Spatial assessment of field-scale water use efficiency of an AI-radar guided irrigation system using UAV-derived and OpenET evapotranspiration

Declining groundwater levels in the Ogallala Aquifer have increased the urgent need to improve water productivity in irrigated cropping systems in the U.S. Great Plains. Precision irrigation strategies that account for sustainable water use may enhance field-scale water use efficiency (WUE f = Grain yield/actual evapotranspiration). This study evaluated a novel irrigation system (AI-Radar; Field A) against a sub-surface drip irrigation system (SDI; Field B) in an on-farm field trial (both fields located in proximity) by quantifying WUE f for soybean ( Glycine max L.) production. WUE f was calculated as a function of crop yield and seasonal evapotranspiration (seasonal ET a ). Seasonal ET a was quantified using UAV-based surface energy balance modelling (HRMET) and daily OpenET ensemble satellite estimates as complementary tools. This was used to calculate WUE f as crop yield divided by seasonal ET a . Results exhibited WUE f was significantly (p-value < 0.05) higher under Field A (UAV: 7.17 ± 2.41 kg ha⁻¹ mm⁻¹; OpenET: 6.66 ± 2.24 kg ha⁻¹ mm⁻¹) than Field B (UAV: 5.70 ± 2.13 kg ha⁻¹ mm⁻¹; OpenET: 5.06 ± 1.89 kg ha⁻¹ mm⁻¹), alongside an estimated 15 to 27% lower seasonal irrigation depth than the SDI (according to growers record). The mean yield under AI-Radar (3189 kg/ha) was 30.8% higher than SDI (2208 kg/ha) (p < 0.05), with a lower coefficient of variation (9.96%), indicating higher yield potential in Field A. Comparison of seasonal ET a estimates derived from UAV and OpenET showed moderate to strong agreement (R 2 = 0.65 to 0.69, RMSE = 0.77 to 0.90 mm day -1 ; NRMSE = 21.8 to 23.4%; MBE = -0.28 to -0.42 mm day -1 ). Both methods (UAV and OpenET) supported the findings of the irrigation performance evaluation. Specifically, OpenET, an open-source, easily accessible tool, offers growers a scalable way to monitor field-scale ETa. By comparing AI-driven radar-based irrigation with SDI system using UAV- and OpenET-derived ETa estimates, this study provides a practical framework for evaluating irrigation performance at the field scale.