Abstract:

Agricultural drought is influenced by multiple factors such as climate and soil，which increases the complexity of drought propagation and makes it difficult to analyze drought characteristics，thereby affecting the warning accuracy. To alleviate this problem，a spatiotemporal propagation warning method for agricultural drought based on graph neural network and multimodal data fusion has been proposed. After obtaining multimodal agricultural drought data such as meteorological，soil，and remote sensing images，the weighted average operation is used to fuse the multimodal data at the decision-making level，thereby constructing an agricultural drought spatiotemporal map. Combining the graph neural network model，by calculating the importance of spatiotemporal graph nodes and eliminating the interference of irrelevant features，the spatiotemporal propagation characteristics of agricultural drought in the spatiotemporal graph can be interpreted. On the premise of fully considering the water demand for crop growth，the corresponding drought risk assessment value is quantitatively calculated，and agricultural drought grading warning is implemented based on the designed grading system. The experimental results show that this method has a high level of accuracy in warning results，with a PR-AUC （precision-recall area under the curve） value of 0.95，indicating excellent application prospects.

Key words: agricultural drought warning, drought spatiotemporal warning, spatiotemporal propagation characteristics, multimodal data fusion, graph neural network