Subterra: Estimating Soil Moisture at Root Zone Depths Using Science-Guided Learning

Accurate prediction of soil moisture (SM) is crucial for applications in agriculture, hydrology, and climate modeling. Traditional data-driven machine learning (ML) approaches often require extensive labeled datasets and fail to incorporate the physical principles governing SM dynamics. In this study, we propose a novel science-guided learning framework to predict SM in the top 20 cm of soil using deep neural networks (DNNs). By integrating physical equations, such as Richards' equation, into the learning process, our approach ensures scientifically consistent predictions while improving model generalizability. Our empirical results show that the proposed graph-based model outperforms traditional ML approaches by more than 30% in accuracy, while predicting SM with an error of less than 5% compared to ground-truth in-situ measurements.