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处理海洋颜色图像时识别光学浅像素的方法。

Approach for identifying optically shallow pixels when processing ocean-color imagery.

作者信息

McKinna Lachlan I W, Werdell P Jeremy

出版信息

Opt Express. 2018 Oct 29;26(22):A915-A928. doi: 10.1364/OE.26.00A915.

DOI:10.1364/OE.26.00A915
PMID:30469992
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6506854/
Abstract

Ocean-color remote sensing is routinely used to derive marine geophysical parameters from sensor-observed spectral water-leaving radiances. However, in clear geometrically shallow regions, traditional ocean-color algorithms can be confounded by light reflected from the seafloor. Such regions are typically referred to as "optically shallow". When performing spatiotemporal analyses of ocean color datasets, optically shallow features such as submerged sand banks and coral reefs can lead to unexpected regional biases. Most contemporary approaches mask or flag suspected optically shallow pixels based on ancillary bathymetric data. However, the extent to which seafloor reflectance contaminates the water-leaving radiance is dependent on bathymetry, water clarity and seafloor albedo. In this paper, an approach for flagging optically shallow pixels has been developed that considers all three of these variables. In the method, the optical depth of the water column at 547 nm, ζ(547), is predicted from bathymetric data and estimated water-column optical properties. If ζ(547) is less then the pre-defined threshold, a pixel is flagged as potentially optically shallow. Radiative transfer modeling was used to identify a conservative threshold value of ζ(547) = 20 for a bright sand seafloor. In addition, pixels in waters shallower than 5 m are also flagged. We also examined how varying bathymetric datasets may affect the optically shallow flag using MODIS data. It is anticipated that the optically shallow flag will benefit end-users when quality controlling derived ocean color products. Further, the flag may prove useful as a mechanism for switching between optically deep and shallow algorithms during ocean color processing.

摘要

海洋颜色遥感通常用于从传感器观测到的光谱离水辐亮度中推导海洋地球物理参数。然而,在几何深度较浅的清澈区域,传统的海洋颜色算法可能会受到海底反射光的干扰。这些区域通常被称为“光学浅水区”。在对海洋颜色数据集进行时空分析时,诸如水下沙洲和珊瑚礁等光学浅水区特征可能会导致意外的区域偏差。大多数现代方法基于辅助测深数据对疑似光学浅水区像素进行掩膜或标记。然而,海底反射率对离水辐亮度的污染程度取决于测深、水体透明度和海底反照率。本文开发了一种标记光学浅水区像素的方法,该方法考虑了所有这三个变量。在该方法中,根据测深数据和估算的水柱光学特性预测547纳米处水柱的光学深度ζ(547)。如果ζ(547)小于预定义阈值,则将一个像素标记为可能是光学浅水区。利用辐射传输模型确定了明亮沙地海底ζ(547) = 20的保守阈值。此外,水深小于5米水域的像素也会被标记。我们还利用中分辨率成像光谱仪(MODIS)数据研究了不同的测深数据集可能如何影响光学浅水区标记。预计在对派生的海洋颜色产品进行质量控制时,光学浅水区标记将使最终用户受益。此外,该标记在海洋颜色处理过程中作为在光学深水区和浅水区算法之间切换的一种机制可能会被证明是有用的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/31934314a11c/nihms-1516752-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/403a31e1be5d/nihms-1516752-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/2b399834acfb/nihms-1516752-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/0358e41f3e7c/nihms-1516752-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/83d3842680e9/nihms-1516752-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/4ecd0ed6b98a/nihms-1516752-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/8f4997ff431d/nihms-1516752-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/6953d916c64f/nihms-1516752-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/955284b888f4/nihms-1516752-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/31934314a11c/nihms-1516752-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/403a31e1be5d/nihms-1516752-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/2b399834acfb/nihms-1516752-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/0358e41f3e7c/nihms-1516752-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/83d3842680e9/nihms-1516752-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/4ecd0ed6b98a/nihms-1516752-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/8f4997ff431d/nihms-1516752-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/6953d916c64f/nihms-1516752-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/955284b888f4/nihms-1516752-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c47/6506854/31934314a11c/nihms-1516752-f0009.jpg

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