Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform

Precise knowledge of crop water consumption is essential to better manage agricultural use, particularly in regions where most countries struggle with increasing and food insecurity. Approaches such as cloud computing remote sensing (RS) have facilitated access, process, visualization big geospatial data map monitor requirements. To find the reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands drylands, we modeled mapped ETa using empirical RS methods across Zayandehrud river basin Iran two decades (2000–2019) on Google Earth Engine platform Normalized Difference (NDVI) Enhanced 2 (EVI2). Developed ET-VI products this study comprise three NDVI-based (ET-NDVI*, ET-NDVI*scaled, ET-NDVIKc) an EVI2-based (ET-EVI2). We (a) applied, first time, ET-NDVI* method a crop-independent index then compared its performance ET-EVI2 ET, (b) assessed ease feasibility transferability these other regions. Comparing four showed that annual ET-NDVI*scaled estimations were close. ET-NDVIKc consistently overestimated ETa. Our findings indicate easy parametrize adopt regions, while are site-dependent sensitive image acquisition time. performed robustly arid semi-arid making it tool. Future research should further develop confirm by characterizing accuracy VI-based over drylands comparing them available examining their crop-specific comparisons.