Contrasting 2D/3D urban morphology drivers of surface and canopy temperatures under different spatial scales

With the rapid progression of global urbanization, the two-dimensional (2D) and three-dimensional (3D) morphology of cities increasingly influence urban environments and their surrounding areas. Urban morphology and its ecological effects are gaining growing attention. However, existing research seldom investigates the effects of 2D and 3D urban morphology on urban canopy and surface temperatures across multiple spatial scales. Focusing on Guangdong Province, China, this study employs interpretable machine learning techniques to uncover the nonlinear relationships between urban morphology and urban temperatures at various spatial scales. The results reveal that urban morphology exhibits significant scale-dependent effects on urban temperatures. At finer spatial scales, 3D building morphology emerges as the dominant factor influencing temperature. As the scale increases, the influence of 2D building morphology becomes more pronounced, except for daytime canopy temperatures. Key parameters such as building roughness, compactness, and impervious surfaces are identified as critical contributors to urban temperature, significantly driving urban warming. Specially, 3D urban morphology primarily affects canopy temperatures, with its influence decreasing as the scale increases at night. 2D urban morphology primarily affects surface temperatures, with its influence increasing with scale during both day and night. These findings advance our understanding of the interplay between urban morphology and the urban thermal environment, offering valuable insights for mitigating urban heat and promoting sustainable urban development.