Evapotranspiration Partitioning in Selected Subtropical Fruit Tree Orchards Based on Sentinel 2 Data Using a Light Gradient-Boosting Machine (LightGBM) Learning Model in Malelane, South Africa

The accurate estimation of evapotranspiration (ET) and its components are vital for water resource management and irrigation planning. This study models tree transpiration (T) and ET for grapefruit, litchi, and mango orchards using light gradient-boosting machine (LightGBM) optimized using the Bayesian hyperparameter optimization. Grounds T and ET for these crops were measured using the heat ratio method of monitoring sap flow and the eddy covariance technique for quantifying ET. The Sentinel 2 satellite was used to compute field leaf area index (LAI). The modelled data were used to partition the orchard ET into beneficial (T) and non-beneficial water uses (orchard floor evaporation—Es). We adopted the 10-fold cross-validation to test the model robustness and an independent validation to test performance on unseen data. The 10-fold cross-validation and independent validation on ET and T models produced high accuracy with coefficient of determination (R2) ≥ 0.88, Kling–Gupta efficiency (KGE) ≥ 0.91, root mean square error (RMSE) ≤ 0.04 mm/h, and mean absolute error (MAE) ≤ 0.03 mm/h for all the crops. The study demonstrates that LightGBM can accurately model the transpiration and evapotranspiration for subtropical tree crops using Sentinel 2 data. The study found that Es which combined soil evaporation and understorey vegetation transpiration contributed 35, 32, and 31% to the grapefruit, litchi and mango orchard evapotranspiration, respectively. We conclude that improvements on orchard floor management practices can be utilized to minimize non-beneficial water losses while promoting the productive water use (T).