A Framework for Soil Moisture Downscaling on the Tibetan Plateau Based on Interpretable Deep Learning

Soil moisture (SM) is crucial for agricultural production and ecological restoration, and high-resolution SM data can be efficiently acquired through downscaling remote sensing products. Although deep learning is widely used for downscaling, its mechanisms often lack interpretability. In this study, a downscaling model based on the architecture of the Kolmogorov–Arnold Network (KAN) was constructed using multi-source remote sensing data to generate SM data at 500 m resolution in the Tibetan Plateau. In addition, the interpretability of the model was effectively improved by fitting mathematical equations between SM and simplified variables. The results showed that (1) the downscaled SM of KANs demonstrated superior accuracy compared to ANN, significantly reducing the RMSE and ubRMSE of the SMAP product while closely aligning with station observations in temporal change trends; (2) correlation and SHAP analysis results showed that NDVI was the most important environmental factor affecting SM changes, followed by DEM and ET; (3) the symbolic equations of SM and simplified variables for each month were obtained by fitting the KAN model, whose downscaling accuracy was less than that of the KAN model but better than that of the SMAP product. Overall, this study explored the relationship between SM and environmental factors, and the obtained high-resolution SM data have good accuracy, which can meet the needs of small- or medium-scale research and provide a reference for subsequent downscaling studies.