| 基于辐射传递模拟及人工神经网络技术的二类水体光学组分的反演 |
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| 引用本文： | 张亭禄,邱国强.基于辐射传递模拟及人工神经网络技术的二类水体光学组分的反演[J].湖泊科学,2009,21(2):173-181. |
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| 作者姓名： | 张亭禄 邱国强 |
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| 作者单位： | 中国海洋大学海洋遥感教育部重点实验室,青岛,266100 |
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| 摘 要： | 介绍了一种基于辐射传递模拟和人工神经网络技术(ANN)的二类水体水色要素(CHL,SPM,CDOM)的反演算法.在辐射传递模拟计算中,纯海水吸收和散射、浮游植物吸收的数据或模型是已发表的被广泛采用的结果.黄色物质和非浮游植物颗粒吸收以及海洋颗粒物的散射模型从COASTLOOC数据中导出.另外,还利用了一个新的海洋颗粒物后向散射概率模型,在该模型中颗粒物后向散射概率是颗粒有机物与SPM比值和波长的甬数.把上述定义的固有光学性质作为输入,经过辐射传递模拟得到海表面以下辐照度反射比数据集,然后将该模拟数据集用于训练不同的人工神经网络,获取水色和水色要素浓度之间甬数关系的最佳近似.利用以上建立的基于人工神经网络的算法,把COASTLOOC数据集和PMNS数据集的辐照度反射比作为输入进行水色要素反演,通过比较反演值和真实测量值来评价算法性能.结果显示,建立的基于ANN的二类水体水色要素反演算法具有很好的性能.
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| 关 键 词： | 海洋辐射传递模拟 人工神经网络 光学组分的反演 二类水体 |
| 收稿时间： | 2008/10/27 0:00:00 |
| 修稿时间： | 2008/12/3 0:00:00 |
Algorithms based on artificial neural network for retrieval of oceanic constituents in Case II waters |
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| ZHANG Tinglu and QIU Guoqiang.Algorithms based on artificial neural network for retrieval of oceanic constituents in Case II waters[J].Journal of Lake Science,2009,21(2):173-181. |
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| Authors: | ZHANG Tinglu and QIU Guoqiang |
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| Institution: | Ocean Remote Sensing Laboratory of Ministry of Education of China;Ocean University of Qingdao;Qingdao 266100;P.R.China |
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| Abstract: | In the present paper, we report an algorithm method to retrieve the oceanic constituent concentrations (CHL, SPM, and CDOM) in Case II waters. The method is derived from the radiative transfer simulations and is subsequently applied in the Artificial Neural Network (ANN) techniques. Information on absorption and total scattering of pure sea water, as well as absorption of phytoplankton and associated particles are taken from measurements or parameterisations in published literatures, and information on abso... |
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| Keywords: | Ocean radiative transfer simulation artificial neural network retrieval of oceanic constituents case II waters |
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