Physics-informed deep-learning model for mitigating spatiotemporal imbalances in FLUXNET2015 global evapotranspiration data

Abstract As a key component of the water cycle, evapotranspiration (ET) plays a critical role in agricultural management and climate prediction. While numerous long-term observation sites have been established in Europe and North America (data-rich regions), fewer short-term observation sites exist in South America and, particularly, in Africa (data-poor regions). Several machine learning approaches have been developed for ET estimations. However, most existing studies in this field have used training and testing data from the same region, potentially leading to poor extrapolation in unseen areas. This paper proposes a physics-informed deep-learning model that considers external environmental variables, enabling more accurate identification of different underlying surfaces. Our results demonstrate that the proposed model effectively transfers the knowledge acquired from its training on data-rich regions to data-poor regions, thereby mitigating spatiotemporal imbalances in global in-situ ET observations. Overall, this approach can support the sustainable development of data-deficient regions or countries.