Comparing cotton ET data from a satellite platform, in situ sensor, and soil water balance method in Arizona

Authors: ELSADEK, ELSAYED AHMED - University Of Arizona; ATTALAH, SAID - University Of Arizona; Williams, Clinton; Thorp, Kelly; Wang, Dong; ELSHIKHA, DIAA ELDIN - University Of Arizona

Submitted to: Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/14/2026
Publication Date: 1/15/2026
Citation: Elsadek, E., Attalah, S., Williams, C.F., Thorp, K.R., Wang, D., Elshikha, D.M. 2026. Comparing cotton ET data from a satellite platform, in situ sensor, and soil water balance method in Arizona. Agriculture. 16(2). Article 228. https://doi.org/10.3390/agriculture16020228.
DOI: https://doi.org/10.3390/agriculture16020228

Interpretive Summary: Accurate evapotranspiration (ET) estimates support US agriculture by assisting producers in increasing crop yields, farm profitability, and water use efficiency. These precise estimates also help growers reduce costs, energy consumption, and adverse environmental impacts, especially crucial in arid regions like Arizona. The study successfully demonstrated that the LI-710 sensor and specific OpenET models provide reliable guidance for efficient cotton irrigation management. The LI-710 acceptably simulated daily ET, with a slight overestimation of 0.21 mm and an acceptable simulation error of 4.40%. Among the satellite-based models, SIMS showed acceptable performance, slightly overestimating daily ETc by 0.16 mm with a simulation error of 3.42%. The SSEBop model also achieved acceptable metrics, underestimating daily ETc by 0.16 mm with a simulation error of -3.35%. Ultimately, these validated tools, including the OpenET Ensemble, provide reliable data to help growers manage water resources effectively in semi-arid climates.

Technical Abstract: Crop production in the desert Southwest of the United States as well as in other arid and semi-arid regions require tools that provide accurate crop evapotranspiration (ET) estimates to support efficient irrigation management. Such tools include the web-based OpenET platform, which provides real-time ET data generated from six satellite-based models and their Ensemble, as well as the LI-710 (LI-COR Inc., Lincoln, Nebraska, USA). This study evaluated simulated ET (ETsim) of cotton (Gossypium hirsutum L.) derived from OpenET models (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop), their Ensemble approach, and LI-710 (LI-COR Inc., Lincoln, Nebraska, USA). Field data were utilized to estimate “measured” cotton ET (ETmea) using the soil water balance method from June to October 2025 in Gila Bend, Arizona, USA. Four evaluation metrics, the normalized root-mean-squared error (NRMSE), mean bias error (MBE), simulation er-ror (Se), and coefficient of determination (R2), were employed to evaluate the performance of OpenET models, their Ensemble, and the LI-710 in estimating cotton ET. Statistical analysis indicated that the ALEXI/DisALEXI, geeSEBAL, and PT-JPL models underestimated ETmea with simulation errors ranging from (-26.92 = Se = -20.57%). The eeMETRIC, SIMS, SSEBop, and Ensemble were considered acceptable based on the statistical metrics (22.57% = NRMSE = 29.85%, -0.36 mm. day-1 = NRMSE = -0.16 mm. day-1, -7.58% = Se = 3.42%, 0.57 = R2 = 0.74). Meanwhile, LI-710 provided an acceptable simulation of cotton ET with a slight tendency to underestimate daily ET by 0.21 mm. A strong positive correlation (r=0.81) was observed between the ETsim and ETmea. Moreover, Se and NRMSE were 4.40% and 23.68%, respectively. These results indicated that OpenET models such as eeMETRIC, SIMS, or SSEBop, OpenET Ensemble, and LI-710 can offer growers and decision-makers reliable guidance for efficient irrigation management of cotton in arid and semi-arid climates.