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A statistical model of marine reflectance

机译:海洋反射率统计模型

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摘要

Based on the analysis of an extensive bio-optical data set, i.e., the NOMAD dataset, the simultaneous stochastic behavior of the marine reflectance and chlorophyll concentration is characterized using nonparametric techniques. A statistical model of the conditional distribution of the marine reflectance given the chlorophyll concentration is proposed, that takes into account the natural correlations between the various optical variables. The model can be used to simulate realistic marine reflectance spectra for a given chlorophyll content, and to define prior distributions for atmospheric correction of satellite ocean-color imagery. It may also help to define bio-optical algorithms for chlorophyll concentration that minimize the influence of phytoplankton type. Conversely, considering a nonparametric regression model to retrieve chlorophyll concentration from marine reflectance leads to an improvement of about 10% on the average relative error over the polynomial OC4v4 algorithm. The prediction error of the nonparametric model provides a lower bound on the possible accuracy of chlorophyll concentration retrievals from in-situ marine reflectance, i.e., 49.2%.
机译:基于对广泛的生物光学数据集(即NOMAD数据集)的分析,使用非参数技术表征了海洋反射率和叶绿素浓度的同时随机行为。提出了一个给定叶绿素浓度的海洋反射率条件分布的统计模型,该模型考虑了各种光学变量之间的自然相关性。该模型可用于模拟给定叶绿素含量的逼真的海洋反射光谱,并定义用于大气校正卫星海洋彩色图像的先验分布。它也可能有助于为叶绿素浓度定义生物光学算法,以最大程度地减少浮游植物类型的影响。相反,考虑使用非参数回归模型从海洋反射率中检索叶绿素浓度,可以使多项式OC4v4算法的平均相对误差提高约10%。非参数模型的预测误差为从原位海洋反射率获取叶绿素浓度的可能准确性提供了一个下限,即49.2%。

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