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author:

Jiang, Yichen (Jiang, Yichen.) [1] | Tang, Yunwei (Tang, Yunwei.) [2] | Jing, Linhai (Jing, Linhai.) [3] | Galdies, Charles (Galdies, Charles.) [4] | Li, Hui (Li, Hui.) [5] | Yan, Lin (Yan, Lin.) [6] | Ding, Haifeng (Ding, Haifeng.) [7] | Xie, Qiyuan (Xie, Qiyuan.) [8] | Dou, Changyong (Dou, Changyong.) [9]

Indexed by:

EI

Abstract:

Aerosol Optical Depth (AOD) is vital for assessing air quality and climate. Nighttime lights (NTL) provide a new data source for urban AOD inversion, but current NTL data suffer from low spatial resolution, limiting detailed AOD studies in smaller urban areas and hindering high-precision AOD algorithms. This study introduces methods for retrieving AOD using Glimmer Imager (GLI) data from the newly launched SDGSAT-1. Three methods were proposed: the standard deviation method, look-up-table method, and collaborative inversion method with VIIRS Day Night Band (DNB) data. Applied to Beijing, these methods produced a 10 m resolution AOD product. The look-up-table method achieved the highest correlation (R = 0.8876) compared to the standard deviation (R = 0.8224) and collaborative inversion (R = 0.8322) methods. The look-up-table method performed best with ample data, while the collaborative inversion method proved more reliable with limited data. These findings demonstrate the effectiveness of SDGSAT-1 GLI data in accurately retrieving nighttime AOD, offering significant potential for monitoring urban pollution. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Keyword:

Air quality Haze pollution Night vision devices Table lookup

Community:

  • [ 1 ] [Jiang, Yichen]International Research Center of Big Data for Sustainable Development Goals, Beijing, China
  • [ 2 ] [Jiang, Yichen]Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • [ 3 ] [Jiang, Yichen]College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
  • [ 4 ] [Tang, Yunwei]International Research Center of Big Data for Sustainable Development Goals, Beijing, China
  • [ 5 ] [Tang, Yunwei]Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • [ 6 ] [Jing, Linhai]School of Information Engineering, China University of Geosciences Beijing, Beijing, China
  • [ 7 ] [Galdies, Charles]Institute of Earth Systems, University of Malta, Msida, Malta
  • [ 8 ] [Li, Hui]International Research Center of Big Data for Sustainable Development Goals, Beijing, China
  • [ 9 ] [Li, Hui]Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • [ 10 ] [Yan, Lin]International Research Center of Big Data for Sustainable Development Goals, Beijing, China
  • [ 11 ] [Yan, Lin]Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • [ 12 ] [Ding, Haifeng]International Research Center of Big Data for Sustainable Development Goals, Beijing, China
  • [ 13 ] [Ding, Haifeng]Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • [ 14 ] [Xie, Qiyuan]Zijin School of Geology and Mining, Fuzhou University, Fuzhou, China
  • [ 15 ] [Dou, Changyong]International Research Center of Big Data for Sustainable Development Goals, Beijing, China
  • [ 16 ] [Dou, Changyong]Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

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Source :

International Journal of Digital Earth

ISSN: 1753-8947

Year: 2024

Issue: 1

Volume: 17

3 . 7 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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