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 Hydrology and Remote Sensing Laboratory: Beltsville, MD
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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: **Location:
                            Hydrology and Remote Sensing Laboratory** Project Number: 8042-13610-030-000-D
                        
                        Project Type: In-House Appropriated Start Date: Dec 22, 2021
                        
                        End Date: Dec 21, 2026 **Objective:**
                        
                        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.


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