Practical irrigation scheduling at the sub-field scale: Comparing OpenET and capacitance soil moisture sensors against a TSEB reference in dry edible beans

Abstract

Accurate estimation of daily crop evapotranspiration (ET) at the sub-field level is critical for precision irrigation scheduling and management, yet few studies have evaluated whether satellite-based ET products can resolve soil-driven spatial variability within a single field. We compared three ET estimation approaches across three management zones delineated by apparent electrical conductivity in a commercial dry edible bean field in western Nebraska, U.S.A. during the 2023 growing season, including Two-Source Energy Balance (TSEB) with ground-installed infrared radiometer thermometers (IRT) as the reference, soil water balance (SWB) from commercial capacitance sensors with factory default calibration, and satellite-based ET from OpenET. Three zones exhibited significant differences (p < 0.05) in available water, vegetation indices, and yield, confirming distinct growing environments under uniform irrigation. The OpenET ensemble showed moderate daily agreement with TSEB (R² = 0.33-0.52, RMSE = 1.0-1.2 mm/day), whereas SWB showed poor agreement (R² = 0.01-0.05, RMSE = 3.1-6.8 mm/day) due to sensor inaccuracies possibly caused by factory default calibration, preferential flow in sandy soils, and unquantifiable deep percolation. Among six individual OpenET models, eeMETRIC achieved the highest correlations (R² = 0.38-0.60) but failed to differentiate ET among zones, while SIMS and SSEBop combined relatively high correlations (R² = 0.35-0.58) with the ability to detect significant zone-level ET differences (p < 0.05). These findings highlight the limitations of capacitance soil moisture sensors for daily ET estimation under typical commercial conditions and demonstrate that select OpenET models can capture sub-field ET variability, supporting their integration into zone-level irrigation scheduling for spatially heterogeneous fields. By making zone-specific ET information available without on-farm instrumentation, these results help open a practical pathway toward broader producer adoption of data-driven precision irrigation scheduling.