Investigating LST evolution and heatwave patterns using machine learning in the Beijing-Tianjin-Hebei Urban agglomeration
4 affiliations
- 1College of Art, Hebei GEO University, Shijiazhuang 050000, China
- 2School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430079, China
- 3College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China
- 4College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Abstract
Rapid urbanization and persistent climate warming are jointly intensifying summer thermal risks in large urban agglomerations, with increasingly pronounced coupling between daytime heat exposure and insufficient nighttime cooling. Using MODIS land surface temperature (LST) data, heatwave metrics, Moran's I and Getis-Ord Gi∗ statistics, and XGBoost-SHAP interpretation, we examined daytime and nighttime thermal evolution across the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) from 2000 to 2024. Daytime LST showed weak net warming with strong interannual fluctuations, whereas nighttime LST increased steadily by 2.3°C. Pixels exposed to compound day-night heatwaves expanded from 6.2% to 18.6%, with persistent hotspots in the southern urban belt and coldspots in northern mountains. SHAP results indicate that elevation exerted the strongest cooling association, while vegetation and albedo mainly shaped daytime LST and nighttime lights and built-up land shaped nighttime LST. These findings may support time-specific and regionally differentiated heat-risk governance.
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BibTeX
@article{liu2026investigating,
title = {{Investigating LST evolution and heatwave patterns using machine learning in the Beijing-Tianjin-Hebei Urban agglomeration}},
author = {Chen Liu and Maomao Zhang},
journal = {iScience},
year = {2026},
month = jun,
doi = {10.1016/j.isci.2026.116545},
url = {https://doi.org/10.1016/j.isci.2026.116545},
note = {CC BY},
}
RIS
TY - JOUR
TI - Investigating LST evolution and heatwave patterns using machine learning in the Beijing-Tianjin-Hebei Urban agglomeration
AU - Chen Liu
AU - Maomao Zhang
PY - 2026
DA - 2026/06/26
JO - iScience
DO - 10.1016/j.isci.2026.116545
UR - https://doi.org/10.1016/j.isci.2026.116545
AB - Rapid urbanization and persistent climate warming are jointly intensifying summer thermal risks in large urban agglomerations, with increasingly pronounced coupling between daytime heat exposure and insufficient nighttime cooling. Using MODIS land surface temperature (LST) data, heatwave metrics, Moran's I and Getis-Ord Gi∗ statistics, and XGBoost-SHAP interpretation, we examined daytime and nighttime thermal evolution across the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) from 2000 to 2024. Daytime LST showed weak net warming with strong interannual fluctuations, whereas nighttime LST increased steadily by 2.3°C. Pixels exposed to compound day-night heatwaves expanded from 6.2% to 18.6%, with persistent hotspots in the southern urban belt and coldspots in northern mountains. SHAP results indicate that elevation exerted the strongest cooling association, while vegetation and albedo mainly shaped daytime LST and nighttime lights and built-up land shaped nighttime LST. These findings may support time-specific and regionally differentiated heat-risk governance.
SN - 2589-0042
ER -