Next Article in Journal
Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe
Next Article in Special Issue
Long-Term Pattern of Primary Productivity in the East/Japan Sea Based on Ocean Color Data Derived from MODIS-Aqua
Previous Article in Journal
Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment
Previous Article in Special Issue
A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake
Open AccessArticle

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

by 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
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Deepak R. Mishra, Eurico J. D’Sa, Sachidananda Mishra, Magaly Koch and Prasad S. Thenkabail
Remote Sens. 2015, 7(11), 14731-14756; https://doi.org/10.3390/rs71114731
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)
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats

Article Access Map

1
Only visits after 24 November 2015 are recorded.
Back to TopTop