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Remote Sens. 2015, 7(11), 14731-14756; doi:10.3390/rs71114731

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

1,* , 1,* , 1,†
,
1,†
,
2,†
,
3,†
and
1,†
1
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
2
School of Geography, Planning of Sun Yat-Sen University, No. 135 Xingang Xi Road, Guangzhou 510275, China
3
College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North Third Ring Road, Chaoyang District, Beijing 100029, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Eurico J. D’Sa, Sachidananda Mishra, Magaly Koch and Prasad S. Thenkabail
Received: 8 June 2015 / Revised: 19 October 2015 / Accepted: 29 October 2015 / Published: 5 November 2015
(This article belongs to the Special Issue Remote Sensing of Water Resources)
View Full-Text   |   Download PDF [1359 KB, uploaded 5 November 2015]   |  

Abstract

Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively. View Full-Text
Keywords: optically complex waters; classification; remote sensing reflectance; inherent optical properties optically complex waters; classification; remote sensing reflectance; inherent optical properties
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shen, Q.; Li, J.; Zhang, F.; Sun, X.; Li, J.; Li, W.; Zhang, B. Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance. Remote Sens. 2015, 7, 14731-14756.

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