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学科主题: Remote Sensing; Imaging Science & Photographic Technology
题名: An appraisal of surface chlorophyll estimation by satellite remote sensing in the South China Sea
作者: Tang, S ; Chen, C ; Zhan, H ; Zhang, J ; Yang, J
通讯作者: sltang@scsio.ac.cn
刊名: INTERNATIONAL JOURNAL OF REMOTE SENSING
发表日期: 2008
卷: 29, 期:21, 页:6217-6226
收录类别: sci
部门归属: [Tang, S.; Chen, C.; Zhan, H.; Zhang, J.; Yang, J.] Chinese Acad Sci, S China Sea Inst Oceanol, LED, Guangzhou 510301, Guangdong, Peoples R China; [Tang, S.] Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China
项目归属: LED
摘要: The OC4v4 algorithm, which is confirmed as a standard algorithm for the retrieval of ocean chlorophyll-a concentration (chl-a) from ocean colour satellite data, was appraised for its performance in the South China Sea (SCS). With in situ optical data and surface chl-a data collected on six cruises from 1997 to 2005 in the SCS, analysis showed a large discrepancy between the in situ and OC4v4-retrieved chl-a. The OC4v4 algorithm generally overestimates the chl-a in the investigated area; this possibly results from the relatively higher absorption of the chromophoric of dissolved organic matter (CDOM). To find a better algorithm, correlation analysis was carried out between the chl-a and the band ratio. Results showed that the remote sensing reflectance ratios, R-555(412) and R-555(443), obtained the best correlation relationship. New algorithms were proposed based on the two band ratios. Compared with OC4v4, the new algorithms show better results and are suggested for the retrieval of chl-a from SeaWiFS or MODIS in the SCS.
WOS记录号: WOS:000260326200011
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.scsio.ac.cn/handle/344004/5945
Appears in Collections:热带海洋环境动力实验室(LTO)_期刊论文

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Recommended Citation:
Tang, S; Chen, C; Zhan, H; Zhang, J; Yang, J.An appraisal of surface chlorophyll estimation by satellite remote sensing in the South China Sea,INTERNATIONAL JOURNAL OF REMOTE SENSING,2008,29(21):6217-6226
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文件名: An appraisal of surface chlorophyll estimation by satellite remote sensing in the South China Sea.pdf
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