Surface Urban Heat Island Across 419 Global Big Cities

Abstract

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (P < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (P = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect.

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

“…For all the cities averaged, the annual SUHII in daytime was higher than in nighttime (1.63 • C vs. 0.88 • C), and the SUHII in summer was higher than in winter (3.07 • C vs. 0.41 • C for daytime and 1.09 • C vs. 0.70 • C for nighttime). These results were similar to previous studies [3,10,32] and can be attributed to vegetation activities. Vegetation can release more latent heat fluxes but less sensible heat fluxes (than the artificial impervious surfaces: roads and buildings) by evapotranspiration and then decrease the temperature [3,26,35].…”

“…However, the SNLD data has a blooming effect; the urban area extracted from SNLD was usually larger than the real urban area [44,47]. Figures 2c and 3c showed that the urban areas at WH extracted from SNLD contained the Yangtze River and some lakes, which should be removed in SUHI's study [3,10,48]. In addition, MODIS LCD can extract the water body accurately (Figures 2d and 3d).…”

“…MODIS LST data has been widely used to study the SUHIs at regional scale [3,10,[31][32][33][34][35][36] because of its high accuracy, wide coverage and high temporal resolution (four times pass every day; Terra satellite: 10:30 a.m. and 10:30 p.m. local time, Aqua satellite: 1:30 a.m. and 1:30 p.m. local time). In this study, the LST was derived from the MOD11A2 data (Terra satellite, 1 km spatial resolution, 8 day composite) for the period 2001-2016.…”

“…The SUHII was calculated using Equation (2) according to previous studies [3,10]: 2001 3376 489 193 453 191 118 277 387 298 85 2002 4313 646 259 499 216 169 296 423 308 92 2003 5813 842 311 579 312 292 381 598 329 249 2004 6767 948 344 667 390 331 438 677 352 255 2005 7831 1003 378 748 405 334 489 711 ...…”

Sustainable Water Systems for the City of Tomorrow—A Conceptual Framework

“…Most metropolitan cities in US and around the world have experienced the massive flooding in severe storm and urban heat island (UHI) effects in hot summer due to the replacement of natural open and vegetated land surfaces with artificial concrete infrastructure and impermeable, dry surfaces [104,105]. Solar energy is converted to more sensible heat, rather than latent heat.…”