引用本文:	余果,付东洋,刘大召,刘贝,廖珊,王立安,张小龙.基于神经网络模型的湛江湾水体有色溶解有机物的遥感估算[J].海洋科学,2018,42(9):73-80.

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基于神经网络模型的湛江湾水体有色溶解有机物的遥感估算
余果^1,2, 付东洋¹, 刘大召¹, 刘贝¹, 廖珊¹, 王立安¹, 张小龙^1,2
1.广东海洋大学电子与信息工程学院, 广东湛江 524088;2.广东海洋大学海洋与气象学院, 广东湛江 524088

摘要:

有色溶解有机物（Colored Dissolved Organic Matter，CDOM）是水体中重要的水质参数之一，是水色遥感的重要研究对象，如何构建适合特定区域的近海二类水体CDOM反演模型一直是国内外研究难点。本文利用2017年5月26~29日对南海西北部海域湛江湾20个站位采集的水样和测量的光谱资料，分析归一化遥感反射率与CDOM浓度a_g（400）的相关性，发现最大负相关系数出现在586 nm处，选择580、585、590、595 nm这四个波段处的归一化遥感反射率与a_g（400）建立了多元线性回归模型、BP （Back-Propagation）神经网络模型和RBF （Radial-Basis Function）神经网络模型，并与其他算法模型进行对比分析。结果发现，BP和RBF神经网络模型的平均相对误差和均方根误差均远小于多元线性回归模型和其他算法模型，神经网络模型的预测值与实测值拟合效果要优于多元线性回归模型。研究表明，神经网络模型更适合于湛江湾有色溶解有机物的遥感估算。

关键词: 湛江湾有色溶解有机物归一化遥感反射率神经网络模型遥感估算

DOI：10.11759/hykx20180409001

分类号:P733.3

基金项目:国家海洋公益专项项目（201305019）；广东省自然科学基金项目（2014A030313603）；广东省科技计划项目（2013B030200002，2016A020222016）；广东海洋大学创新强校项目（GDOU2014050226）；广东省哲学社会科学规划项目（GD12YGL04）；广东省普通高校优秀青年创新人才培养计划项目（2012WYM_0077）；广东海洋大学博士科研启动项目（E11097）

Remote sensing estimation of colored dissolved organic matter in the water body of Zhanjiang Bay based on neural network model

YU Guo^1,2, FU Dong-yang¹, LIU Da-zhao¹, LIU Bei¹, LIAO Shan¹, WANG Li-an¹, ZHANG Xiao-long^1,2

1.Guangdong Ocean University, College of Electronic and Information Engineering, Zhanjiang 524088, China;2.Guangdong Ocean University, College of Oceanography and Meteorology, Zhanjiang 524088, China

Abstract:

Colored dissolved organic matter (CDOM), one of the important water quality parameters, is an important research object of ocean color remote sensing. How to construct CDOM inversion model in Case-Ⅱ Waters of coastal which is applied to particular locations has always been a research challenge at home and abroad. In this study we use the water sample extracted from 20 stations of the Zhanjiang Bay, which is the northwestern part of the South China Sea, and live spectral measurements of these stations from May 26 to 29, 2017 to analysis the correlation between normalized remote sensing reflectance and CDOM concentration[a_g(400)], the maximum negative correlation coefficient appears at 586nm. The multiple linear regression model, BP neural network model and RBF neural network model were built with a_g(400) and the normalized remote sensing reflectivity at this four bands of 580 nm, 585 nm, 590 nm, 595 nm, and then compared with other CDOM algorithm models. The results show that the average relative error and root mean square error of the BP and RBF neural network model were far less than other models and that the fitting effect between the predicted values of the neural network model and the measured values was better than the effect came from the multiple linear regression model. That is to say the neural network model is more suitable for the remote sensing estimation of colored dissolved organic matter in Zhanjiang Bay.

Key words: Zhanjiang Bay colored dissolved organic matter normalized remote sensing reflectance neural network model remote sensing estimation