A UAV-based method for root zone soil moisture modeling of different farmland scale with grain and economic crops

Rapid and accurate estimation of crop root zone soil moisture (RZSM) is critical for precision agricultural water management, especially in arid and semi-arid regions. This study integrates unmanned aerial vehicle (UAV) multispectral remote sensing with the Remote Sensing-based Water Balance Assessment Tool (RWBAT) model to estimate RZSM for four representative crop types—wheat, maize, rapeseed, and apple trees—in the Loess Plateau region of China. High-resolution vegetation indices (VIs) derived from UAV multispectral imagery and field-measured meteorological data were used to drive the RWBAT model and simulate multi-depth soil moisture dynamics throughout the crop growth period. Based on correlation analysis, NDVI, EVI, SAVI, and DVI were selected to construct crop-specific LAI estimation models, achieving R² values ranging from 0.60 to 0.87. The RWBAT model was calibrated and validated using in-situ soil moisture data from 0 to 140 cm depth, demonstrating high simulation accuracy, particularly at 120–140 cm, with R² values of 0.91 (wheat), 0.76 (apple trees), 0.78 (rapeseed), and 0.80 (maize). Sensitivity analysis revealed that increases in relative humidity and precipitation enhance soil moisture across all crops, with precipitation having a greater influence at deeper soil depths. Overall, the proposed UAV-RWBAT integrated approach demonstrates strong potential for high-resolution, crop-specific root zone soil moisture estimation, offering a promising tool for field-scale water resource management and precision irrigation planning in heterogeneous agricultural landscapes.