High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical ‘ocean colour’ blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll-a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m⁻³ to 1.11 mg m⁻³, whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m⁻³ for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll-a and turbidity cannot be fully established without further availability of in situ optical measurements.

高分辨率成像光谱仪是解决内陆光学水质监测中重大数据缺口的先决条件。在这项工作中，我们提供了来自 Sentinel-2 多光谱成像仪 (MSI) 与相应的 Sentinel-3 海洋和陆地颜色仪器 (OLCI) 产品的叶绿素a和浊度的数据驱动比对。对于叶绿素一检索、经验性“海洋颜色”蓝绿波段比率和近红外 (NIR) 波段比率算法，以及半分析三波段 NIR-红色比率算法，都包含在分析中。对跨越五大洲 24 个湖泊的 MSI 和 OLCI 的 600 万次联合注册进行了分析。在使用 POLYMER 进行大气校正后，红色和 NIR 波段的反射率分布显示出两个传感器之间的相似性，而与 OLCI 相比，MSI 结果中蓝色和绿色波段的分布呈正偏斜。虽然从该分析中无法确定使用 MSI 或 OLCI 结果检索的反射率的准确性，但针对 MSI 的水质算法与先前为 Envisat 中分辨率成像光谱仪 (MERIS) 及其后续 OLCI 得出的算法进行优化，支持更广泛地将 MSI 用于水上应用。叶绿素-因此，针对并发 OLCI 观察结果针对 MSI 调整了算法，从而对原始算法系数进行了显着改进。蓝绿带比算法的平均绝对差 (MAD) 从 1.95 mg m ^-3降低到 1.11 mg m ^-3，而相关系数从 0.61 增加到 0.80。对于 NIR-红带比算法的改进是适度的，MAD 从 4.68 降低到 4.64 mg m ^-3对于经验红带比算法，3.73 到 3.67 用于半解析 3 带算法。浊度算法的三种实现在调整后显示出改进，结果分布具有减少的偏差。对于 665、708 和 778 nm 实现，MAD 分别从 0.85 减少到 0.72、1.22 到 1.10 和 1.93 到 1.55 FNU。然而，仍有几个不确定性来源：相邻陆地显示传感器之间的高度差异，表明陆地附近的高产品不确定性仍然是小型水体的问题，而此时无法说明 MSI 或 OLCI 结果是否受到不同影响. MSI 的光谱更宽频带对算法对叶绿素a 的敏感性的影响 如果没有进一步的原位光学测量，就无法完全确定浊度。