Roby, M.C., Ciraolo, G., Decaro, D., Ippolito, M., Corbari, C., Ceppi, A., Mancini, M., Kustas, W.P. 2024. A fully remote sensing-based implementation of the two-source energy balance model: an application over Mediterranean crops. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2024.109207.
Meza, K., Torres-Rua, A., Hipps, L., Kopp, K., Straw, C., Kustas, W.P., Christiansen, L., Coopmans, C., Gowing, I. 2025. Relating spatial turfgrass quality to actual evapotranspiration for precision golf course irrigation. Crop Science. https://doi.org/10.1002/csc2.21446.
Taylor, K.J., Sharp, S.J., Stewart, G.A., Williams, M.R., Mccarty, G.W., Palmer, M.A. 2024. Diel greenhouse gas emissions demonstrate a strong response to vegetation patch types in a freshwater wetland. Journal of Geophysical Research-Biogeosciences. 129(11). https://doi.org/10.1029/2024JG008193.
Cammalieri, C., Anderson, M.C., Bambach, N., Mcelrone, A.J., Knipper, K.R., Roby, M.C., Kustas, W.P. 2024. Field scale partitioning of Landsat land surface temperature into soil and canopy components for evapotranspiration assessment using a two-source energy balance model. Irrigation Science. https://doi.org/10.1007/s00271-024-00976-w.
Tsegaye, T., Marlen, E., Hapeman, C.J., Kleinman, P.J., Baffaut, C., Browning, D.M., Coffin, A.W., Spiegal, S.A. 2024. The Long-Term Agroecosystem Research Network: Cross-site transdisciplinary science to support a sustainable and resilient agriculture. Journal of Environmental Quality. 53(6):777-786. https://doi.org/10.1002/jeq2.20649.
Sanchez-Zapero, J., Martinez-Sanchez, E., Camacho, F., Wang, Z., Carrer, D., Schaaf, C., Garcia-Haro, F., Nickeson, J., Cosh, M.H. 2023. Surface Albedo VALidation (SALVAL) platform: towards CEOS LPV validation stage 4. Application to three global albedo climate data records. Remote Sensing. 15:1081. https://doi.org/10.3390/rs15041081.
Zahn, E., Ghannam, K., Chameckecki, M., Moene, A., Kustas, W.P., Good, S., Bou-Zeild, E. 2024. Numerical investigation of observational flux partitioning methods for water vapor and carbon dioxide. Biogeosciences. 129. Article e2024JG008025. https://doi.org/10.1029/2024JG008025.
Wang, T., Alfieri, J.G., Mallick, K., Ortiz, A., Anderson, M.C., Fisher, J., Girotto, M., Szutu, D., Verfaillie, J., Baldocchi, D. 2024. How advection affects the surface energy balance and its closure at an irrigated alfalfa field. Agricultural and Forest Meteorology. 357. https://doi.org/10.1016/j.agrformet.2024.110196.
Sara, K., Rajasekaran, E., Kustas, W.P., Alfieri, J.G., Prueger, J.H., Alsina, M., Hipps, L.E., Mckee, L.G., Mcelrone, A.J., Castro, S., Bambach, N. 2024. Combining spatial downscaling technique and diurnal temperature cycle model to acquire diurnal patterns of land surface temperature at field scale. Journal of Photogrammetry and Remote Sensing. 92:723-740. https://doi.org/10.1007/s41064-024-00291-1.
Li, M., Lang, R.H., Cosh, M.H. 2024. P- and L-band radiometry retrieval of subsurface soil moisture and temperature profiles. IEEE Transactions on Geoscience and Remote Sensing. 62. https://doi.org/10.1109/TGRS.2024.3416988.
Wei, Z., Miao, L., Zhao, T., Meng, L., Lu, H., Peng, Z., Cosh, M.H., Fang, B., Lakshmi, V., Shi, J.C. 2024. Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning. IEEE Transactions on Geoscience and Remote Sensing. 313. https://doi.org/https://doi.org/10.1016/j.rse.2024.114371.
