A Meteorology-Driven Transformer Network to Predict Soil Moisture for Agriculture Drought Forecasting
Zhenhua Xiong, Zhicheng Zhang, Hanliang Gui, Xiaoyou Chen, Shi Hu, Lun Gao, Yang He, Jianxiu Qiu, Qinchuan Xin
Abstract
Since agricultural drought plays a leading role in restricting agricultural productivity, accurate forecasting is crucial for agricultural management. Although soil moisture (SM) is the primary variable for identifying and forecasting agricultural drought, accurately predicting SM is challenged by its strong interaction with external meteorological forcings that change rapidly across space and time. To provide a predictive method that reduces uncertainty in SM modeling and compensates for the latency in satellite-based SM products, we propose a meteorologically driven Transformer framework (MDTF). The framework can predict global surface (0–5 cm) and root-zone (0–100 cm) SM utilizing meteorological forecasts and soil physical properties with a prediction latency of 35 days. When validated against satellite-based SM data and in situ measurements, our proposed model more accurately predicts global SM spatial patterns and seasonal dynamics compared with the physics-based Common Land Model (CoLM), demonstrating consistent performance across different land covers. MDTF outperforms popular machine learning models, achieving unbiased root-mean-square error (ubRMSE) values of 0.0297 m3/m3 and 0.0211 m3/m3 for surface and root-zone SM. The predicted seamless global daily SM information could be effectively utilized for agricultural drought forecasting. Our research demonstrates that the MDTF model has unique advantages in modeling key hydrological variables of the Earth system, providing a reference for predicting time series of global SM and agricultural drought dynamics.