Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China

Xiaoxi Wang, Yaojun Zhang, Danlin Yu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to the livable, healthy, and sustainable development of cities. In this study, we focused on the potential effect of human daily activities on LST from a short-term perspective. Beijing was selected as a case city, and Weibo check-in data were employed to measure the intensity of human daily activities. MODIS data were analyzed and used for urban LST measurement. We adopted spatial autocorrelation analysis, Pearson correlation analysis, and spatial autoregressive model to explore the influence mechanism of LST, and the study was performed at both the pixel scale and subdistrict scale. The results show that there is a significant and positive spatial autocorrelation between LSTs, and urban landscape components are strong explainers of LST. A significant and positive effect of human daily activities on LST is captured at night, and this effect can last and accumulate over a few hours. The variables of land use functions and building forms show varying impacts on LST from daytime to nighttime. Moreover, the comparison between results at different scales indicates that the relationships between LST and some explainers are sensitive to the study scale. The current study enriches the literature on LST and offers meaningful and practical suggestions for the monitoring, early warning, and management of urban thermal environment with remote sensing technology and spatial big data sources.

Original languageEnglish
Article number1783
JournalRemote Sensing
Volume15
Issue number7
DOIs
StatePublished - Apr 2023

Keywords

  • Beijing
  • MODIS
  • Weibo Check-in
  • human daily activities
  • land surface temperature
  • spatial autoregressive model
  • spatial big data

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