Long-Term Dynamics of Normalized Difference Vegetation Index and Land Surface Temperature in a Rapidly Urbanizing City: Insights from Google Earth Engine

Understanding the long-term dynamics and spatial associations between vegetation and land surface temperature (LST) is important for characterizing urban thermal environments and informing climate-resilient urban planning. This study examined the spatiotemporal dynamics of the Normalized Difference Vegetation Index (NDVI) and LST in Zhengzhou, China, from 1990 to 2022 using Landsat imagery processed on the Google Earth Engine (GEE) platform. Annual NDVI and LST composites were generated from cloud- and cloud-shadow-masked Landsat observations. City-wide annual trends were examined using linear regression, while pixel-level trends were assessed using the Theil–Sen estimator and the Mann–Kendall (MK) test with trend-free pre-whitening (TFPW). Spatial patterns in five representative years (1990, 2000, 2010, 2020, and 2022) were further examined using district-level statistics and bivariate Moran’s I analysis to characterize spatial heterogeneity and local spatial associations. The results showed an overall increase in city-wide NDVI (0.00452/year) and city-wide LST (0.0667 °C/year). At the pixel level, significant increasing trends (p < 0.01) were identified for both NDVI and LST, with mean Sen’s slopes of 0.002434/year and 0.2957 °C/year, respectively. NDVI increased mainly in the southwestern mountainous areas and along the Yellow River, whereas relatively low NDVI and high LST persisted in densely urbanized districts. Bivariate Moran’s I analysis further revealed a significant negative spatial association between NDVI and neighboring LST values, indicating that areas with greater vegetation tended to be spatially associated with lower LST in neighboring areas. This spatial association should be distinguished from the temporal trends of the two variables, which both increased over the study period. However, vegetation improvement was insufficient to fully offset the warming associated with rapid urbanization. These findings highlight the spatial heterogeneity of long-term vegetation and thermal changes and demonstrate the value of annual multi-observation satellite composites and GEE for urban environmental monitoring. They also provide spatially explicit evidence to support targeted green infrastructure planning and urban heat mitigation.