期刊
REMOTE SENSING
卷 7, 期 11, 页码 14731-14756出版社
MDPI
DOI: 10.3390/rs71114731
关键词
optically complex waters; classification; remote sensing reflectance; inherent optical properties
类别
资金
- National Natural Science Foundation of China [41571361, 41325004, 41001205, 41471308, 41201356]
- China's 863 program [2013AA12A302]
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 (R-rs ()) 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 R-rs (709)/R-rs (681), R-rs (560)/R-rs (709), R-rs (560)/R-rs (620), and R-rs (709)/R-rs (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.
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