对比地表温度与人体热应力，发现城市形态对人体会产生非线性影响。
From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphology
- 用卫星数据和高精度模型比较地表温与人体热感差异
- 揭示遮蔽率是影响人体热应激的关键因素，但地表温未体现这一点
- 适合城市规划者和气候适应研究者参考
热暴露连接建成环境与公共健康，直接影响城市的宜居性与可持续性。理解热暴露的空间异质性及其驱动因素对气候适应型城市规划至关重要。然而，多数规划研究依赖地表温度（LST），其是否充分反映人体热暴露，以及与生理相关的热应力有何差异，尚缺乏深入探讨。本研究基于新加坡的Landsat获取的30米分辨率LST与GPU加速的1米分辨率通用热气候指数（UTCI），构建“建模-比较-评估”框架，系统分析两种指标的空间与机制差异。采用新颖的地理加权XGBoost（GW-XGBoost）与广义可加模型（GAM）方法，揭示二者与城市要素间显著的非平稳与阈值关系。结果表明，LST与UTCI在空间分布上存在明显差异，且二维与三维城市因子的影响具有显著空间异质性，解释性GW-XGBoost模型测试R²分别为0.855（LST）与0.905（UTCI）。关键发现：天空视野因子对UTCI变异有核心解释作用，但对LST独立贡献较小，说明LST无法捕捉遮蔽与辐射过程对实际人体热应力的影响；同时SHAP-GAM分析显示更高反照率与更高UTCI相关。研究为融合生理相关热指数提供决策支持，助力精准热风险管理和以人为本的城市规划。
原文摘要 · Abstract (English)
Heat exposure connects the built environment and public health, directly shaping the livability and sustainability of urban areas. Understanding the spatial heterogeneity of heat exposure and its drivers is vital for climate-adaptive urban planning. However, most planning-oriented studies rely on land surface temperature (LST), and whether LST adequately represents human heat exposure and how it differs from physiologically relevant heat stress remains insufficiently examined. Here, using Landsat-retrieved 30-m LST and GPU-accelerated 1-m universal thermal climate index (UTCI) in Singapore, this study establishes a comprehensive "Modeling-Comparing-Assessing" framework to systematically evaluate the spatial and mechanistic differences between these two metrics. We further investigate their pronounced non-stationary and threshold-based relationships with urban factors using a novel geographically weighted XGBoost (GW-XGBoost) and generalized additive model (GAM) workflow. Our results reveal substantial differences in the spatial patterns of LST and UTCI, along with marked spatial heterogeneity in how 2D and 3D urban factors impact these thermal metrics, as demonstrated by explainable GW-XGBoost models (test R2 = 0.855 for LST and 0.905 for UTCI). Crucially, spatially explicit SHAP shows that sky view factor plays a central role in explaining UTCI variability but exhibits a comparatively marginal independent contribution to LST, indicating that LST inadequately captures shading-driven and radiative processes governing actual human heat stress. Moreover, SHAP-GAM analysis indicates that higher albedo is associated with increased UTCI. These findings provide model-informed planning implications for integrating physiologically relevant thermal indices to support targeted heat risk management and human-centric urban planning.
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