A Factorial-Clustered Copula Covariate Analysis for Interaction Effects of Multiple Climate Factors on Vegetation Cover in China

Vegetation is a vital component of ecosystems and an indicator of global environmental change. It is significantly influenced by climate factors. Previous studies have often overlooked the nonlinear relationships, spatiotemporal variability, and interaction effects of climate factors on vegetation, focusing instead on simplistic trends or regional classifications based on vegetation type, climate zone, or ecosystem. In this study, a factorial-clustered copula covariate analysis model was developed to investigate the effects of climate factors on vegetation cover (NDVI) in China from 2000 to 2023. The results showed that temperature had the strongest correlation with NDVI (0.66), followed by precipitation and solar radiation (both 0.46), and soil moisture (0.14). The NDVI exhibited significant spatial variability, with low values (<0.1) in 17.6% and high values (>0.8) in 12.7% of the areas. Regional variations were observed: precipitation-dominated NDVI changes in arid regions (Cluster 1, 43%), solar radiation in tropical areas (Clusters 4 and 5, >79%), and soil moisture in humid zones (Cluster 2, 29%). Interaction effects, such as Pre:Temp and Pre:Temp:SM, further influenced NDVI dynamics. Joint probability analysis revealed diverse dependency patterns across clusters, highlighting the complex interplay between climatic and non-climatic factors. These findings emphasize the need for tailored management strategies to address region-specific vegetation dynamics under changing climatic conditions.