Urban environments have a strong influence on the land surface temperature (LST) in urban areas. Understanding the relationship between LST and urban environmental factors can help develop effective strategies to reduce high LSTs in urban areas, which is critical for mitigating the urban heat island effect. Previous studies have focused on the correlation between LST and the environmental factors that drive its formation, without considering the influences of the neighboring environment and the vertical expansion of highly urbanized areas. Notably, the correlation between LST and its neighboring environment in different seasons remains unclear. In this study, we selected central Beijing in China as our study area and employed the moving window method to characterize the environmental factors of the neighboring environment of the central LST cell. We explored eight environmental factors from three layers: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI), building density (BD), building height (BH), building volume (BV), sky view factor (SVF), and road density (RD). The Pearson correlation and extreme gradient boosting (XGB) regression methods were applied to measure the correlation between LST and the different factors in moving windows of different sizes. The results indicated that the correlation between NDVI, MNDWI, and LST was considerably different in the winter and other seasons. However, NDBI was positively correlated with LST in all seasons, although the correlation was strongest/weakest in summer/winter. Among building-related factors, BD and BH were more strongly correlated with LST, and the positive/negative correlation between BD/BH and LST was stronger in summer/winter. The correlation between LST and its neighboring environment varied with increasing window size, and this variation differs significantly between winter and other seasons. In spring, summer, and autumn, the strength of the correlation between LST and its neighboring environment showed an “inverted V” pattern with increasing window size. The optimal spatial scales to explore the influence of neighboring environments on the LST of 30-m cells were 210 m and 270 m. This study revealed the seasonal correlation between LST and its neighboring environment while explaining the variation at a spatial scale. Notably, this study can provide a new perspective for understanding the driving mechanism of the urban thermal environment, while contributing to its scientific optimization and management.

Local landscape patterns in cities can substantially alter surrounding land surface temperature (LST) and affect the surface urban heat island effect. Previous studies have confirmed that some urban functional areas produce pronounced local heat/cold island effects in cities; however, the mitigating of this effect from the perspective of surrounding landscapes has not been investigated in depth. Additionally, different types of industrial plants have not been individually studied across multiple cities as major contributors to the local heat island effect. Therefore, based on 17 mega plants in various cities in the Northern Hemisphere, this study explores the impact of surrounding landscapes on the surface industrial heat island (SIHI) effect, proposes cooling metrics from a new landscape patch perspective, and quantifies the impact of different patch configurations on the SIHI using Extreme Gradient Boosting regression models and Shapley Additive exPlanations. The primary results are as follows: 1) Regarding to the footprint of the SIHI effect, the coverage of impervious surfaces dominates the ambient LST pattern. Industrial plant types and latitudes are moderately influencing factors. 2) In terms of the cooling effect, landscape patch size and width relative to industrial plant size has a pronounced impact on the LST cooling speed. Besides, various land cover types have distinct relative cooling thresholds for patch area and distance from the plants. The influence of patch attributes on LST cooling speed is determined by the distinct land cover type. 3) What is prominent is that the patches of water bodies with larger relative footprints often exhibit higher cooling speeds. This study aims to provide guidelines for urban planners in assessing the local thermal environment and mitigating further urban warming.