Mane, S., Singh, G., Das, N., Kanungo, A., Cosh, M.H., Dong, Y. 2025. Development of low-cost handheld soil moisture measurement device for farmers and citizen scientists. Frontiers in Environmental Science. 13. https://doi.org/10.3389/fenvs.2025.1590662.
Ayres, E., Reichle, R., Colliander, A., Cosh, M.H., Smith, L. 2024. Validation of remotely sensed and modelled soil moisture at forested and unforested NEON sites. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17:14248-14264. https://doi.org/10.1109/JSTARS.2024.3430928.
Walker, V.A., Cosh, M.H., Ochsner, T. 2024. Calculating a minimum overlap period for successful intercalibration of soil moisture sensors. Vadose Zone Journal. 23(4). Article e20346. https://doi.org/10.1002/vzj2.20346.
Bai, G., Burdette, B., Scoby, D., Irmak, S., Luck, J., Neale, C., Schnable, J., Awada, G., Kustas, W.P., Ge, Y. 2024. High-throughput physiological phenotyping of crop evapotranspiration at the plot scale. Field Crops Research. https://doi.org/10.1016/j.fcr.2024.109507.
Crow, W.T., Feldman, A. 2024. Vegetation crosstalk present in official SMAP surface soil moisture retrievals. Remote Sensing of Environment. 316. https://doi.org/10.1016/j.rse.2024.114466.
Munusamy, S., Rajasekjaran, E., Saraswat, D., Kustas, W.P., Bambach, N., Mcelrone, A., Castro, S.J., Prueger, J.H., Alfieri, J.G., Alsina, M.M. (2024) The utility and applicability of vegetation index based models for the spatial disaggregation of evapotranspiration. Irrigation Science. https://doi.org/10.1007/s00271-024-00963-1.
Huang, M., Carmichael, G.R., Crawford, J.H., Bowman, K.W., De Smedt, I., Colliander, A., Cosh, M.H., Kumar, S., Guenther, A.B., Janz, S.J., Stauffer, R.M., Thompson, A.M., Fedkin, N.M. 2025. Reactive nitrogen in and around the northeastern and Mid-atlantic US: Sources, sinks, and connections with ozone. Atmospheric Chemistry and Physics. 25:1449-1476. https://doi.org/10.5194/acp-25-1449-2025.
Osman, M., Zaitchik, B.F., Otkin, J., Anderson, M.C. 2024. A global flash drought inventory based on soil moisture volatility. Scientific Data. https://doi.org/10.1038/s41597-024-03809-9.
Gao, B., Li, R., Yang, Y., Anderson, M.C. 2024. Correction of thin cirrus absorption effects in Landsat 8 TIRS Images using the OLI cirrus band on the same satellite platform. Sensors. 24. Article 4697. https://doi.org/10.3390/s24144697.
Ghosh, A., Farhadi, M., Hoque, M.E., Boyd, D., Bourgeau-Chavez, L., Cosh, M.H., Colliander, A., Kurum, M. 2025. Estimating vegetation optical depth with mobile GNSS transmissiometry in temperate forests during SMAPVEX’22. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18:6451-6463. https://doi.org/10.1109/JSTARS.2025.3541182.
Chang, J., Gao, F.N., Anderson, M.C., Cirone, R.J., Zhao, H. 2025. Regionalization analysis of environmental drivers of CONUS grazing land biomass. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18:12634-12644. https://doi.org/10.1109/JSTARS.2025.3568771.
Walker, V.A., Cosh, M.H., White, W.A., Colliander, A., Kelly, V., Siqueira, P. 2025. Soil surface roughness in temperate forest during SMAPVEX19-22. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18:4640-4647. https://doi.org/10.1109/JSTARS.2025.3530710.
