Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM

We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables.

本研究开发了注意力增强长短期记忆（attention-enhanced long and short memory, AELSTM）模型，用于基于气候数据预测植被叶面积指数（Leaf Area Index, LAI）时间序列。该AELSTM模型能够构建植被叶面积指数时间序列与气候变量之间的关联关系。