Evaluating the Performance of OpenET Models for Alfalfa in Arizona

This study was conducted to evaluate six satellite-based ET models (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and their Ensemble, derived from the OpenET platform, in estimating the actual evapotranspiration (ET) of alfalfa. Then, identify the best-performing OpenET model for alfalfa irrigation management under arid climate conditions in Arizona, USA. Five statistical metrics, the index of agreement (Dindex), Nash-Sutcliffe efficiency coefficient (NSE), mean bias simulation error (MBE), prediction/simulation error (Pe), and coefficient of determination (R²), were used to evaluate the seven alternative estimates in comparison with measured ET (ETmea) at a field scale with four replicates during the 2023 alfalfa growing season in Buckeye, Arizona. Overall, OpenET models and their Ensemble were linearly correlated to average ETmea with R² > 0.71. Our findings showed that ALEXI/DisALEXI, geeSEBAL, and PT-JPL had a general tendency to underestimate actual ET with acceptable to poor prediction errors (Pe ≤ -35.18 for PT-JPL). In contrast, eeMETRIC, SIMS, and SSEBop overestimated ETmea with acceptable to poor prediction errors (1.86 ≤ Pe ≤ 28.39). Our results highlighted the limitations of using the PT model in arid to semi-arid areas, even after the PT-JPL aridity correction. The Ensemble approach, which combined all OpenET models, showed a high degree of agreement (0.93 ≤ Dindex ≤ 0.96) with the ETmea of alfalfa during the growing period in 2023. R² ranged from 0.77 to 0.86, with positive NSE values between 0.67 and 0.82. Moreover, the Ensemble approach had significantly lower prediction errors (-6.92 ≤ Pe ≤ 4.04) when compared with six OpenET models, making it the best to simulate alfalfa’s actual evapotranspiration over the study area. This will contribute to providing farmers and decision-makers with the best satellite-based approach for efficient irrigation management and water use in arid regions.