DeepSoil: Physics-Guided Learning for High-Resolution Soil Moisture Mapping from Surface to Root-Zone via Multimodal Sensing and Process-Based Modeling

Soil moisture plays a critical role in several domains and can be used to inform decision-making in agricultural settings, drought forecasting, forest fire predictions, and water conservation. Soil moisture is measured using in-situ and remote-sensing equipments. Depending on the type of equipment that is used, some challenges must be reconciled, including the density of observations, the measurement precision, and the resolutions at which these measurements are available. In general, in-situ measurements are high-precision but sparse, while remote sensing measurements benefit from spatial coverage, albeit at lower precision and coarser resolutions. The crux of this study is to produce higher-precision soil moisture estimates at the top soil and root zone and generate high resolution maps (30m). Our methodology combines scientific models, deep neural networks, topographical characteristics, and information about ambient conditions alongside both in-situ and remote sensing data to accomplish this. Domain science infuses several aspects of our methodology. In particular, we (1) construct science-infused training targets from physics-based soil moisture products, (2) regulate learning using multipart loss functions that enforce consistency with soil hydraulic behavior, and (3) scale training through region-aware clustering to balance tractability and spatial specialization. Our empirical benchmarks profile several aspects and demonstrate that our methodology accounts for spatial variability while accounting for both static (soil properties and elevation) and dynamically varying phenomena to generate accurate, high-precision 30m resolution soil moisture (SMC) maps. Finally, we extend DeepSoil to root-zone estimation by training a dedicated 20 cm model using supervision from mechanistic soil-water simulations and a physics-guided composite loss that enforces hydraulic bounds and matric-potential consistency, enabling high-resolution mapping beyond the surface layer.