Comparing evapotranspiration estimations using crop model-data fusion and satellite data-based models with lysimetric observations: Implications for irrigation scheduling

Irrigation scheduling relies on accurately estimating actual evapotranspiration (ETa). However, achieving this goal remains challenging, with current trends attempting to integrate sensor data into biophysically-sound models. In this study, we used ETa models that integrate satellite-derived data. Model outputs were compared with weighing lysimeter measurements collected at Bushland, Texas, focusing on the uncertainties associated with model estimations and lysimetric observations of ETa. The data were for maize (Zea mays L) during the seasons 2013, 2016, and 2018, irrigated using a linear-move system applying 100 % and 75 % of ETa, and a subsurface drip irrigation system applying 100 % ETa. The models were CropSyst-W, a crop model integrating the normalized difference vegetation index to derive green canopy cover, and two remote sensing data-based energy balance models: EEFlux and OpenET. The average Willmott index of agreement (d, 0 = no agreement, 1 = perfect agreement) of 3 years and four lysimeters were 0.93 and 0.77 for CropSyst-W and EEFlux, respectively. OpenET estimates were only available in 2016 and 2018, with an average d of 0.89. The average normalized root mean square deviation was 0.31 and 0.47 for CropSyst-W and EEFlux and 0.32 for OpenET. Uncertainty factors affecting modeled and observed ETa highlight the difficulty in defining model accuracy for adaptive irrigation scheduling, which is also affected by the spatial variability of ETa and irrigation uniformity. Research on pathways for model improvement should continue, searching for practical solutions that integrate models and sensors and account for the limitations discussed here.