Zhao, H., Gao, F.N., Anderson, M.C., Cirone, R.J., Chang, J. 2025. Improving crop condition monitoring using phenologically aligned vegetation index anomalies – A case study in central Iowa. International Journal of Applied Earth Observation and Geoinformation. 139. https://doi.org/10.1016/j.jag.2025.104526.
Jeong, J., Tsang, L., Kurum, M., Ghosh, A., Colliander, A., Yueh, S., Mcdonald, K., Steiner, N., Cosh, M.H. 2025. Full-wave simulations of forest at L-band with fast hybrid multiple scattering theory method and comparison with GNSS signals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18:5395-5405. https://doi.org/10.1109/JSTARS.2025.3533313.
Kim, S., Xu, X., Colliander, A., Cosh, M.H., Kraatz, S.G., Kelly, V., Siqueira, P. 2025. Soil moisture estimates using -band airborne SAR over forests replicating NISAR observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18:7364-7373. https://doi.org/10.1109/JSTARS.2025.3544095.
Colliander, A., Cosh, M.H., Bourgeau-Chavez, L., Kelly, V., Kraatz, S.G., Siqueira, P., Walker, V.A., Chen, X., Roy, A., Lakhankar, T., Mcdonald, K., Steiner, N., Kurum, M. 2025. SMAP Validation Experiment 2019-2022 (SMAPVEX19-22): Field campaign to improve soil moisture and vegetation optical depth retrievals in temperate forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18:10749-10771. https://doi.org/10.1109/JSTARS.2025.3553085.
Berg, A., Wicks, K., Thomas, J., Roy, A., Magagi, R., Helgason, W., Colliander, A., Cosh, M.H., Tetlock, E., Gorrab, A., Roy, C., Salmabadi, H., Amini, Y. 2025. Soil Moisture Active Passive Soil Moisture Validation Experiment withing the Canadian Boreal Forest in 2022 (SMAPVEX22-Boreal). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18:11816-11834. https://doi.org/10.1109/JSTARS.2025.3564195.
Du, L., Mccarty, G.W., Li, X., Zhang, X., Rabenhorst, M.C., Lang, M.W., Zou, Z., Zhang, X., Hinson, A.L. 2023. Drainage ditch network extraction from lidar data using deep convolutional neural networks in a low relief landscape . Journal of Hydrology. 628. https://doi.org/10.1016/j.jhydrol.2023.130591.
Wang, Y., Zhou, Y., Zhang, X., Franz, K., Jia, G. 2024. Water regulation mitigates but does not eliminate water scarcity under rapid economic growth . Resources Conservation and Recycling. 215. https://doi.org/10.1016/j.resconrec.2024.108098.
Knipper, K.R., Anderson, M.C., Bambach, N., Melton, F., Ellis, Z., Yang, Y., Volk, J., McElrone, A.J., Kustas, W.P., Roby, M.C., Carrara, W., Castro, S., Kilic, A., Fisher, J., Ruhoff, A., Senay, G.B., Morton, C., Saa, S., Allen, R. 2024. A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards. Agriculture and Forest Meteorology. 355. Article 110146. https://doi.org/10.1016/j.agrformet.2024.110146.
Qiu, L., Xue, Q., Wu, Y., Zhang, X., Wang, Y., Yang, K., Zhao, F., Yin, X. 2024. Responses of precipitation and water vapor budget on the Chinese Loess Plateau to global land cover change forcing. Journal of Environmental Management. 365. https://doi.org/10.1016/j.jenvman.2024.121588.
Wang, Y., Zhang, X., Zhao, K., Singh, D. 2024. Streamflow characteristics and changes over the conterminous United States. Scientific Data. https://doi.org/10.1038/s41597-024-03618-0.
Karki, R., Qi, J., Zhang, X., Srivastava, P.K. 2024. Evaluating SWAT-3PG simulation of hydrologic and water quality processes in a forested watershed: A case study in the St. Croix River basin. Journal of Hydrology. 648. https://doi.org/10.1016/j.jhydrol.2024.132393.
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