Abstract:

[Objective] Agricultural drought has a negative impact on the development of agricultural production and even poses a threat to food security. To reduce disaster losses and ensure stable crop yields, accurately predicting and classifying agricultural drought severity based on the standardized soil moisture index (SSMI) is of significant importance. [Methods] An agricultural drought prediction model, GCN-BiGRU-STMHSA was proposed, which integrated a graph convolutional network (GCN), a bidirectional gated recurrent unit (BiGRU), and a multi-head self-attention (MHSA) mechanism, based on remote sensing data. In terms of model design, the proposed method first employed GCN to fully capture the spatial correlations among different meteorological stations. By utilizing GCN, a spatial graph structure based on meteorological stations was constructed, enabling the extraction and modeling of spatial dependencies between stations. Additionally, a spatial multi-head self-attention mechanism (S-MHSA) was introduced to further enhance the model's ability to capture spatial features. For temporal modeling, BiGRU was utilized as the time-series feature extraction module. BiGRU considers both forward and backward dependencies in time-series data, enabling a more comprehensive understanding of the temporal dynamics of agricultural drought. Meanwhile, a temporal multi-head self-attention mechanism (T-MHSA) was incorporated to enhance the model's capability to learn long-term temporal dependencies and improve prediction stability across different time scales. Finally, the model employed a fully connected layer to perform regression prediction of the SSMI. Based on the classification criteria for agricultural drought severity levels, the predicted SSMI values were mapped to the corresponding drought severity categories, achieving precise agricultural drought classification. To validate the effectiveness of the proposed model, the global land data assimilation system (GLDAS_2.1) dataset and conducted modeling and experiments was utilized on five representative meteorological stations in the North China Plain (Xinyang, Gushi, Fuyang, Huoqiu, and Dingyuan). Additionally, the proposed model was compared with multiple deep learning models, including GRU, LSTM, and Transformer, to comprehensively evaluate its performance in agricultural drought prediction tasks. The experimental design covered different forecasting horizons to analyze the model's generalization capability in both short-term and long-term predictions, thereby providing a more reliable early warning system for agricultural drought. [Results and Discussions] Experimental results demonstrated that the proposed GCN-BiGRU-STMHSA model outperforms baseline models in both SSMI prediction and agricultural drought classification tasks. Specifically, across the five study stations, the model achieved significantly lower mean absolute error (MAE) and root mean squared error (RMSE), while attaining higher coefficient of determination ( R²), classification accuracy (ACC), and F₁-Score ( F₁). Notably, at the Gushi station, the model exhibited the best performance in predicting SSMI 10 days ahead, achieving an MAE of 0.053, a RMSE of 0.071, a R² of 0.880, an ACC of 0.925, and a F₁ of 0.924. Additionally, the model's generalization capability was investigated under different forecasting horizons (7, 14, 21, and 28 days). Results indicated that the model achieved the highest accuracy in short-term predictions (7 days). Although errors increase slightly as the prediction horizon extends, the model maintained high classification accuracy even for long-term predictions (up to 28 days). This highlighted the model's robustness and effectiveness in agricultural drought prediction over varying time scales. [Conclusions] The proposed model achieves superior accuracy and generalization capability in agricultural drought prediction and classification. By effectively integrating spatial graph modeling, temporal sequence feature extraction, and self-attention mechanisms, the model outperforms conventional deep learning approaches in both short-term and long-term forecasting tasks. Its strong performance provides accurate drought early warnings, assisting agricultural management authorities in formulating efficient water resource management strategies and optimizing irrigation plans. This contributes to safeguarding agricultural production and mitigating the potential adverse effects of agricultural drought.

Key words: agricultural drought prediction, BiGRU, multi-head self-attention mechanism, graph convolutional network, standardized soil moisture index