Physics‐Informed Spatiotemporal Graph Neural Network for Soil Temperature Forecasting
TL;DR
Results indicate that the model outperforms conventional data‐driven approaches while maintaining strong physics‐inspired interpretability, and provides a robust and generalisable tool for climate system modelling, regional environmental monitoring and data‐driven agricultural management.
Abstract
Soil temperature is a key component of the climate system, influencing surface–atmosphere heat exchange, ecosystem processes and agricultural microclimates. Its temporal evolution is governed by complex interactions among environmental factors, surface disturbances and vertical heat conduction, making accurate prediction challenging. To address these challenges, this study introduces a Physics‐Inspired Time‐Gradient Adaptive Graph Convolutional Network (PTAGCN) coupled with a Physics‐Inspired attention mechanism. The framework captures local temporal dynamics within soil layers while modelling thermally coupled interactions across layers and enforces gradient and scale consistency to ensure physically plausible predictions. Experiments conducted across distinct climate regions in China—Fengyun Village, Chongqing and Naiman, Inner Mongolia—demonstrate the effectiveness of the proposed approach, achieving an RMSE of 1.5633°C, an MAE of 1.2670°C and a Nash–Sutcliffe efficiency (NSE) of 0.9759 in forecasting 20 cm soil temperature. These results indicate that the model outperforms conventional data‐driven approaches while maintaining strong physics‐inspired interpretability. The framework provides a robust and generalisable tool for climate system modelling, regional environmental monitoring and data‐driven agricultural management.