A Hybrid Physics‐Guided Deep Learning Modeling Framework for Predicting Surface Soil Moisture

Abstract Accurate prediction of surface soil moisture (SSM) is vital for understanding the complex interactions between terrestrial and atmospheric processes with significant implications for weather forecasting, agriculture, and water management. In this study, we introduce an innovative physics‐guided deep learning (PGDL) model by integrating the process‐based insights of the terrestrial ecosystem model (TEM) with the dynamic predictive capabilities of long short‐term memory (LSTM) networks to improve SSM prediction. The PGDL model leverages the complementary strengths of the deterministic framework of TEM and the data‐driven prowess of LSTM, providing predictions that are deeply rooted in physical processes while capturing complex patterns in data. We evaluated the PGDL model against traditional process‐based (PB) models and pure deep learning (DL) approaches in both single‐site and multisite simulations. For single‐site simulations, we performed time‐based partitioning on 7 sites with different vegetation types, whereas for multisite simulation, data from 13 sites within the same vegetation type were used for model training and evaluation. In single‐site simulations, PGDL achieved significantly lower RMSE (0.04) and higher R 2 (0.66) compared to PB (RMSE: 0.13, R 2 : −2.32) and DL (RMSE: 0.06, R 2 : 0.36). In multisite simulations, PGDL (RMSE: 0.06, R 2 : 0.63) also outperformed PB (RMSE: 0.09, R 2 : 0.28) and DL (RMSE: 0.08, R 2 : 0.38). Our results show that the PGDL modeling framework improves the predictive accuracy of DL models and the physical interpretability of PB models, which can serve as a robust tool to predict SSM dynamics.