Exploring the Impact of Urban Characteristics on Diurnal Land Surface Temperature Based on LCZ and Machine Learning

Xinyu Zhang and Jun Zhang ()
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Xinyu Zhang: School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China
Jun Zhang: School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China

Land, 2025, vol. 14, issue 9, 1-21

Abstract: The urban heat island (UHI) effect has become a critical environmental issue affecting urban livability and public health, attracting widespread attention from both academia and society. Although numerous studies have examined the influence of urban characteristics on land surface temperature (LST), most have been restricted to single variables or single time points, and the traditional “urban–rural dichotomy” approach fails to capture intra-urban thermal heterogeneity. To address this limitation, this study integrates the Local Climate Zone (LCZ) framework with machine learning techniques to systematically analyze the diurnal variation patterns of LST across different LCZ types in Beijing and explore the interactive effects of urban characteristic variables on LST. The results show the following: (1) Compact building zones (LCZ 1–3) exhibit significantly higher daytime LST than open building zones (LCZ 4–6), with reduced differences at night; high-rise buildings cool daytime surfaces through shading but increase nighttime LST due to heat storage. (2) Blue–green space variables, such as NDVI and tree coverage (T PLAND ), substantially lower daytime LST through evapotranspiration, but their nighttime cooling effect is weak; cropland coverage (C PLAND ) plays a particularly important role in lowering nighttime LST. (3) Blue–green space and urban form variables exhibit significant interaction effects on LST, with contrasting impacts between day and night. (4) Population activity variables are strongly correlated with increased LST, especially at night, when their warming effects are more prominent. This study reveals the relative importance and nonlinear relationships of different variables across diurnal cycles, providing a scientific basis for optimizing blue–green space configuration, improving urban morphology, regulating human activity, and formulating effective UHI mitigation strategies to support the development of more sustainable urban environments.

Keywords: urban thermal environment; land surface temperature; diurnal variation; Local Climate Zone; urban characteristics; machine learning; SHAP; ECOSTRESS product or data (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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