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Review of machine learning methods for aerosol quantitative remote sensing

  • role： First author第一作者
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：chenxf@aircas.ac.cn
  • Introduction：陈兴峰，1984年生，男，副研究员，研究方向为大气环境遥感。E-mail： chenxf@aircas.ac.cn
CHEN Xingfeng1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Space Information, Space Engineering University, Beijing 101416, China

  • Email：zhengfengjie84@163.com
  • Introduction：郑逢杰，1984年生，女，讲师，研究方向为大气环境遥感。E-mail： zhengfengjie84@163.com
ZHENG Fengjie2*,  
  • Affiliation：

    China Academy of Space Technology, Beijing 100094, China

GUO Ding3,  
  • Affiliation：

    State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

WANG Lili4,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHAO Limin1,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Jiaguo1,  
  • Affiliation：

    Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China

LI Lei5,  
  • Affiliation：

    Center for Satellite Application on Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China

ZHANG Yuhuan6,  
  • Affiliation：

    School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China

ZHANG Kainan7,  
  • Affiliation：

    Key Laboratory of Space Ocean Remote Sensing and Applications, Beijing 100081, China

XI Meng8,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Kaitao1

résumé

Machine learning methods have made breakthroughs in recent years, and their remote sensing applications have developed from remote sensing image recognition and classification to many fields in quantitative retrievals. Owing to their complicated mechanism in quantitative aerosol remote sensing, the types and accuracy of retrieved parameters are limited. Machine learning introduces new research and application techniques to aerosol remote sensing. Existing aerosol retrieval machine learning methods are summarized into four categories: satellite aerosol optical depth remote sensing, other aerosol parameters’ satellite remote sensing, particulate matter concentration, and ground-based aerosol remote sensing. In consideration of the authors’ research, through analysis and discussion, the conditions for machine learning to be used for aerosol quantitative remote sensing are summarized as follows: (1) Physical models could not be utilized. (2) Existing models have low accuracy. (3) Existing models have low computational efficiency. From the perspective of application, relevant inputs can be used to utilize machine learning to improve retrieved product types, retrieval accuracy, and calculation efficiency. For the quantitative remote sensing research field, how to mine information from remote sensing data should also be paid attention to improve the retrieval capability. Machine learning can also feedback the understanding of remote sensing mechanisms through error analysis, such that machine learning and remote sensing mechanism research can promote each other.

mots-clés

aerosol;quantitative remote sensing;Retrieval;machine learning;deep learning

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