Improving generalization of soil moisture prediction using self-supervised learning

Accurate soil moisture prediction is increasingly important due to its critical role in water resource management and agricultural sustainability under global climate change. While machine learning models have achieved high accuracy in soil moisture prediction, their ability to generalize to different environmental and meteorological conditions remains a significant challenge. Existing models often perform poorly when applied to conditions that differ from their training data, highlighting the need for approaches that improve generalization while effectively capturing underlying soil moisture dynamics.

In this study, we propose a novel soil moisture prediction model that combines self-supervised learning with a Transformer architecture. The performance of the model was compared with the widely used Long Short-Term Memory (LSTM)-based approach to evaluate its ability to generalize. The proposed model outperformed the baseline in tasks such as capturing extreme soil dryness, adapting to unobserved meteorological humidity conditions, and forecasting soil moisture dynamics at untrained depths. Further analysis revealed that the model’s success stems from its capability to learn comprehensive representations of underlying soil moisture processes. These results highlight the potential of advanced deep learning methods to improve prediction and our process understanding of soil hydrology in a changing climate.