Abstract Productive upwelling zones such as the southern Benguela can exhibit phytoplankton biomass variability over several orders of magnitude, from near oligotrophic offshore waters to hypertrophic inshore blooms of >100 mg m −3. This introduces complexity for ocean colour applications such as Harmful Algal Bloom (HAB) monitoring. As low and high biomass algorithmic approaches for ocean colour differ, no single algorithm can optimally retrieve accurate Chl a over such a wide range of biomass. We propose a novel technique to apply and blend two different Chl a algorithms — an empirical blue-green algorithm for low to moderate biomass and a red-NIR band-ratio algorithm for moderate to high biomass. The blending method is based on the 708 and 665 nm reflectance wavelength ratio, where the blue-green algorithm is applied when the ρw(708)/ρw(665) ratio is 1.15, whilst the two are blended using a weighted approach in between these values. When applied to in situ and satellite match-up data this method provides a median absolute relative difference (MARD) of 37.9 and 45.7%, respectively, and a RMSD of 0.27 and 0.35 respectively, over Chl a concentrations spanning three orders of magnitude. Application is demonstrated for both MERIS and OLCI sensors, providing a smooth transition between different biomass levels and algorithm Chl a returns.

中文翻译：

---

浮游植物占主导地位的水域中MERIS和OLCI的优化叶绿素a切换算法

摘要 生产性上升流区（如本格拉南部）可以表现出几个数量级的浮游植物生物量变化，从近贫营养近海水域到 >100 mg m -3 的肥厚近海水华。这给海洋颜色应用带来了复杂性，例如有害藻华 (HAB) 监测。由于海洋颜色的低生物量和高生物量算法方法不同，没有单一的算法可以在如此广泛的生物量范围内最佳地检索准确的 Chl a。我们提出了一种新技术来应用和混合两种不同的 Chl a 算法——一种适用于中低生物量的经验蓝绿算法和适用于中高生物量的红色近红外波段比算法。混合方法基于 708 和 665 nm 反射波长比，其中当 ρw(708)/ρw(665) 比率为 1.15 时应用蓝绿算法，同时使用这些值之间的加权方法将两者混合。当应用于原位和卫星匹配数据时，该方法提供的中值绝对相对差 (MARD) 分别为 37.9% 和 45.7%，RMSD 分别为 0.27 和 0.35，跨越三个数量级的 Chl a 浓度。对 MERIS 和 OLCI 传感器的应用进行了演示，提供了不同生物量水平和算法 Chl a 之间的平滑过渡。Chl a 浓度跨越三个数量级。对 MERIS 和 OLCI 传感器的应用进行了演示，提供了不同生物量水平和算法 Chl a 之间的平滑过渡。Chl a 浓度跨越三个数量级。对 MERIS 和 OLCI 传感器的应用进行了演示，提供了不同生物量水平和算法 Chl a 之间的平滑过渡。