Research on dynamic prediction of vegetation coverage by precipitation-evapotranspiration in arid regions based on CNN-LSTM hybrid model

Aiming to address the challenges of phenological lag and the dominant role of water factors (precipitation and evapotranspiration) in dynamically predicting vegetation coverage in arid regions, this study proposes a hybrid CNN-LSTM-based prediction method. By employing the Standardized Precipitation Evapotranspiration Index (SPEI) to classify the study area into different arid region types, it provides diverse samples of vegetation coverage changes under various drought conditions for training the CNN-LSTM model. The model integrates precipitation-evapotranspiration data from multiple arid region types along with target prediction data. The CNN extracts spatial features of precipitation and evapotranspiration, while the LSTM captures long-term temporal dependencies, enabling dynamic vegetation coverage prediction. Furthermore, the study quantifies both the single-year and comprehensive dynamic change indices of vegetation coverage to characterize its temporal variations. Experimental results demonstrate that the model effectively identifies differences in rainfall characteristics across areas with different drought severity levels. In arid regions, the contribution rate of heavy rainfall to the total rainfall anomaly (25.15%) is substantially higher than that in semi-arid regions (1.55%), with statistical tests confirming a significant difference (P < 0.05). On the test set, the Pearson correlation coefficient between the model's predictions and the measured data reaches 0.95, indicating strong agreement with reality. The dynamic change indices accurately capture phased variations in vegetation coverage from 2000 to 2022.