This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation.

中文翻译：

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卫星叶绿素 a 浓度的不确定性来源——亚得里亚海案例研究

本文分析了1997年至2019年亚得里亚海叶绿素-a剖面的时间序列，并将数据与卫星产品进行比较，以分析和减少相应卫星产品的不确定性。研究了卫星叶绿素-a 浓度的三个不确定性来源：(a) 算法本身； (b) 水柱的垂直结构； (c) 浮游植物群落结构。研究了全局和区域算法，以及使用时间序列数据调整的局部算法。与现场数据相比，全局算法产生的不确定性最大，其次是区域和局部算法。本地算法的相关系数为 0.690，与区域算法的相关系数 0.420 和全局算法的相关系数 0.042 相比显着增加。全局和区域算法都表现出与叶绿素a浓度成反比的系统误差，而局部算法显示系统误差有所减少，突出了局部观测的价值，对于改进次区域和局部算法检索来自卫星海洋颜色数据的叶绿素 a 浓度。虽然混合层没有显示出与不确定性的任何直接相关性，但它可能会促进夏季开花后叶绿素-a 剖面中异常强烈的垂直梯度，这是卫星观测与表面叶绿素-a 浓度之间存在巨大差异的主要来源。因此，用垂直剖面补充卫星测量非常重要，以确保读数有效，并在处理水华后数据时保持谨慎。这些情况仅占所有病例的不到 3%。浮游植物群落结构的差异与估计误差直接相关——Miozoa与低误差相关，Bacillariophyta与高误差相关，而Phytoflagellates丰度则提示低估和高估之间的误差。