. 2026 Feb 10;16:8065. doi: 10.1038/s41598-026-39813-9

Table 3.

Multi-source data, preprocessing and factor selection rationale.

Dataset Name	Source	Preprocessing Result	Factor Selection Rationale (Scientific and Empirical Basis)
Landsat5/8 Collection 2Level-2L2)	usgs.gov²⁶	30 m resolution LST raster	LST is the core indicator for quantifying SUHI intensity and its spatial–temporal variability. Satellite-derived LST is widely adopted in urban climate studies due to its spatial continuity and comparability across periods²⁷.
Administrative Boundaries	National Geographic Information Public Service Platform²⁸	/	Administrative boundaries are used to delineate the study area and ensure spatial consistency in data integration and factor analysis.
Land Cover	Yang & Huang (2024)²⁵	30 m resolution land cover raster	Land cover types strongly influence surface thermal behaviour, as impervious surfaces and vegetated areas exhibit contrasting heat storage and radiation properties, making land cover a primary driver of SUHI patterns²⁹.
DEM Data	Copernicus Global DEM³⁰	100 m resolution DEM raster	Topographic attributes, such as elevation, slope and aspect, are included because they influence the receipt of solar radiation, airflow patterns and microclimatic conditions, which in turn affect spatial variations in LST. Studies using spatial econometric and machine-learning approaches have demonstrated that slope and other terrain features contribute significantly to urban heat island intensity, especially in landscapes with noticeable surface undulation^[31].
		100 m resolution slope raster
		100 m resolution aspect raster
Vector Map Data	OPENSTREETMAP³²	100 m resolution road density raster	Urban form metrics (density, height, road density) strongly influence surface thermal conditions through shading, heat storage and reduced ventilation^33,34.
		100 m resolution building height raster
		100 m resolution building density raster
Population Density	Chen, Xu, Ge, Zhang, and Zhou (2024)³⁵	100 m resolution population density raster	Population density is commonly used as a proxy for anthropogenic activity intensity and associated heat emissions, which contribute to urban thermal anomalies³⁴.
Normalised Difference Vegetation Index (NDVI)	National Ecosystem Science Data Center³⁶	100 m resolution NDVI raster	The NDVI quantifies vegetation cover, which mitigates surface temperature through shading and evapotranspiration and typically shows a negative correlation with LST³⁴.