文献类型： 外文期刊
作者： Zhan, HG 1 ; Shi, P 2 ; Chen, CQ 2 ;
作者机构： 1.Chinese Acad Sci, S China Sea Inst Oceanog, Guangzhou 510301, Peoples R China
2.Chinese Acad Sci, S China Sea Inst Oceanog, Guangzhou 510301, Peoples R China; Chinese Acad Fisheries Sci, S China Sea Fisheries Inst, Guangzhou 510300, Peoples R China
关键词： neural network;chlorophyll concentration;remote sensing reflectance;inversion;Case II waters
期刊名称：CHINESE SCIENCE BULLETIN （ 影响因子：1.649； 五年影响因子：1.738 ）
ISSN： 1001-6538
年卷期： 2001 年 46 卷 2 期
页码：
收录情况： SCI
摘要： Neural networks (NNs) for the inversion of chlorophyll concentrations from remote sensing reflectance measurements were designed and trained on a subset of the SeaBAM data set. The remaining SeaBAM data set was then applied to evaluating the performance of NNs and compared with those of the SeaBAM empirical algorithms, NNs achieved better inversion accuracy than the empirical algorithms in most of chlorophyll concentration range, especially in the intermediate and high chlorophyll regions and Case II waters, Systematic overestimation existed in the very low chlorophyll (<0.031 mg/m(3)) region, and little improvement was obtained by changing the size of the training data set.
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