Surface urban heat island detected by all-weather satellite land surface temperature

Since the existing satellite thermal infrared (TIR) land surface temperature (LST) is susceptible to cloud contamination and other factors, surface urban heat island (SUHI) studies based on TIR LST are limited to clear-sky conditions and are not representative of SUHI under all-weather conditions, which result in a possible clear-sky bias for SUHI. This study introduces a newly released 1-km all-weather LST product (TRIMS LST), which is spatiotemporally seamless, to investigate the real SUHI under all-weather conditions for five megacities (i.e. Harbin, Beijing, Shanghai, Guangzhou, and Chengdu) in China. Firstly, this study compares TRIMS SUHI with MODIS SUHI under clear-sky, partial-cloudy, and cloudy conditions. Secondly, the extent of the influence of cloudiness on SUHI is quantified. Finally, the monthly TRIMS SUHI is used to analyze the clear-sky bias that is caused by using only clear-sky data for the SUHI. Results indicate that (i) the absence of pixel data leads to negative offsets in the SUHI intensities calculated by MODIS LST, and these offsets expand gradually with increases in the number of missing-pixel data, causing the maximum offset to reach -1.83 degrees C under cloudy conditions in Chengdu; (ii) cloud can mitigate the SUHI for most cities: when the cloud coverage in Guangzhou reaches 90-100%, the daytime SUHI intensity decreases from 2.66 degrees C for clear-sky conditions to 1.70 degrees C; the mitigating effect differs at daytime and nighttime; and (iii) clear-sky bias varies significantly across climate zones and seasons, with a varying range of-1.6-1.2 degrees C for the five selected cities. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a newly released all-weather land surface temperature (LST) product (TRIMS LST) to investigate the real surface urban heat island (SUHI) under different weather conditions in five megacities in China. It compares TRIMS SUHI with MODIS SUHI under different sky conditions and quantifies the influence of cloudiness on SUHI. The study also analyzes the clear-sky bias of SUHI using monthly TRIMS SUHI data.