The Multi-feature Parameter Classification of Aerosol Based on OMI Remote Sensing Data: A Case Study in Guangdong Province

ZHENG Yangcheng; LI Lili; WANG Yunpeng

doi:10.6054/j.jscnun.2021060

Journal of South China Normal University (Natural Science Edition) > 2021 > 53(4): 68-75. > DOI: 10.6054/j.jscnun.2021060

ZHENG Yangcheng, LI Lili, WANG Yunpeng. The Multi-feature Parameter Classification of Aerosol Based on OMI Remote Sensing Data: A Case Study in Guangdong Province[J]. Journal of South China Normal University (Natural Science Edition), 2021, 53(4): 68-75. DOI: 10.6054/j.jscnun.2021060

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The Multi-feature Parameter Classification of Aerosol Based on OMI Remote Sensing Data: A Case Study in Guangdong Province

ZHENG Yangcheng^{1, 2, 3},
LI Lili^4, ,,
WANG Yunpeng^{1, 2}

1.
Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
2.
Center for Excellence in Deep Earth Science, Chinese Academy of Sciences, Guangzhou 510640, China
3.
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 510640, China
4.
College of Environmental Science and Engineering, Zhongkai University of Agricultural and Engineering, Guangzhou 510225, China

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Received Date: December 07, 2020
Available Online: September 02, 2021

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Abstract

Abstract

In order to solve the problems of low precision and redundant feature parameters in the process of aerosol classification, several significant aerosol feature parameters were extracted from the OMI (Ozone Monitoring Instrument) remote sensing products and Random Forest (RF) algorithm was used for aerosol type classification and verification. Aerosols of Guangdong Province in 2014 were divided into three types: desert dust (DST), carbonaceous aerosols associated with biomass burning (CRB) and sulfate-based urban-industrial aerosols (SLF). The Random Forest classification results and the importance of feature parameters were analyzed. The spatial distribution of cla-ssification results were compared to that of OMI aerosol type products. The following results are obtained. First, with the RF algorithm, a total precision of over 97% can be reached with a few training samples. Second, calculating the importance of different aerosol feature parameters in RF shows that the most important feature parameters are angstrom exponent, UVAI, RI₃₈₈, RI₃₅₄, SSA₅₀₀ and AAOD₅₀₀ in turn, indicating that size distribution and absorption ability of aerosols play the key roles. Third, the spatial distribution of three aerosol types shows that sulfate-based urban-industrial aerosols are dominant in the Pearl River Delta. Proportion of biomass burning and desert dust aerosols are highest in the Pearl River Delta and lowest in the eastern and northern Guangdong.
- aerosol classification,
- random forest,
- OMI,
- feature parameter,
- angstrom exponent,
- UVAI

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