Evaluation of four remote sensing algorithms in estimating actual evapotranspiration in agricultural environments

Abstract

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 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 ha. 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 and validated using a smart field weighing lysimeter while the algorithm estimates were evaluated at irrigation-scheme scale using weather-station-based extrapolation. The VI-based algorithm performed best, with r=0.92, R2=0.85 and the lowest errors (RMSE = 0.58 mm d−1; MAE = 0.44 mm d−1), followed closely by SEB (r=0.92, R2=0.84, RMSE = 0.83 mm d−1, MAE = 0.69 mm d−1). The SEBS algorithm showed moderate performance, while CWSI performed poorly. While SEBAL is the second-best performing algorithm, the validation of VI-ETa approach in estimating ETa against lysimeter measurements demonstrated its reliability with strong correlations and low error metrics, as a result, the VI-ETa algorithm is a computationally efficient alternative. The uncertainty analysis indicated that extrapolated ETa estimates were reliable within ±7 %–12 %. The study demonstrates the potential use of some remote sensing algorithms for accurate ETa estimation to support irrigation scheduling, which may lead to reduced water overuse in water-scarce environments.