Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model

Abstract

The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models. The accurate spatiotemporal predictions of soil moisture provide insights into future conditions, supporting decision making in applications, such as crop yield optimization, irrigation planning, and drought management. However, existing models face limitations in capturing complex spatiotemporal dependencies and dynamic causal interactions. This article proposes a spatiotemporal prediction framework that integrates causal inference with deep learning, termed the causal-guided spatiotemporal Swin transformer (Causal ST-SwinT). The model introduces a dynamic causal weight adjustment mechanism to adaptively optimize the causal relationship intensity between variables and adopts a hierarchical multilevel feature extraction strategy to effectively capture complex spatiotemporal dependencies, thereby enhancing prediction accuracy and model interpretability. The proposed method is validated on the ERA5 and soil moisture active passive (SMAP) datasets over the Tibetan Plateau and compared with multiple models. Experimental results show that Causal ST-SwinT significantly outperforms the classical convolutional long short-term memory model, reducing mean absolute error from 0.0146 to 0.0055 m³/m³ on the ERA5 dataset and from 0.0088 to 0.0046 m³/m³ on the SMAP dataset. Robustness analysis reveals that Causal ST-SwinT maintains high prediction accuracy under various environmental conditions. Ablation experiments further confirm the critical role of the causal attention module in improving model performance. These findings demonstrate that integrating causal knowledge with deep learning models effectively enhances the modeling capabilities of complex spatiotemporal systems, providing a novel solution for broader spatiotemporal prediction tasks.