Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis

As global climate change intensifies, its impact on the ecological environment is becoming increasingly pronounced. Among these, land surface temperature (LST) and vegetation cover status, as key ecological indicators, have garnered widespread attention. This study analyzes the spatiotemporal dynamics of LST and the Kernel Normalized Difference Vegetation Index (KNDVI) in 11 provinces along the Yangtze River and their response to climate change based on MODIS Terra satellite data from 2000 to 2020. The linear regression showed a significant KNDVI increase of 0.003/year (p < 0.05) and a LST rise of 0.065 °C/year (p < 0.01). The Principal Component Analysis (PCA) explained 74.5% of the variance, highlighting the dominant influence of vegetation cover and urbanization. The K-means clustering identified three regional patterns, with Shanghai forming a distinct group due to low KNDVI variability. The Generalized Additive Model (GAM) analysis revealed a nonlinear LST–KNDVI relationship, most evident in Hunan, where cooling effects weakened beyond a KNDVI threshold of 0.25. Despite a 0.07 KNDVI increase, high-temperature areas in Chongqing and Jiangsu expanded by over 2500 km2, indicating limited LST mitigation. This study reveals the complex interaction between LST and the KNDVI, which may provide scientific basis for the development of regional ecological management and climate adaptation strategies.