The relationship between land surface temperature and artificial impervious surface fraction in 682 global cities: spatiotemporal variations and drivers

Abstract

Abstract The artificial impervious surface (AIS) counts among the most important components of the urban surface, and understanding how temperature changes with the AIS fraction (AISF) is crucial for urban ecology and sustainability. Considering the high heterogeneity among existing local studies, this study systematically analyzed the relationship between land surface temperature (LST) and AISF in 682 global cities. The LST–AISF relation was quantified by the coefficient (δLST, ΔLST/ΔAISF) of a linear regression model, which measures the LST change by 1 unit (1%) increase in AISF. The LST was acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) daily products during 2014–2016, while the AISF was calculated as the proportion of AIS in each MODIS pixel according to the high-resolution Global Artificial Imperious Area (GAIA) product in 2015. Major results can be summarized as follows: (a) LST shows an increasing trend along AISF gradients (positive δLST) in most cities, with annually average daytime and nighttime δLST of 0.0219 (0.0205, 0.0232) °C/% (values in parenthesis define the 95% confidence interval, hereinafter) and 0.0168 (0.0166, 0.0169) °C/%, respectively, for global cities. (b) Daytime δLST varies substantially among cities, with generally stronger values in tropical and temperate cities, but weaker or even negative values in arid cities; while at night, cities located in the cold climate zone tend to have larger δLST. (c) The LST–AISF relation is also season-dependent, characterized by a greater δLST in warm months, especially for cities located in temperate and cold climate zones. (d) Driver analyses indicate that changes in surface biophysical properties, including vegetation conditions and albedo, are main contributors to the spatiotemporal variation of daytime and nighttime δLST, respectively. These results help us to get a quantitative and systematic understanding of the climatic impacts of urbanization.