Journal of Geographical Sciences

Investigating nonlinear factors influencing multi-scale urban land surface temperature using machine learning models

SHI Yue, FAN Qiang, SUN Shuang, SONG Xiaonan, ZHANG Bing

Journal of Geographical Sciences, 2025, 35(9): 1998-2014. DOI: 10.1007/s11442-025-2400-x

Factor classification	Code	Factor
Terrain	X₁	DEM
Terrain	X₂	Slope
Surface parameter	X₃	EVI
Surface parameter	X₄	NDVI
Population	X₅	Population density
Underlying surface type proportion	X₆	Proportion of cropland
	X₇	Proportion of forest
	X₈	Proportion of water body
	X₉	Proportion of impervious land
Landscape	X₁₀	PD
	X₁₁	LPI
	X₁₂	LSI
	X₁₃	CONTAG
	X₁₄	SHDI
	X₁₅	SHEI
	X₁₆	AI

Table 3 Factors influencing LST

Other figure/table from this article

Figure 1 Location of Shenyang city, Liaoning province, Northeast China (Note: With the map approval number GS(2024)0650, the base map remains unmodified.)
Table 1 Classification rules for different temperature zones
Table 2 Landscape index and its significance
Figure 2 Spatial variation map of influencing factors in Shenyang for 2020 (Note: Owing to space limitations, only the spatial distribution of the influencing factors for 2020 is shown.)
Figure 3 Mean standard deviation classification map of LST in Shenyang for 2005, 2010, 2015 and 2020
Table 4 Area and proportion of temperature classifications
Figure 4 Pearson’s analysis results at different scales for 2005, 2010, 2015 and 2020 (*p < 0.05, ** p < 0.01)
Figure 5 Training and test set results
Figure 6 Significance ranking of factors (The size of the symbols represents the strength of the influence, while the colour gradient visually illustrates the impact of each factor on LST.)