Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance
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 ty...
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doaj-9446dea464a443e08c6953750316a50a2020-11-24T21:04:41ZengMDPI AGRemote Sensing2072-42922015-11-01711147311475610.3390/rs71114731rs71114731Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing ReflectanceQian Shen0Junsheng Li1Fangfang Zhang2Xu Sun3Jun Li4Wei Li5Bing Zhang6Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaSchool of Geography, Planning of Sun Yat-Sen University, No. 135 Xingang Xi Road, Guangzhou 510275, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North Third Ring Road, Chaoyang District, Beijing 100029, ChinaInstitute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaDetermining 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.http://www.mdpi.com/2072-4292/7/11/14731optically complex watersclassificationremote sensing reflectanceinherent optical properties |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qian Shen Junsheng Li Fangfang Zhang Xu Sun Jun Li Wei Li Bing Zhang |
spellingShingle |
Qian Shen Junsheng Li Fangfang Zhang Xu Sun Jun Li Wei Li Bing Zhang Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance Remote Sensing optically complex waters classification remote sensing reflectance inherent optical properties |
author_facet |
Qian Shen Junsheng Li Fangfang Zhang Xu Sun Jun Li Wei Li Bing Zhang |
author_sort |
Qian Shen |
title |
Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance |
title_short |
Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance |
title_full |
Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance |
title_fullStr |
Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance |
title_full_unstemmed |
Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance |
title_sort |
classification of several optically complex waters in china using in situ remote sensing reflectance |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2015-11-01 |
description |
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. |
topic |
optically complex waters classification remote sensing reflectance inherent optical properties |
url |
http://www.mdpi.com/2072-4292/7/11/14731 |
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