Microtopography plays a crucial role in regulating soil moisture in arid and semi-arid regions, thereby significantly influencing vegetation growth and distribution. The Loess Plateau, characterized by a deeply incised and fragmented landscape, necessitates an in-depth understanding of the microtopograph–soil moisture–vegetation relationship to guide effective vegetation restoration. This study, based on field investigations and laboratory analyses in the hilly-gully region of the Loess Plateau, employed one-way ANOVA, Duncan’s multiple range test, and structural equation modeling to examine the effects of microtopography on vegetation community characteristics. The results revealed that microtopography significantly affects vegetation diversity and stability. Vegetation diversity and stability were higher on shady slopes than on sunny slopes, with diversity indices increasing by approximately 38% in certain regions. Additionally, downslope positions exhibited greater vegetation diversity than upslopes, with richness indices increasing by approximately 33% and the M. Godron index decreasing by 8.49, indicating enhanced stability. However, the effects of gullies varied significantly across different regions. Soil moisture content was higher on shaded slopes than on sunny slopes and greater at downslope positions than at upslopes, reaching up to 12.89% in gullies. Slope position exerted a direct and significant positive effect on soil moisture, which, in turn, indirectly influenced vegetation diversity and stability. This study reveals the dominant regulatory role of slope position in soil moisture, vegetation diversity, and stability, providing new perspectives and evidence for developing vegetation restoration strategies on the Loess Plateau and promoting the sustainable growth of regional vegetation.

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.