Abstract Accurate remote assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid case-2 waters is a challenge, owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not co-vary with phytoplankton. Here, we propose an improved Quasi-Analytical Algorithm (QAA) (denoted as TC2) for retrieving Chla concentrations from remote sensing reflectance (Rrs(λ)) which can be applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in turbid case-2 waters. TC2 has two main extensions when compared with QAA. First, TC2 makes an additional assumption to separate the total non-water absorption at 665 nm (anw(665)) into phytoplankton absorption (aph(665)) and yellow matter (aym(665)), which is the sum of colored dissolved matter (CDOM) and detritus. Second, for selecting the position of the near-infrared (NIR) band which is used to estimate the signal of total backscattering coefficient (bb(λ0)) at QAA reference band (λ0), we take into account the assumption that the absorption of pure water should be dominant at this band, as well as the impact of the signal-to-noise ratio (SNR) in the NIR band on the Chla concentration estimating model. When applied to in situ Rrs(λ) and OLCI match-up Rrs(λ) data in this study, TC2 provided more accurate Chla estimation than previous Cha concentration retrieval algorithms for turbid case-2 waters. TC2 has the potential for use as a simple and effective algorithm for monitoring Chla concentrations in the turbid case-2 waters at a global scale from space.

中文翻译：

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一种基于 OLCI 的半经验分配吸收系数和估计各种混浊案例 2 水中叶绿素 a 浓度的算法

摘要 在混浊的案例 2 水域中准确远程评估浮游植物叶绿素 a (Chla) 浓度是一项挑战，主要是由于陆地物质（如矿物质和腐殖质）具有光学意义但不与浮游植物共变。在这里，我们提出了一种改进的准分析算法 (QAA)（表示为 TC2），用于从遥感反射率 (Rrs(λ)) 中检索 Chla 浓度，该算法可应用于 Sentinel-3 海洋和陆地颜色仪器 (OLCI) 图像浑浊的案例2水域。与 QAA 相比，TC2 有两个主要扩展。首先，TC2 做出额外假设，将 665 nm 处的总非水吸收 (anw(665)) 分为浮游植物吸收 (aph(665)) 和黄色物质 (aym(665))，后者是有色溶解的总和物质 (CDOM) 和碎屑。第二，为了选择用于估计 QAA 参考波段 (λ0) 处的总后向散射系数 (bb(λ0)) 信号的近红外 (NIR) 波段的位置，我们考虑了纯水吸收的假设应该在该波段占主导地位，以及 NIR 波段的信噪比 (SNR) 对 Chla 浓度估计模型的影响。当应用于本研究中的原位 Rrs(λ) 和 OLCI 匹配 Rrs(λ) 数据时，TC2 为混浊案例 2 水域提供了比以前的 Cha 浓度检索算法更准确的 Chla 估计。TC2 有可能用作一种简单有效的算法，用于从空间监测全球范围内浑浊的案例 2 水域中的 Chla 浓度。