Drivers of Soil Moisture Dynamics over Continental United States

Soil moisture dynamics play a crucial role in hydrological processes, influencing runoff generation, drought stress, and water management. To better understand the complex drivers of soil moisture dynamics, we present a novel hybrid architecture integrating Vision Transformers (ViT), spatial attention mechanisms, and Long Short-Term Memory (LSTM) networks. This architecture enables investigation of controlling factors across diverse landscapes in the Continental United States (CONUS) by incorporating spatial awareness at two levels: through ViT's ability to capture spatial patterns and through explicit spatial attention between neighboring stations. We leverage a comprehensive set of environmental data sources, including in-situ measurements from the International Soil Moisture Network (ISMN), ERA5 climate reanalysis, USGS elevation products, MODIS land cover, and SoilGrids soil characteristics. Initial results from a one-year training period and three-month testing period (R² = 0.73, 0.72, 0.73 for 24h, 48h, and 72h predictions) reveal important insights about the hierarchical importance of different drivers across prediction windows. Our preliminary analysis shows that static physical properties (particularly slope and soil structure) and hydraulic characteristics maintain high importance across temporal scales, while the influence of dynamic weather features varies with prediction horizon. The model's dual spatial attention mechanisms and temporal components enable discovery of both local and regional controls on soil moisture dynamics. The identified feature importance hierarchies provide initial insights into the spatiotemporal controls on soil moisture dynamics across CONUS. Ongoing work extends the training to the full temporal extent of available data to develop a more comprehensive understanding of these driving factors. This approach advances our fundamental understanding of soil moisture processes at continental scales, with implications for a future tool for land characterization and ecological site classification.