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Proceedings Paper

The quasi-analytic remote sensing algorithm of CDOM in the China Yellow Sea and East Sea
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Paper Abstract

Colored dissolved organic matter (CDOM) is one of the most important optical components which affect the sea surface spectral. Because of the similar spectral absorption properties between CDOM and organic detritus, it is difficult to separate them, especially in the coastal water with high turbidity. In many study, the total absorption coefficient of CDOM and organic detritus was combined as a optical parameter retrieved by the remote sensing algorithms. In this paper, a quasi-analytic remote sensing algorithm of CDOM has been developed, which can separate the absorption between CDOM and organic detritus. Firstly, the absorption spectrums of CDOM and organic detritus have been analyzed using the in-situ optical dataset measured by the Case II water optical investigation program in China Yellow Sea and East Sea in the spring, 2003. And then, a quasi-analytic remote sensing algorithm of CDOM has been developed to derive the absorption coefficients of the CDOM and organic detritus separately from the total absorption coefficient which can be retrieved from ocean color remote sensing data directly. The algorithm has been validated using the in-situ optical dataset of China Yellow Sea and East China Sea in the spring of 2003, and the synthesis optical data set and global in situ data set from IOCCG, and the results show that this algorithm perform well.

Paper Details

Date Published: 7 November 2008
PDF: 11 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 714718 (7 November 2008); doi: 10.1117/12.813245
Show Author Affiliations
Yan Bai, State Oceanic Administration (China)
Delu Pan, State Oceanic Administration (China)
Xianqiang He, State Oceanic Administration (China)
Fang Gong, State Oceanic Administration (China)


Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images

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