Determining the primary sources of uncertainty in the retrieval of marine remote sensing reflectance from satellite ocean color sensors II. Sentinel 3 OLCI sensors

A. Gilerson, Eder Herrera-Estrella, Jacopo Agagliate, Robert Foster, J. I. Gossn, D. Dessailly, E. Kwiatkowska
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引用次数: 1

Abstract

Uncertainties in remote sensing reflectance R r s for the Ocean Color sensors strongly affect the quality of the retrieval of concentrations of chlorophyll-a and water properties. By comparison of data from SNPP VIIRS and several AERONET-OC stations and MOBY, it was recently shown that the main uncertainties come from the Rayleigh-type spectral component (Gilerson et al., 2022), which was associated with small variability in the Rayleigh optical thickness in the atmosphere and/or its calculation. In addition, water variability spectra proportional to R r s were found to play a significant role in coastal waters, while other components including radiances from aerosols and glint were small. This work expands on the previous study, following a similar procedure and applying the same model for the characterization of uncertainties to the Sentinel-3A and B OLCI sensors. It is shown that the primary sources of uncertainties are the same as for VIIRS, i.e., dominated by the Rayleigh-type component, with the total uncertainties for OLCI sensors typically higher in coastal areas than for VIIRS.
确定卫星海洋颜色传感器反演海洋遥感反射率的主要不确定源2。哨兵3 OLCI传感器
海洋颜色传感器遥感反射率R R s的不确定性强烈影响叶绿素-a浓度和水性质的反演质量。通过比较SNPP VIIRS和几个AERONET-OC站以及MOBY的数据,最近发现主要的不确定性来自瑞利型光谱分量(Gilerson et al., 2022),这与大气中瑞利光学厚度和/或其计算的小变率有关。此外,发现与R R s成比例的水变率光谱在沿海水域发挥重要作用,而其他组分,包括气溶胶辐射和闪烁,则较小。这项工作扩展了先前的研究,遵循类似的程序,并将相同的模型应用于Sentinel-3A和B OLCI传感器的不确定性表征。结果表明,不确定性的主要来源与VIIRS相同,即以瑞利型分量为主,沿海地区OLCI传感器的总不确定性通常高于VIIRS。
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