Evaluation of remote sensing algorithms for estimating actual evapotranspiration in arid agricultural environments

Abstract. Accurate estimation of actual evapotranspiration (ETa) at both field and larger spatial scales is crucial for understanding crop water use and hydrological interactions particularly in arid regions facing water scarcity. In South Africa, ETa data gaps hinder effective agricultural water management. Advances in geospatial techniques combining Geographical Information Systems (GIS) and remote sensing have made it possible to estimate ETa over large areas. However, the reliability of this depends on the accuracy of algorithms used which must be validated against ground measurements. With the lack of direct ETa measurements in South Africa, this has been a challenging task. This study evaluated ETa variability at farm level to the level of an irrigation Scheme, covering over 36,000 hectares. A total of 22 Landsat 8 satellite images from 2019 to 2021 were used to estimate ETa based on four algorithms: the Surface Energy Balance Algorithm for Land (SEBAL), Surface Energy Balance System (SEBS), Vegetation Index (VI)-based ETa and Crop Water Stress Index (CWSI)-based ETa. Field-scale estimates were compared to measurements from a smart field weighing lysimeter, while larger-scale estimates were validated against extrapolated ETa values. The SEBAL, SEBS and VI-based ETa algorithms correlated well with field-scale lysimeter data, while the CWSI-based algorithm showed poor correlation. SEBAL emerged as the best-performing algorithm, with high correlation coefficients (r=0.91–0.96), strong R² values (0.83–0.92) and the lowest errors (RMSE 0.31–0.89 mm d⁻¹, MAE 0.27–0.82 mm d⁻¹). Findings from this study forms a foundation of improved water management strategies to reduce the overuse of water in agriculture.