Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #440288

Research Project: Accurate Estimation of Crop Transpiration in Orchards and Vineyards using UAV and Satellite Technologies

Location: Hydrology and Remote Sensing Laboratory

Project Number: 8042-13610-030-023-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Jun 1, 2021
End Date: Aug 31, 2022

Objective:
This project aims to develop an accurate estimation of crop transpiration (T) for direct, low cost, and minimal calibration for crop water use and stress estimation in orchards and vineyards using unmanned aerial vehicle and satellite technologies. This information is critical for growers, producers, and water managers to accurately monitor and control water stress and improve water use and irrigation efficiency. Objective 1: Enable UAV-based T estimates for immediate, direct, with minimal calibration and low-cost crop water stress estimation from plant to field and landscape scales spanning multiple fields. Objective 2: Enable satellite-based T estimates for seasonal monitoring of crop water use and stress from field to regional scales.

Approach:
The Two-Source Energy Balance (TSEB) model using remote sensing developed by HRSL scientists for satellites and UAVs can provide the most effective means to compute crop transpiration T and thus the Crop Water Stress Index (CWSI) at field and regional scales. The Cooperator will provide a Ph.D. student to conduct final evaluation of the internal algorithms for estimating T and expedite the transfer of the updated pyTSEB (open-source code of the TSEB model written in python) and USDA ET Toolkit products to commercial irrigation scheduling tools to better meet the needs for crop water use and stress assessment in the US vineyard and orchard industry. Specifically, the Cooperator will use the funding 1) to improve the T estimation at plant- and farm- and landscape-scale using the pyTSEB model and UAV information with historical data collected by HRSL and the Cooperator as part of a completed NASA funded Applied Sciences project, and 2) to apply the T estimation to the USDA ET Toolkit using satellite information for field and regional assessment of crop water use and stress.