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Proceedings Paper

Quantitative retrieval of aerosol optical thickness from FY-2 VISSR data
Author(s): Linyan Bai; Yong Xue; Chunxiang Cao; Jianzhong Feng; Hao Zhang; Jie Guang; Ying Wang; Yingjie Li; Linlu Mei; Jianwen Ai
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Paper Abstract

Atmospheric aerosol, as particulate matter suspended in the air, exists in a variety of forms such as dust, fume and mist. It deeply affects climate and land surface environment in both regional and global scales, and furthermore, lead to be hugely much influence on human health. For the sake of effectively monitoring it, many atmospheric aerosol observation networks are set up and provide associated informational services in the wide world, as well-known Aerosol robotic network (AERONET), Canadian Sunphotometer Network (AeroCan) and so forth. Given large-scale atmospheric aerosol monitoring, that satellite remote sensing data are used to inverse aerosol optical depth is one of available and effective approaches. Nowadays, special types of instruments aboard running satellites are applied to obtain related remote sensing data of retrieving atmospheric aerosol. However, atmospheric aerosol real-timely or near real-timely monitoring hasn't been accomplished. Nevertheless, retrievals, using Fengyun-2 VISSR data, are carried out and the above problem resolved to certain extent, especially over China. In this paper, the authors have developed a new retrieving model/mode to retrieve aerosol optical depth, using Fengyun-2 satellite data that were obtained by the VISSR aboard FY-2C and FY-2D. A series of the aerosol optical depth distribution maps with high time resolution were able to obtained, is helpful for understanding the forming mechanism, transport, influence and controlling approach of atmospheric aerosol.

Paper Details

Date Published: 3 November 2010
PDF: 8 pages
Proc. SPIE 7840, Sixth International Symposium on Digital Earth: Models, Algorithms, and Virtual Reality, 784022 (3 November 2010); doi: 10.1117/12.872969
Show Author Affiliations
Linyan Bai, Institute of Remote Sensing Applications (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Ctr. for Earth Observation and Digital Earth (China)
Yong Xue, Institute of Remote Sensing Applications (China)
London Metropolitan Univ. (United Kingdom)
Chunxiang Cao, Institute of Remote Sensing Applications (China)
Jianzhong Feng, Institute of Agricultural Resources and Regional Planning (China)
Hao Zhang, Institute of Remote Sensing Applications (China)
Jie Guang, Institute of Remote Sensing Applications (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Ying Wang, Institute of Remote Sensing Applications (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Yingjie Li, Institute of Remote Sensing Applications (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Linlu Mei, Institute of Remote Sensing Applications (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Jianwen Ai, Institute of Remote Sensing Applications (China)
Graduate Univ. of the Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 7840:
Sixth International Symposium on Digital Earth: Models, Algorithms, and Virtual Reality
Huadong Guo; Changlin Wang, Editor(s)

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