A Novel Framework Based on Data Fusion and Machine Learning for Upscaling Evapotranspiration from Flux Towers to the Regional Scale

Accurate quantification of regional ET is essential for agricultural water management. Upscaling methods based on flux tower observations have been widely applied in large-scale ET estimation. However, the coarse spatial resolution of existing upscaling approaches limits their utility in field-scale management. Therefore, this study proposes an integrated upscaling framework that combines data fusion and machine learning, enabling spatiotemporally continuous ET estimation at the field scale (30 m × 30 m). First, daily 30 m resolution land surface temperature (LST) and vegetation indices were generated by fusing MODIS, Landsat, and China Land Data Assimilation System (CLDAS) datasets. These variables, along with meteorological data and the footprint model, were used as inputs for machine learning. The upscaled ET was evaluated under varying surface heterogeneity using optical-microwave scintillometers (OMS). The results show that a one-dimensional convolutional neural network (1D CNN) using both remote sensing and meteorological data performed best in relatively homogeneous croplands, achieving a correlation coefficient (R) of 0.90, a bias of −0.14 mm/d, a mean absolute error (MAE) of 0.46 mm/d, and a root mean square error (RMSE) of 0.66 mm/d. In contrast, for heterogeneous urban-agricultural landscapes, the 1D CNN using only remote sensing data outperformed other models, with R, bias, MAE, and RMSE of 0.93, −0.14 mm/d, 0.66 mm/d, and 0.88 mm/d, respectively. Furthermore, SHapley Additive exPlanations (SHAP) revealed that LST and the two-band enhanced vegetation index (EVI2) were the most influential drivers in the models. The framework successfully enables ET modeling and spatial extrapolation in heterogeneous regions, providing a foundation for precision water resource management.