Tunca, EmreKoksal, Eyup SelimTaner, Sakine Cetin2026-07-012026-07-0120250342-71881432-1319https://doi.org/10.1007/s00271-025-01052-7https://hdl.handle.net/20.500.12684/23713Accurate estimation of root-zone soil moisture (SM) is critical for agricultural water management and sustainable crop production. This study develops and evaluates a methodology to estimate SM in sorghum root zones by high-resolution unmanned aerial vehicle (UAV) multispectral and thermal imagery with machine learning (ML). A two-season field experiment (2020-2021) with four irrigation regimes provided UAV data and concurrent ground-based SM measurements. This study conducted a comparative analysis of four ML algorithms: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGB), and K-Nearest Neighbors (KNN). The models were evaluated both as standalone predictors and as components of ensemble structures. Among single models, RF achieved the highest test performance (R-2 = 0.84, RMSE = 11.22 mm/90 cm, MAE = 9.32 mm/90 cm). An ensemble combining XGBoost, LGB, and KNN yielded a slight improvement (R-2 = 0.85, RMSE = 11.124 mm/90 cm, MAE = 8.775 mm/90 cm), indicating that ensemble learning can modestly enhance model performance. The proposed workflow offers a practical approach for field-scale SM monitoring, demonstrating potential applications in irrigation scheduling and agricultural water management.en10.1007/s00271-025-01052-7info:eu-repo/semantics/closedAccess[Keyword Not Available]Article4412-s2.0-105023424787WOS:001627744900008Q1Q1