Reliable satellite estimates of chlorophyll-a concentration (Chl-a) are needed in coastal waters for applications such as eutrophication monitoring. However, because of the optical complexity of coastal waters, retrieving accurate Chl-a is still challenging. Many algorithms exist and give quite different performance for different optical conditions but there is no clear definition of the limits of applicability of each algorithm and no clear basis for deciding which algorithm to apply to any given image pixel (reflectance spectrum). Poor quality satellite Chl-a data can easily reach end-users. To remedy this and provide a clear decision on when a specific Chl-a algorithm can be used, we propose simple quality control tests, based on MERIS water leaving reflectance (ρ_w) bands, to determine on a pixel-by-pixel basis if any of three popular and complementary algorithms can be used. The algorithms being tested are: 1. the OC4 blue-green band ratio algorithm which was designed for open ocean waters; 2. the OC5 algorithm which is based on look-up tables and corrects OC4 overestimation in moderately turbid waters and 3. a near infrared-red (NIR-red) band ratio algorithm designed for eutrophic waters.

Using a dataset of 348 in situ Chl-a / MERIS matchups, the conditions for reliable performance of each of the selected algorithms are determined. The approach proposed here looks for the best compromise between the minimization of the relative difference between In situ measurements and satellite estimations and the number of pixels processed. Conditions for a reliable application of OC4 and OC5 depend on ρ_w412/ρ_w443 and ρ_w560, used as proxies of coloured dissolved organic matter and suspended particulate matter (SPM), as compared to ρ_w560/ρ_w490, used as a proxy for Chl-a. Conditions for reliable application of the NIR-red band ratio algorithm depend on Chl-a and SPM. These conditions are translated into pixel-based quality control (QC) tests with appropriately chosen thresholds. Results show that by removing data which do not pass QC, the performance of the three selected algorithms is significantly improved. After combining these algorithms, 70% of the dataset could be processed with a median absolute percent difference of 30.5%. The QC tests and algorithm merging methodology were then tested on four MERIS images of European waters. The OC5 algorithm was found to be suitable for most pixels, except in very turbid and eutrophic waters along the coasts where the NIR-red band ratio algorithm helps to fill the gap. Finally, a test was performed on an OLCI-S3A image. Although some validations of water reflectance are still needed for the OLCI sensors, results show similar behavior to the MERIS applications which suggests that when applied to OLCI data the present methodology will help to accurately estimate Chl-a in coastal waters for the next decade.

对于诸如富营养化监测的应用，需要在沿海水域中对卫星的叶绿素a浓度（Chl-a）进行可靠的估算。但是，由于沿海水域的光学复杂性，如何获取准确的Chl-a仍然具有挑战性。存在许多算法，并且对于不同的光学条件，它们给出的性能完全不同，但是没有明确定义每种算法的适用范围，也没有明确的依据来确定哪种算法适用于任何给定的图像像素（反射光谱）。劣质的卫星Chl-a数据很容易到达最终用户。为了解决这个问题，并提供时，可以使用特定的叶绿素a算法上一个明确的决定，我们提出简单的质量控制测试的基础上，MERIS离水反射率（ρ _{w ^}）频段，以逐像素确定是否可以使用三种流行且互补的算法中的任何一种。被测试的算法是：1. OC4蓝绿带比算法，设计用于开阔海水；2.基于查找表的OC5算法，可纠正中度浑浊水域中的OC4高估；以及3.为富营养化水域设计的近红外红（NIR-红）带比算法。

使用348个原位Chl-a / MERIS匹配的数据集，确定每种选定算法可靠运行的条件。这里提出的方法在最小化原位测量和卫星估计之间的相对差异与处理的像素数量之间寻求最佳的折衷。为OC4和OC5的可靠的应用条件取决于ρ_瓦特412 /ρ_瓦特443和ρ_瓦特560，用作彩色溶解有机物代理和悬浮颗粒物（SPM），相比于ρ_瓦特560 /ρ_瓦特490，用作Chl-a的代理。NIR红带比例算法可靠应用的条件取决于Chl-a和SPM。这些条件翻译成具有适当选择的阈值的基于像素的质量控制（QC）测试。结果表明，通过删除未通过QC的数据，可以显着提高三种选定算法的性能。组合这些算法后，可以处理70％的数据集，其中位数绝对百分比差为30.5％。然后在欧洲水域的四张MERIS图像上测试了QC测试和算法合并方法。发现OC5算法适用于大多数像素，但在沿海非常混浊和富营养化的水域中，NIR-红谱带比例算法有助于填补空白。最后，对OLCI-S3A图像进行了测试。