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

Remote Sensing

Qian Shen⁴  Junsheng Li⁴  Fangfang Zhang⁴  Xu Sun⁴  Jun Li¹  Wei Li²  Bing Zhang⁴  Deepak R. Mishra³  Eurico J. D’Sa³  Sachidananda Mishra³  Magaly Koch³ 

[1] School of Geography, Planning of Sun Yat-Sen University, No. 135 Xingang Xi Road, Guangzhou 510275, China; E-Mail:;College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North Third Ring Road, Chaoyang District, Beijing 100029, China; E-Mail:Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China;;Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; E-Mails:

关键词: optically complex waters;    classification;    remote sensing reflectance;    inherent optical properties;   

DOI  :  10.3390/rs71114731

来源: mdpi

【 摘 要 】

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 (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 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.