An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.
An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.
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DOI：
10.1016/j.rse.2013.11.021
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发表时间：
2014-03-05
影响因子：
13.5
通讯作者：
Ruiz Verdu, Antonio
中科院分区：
文献类型：
--
作者：
Moore, Timothy S.;Dowell, Mark D.;Bradt, Shane;Ruiz Verdu, Antonio
关键词：
生物光学模型是基于光谱遥感反射率和水中成分的光学性质之间的关系。可以利用这些信息的波长范围根据水的特性而变化。在低叶绿素a水域，光谱的蓝/绿区域对叶绿素a浓度的变化更加敏感，而红/近红外区域在混浊和/或富营养化的水体中变得更加重要。在这项工作中，我们提出了一种方法来管理从蓝/绿比到基于红/近红外的叶绿素-a算法的转变，用于光学复杂的水域。根据沿海和内陆水域的联合现场数据集，两种叶绿素a算法--基于蓝/绿波段的标准NASA OC4算法和基于红/近红外波段的MERIS3波段算法--的总体算法不确定性度量大致相等，每种算法的均方根误差分别为0.416和0.437。然而，很明显，每种算法在不同的叶绿素-a范围内都表现得更好。当使用基于光学水类型分类的混合方法时，总体均方根误差降低到0.320。与任何一种单一算法产品相比，在评估混合的叶绿素a产品时，偏差和相对误差也减少了。作为海洋颜色应用的演示，算法混合方法被应用于伊利湖上的MERIS图像。我们还检查了这种方法在几个沿海海洋环境中的使用，并检查了OWTs对伊利湖上空MODIS-Aqua图像的长期频率。
Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-a algorithms—the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands—with RMS error of 0.416 and 0.437 for each in log chlorophyll-a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll-a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.
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