A Novel Classifier-Guided Ensemble Framework for Global Terrestrial Evapotranspiration Estimates

Abstract. Evapotranspiration (ET) is a key hydrological and meteorological variable, serving as the critical nexus between water and energy exchanges. However, accurate estimation of global ET remains a challenging task, as process-based ET algorithms are often inadequate to capture the nonlinear relationship among environmental factors, and the application of data-driven ET algorithms is hindered by sparse and uncertain ET observations. In this study, we developed a novel ensemble framework that integrates three existing ET models (process-based algorithm, machine learning-based ET model, and hybrid model), aiming to provide high-precision terrestrial ET estimates. The framework is guided by an additional classifier that can achieve dynamic per-pixel model selection, thus fully utilizing the spatiotemporal dynamics of each model's distinct advantages in mapping global ET and avoiding the typical underestimation of high values by ensemble methods. Comprehensive validation of the model was carried out using in-situ ET observations from the FLUXNET2015 dataset, catchment-scale water balance ET dataset, and six global-scale ET products, including comparisons to individual base models and another Attention-Based ensemble model. The quantitative comparisons across statistical metrics (RMSE, MAE, R², KGE) indicate that our ensemble model outperforms other evaluated models, especially in extreme samples. Meanwhile, the introduction of classifier can not only significantly enhance the algorithmic robustness and generalizability, but also allow us to gain a basic understanding of the mechanisms behind model selection by interpretability analysis. The study demonstrated the effectiveness of the proposed framework in enhancing ET estimation robustness, thereby providing a valuable reference for the estimation of other similar variables.