TY - JOUR
T1 - Divergent urbanization-induced impacts on global surface urban heat island trends since 1980s
AU - Li, Long
AU - Zhan, Wenfeng
AU - Hu, Leiqiu
AU - Chakraborty, T. C.
AU - Wang, Zhihua
AU - Fu, Peng
AU - Wang, Dazhong
AU - Liao, Weilin
AU - Huang, Fan
AU - Fu, Huyan
AU - Li, Jiufeng
AU - Liu, Zihan
AU - Du, Huilin
AU - Wang, Shasha
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Urbanization experiences different speeds and forms under diverse development stages across the globe. However, urbanization-induced impacts on long-term surface urban heat island intensity (Is) trends across global cities and the regulators of such impacts remain understudied Here we estimate interannual trends in daytime Is (i.e., urban-rural differences in surface temperatures) across 511 major cities for 1985–2020 using annual averages calculated using reconstructed land surface temperature data derived from >250,000 Landsat thermal images. Our study reveals that the global mean Is growth rate is 0.156 °C/decade. We further examine Is change associated with per 1% impervious land growth (denoted as β) in each city throughout the research period and during different periods. The global mean β is 0.018 ± 0.025 °C/% (mean ± 1 standard deviation) for the whole period, with greater values in humid than in arid climates; and the β may change during different periods, e.g., it has more than tripled when urban impervious land exceeds 30%, indicating the spatiotemporally divergent impacts of urbanization on Is trends across global cities. The spatial variations in β across global cities are well correlated with rural vegetation abundance and precipitation but not with urban population. Among these three factors, rural vegetation abundance possesses the greatest standardized regression coefficient of partial least-squares model, signifying the critical role of biome background in regulating β. The finding implies that future urbanization over densely vegetated regions should be more carefully and strategically planned due to the greater urbanization-induced surface warming effect.
AB - Urbanization experiences different speeds and forms under diverse development stages across the globe. However, urbanization-induced impacts on long-term surface urban heat island intensity (Is) trends across global cities and the regulators of such impacts remain understudied Here we estimate interannual trends in daytime Is (i.e., urban-rural differences in surface temperatures) across 511 major cities for 1985–2020 using annual averages calculated using reconstructed land surface temperature data derived from >250,000 Landsat thermal images. Our study reveals that the global mean Is growth rate is 0.156 °C/decade. We further examine Is change associated with per 1% impervious land growth (denoted as β) in each city throughout the research period and during different periods. The global mean β is 0.018 ± 0.025 °C/% (mean ± 1 standard deviation) for the whole period, with greater values in humid than in arid climates; and the β may change during different periods, e.g., it has more than tripled when urban impervious land exceeds 30%, indicating the spatiotemporally divergent impacts of urbanization on Is trends across global cities. The spatial variations in β across global cities are well correlated with rural vegetation abundance and precipitation but not with urban population. Among these three factors, rural vegetation abundance possesses the greatest standardized regression coefficient of partial least-squares model, signifying the critical role of biome background in regulating β. The finding implies that future urbanization over densely vegetated regions should be more carefully and strategically planned due to the greater urbanization-induced surface warming effect.
KW - Data reconstruction
KW - Land surface temperature
KW - Landsat data
KW - Surface urban heat island
KW - Thermal remote sensing
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U2 - 10.1016/j.rse.2023.113650
DO - 10.1016/j.rse.2023.113650
M3 - Article
AN - SCOPUS:85160704715
SN - 0034-4257
VL - 295
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 113650
ER -