Optimal lag time analysis and relative importance decomposition for insights into ecosystem climate adaptation

Vegetation dynamics are strongly associated with climate variability, yet large-scale quantification of vegetation's temporal lag responses to climatic drivers remains poorly understood. We use multiple linear regression, relative importance contribution analysis and time-lag correlation analysis to systematically examine the sensitivity and optimal lag times (OLT) of vegetation activity (NDVI) to precipitation, temperature, and solar radiation across China during 2001–2024. The results demonstrate that vegetation responses are primarily governed by short lag effects, with temperature exhibiting the shortest mean lag (0.73 ± 0.80 months), followed by precipitation (0.94 ± 0.92 months) and solar radiation (1.87 ± 0.93 months). Spatial heterogeneity was observed in lag structures and vegetation sensitivity among climatic zones and vegetation functional groups. Temperature acts as the dominant driver and explains most of the NDVI variation, while precipitation and solar radiation exert secondary but region-specific impacts. Compared with synchronous correlation analysis, incorporating lag effects greatly improves the explanatory capacity of climate-vegetation relationships. The results emphasize the essential role of temporal memory in vegetation-climate interactions and provide improved understanding of the mechanisms regulating ecosystem responses to climate change. This study provides a robust analytical framework for enhancing vegetation dynamics models and supports more reliable predictions of ecosystem responses under future climate scenarios.