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Remote Sens. 2016, 8(4), 321; doi:10.3390/rs8040321

Spectral Classification of the Yellow Sea and Implications for Coastal Ocean Color Remote Sensing

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
2
University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
3
National Ocean Technology Center, No. 219 Jieyuanxi Road, Nankai District, Tianjin 300112, China
4
National Remote Sensing Center of China, No. 8A Liulinguan Nanli, Haidian District, Beijing 100036, China
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Richard W. Gould, Xiaofeng Li and Prasad S. Thenkabail
Received: 31 December 2015 / Revised: 28 March 2016 / Accepted: 31 March 2016 / Published: 12 April 2016
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
View Full-Text   |   Download PDF [11107 KB, uploaded 14 April 2016]   |  

Abstract

Remote sensing reflectance (Rrs) classification of coastal waters is a useful tool to monitor environmental processes and manage marine environmental resources. This study presents classification work for data sets that were collected in the Yellow Sea during six cruises (spring and autumn, 2003; summer and winter, 2006/2007; and spring and autumn, 2007). Specifically, we analyzed classification features of Rrs spectra and obtained spatio-temporal characteristics of reflectance and bio-optical properties in the coastal waters. Yellow Sea waters were classified into the following four typical regions based on their spatial distribution characteristics: middle of the Yellow Sea (MYS), north Yellow Sea (NYS), coastal Shandong (CS), and Jiangsu shoal (JS), and five water type categories consisting of Classes A–E were used to represent water colors from clear to very turbid. Application of this classification scheme to Medium Resolution Imaging Spectrometer (MERIS) imagery revealed seasonal variations in the data, which suggests that the water types have both significant temporal and spatial distributions. In particular, the area of Class E waters in the Jiangsu shoal tended to gradually shrink in summer and expand in winter. The spatio-temporal variability was due to the influence of various environmental factors such as currents, tidal activity, fresh water discharges, monsoon winds, and typhoons. View Full-Text
Keywords: Yellow Sea; spectral reflectance classification; MERIS Yellow Sea; spectral reflectance classification; MERIS
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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

Ye, H.; Li, J.; Li, T.; Shen, Q.; Zhu, J.; Wang, X.; Zhang, F.; Zhang, J.; Zhang, B. Spectral Classification of the Yellow Sea and Implications for Coastal Ocean Color Remote Sensing. Remote Sens. 2016, 8, 321.

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