Weak Physics‐Guided Multi‐Agent Learning for Surface to Subsurface Moisture Estimation Across Diverse Climate and Soil Conditions

Abstract Estimating subsurface soil moisture remains challenging due to limited in situ observations and the complexity of soil water dynamics. Although surface soil moisture can be retrieved from satellites with high accuracy, deeper layers are not directly observable. Traditional physics‐based models that predict subsurface soil moisture require site‐specific hydraulic properties of the soils. This limits their large‐scale applicability. Alternative data‐driven machine learning models for subsurface soil moisture estimation generally lack physical interpretability. To address the limitation of physical and machine learning models, we propose a weakly physics‐constrained, multi‐agent diffusion model for subsurface soil moisture estimation. The model employs lightweight physical regularization (flux smoothness and feasible‐range constraints) that guide predictions without enforcing strict parameterization, while a multi‐agent structure allows specialization across dry, intermediate, and wet soil regimes. This framework balances predictive flexibility with hydrological consistency and provides uncertainty quantification through stochastic diffusion sampling. The mo