Amidst the rapid global urbanization and economic integration, coastal cities have undergone significant changes in urban spatial patterns. These changes have further worsened the complex urban thermal environment, making it crucial to study the interaction between human-driven development and natural climate systems. To address the insufficient quantification of marine elements in the urban planning of subtropical coastal zones, this study takes Xiamen, a typical deep-water port city, as an example to construct a spatial analysis framework integrating marine boundary layer parameters. This research employs interpolation simulation, atmospheric correction, and other techniques to simulate the inversion of land use and Landsat 8 data, deriving urban morphological elements and Land Surface Temperature (LST) data. These data were then assigned to 500 m grids for analysis. A bivariate spatial auto-correlation model was applied to examine the relationship between urban carbon emission and LST. The study area was categorized based on the influence of marine factors, and the spatial relationships between urban morphological elements and LST were analyzed using a multiscale geographically weighted regression model. Three Xiamen-specific discoveries emerged: (1) the marine exerts a significant thermal mitigation effect on the city, with an average influence range of 7.94 km; (2) the relationship between urban morphology and the thermal environment exhibits notable spatial heterogeneity across different regions; and (3) to mitigate urban thermal environments, connected green corridors should be established in the southern coastal areas of outer districts in regions significantly influenced by the ocean. In areas with less marine influence, spatial complexity should be introduced by disrupting relatively intact blue–green spaces, while regions unaffected by the ocean should focus on increasing green spaces and reducing impervious surfaces and water bodies. These findings directly inform Xiamen’s 2035 Master Plan for combating heat island effects in coastal special economic zones, providing transferable metrics for similar maritime cities.

Green Infrastructure (GI) has garnered increasing attention from various regions due to its potential to mitigate urban heat island (UHI), which has been exacerbated by global climate change. This study focuses on the central area of Fuzhou city, one of the “furnace” cities, and aims to explore the correlation between the GI pattern and land surface temperature (LST) in the spring and autumn seasons. The research adopts a multiscale approach, starting from the urban scale and using urban geographic spatial characteristics, multispectral remote sensing data, and morphological spatial pattern analysis (MSPA). Significant MSPA elements were tested and combined with LST to conduct a geographic weighted regression (GWR) experiment. The findings reveal that the UHI in the central area of Fuzhou city has a spatial characteristic of “high temperature in the middle and low temperature around,” which is coupled with a “central scattered and peripheral concentrated” distribution of GI. This suggests that remote sensing data can effectively be utilised for UHI inversion. Additionally, the study finds that the complexity of GI, whether from the perspective of the overall GI pattern or the classification study based on the proportion of the core area, has an impact on the alleviation of UHI in both seasons. In conclusion, this study underscores the importance of a reasonable layout of urban green infrastructure for mitigating UHI.

Urban landscape patterns significantly impact land surface temperature (LST) and the urban heat island (UHI) effect. This study employs the boosted regression tree (BRT) model and variance partitioning analysis to examine the contributions and relationships of two-dimensional and three-dimensional building and vegetation patterns to LST, and their marginal effects at different heights. The results show that the dominant indicators affecting LST differ between buildings and vegetation, with three-dimensional building features being slightly more important than two-dimensional features (percentage of landscape of buildings) and two-dimensional vegetation features (three-dimensional green index) having a greater impact than three-dimensional features. When both buildings and vegetation are considered, building patterns still have significant explanatory power. Building height differences influence each indicator’s contribution and marginal effects on LST, with lower-height areas seeing a joint dominance of buildings and vegetation on LST changes, and higher-height areas showing greater impact from vegetation indicators. Increasing the percentage of landscape of vegetation (PLAND_V) provides the best cooling effect in lower-building-height areas, but in higher-building-height areas, the cooling effect weakens, requiring additional vegetation indicators to assist in cooling.