From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphology
6 affiliations
- 1Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower, Singapore 138602, Singapore
- 2Department of Architecture, National University of Singapore, Singapore 117566, Singapore
- 3Department of City and Regional Planning, University of Pennsylvania, Philadelphia, PA 19104, USA
- 4Urban Analytics Subject Group, Urban Studies & Social Policy Division, University of Glasgow, United Kingdom
- 5Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- 6Design Group, Department of Architecture, University of Cambridge, Cambridge, United Kingdom
Abstract
Heat exposure connects the built environment and public health, directly shaping the livability and sustainability of urban areas. Fully understanding the spatial heterogeneity of heat exposure and its driving factors is therefore a vital prerequisite 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 (the test R 2 = 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 model-informed planning implications for integrating physiologically relevant thermal indices to support targeted heat risk management and climate-adaptive urban planning.
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BibTeX
@article{wang2026from,
title = {{From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphology}},
author = {Yuan Wang and Shengao Yi and Xiaojiang Li and Pengyuan Liu and Zhiwei Yang and Ronita Bardhan and Rudi Maria Frans Anne Stouffs},
journal = {Sustainable Cities and Society},
year = {2026},
month = jul,
doi = {10.1016/j.scs.2026.107659},
url = {https://doi.org/10.1016/j.scs.2026.107659},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphology
AU - Yuan Wang
AU - Shengao Yi
AU - Xiaojiang Li
AU - Pengyuan Liu
AU - Zhiwei Yang
AU - Ronita Bardhan
AU - Rudi Maria Frans Anne Stouffs
PY - 2026
DA - 2026/07/13
JO - Sustainable Cities and Society
DO - 10.1016/j.scs.2026.107659
UR - https://doi.org/10.1016/j.scs.2026.107659
AB - Heat exposure connects the built environment and public health, directly shaping the livability and sustainability of urban areas. Fully understanding the spatial heterogeneity of heat exposure and its driving factors is therefore a vital prerequisite 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 (the test R 2 = 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 model-informed planning implications for integrating physiologically relevant thermal indices to support targeted heat risk management and climate-adaptive urban planning.
SN - 2210-6707
SN - 2210-6715
ER -