Remote sensing vegetation Indices-Driven models for sugarcane evapotranspiration estimation in the semiarid Ethiopian Rift Valley

Evapotranspiration (ET), which represents water loss due to soil evaporation and crop transpiration, is a critical hydrological parameter for managing available water resources in irrigation systems. Traditional methods for monitoring actual evapotranspiration (ETa) involve field measurements. While accurate, they lack scalability, are labor-intensive, and incur high costs. Remote sensing satellites can help address these practical challenges by providing high-resolution imagery for spatially explicit mapping and near-real-time monitoring of ETa. This study aimed to develop simple yet robust models for estimating ETa using Sentinel-2 (S2A and S2B) satellite vegetation indices (VIs)—the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—and the Google Earth Engine (GEE) cloud platform for irrigated sugarcane plantations of the Metehara Sugarcane Estate in the semiarid landscape of the Ethiopian Rift Valley. Six empirical ET-VI models that combined NDVI-based proxies (NDVIKc, NDVI*, and NDVI*scaled) and EVI-based proxies (EVIKc, EVI*, and EVI*scaled) for the crop coefficient (Kc) with the reference ET (ETo) were developed and evaluated for growing seasons between 2020 and 2022. Model validation using independently estimated sugarcane ET (ETsugarcane) and open-access remote sensing ET, Actual EvapoTranspiration and Interception (ETIa) showed that all ET-VI models captured spatiotemporal dynamics in the consumptive fraction of sugarcane water use, with a higher coefficient of determination (R2) of ≥ 0.91. However, comparative analyses of ETa retrieval models indicated improved accuracy of the ET-EVI models (root mean square error (RMSE) of ± 8 mm for ETsugarcane and ± 4 mm for ETIa) compared with the ET-NDVI models. Among the EVI models, ET-EVIKc achieved the highest R2 of 0.98, RMSE of ≤ 30 mm, and percentage bias (PBIAS) of ≤ 15 %. The results also revealed a strong correlation between the scaled VI-derived models and the reference ETIa (R2 = 0.94–0.97), which best explained the field-by-field variability, with the ET-EVI*scaled model achieving a lower RMSE of 18 mm than the ET-NDVI*scaled model (RMSE= 32 mm), while both the models showed similar levels of bias (∼17 %). Moreover, compared to the referenced ETsugarcane, the bias was minimal at − 9 % for ET-NDVI*scaled and − 1 % for ET-EVI*scaled. At the field scale, the NDVI and EVI models estimated the mean monthly ETa ranging from 99 to 129 mm m−1 and 89 to 148 mm m−1, respectively, with total annual averages of 1188–1537 mm yr−1 and 1296–1566 mm yr−1. In this context, the modeled ETa provided improved insights into consumptive water use in irrigated sugarcane plantations with limited field measurements. The statistical model evaluation metrics indicated that ET-EVIKc was the optimal model in characterizing ETsugarcane, outperforming the ET-NDVIKc and ET-EVI*scaled models, which ranked second by > 6 %, and ET-NDVI*scaled model, which ranked third by > 20 %. Our findings demonstrate the potential of multispectral VI-driven models as cost-effective and practical tools for the rapid estimation and mapping of ETa, thereby supporting the development of sustainable water conservation practices. A major advantage of the empirical modeling framework presented in this study is the straightforward parametrization of spatially consistent Kc distributions using remote sensing VIs and local weather station data. However, further improvements and operational applications of standardized VI-based ET models in croplands of other large irrigation schemes in semiarid regions should consider atmospheric impacts, variations in scene characteristics, and bare ground/soil exposure.