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《Geo-information Science》 2004-02
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A Study on the Remote Sensing Inversion of Ocean Chlorophyll Concentration by Using Neural Network Method

HUANG Haiqing, HE Xianqiang, WANG Difeng, PAN Delu(Key Lab of Ocean Dynamic Processes and Satellite Oceanography, SIO, SOA, PRC, Hangzhou 310012, China)  
One of the tasks of ocean color remote sensing is the measurement of marine primary production variation with time and space, whose important index is chlorophyll concentration of phytoplankton. The study on the inversion model of chlorophyll by remote sensing has been carried out for more than 30 years, and the main method is based on the ratio of blue and green channels. With the development of new ocean color remote sensors and new inversion methodology, the method based on fluorescence line high and neural network has being developed. In this paper, the neural network inversion is studied by means of the data set of global chlorophyll and in-situ radiance which was collected by SeaWiFS Bio-Optical Algorithm Mini-Workshop (SeaBAM). Finally, the result by neural network in the study is compared with that by Sea BAM’s empirical inversion.
【Fund】: 863青年基金项目2002AA639490资助。
【CateGory Index】: TP183
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