Soil Moisture Prediction Using the VIC Model Coupled with LSTMseq2seq - Xiuping Zhang; Xiufeng He; Rencai Lin; Xiaohua Xu; Yanping Shi; Zhenning Hu (Remote Sensing, 2025)
Soil moisture (SM) is a key variable in agricultural ecosystems and is crucial for drought prevention and control management. However, SM is influenced by underlying surface and meteorological conditions, and it changes rapidly in time and space. To capture the changes in SM and improve the accuracy of short-term and medium-to-long-term predictions on a daily scale, an LSTMseq2seq model driven by both observational data and mechanism models was constructed. This framework combines historical meteorological elements and SM, as well as the SM change characteristics output by the VIC model, to predict SM over a 90-day period. The model was validated using SMAP SM. The proposed model can accurately predict the spatiotemporal variations in SM in Jiangxi Province. Compared with classical machine learning (ML) models, traditional LSTM models, and advanced transformer models, the LSTMseq2seq model achieved R2 values of 0.949, 0.9322, 0.8839, 0.8042, and 0.7451 for the prediction of surface SM over 3 days, 7 days, 30 days, 60 days, and 90 days, respectively. The mean absolute error (MAE) ranged from 0.0118 m3/m3 to 0.0285 m3/m3. This study also analyzed the contributions of meteorological features and simulated future SM state changes to SM prediction from two perspectives: time importance and feature importance. The results indicated that meteorological and SM changes within a certain time range prior to the prediction have an impact on SM prediction. The dual-driven LSTMseq2seq model has unique advantages in predicting SM and is conducive to the integration of physical mechanism models with data-driven models for handling input features of different lengths, providing support for daily-scale SM time series prediction and drought dynamics prediction.