Evaluating Irrigation Performance and Water Productivity Using EEFlux ET and NDVI

Southern California’s Imperial Valley (IV) faces serious water management concerns due to its semi-arid environment, water-intensive crops and limited supply. Accurate reliable irrigation system performance productivity information is required in order assess improve the current strategies. This study evaluates spatially distributed equity, adequacy crop (CWP) for two crops, alfalfa sugar beet, using remotely sensed data a geographical 2018/2019 growing season. The actual evapotranspiration (ETa) was mapped Google Earth Engine Evapotranspiration Flux, linear interpolation method R version 4.0.2. approx() function base used produce daily ETa maps, then totaled compute whole equity were determined according ETa’s coefficient of variation (CV) relative (RET), respectively. classification performed machine learning approach (a random forest algorithm). CWP computed as ratio yield use, employing disaggregation map yield, county-level production statistics normalized difference vegetation index (NDVI) images. errors (RE) compared reported literature values 7–27% 0–3% beet. average low; however, spatial within fields showed that 35% had variability greater than 10%. RET high, indicating adequate irrigation; 31.5% 12% beet clustered Valley’s central corner consuming more their potential visibly. wide variation, with CVs 32.92% 25.4% signifying substantial scope enhancement. correlation between CWP, reducing approximately 1500 mm 1200 would help boost without decreasing which nearly equivalent 44.52M cu. m (36,000 acre-ft) water. study’s results could managers identify poorly performing where conservation be focused.