A Transferable Modeling Framework for Improving the Cooling Effect of Urban Green Space: Multi-Temporal Sampling, 3D Morphological Reconstruction and Bayesian Network

Accurate assessment of the cooling effect from urban green space (UGS) is largely hindered by insufficient field samples or consideration of the internal and surrounding three-dimensional (3D) structure. This study developed a transferable modeling-optimization framework that integrated a multi-temporal sampling strategy, multimodal 3D environmental reconstruction, and Bayesian-based optimization. First, the potential influencing factors of the cooling effect were quantified from three aspects of inner 2D/3D structure, surrounding building ventilation, and background meteorology through fusing field measurements, multi-spectral UAV images, and Sentinel-2 images. Then, a generalized additive mixed-effects model was used to explore cooling-related patterns of UGS, and a Bayesian network was further applied to identify potential optimized configurations. The results suggest the following: (1) The adopted multi-temporal sampling strategy enhances the stability of detected cooling signals and minimizes spatial interference among neighboring UGS patches and water bodies. (2) Temporal changes in the cooling effect are mainly driven by average air temperature and maximum wind speed, while the spatial variation by the UGS inner characteristics of area and shape index and surrounding ventilation. (3) The “win–win” situation of cooling intensity and range occurred in UGSs with larger areas, higher shape regularity, and medium ventilation. This approach is useful for model-based planning of climate-responsive green infrastructure and city-scale ventilation systems in heat-vulnerable environments.