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Comparison of Chlorophyll Algorithms in the Bohai Sea of China  

Xiu, Peng (College of Physical and Environmental Oceanography, Physical Oceanography Laboratory, Ocean University of China)
Liu, Yuguang (College of Physical and Environmental Oceanography, Physical Oceanography Laboratory, Ocean University of China)
Rong, Zengrui (College of Physical and Environmental Oceanography, Physical Oceanography Laboratory, Ocean University of China)
Zong, Haibo (College of Physical and Environmental Oceanography, Physical Oceanography Laboratory, Ocean University of China)
Li, Gang (Shenzhen Key Laboratory for Coastal and Atmospheric Research, Pku-hkust Shenzhen-hongkong Institution, China)
Xing, Xinogang (College of Physical and Environmental Oceanography, Physical Oceanography Laboratory, Ocean University of China)
Cheng, Yongcun (College of Physical and Environmental Oceanography, Physical Oceanography Laboratory, Ocean University of China)
Publication Information
Ocean Science Journal / v.42, no.4, 2007 , pp. 199-209 More about this Journal
Abstract
Empirical band-ratio algorithms and artificial neural network techniques to retrieve sea surface chlorophyll concentrations were evaluated in the Bohai Sea of China by using an extensive field observation data set. Bohai Sea represents an example of optically complex case II waters with high concentrations of colored dissolved organic mattei (CDOM). The data set includes coincident measurements of radiometric quantities and chlorophyll a concentration (Chl), which were taken on 8 cruises between 2003 and 2005, The data covers a range of variability in Chl in surface waters from 0.3 to 6.5 mg $m^{-3}$. The comparison results showed that these empirical algorithms developed for case I and case II waters can not be applied directly to the Bohai Sea of china, because of significant biases. For example, the mean normalized bias (MNB) for OC4V4 product was 1.85 and the root mean square (RMS) error is 2.26.
Keywords
chlorophyll concentration; ocean color; remote sensing model; case II waters; Artificial Neural Network;
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