Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables
Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables
复制标题
通过半解析模型优化和查找表根据 MERIS 卫星数据估算案例 II 水域中的成分浓度
DOI：
10.1016/j.rse.2011.01.007
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发表时间：
2011-05
影响因子：
13.5
通讯作者：
Yang, Wei
中科院分区：
文献类型：
--
作者：
Fukushima, Takehiko;Chen, Jin;Matsushita, Bunkei;Yang, Wei
由于浮游植物、海洋生物、彩色溶解有机物（CDOM）和纯水之间复杂的相互作用，对案例II水域水成分浓度的远程估计一直是一个巨大的挑战。估计成分浓度的半分析算法是有效且易于实现的，但仍然存在两个挑战。首先，需要一个没有抽样偏差的数据集来校准估计模型；其次，半分析性指标是基于几个可能不普遍适用的特定假设而开发的。本研究提出了一种半解析模型优化和查找表（SAMO-LUT）方法来解决这两个问题。SAMO-LUT方法是基于先前的三种半解析模型来估计叶绿素a、tripton和CDOM。采用了查找表和迭代搜索策略来获取模型中最合适的参数。利用无噪声模拟数据、原位数据和中分辨率成像光谱仪（MERIS）卫星数据3个数据集验证了该方法的有效性。结果表明，SAMO-LUT方法对理想的模拟数据集产生无误差的结果；并且即使对于原位和MERIS数据，也能够准确地估计水成分浓度，其平均偏差（平均归一化偏差，MNB）低于9%，相对随机不确定性（归一化均方根误差，NRMS）低于34%。这些结果证明了该算法在基于卫星观测准确监测内陆和沿海水域方面的潜力。
Remote estimation of water constituent concentrations in case II waters has been a great challenge, primarily due to the complex interactions among the phytoplankton, tripton, colored dissolved organic matter (CDOM) and pure water. Semi-analytical algorithms for estimating constituent concentrations are effective and easy to implement, but two challenges remain. First, a dataset without a sampling bias is needed to calibrate estimation models; and second, the semi-analytical indices were developed based on several specific assumptions that may not be universally applicable. In this study, a semi-analytical model-optimizing and look-up-table (SAMO-LUT) method was proposed to address these two challenges. The SAMO-LUT method is based on three previous semi-analytical models to estimate chlorophyll a, tripton and CDOM. Look-up tables and an iterative searching strategy were used to obtain the most appropriate parameters in the models. Three datasets (i.e., noise-free simulation data, in situ data and Medium Resolution Imaging Spectrometer (MERIS) satellite data) were collected to validate the performance of the proposed method. The results show that the SAMO-LUT method yields error-free results for the ideal simulation dataset; and is able also to accurately estimate the water constituent concentrations with an average bias (mean normalized bias, MNB) lower than 9% and relative random uncertainty (normalized root mean square error, NRMS) lower than 34% even for in situ and MERIS data. These results demonstrate the potential of the proposed algorithm to accurately monitor inland and coastal waters based on satellite observations.
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影响因子：
1.9
作者：
HALE, GM;QUERRY, MR
通讯作者：
QUERRY, MR
影响因子：
3.4
作者：
D. Doxaran;J. Froidefond;P. Castaing
通讯作者：
D. Doxaran;J. Froidefond;P. Castaing
影响因子：
3.4
作者：
K. Gin;S. T. Koh;I. Lin
通讯作者：
K. Gin;S. T. Koh;I. Lin
DOI：
--
发表时间：
2000
期刊：
--
影响因子：
--
作者：
G. Fargion;J. Mueller
通讯作者：
G. Fargion;J. Mueller
影响因子：
3.6
作者：
Garver, SA;Siegel, DA
通讯作者：
Siegel, DA