摘要： 【目的/意义】 农业干旱对中国农业生产发展具有消极影响，甚至威胁到粮食安全。为了降低灾害损失，保障中国的作物产量，根据标准化土壤湿度指数（Standardized Soil Moisture Index, SSMI）对农业干旱进行准确预测和等级分类具有重要意义。 【方法】 基于遥感数据，采用深度学习相关模型实现了农业干旱预测。首先，考虑了农业干旱的空间特点，提出了一种结合图神经网络、双向门控循环单元（Bi-Directional Gated Recurrent Unit, BiGRU）和多头自注意力机制的农业干旱预测模型GCN-BiGRU-STMHSA（Graph Convolutional Networks-Bidirectional Gated Recurrent Unit-Spatio-Temporal Multi-Head Self-Attention）。其次，使用日尺度的SSMI作为农业干旱指标。最后，根据搭建的GCN-BiGRU-STMHSA模型实现对SSMI的精准预测和分类。采用全球陆地数据同化系统2.1（Global Land Data Assimilation System-2.1, GLDAS-2.1）为数据集，在该数据集上训练GCN-BiGRU-STMHSA模型，以预测SSMI值并进行农业干旱等级分类。并与经典深度学习模型进行了比较。 【结果和讨论】 实验结果表明，GCN-BiGRU-STMHSA模型结果优于其他模型。在5个研究地点中，固始县数据集上误差最小，预测10天后的SSMI时，其平均绝对误差（Mean Absolute Error, MAE）为0.053、均方根误差（Root Mean Square Error, RMSE）为0.071、决定系数（Coefficient of Determination, R2）为0.880，准确率（Accuracy, ACC）为0.925，调和平均值（ F1）为0.924。预测步长越短，预测的效果越好，当预测步长为28天时，模型预测干旱分类表现依然良好。 【结论】 该模型在农业干旱预测和分类任务中具有更高的精度和更好的泛化能力。
【目的/意义】 农业干旱对中国农业生产发展具有消极影响，甚至威胁到粮食安全。为了降低灾害损失，保障中国的作物产量，根据标准化土壤湿度指数（Standardized Soil Moisture Index, SSMI）对农业干旱进行准确预测和等级分类具有重要意义。 【方法】 基于遥感数据，采用深度学习相关模型实现了农业干旱预测。首先，考虑了农业干旱的空间特点，提出了一种结合图神经网络、双向门控循环单元（Bi-Directional Gated Recurrent Unit, BiGRU）和多头自注意力机制的农业干旱预测模型GCN-BiGRU-STMHSA（Graph Convolutional Networks-Bidirectional Gated Recurrent Unit-Spatio-Temporal Multi-Head Self-Attention）。其次，使用日尺度的SSMI作为农业干旱指标。最后，根据搭建的GCN-BiGRU-STMHSA模型实现对SSMI的精准预测和分类。采用全球陆地数据同化系统2.1（Global Land Data Assimilation System-2.1, GLDAS-2.1）为数据集，在该数据集上训练GCN-BiGRU-STMHSA模型，以预测SSMI值并进行农业干旱等级分类。并与经典深度学习模型进行了比较。 【结果和讨论】 实验结果表明，GCN-BiGRU-STMHSA模型结果优于其他模型。在5个研究地点中，固始县数据集上误差最小，预测10天后的SSMI时，其平均绝对误差（Mean Absolute Error, MAE）为0.053、均方根误差（Root Mean Square Error, RMSE）为0.071、决定系数（Coefficient of Determination, R2）为0.880，准确率（Accuracy, ACC）为0.925，调和平均值（ F1）为0.924。预测步长越短，预测的效果越好，当预测步长为28天时，模型预测干旱分类表现依然良好。 【结论】 该模型在农业干旱预测和分类任务中具有更高的精度和更好的泛化能力。
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.
[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.
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权家璐, 陈雯柏, 王一群, 程佳璟, 刘亦隆. 基于GCN-BiGRU-STMHSA的农业干旱预测研究[J]. 智慧农业(中英文), 2025, 7(1): 156-164.
QUAN Jialu, CHEN Wenbai, WANG Yiqun, CHENG Jiajing, LIU Yilong. Research on Agricultural Drought Prediction Based on GCN-BiGRU-STMHSA[J]. Smart Agriculture, 2025, 7(1): 156-164.