Driving factors of land surface temperature in urban agglomerations: A case study in the pearl river delta, china

Wenxiu Liu, Qingyan Meng*, Mona Allam, Linlin Zhang, Die Hu, Massimo Menenti

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
17 Downloads (Pure)


Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations.

Original languageEnglish
Article number2858
Number of pages25
JournalRemote Sensing
Issue number15
Publication statusPublished - 2021


  • Driving factors
  • Geo-detector metric
  • Land surface temperature
  • Spatial analysis
  • Urban agglomeration


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