Integrating OPTRAM and machine learning with multimodal EO proxies for optimized irrigation scheduling in smallholder systems: a Vhembe District case study

Climate variability and recurrent droughts pose increasing irrigation challenges for smallholder maize farmers in southern Africa. This study developed a scalable Earth observation and artificial intelligence (EO–AI) framework combining satellite data, machine learning, and crop water modeling to estimate daily maize actual crop evapotranspiration (ETc) in South Africa’s Vhembe District. Five machine learning models were rigorously validated against benchmark ET c derived from the FAO-56 Penman–Monteith reference evapotranspiration (ET 0 ) method multiplied by locally calibrated crop coefficients (K c ). Random Forest and k-Nearest Neighbors models demonstrated superior performance, with R 2 consistently exceeding 0.99, root mean square error (RMSE) below 0.06 mm/day, and normalized RMSE (NRMSE) less than 2%, outperforming support vector machine, MARS, and XGBoost models. The EO–AI framework effectively captured fine-scale spatial and temporal ET c variability, with daily actual maize ET c at 6.5 mm/day during peak crop sensitivity periods. An operational irrigation decision-support prototype translated these predictions into targeted field-level water-deficit alerts for farmers. This work highlights the value of EO–AI frameworks for delivering high-resolution, daily ET c mapping in fragmented, cloud-prone landscapes, enabling more precise and resilient irrigation strategies for smallholder systems.