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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





- Alfieri, Joseph
- Water Availability and Watershed Management (211)
- Sustainable Agricultural Systems Research (216)

Research Project: **Location:
                            Hydrology and Remote Sensing Laboratory** Project Number: 8042-13610-030-072-R
                        
                        Project Type: Reimbursable Cooperative Agreement Start Date: Dec 1, 2022
                        
                        End Date: Nov 30, 2025 **Objective:**
                        
                        Enhance wine-grape quality through the effective use of limited water resources by developing remote sensing-based tools to monitor vine water consumption and stress levels to manage irrigation and other vineyard activities. **Approach:**
                        
                        With ground truth eddy covariance, plant biophysical and remote sensing data from unmanned aerial vehicles (UAVs) collected in California vineyards and similar data collected in Israeli vineyards,  improve the ability of the correlation-based flux partitioning technique (CPET) to accurately isolate the contribution of soil evaporation (E) and transpiration (T) to the total moisture flux (ET) by developing a crop-specific relationship between water use efficiency (WUE), interstitial CO2, atmospheric demand, and other environmental factors.  The ground truth data will be used to enhance  the ability of the remote sensing-based Two-Source Energy Balance (TSEB) model using AUV imagery to accurately partition ET between soil evaporation and vine canopy and cover crop transpiration by developing and incorporating an improved remote sensing-based retrieval approach for vineyard leaf area index into the model, along with wind and radiation extinction algorithms for the vine/sub-canopy air layer, that account for the unique architecture of vineyard canopies.   The impact of using the modified TSEB model to estimate vine transpiration for stress detection versus prior approaches using ET without partitioning over climatic gradients in the US and Israel will be evaluated using both UAV and satellite remote sensing data and the CPET ground truth data.


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