Beyond Land Surface Temperature: Explainable Spatial Machine Learning Reveals Urban Morphology Effects on Human-Centric Heat Stress

arXiv cs.LG / 4/27/2026

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Key Points

The study argues that land surface temperature (LST) is often an incomplete proxy for human-relevant heat stress and develops a framework to compare LST with the more physiologically grounded UTCI in Singapore.
Using high-resolution Landsat-derived 30 m LST and GPU-accelerated 1 m UTCI, the authors quantify spatial and mechanistic differences between the two metrics with a “Modeling-Comparing-Assessing” workflow.
They apply a geographically weighted XGBoost (GW-XGBoost) and generalized additive model (GAM) to uncover non-stationary, threshold-based relationships between thermal metrics and urban form factors.
Explainability results show sky view factor strongly drives UTCI variability but contributes much less to LST, suggesting LST underrepresents shading/radiative processes that shape actual human heat stress.
The research also finds albedo relates to higher UTCI in their SHAP-GAM analysis, supporting the use of physiologically relevant thermal indices for targeted heat-risk management and climate-adaptive planning.

Abstract

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, adopting 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 discrepancies between the two metrics. We further investigate pronounced non-stationary and threshold-based quantitative relationships of the two metrics with urban factors by employing a novel geographically weighted XGBoost (GW-XGBoost) and generalized additive model (GAM) workflow. Our results demonstrate notable discrepancies in spatial patterns of LST and UTCI, along with substantial spatial heterogeneity in how 2D and 3D urban factors impact these two thermal metrics, as revealed by explainable GW-XGBoost models (global out-of-bag R2 = 0.855 for LST and 0.905 for UTCI, respectively). Crucially, spatially explicit SHAP interprets 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. Notably, SHAP-GAM analysis indicates that higher albedo is associated with increased UTCI. These novel findings provide evidence for integrating physiologically relevant thermal indices to inform targeted heat risk management and climate-adaptive urban planning.