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Spectral optimization for constituent retrieval in Case 2 waters I: Implementation and performance

机译:案例2水域中成分检索的光谱优化I:实施和性能

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We describe in detail the implementation of the spectral optimization algorithm (SCA) for Case 2 waters for processing of ocean color data. This algorithm uses aerosol models and a bio-optical reflectance model to provide the top-of atmosphere (TOA) reflectance. The parameters of both models are then determined by fitting the modeled TOA reflectance to that observed from space, using non-linear optimization. The algorithm will be incorporated into the SeaDAS software package as an optional processing switch of the Multi-Sensor Level-1 to Level-2 code. To provide potential users with an understanding of the accuracy and limitations of the algorithm, we generated a synthetic data set and tested the performance of the SCA with both correct and incorrect bio-optical model parameters. Application of the SOA to actual SeaWiFS data in the Lower Chesapeake Bay (for which surface measurements were available) showed that 20% errors in the bio-optical model parameters still enabled retrieval of chlorophyll a and the total absorption coefficient dissolved plus particulate detrital material at 443 nm with an error of less than 30% and 20%, respectively. In a companion paper we present a validation study of the application of the algorithm in the Chesapeake Bay.
机译:我们详细描述了案例2水域中用于处理海洋颜色数据的光谱优化算法(SCA)的实现。该算法使用气溶胶模型和生物光学反射率模型来提供最高大气(TOA)反射率。然后,使用非线性优化,通过使建模的TOA反射率与从空间观察到的反射率拟合,来确定两个模型的参数。该算法将作为多传感器Level-1到Level-2代码的可选处理开关并入SeaDAS软件包。为了使潜在用户了解该算法的准确性和局限性,我们生成了一个综合数据集,并使用正确和错误的生物光学模型参数测试了SCA的性能。将SOA应用于下切萨皮克湾的实际SeaWiFS数据(可进行表面测量)表明,生物光学模型参数中有20%的误差仍使得能够获取叶绿素a和总吸收系数以及颗粒状碎屑物质的溶解量。 443 nm,误差分别小于30%和20%。在同伴论文中,我们提出了对该算法在切萨皮克湾的应用的验证研究。

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