A scalable deep learning framework for evapotranspiration estimation using open-access climate services and remote sensing

Abstract

By addressing the operational and maintenance constraints of physical ground-based weather stations, this study proposes a deep learning (DL) framework for estimating reference evapotranspiration (ET₀) by combining open-access climate services and remote sensing (RS) data. The proposed approach is benchmarked against traditional machine learning (ML) models, while multiple deep neural network (DNN) architectures are also evaluated, including multilayer perceptron (MLP), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Experiments conducted on three agricultural plots in southeastern Spain, representing contrasting meteorological conditions, demonstrate that RNNs achieve the best performance, with a coefficient of determination of R² = 0.92. Furthermore, model interpretability was addressed using SHapley Additive exPlanations analysis, which confirmed the biophysical consistency of the predictions and identified land surface temperature as the primary driver of the model's estimations. A key contribution is the demonstration that infrastructure-free models trained solely on open-access satellite and climate data can match or even surpass conventional meteorology-based methods, providing a scalable solution for ET₀ prediction. Based on a validation performed upon three agricultural plots in southeastern Spain, which represent contrasting semi-arid meteorological conditions, the framework showcases its potential applicability for global scalability by utilizing location-agnostic, open-access data. Moreover, the integration of crop coefficients enables accurate forecasting of daily irrigation demand. Overall, the proposed methodology illustrates the feasibility of artificial intelligence-driven irrigation management across diverse climates and highlights its potential to advance sustainable water use in agriculture.