Remote sensing reflectance anomalies in the ocean
Remote sensing reflectance anomalies in the ocean
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DOI：
10.1016/j.rse.2016.06.002
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发表时间：
2016-10-01
影响因子：
13.5
通讯作者：
Antoine, David
中科院分区：
文献类型：
--
作者：
Huot, Yannick;Antoine, David
与海洋平均遥感反射率（Rrs）的微小光谱差异-异常-可以从海洋颜色卫星数据中提供独特的环境信息。首先，我们描述了三个输入光谱波段和输出波段之间的平均关系，通过开发一个查找表（WT）的基础上完全归一化Rrs从MODIS AQUA传感器。通过将在输出波长处测量的Rrs除以来自LUT的预测，我们得到取决于输入和输出频带的组合的几个异常。在全球范围内，这些异常都与叶绿素浓度无关。某些异常与先前描述的数据产品（例如，CDOM指数，半分析反演模型的后向散射系数），但其他数据与美国航天局目前分发的任何产品都不相关。在后一种情况下，从海洋颜色光谱中提取有关海洋光学特性的新信息，从而可以识别水团，而这在标准海洋颜色产品中是不可能的。在某些情况下，不可能确定这种信息的光源，因为这种信息可能在空间和时间上都是可变的。我们还表明，通过删除的主要来源的变化，异常显示出有趣的潜力，以确定在卫星时间序列传感器响应的微妙变化。(C)2016 Elsevier Inc. All rights reserved.
Small spectral differences from the mean remote sensing reflectance (Rrs) of the ocean - anomalies - can provide unique environmental information from ocean color satellite data. First, we describe the average relationship between three input spectral bands and an output band by developing a look-up table (WT) based on the fully normalized Rrs from the MODIS AQUA sensor. By dividing the Rrs measured at the output wavelength by the prediction from the LUT, we obtain several anomalies depending on the combination of input and output bands. None of these anomalies are correlated with chlorophyll concentration on the global scale. Some anomalies are strongly correlated with previously described data products (e.g., CDOM index, backscattering coefficients from semi-analytical inversion models), but others are not correlated with any product currently distributed by NASA. In the latter case, new information about oceanic optical properties is extracted from the ocean color spectra, which allows identification of water masses that was otherwise impossible with standard ocean color products. It was not possible, in some cases, to identify the optical source of this information, which may be spatially and temporally variable. We also show that by removing the main source of variability, the anomalies show interesting potential to identify subtle shifts in sensor response in satellite time series. (C) 2016 Elsevier Inc. All rights reserved.