A Soil Moisture Prediction Model Based on GCN-LSTM Network Incorporating Channel and Temporal Attention

Getting soil moisture right matters for fighting drought and stopping land from turning into desert. Aiming at the problems of insufficient spatiotemporal modeling and redundant attention mechanisms in global soil moisture prediction, we built a new deep learning model called CTA-GraphConvLSTM to better capture how soil moisture changes across both space and time, and provide technical support for drought early warning, precision agriculture and water resource management. It combines graph convolutional networks to map geographic relationships and uses a 3D-SENet attention mechanism to pull out key temporal patterns. Using the LandBench dataset, we compared the proposed model with LSTM, GraphLSTM, and ConvLSTM across multiple lead times and drought levels. Performance was evaluated using root mean square error (RMSE) and R${}^2$. The CTA-GraphConvLSTM achieved the highest predictive accuracy (R${}^2$ = 0.555 for 1-day lead), outperforming ConvLSTM (R${}^2$ = 0.444), LSTM (R${}^2$ = 0.430), and GraphLSTM (R${}^2$ = 0.088). This value reveals that the model can hardly explain the variance in the data and presents extremely poor prediction performance, performing just slightly better than a simple mean predictor. The comparison results fully verify that the proposed model has higher prediction accuracy. These results demonstrate the effectiveness of graph-scale spatiotemporal modeling for soil moisture prediction. Our research has direct practical applications: it can support precision agriculture by optimizing irrigation schedules, enhance water resource management through improved reservoir operation, and strengthen drought early warning systems, thereby contributing to sustainable land use and food security.