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Investigating LST evolution and heatwave patterns using machine learning in the Beijing-Tianjin-Hebei Urban
Chen Liu1,2, Maomao Zhang3,4
1College of Art, Hebei GEO University, Shijiazhuang 050000, China.
Iscience
|July 23, 2026
Summary
Urban heat risks are rising due to urbanization and warming, especially at night. Compound heatwaves now affect 18.6% of the Beijing-Tianjin-Hebei region, highlighting the need for targeted heat governance.
Area of Science:
- Environmental Science
- Urban Climatology
- Remote Sensing
Background:
- Urban agglomerations face intensifying summer thermal risks from urbanization and climate warming.
- A significant coupling exists between daytime heat exposure and insufficient nighttime cooling.
Purpose of the Study:
- To examine the daytime and nighttime thermal evolution in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) from 2000 to 2024.
- To identify factors influencing urban thermal patterns and heatwave expansion.
Main Methods:
- Utilized MODIS land surface temperature (LST) data and heatwave metrics.
- Employed spatial statistics (Moran's I, Getis-Ord Gi*) and XGBoost-SHAP for analysis.
- Investigated thermal evolution across the BTHUA over a 25-year period.
Main Results:
- Nighttime LST increased steadily by 2.3°C, while daytime LST showed fluctuations.
- Compound day-night heatwave exposure expanded from 6.2% to 18.6% of pixels.
- Elevation was the primary cooling factor; vegetation/albedo influenced daytime LST; nighttime lights/built-up land influenced nighttime LST.
Conclusions:
- Urban heat risks, particularly nighttime warming and compound heatwaves, are escalating in the BTHUA.
- Distinct geographical patterns of heat hotspots and coldspots persist.
- Findings support the development of time-specific and regionally differentiated heat-risk governance strategies.
