The Spatiotemporal Evolution and Driving Forces of the Urban Heat Island in Shijiazhuang

As a comprehensive reflection of the thermal characteristics of the urban environment, the urban heat island (UHI) effect has triggered a series of ecological and environmental issues. Existing studies on the UHI effect in Shijiazhuang, the capital of Hebei Province, China, have primarily focused on spatial–temporal distribution characteristics and migration trends, with less focus on the influences of other contributing factors. This study focuses on Shijiazhuang city, using Landsat ETM+/OLI data from 2000 to 2020 to analyze the spatiotemporal traits of the UHI effect. The mono-window algorithm (MW) was used to retrieve land surface temperatures (LSTs), and the seasonal autoregressive integrated moving average (SARIMA) model was used to predict LST trends. Key factors such as the normalized difference vegetation index (NDVI), digital elevation model (DEM), population (POP), precipitation (PPT), impervious surface (IPS), potential evapotranspiration (PET), particulate matter 2.5 (PM2.5), and night light (NL) were analyzed using spatial autocorrelation to explore their dynamic relationship with the UHI. Specifically, a multi-scale analysis model was developed to search for the optimum urban spatial scale, enabling a comprehensive assessment of the spatiotemporal evolution and drivers of the UHI in Shijiazhuang. The UHI showed pronounced spatial clustering, expanding annually by 44.288 km2, with a southeastward shift. Autumn exhibited the greatest reduction in UHI, while predictions suggested peak temperatures in summer 2027. According to the bivariate clustering analysis, the NDVI was the most influential factor in mitigating the UHI, while the IPS spatially showed the most significant enhancement in the UHI in the central urban areas. Other factors generally promoted the UHI after 2005. The multi-scale geographically weighted regression (MGWR) model was best fitted at a 3 km × 3 km scale. Considering the joint effects of multiple factors, the ranking of contributing factors to the model prediction is as follows: PET > DEM > NDVI > IPS > PPT > PM2.5 > NL > POP. The interactive effects, especially between the PET and DEM, reach a significant value of 0.72. These findings may address concerns regarding both future trends and mitigation indications for UHI variations in Shijiazhuang.