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<p>Land cover, terrain, precipitation, and socioeconomic conditions directly or indirectly influence urban surface temperature. Against the backdrop of global climate change, exacerbated by the urban heat island effect, these impacts become more pronounced. However, there is a lack of in-depth research on the combined effects and mechanisms of various natural and social factors on land surface temperature (LST), especially in typical inland cities of northwestern China. This study focuses on Lanzhou City, using remote sensing techniques and the Google Earth Engine platform to retrieve and compute LST and related influencing factors. Analyzed using a geographical detector model, the study reveals the primary drivers and seasonal variations of LST changes in Lanzhou. The results indicate that both warm and cold season methods for retrieving LST in Lanzhou demonstrate high validity and reliability. Geographical detector factor analysis reveals distinct primary drivers of LST in Lanzhou during warm and cold seasons. During the warm season, elevation and the Normalized Difference Vegetation Index (NDVI) demonstrate the highest explanatory power for LST in Lanzhou, each with a q-value of 0.4. Conversely, aspect exhibits the highest explanatory power during the cold season, with a q-value of 0.45. Interaction detection reveals significant impacts: NDVI-GDP interaction strongly influences LST in the warm season (q = 0.598), while aspect-slope interaction dominates in the cold season (q = 0.615). Ecological detection shows significant differences in most factors during the warm season at a 95% confidence level. This highlights the need to study the complex interactions between natural and social factors.</p>

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Effects of natural and social factors on the surface temperature of warm and cold seasons in typical city of northwest China

  • 期刊论文

  • 发表日期：

    2025年2月4日

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  • L. Yifeng,
  • W. Cuirong,
  • Y. Lina,
  • S. Yifan,
  • W. Xiaoyi,
  • L. Maosen

摘要

Land cover, terrain, precipitation, and socioeconomic conditions directly or indirectly influence urban surface temperature. Against the backdrop of global climate change, exacerbated by the urban heat island effect, these impacts become more pronounced. However, there is a lack of in-depth research on the combined effects and mechanisms of various natural and social factors on land surface temperature (LST), especially in typical inland cities of northwestern China. This study focuses on Lanzhou City, using remote sensing techniques and the Google Earth Engine platform to retrieve and compute LST and related influencing factors. Analyzed using a geographical detector model, the study reveals the primary drivers and seasonal variations of LST changes in Lanzhou. The results indicate that both warm and cold season methods for retrieving LST in Lanzhou demonstrate high validity and reliability. Geographical detector factor analysis reveals distinct primary drivers of LST in Lanzhou during warm and cold seasons. During the warm season, elevation and the Normalized Difference Vegetation Index (NDVI) demonstrate the highest explanatory power for LST in Lanzhou, each with a q-value of 0.4. Conversely, aspect exhibits the highest explanatory power during the cold season, with a q-value of 0.45. Interaction detection reveals significant impacts: NDVI-GDP interaction strongly influences LST in the warm season (q = 0.598), while aspect-slope interaction dominates in the cold season (q = 0.615). Ecological detection shows significant differences in most factors during the warm season at a 95% confidence level. This highlights the need to study the complex interactions between natural and social factors.