Abstract The active hydrodynamics and complex circulation patterns in the northern Gulf of Mexico (nGoM) provide a dynamic scenario to investigate mesoscale responses of the size structure of phytoplankton communities to environmental variability. In this study, a large in-situ dataset acquired in inland, estuarine and coastal waters of the nGoM and U.S. East Coast were utilized to achieve a regional parameterization of the red-NIR ratio-based chlorophyll a (Chl a) algorithm for the high-spatial and spectral resolution Sentinel 3-OLCI ocean color sensor. Using an adaptive scheme, the algorithm was used in combination with other standard Chl a algorithms such as the Neural Network (C2RCC) and OC4ME to optimally extract Chl a in water types ranging from turbid estuarine to clear oceanic waters in the nGoM. This adaptive methodology (Chl a_AD) showed better performance in estimating OLCI-Chl a (R2=0.84, N = 178) compared to any single algorithm in the estuarine-coastal-ocean waters of the nGoM. Next, OLCI-derived phytoplankton spectral absorption coefficients (OLCI-aphy) were obtained for comparison, using a regionally-tuned 3rd order function of OLCI-Chl a based on Multi-regression (MR), and an estuarine-to-ocean adaptive Quasi-Analytical Algorithm (QAA-AD). Finally, an Empirical Orthogonal Function (EOF) algorithm was applied to OLCI-aphy to retrieve phytoplankton size fractions (PSFs) for the river-dominated nGoM region. Results indicate relatively better performance of MR-EOF in estimating fmicro (R2=0.87), fnano(R2=0.76) and fpico(R2=0.64) compared to QAA-AD-EOF. Chl a time-series (Oct 2016 – Jan 2020) obtained in the estuaries and shelf waters of the nGoM revealed seasonality (peak/lows in spring/fall) linked to river discharge, where hydrodynamics and wind driven variability due to storms further influenced the phytoplankton biomass spatiotemporal distribution. Microphytoplankton dominated in estuaries and the nGOM inner shelf waters, including the midshelf region during the spring peak river discharge period. PSF dynamics in the outer shelf is strongly influenced by Loop Current eddies where warm offshore waters contribute to the dominance of the picophytoplankton fraction. Floodwater discharge and strong winds due to Hurricane Barry (2019) transported estuarine microphytoplankton into the outer shelf and also increased nanophytoplankton fraction. Such fluctuations in phytoplankton size structure after hurricanes or storms can modify the pelagic food web dynamics in coastal systems.

中文翻译：

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使用自适应哨兵 3-OLCI 叶绿素 a 和光谱经验正交函数在墨西哥湾北部河口 - 大陆架水域浮游植物群落大小结构的生物地理学趋势

摘要 墨西哥湾北部 (nGoM) 活跃的流体动力学和复杂的环流模式为研究浮游植物群落大小结构对环境变化的中尺度响应提供了一个动态场景。在这项研究中，利用在 nGoM 和美国东海岸的内陆、河口和沿海水域获得的大型原位数据集，实现了基于红近红外比的叶绿素 a (Chl a) 算法的区域参数化，用于高-空间和光谱分辨率 Sentinel 3-OLCI 海洋颜色传感器。使用自适应方案，该算法与其他标准 Chl a 算法（例如神经网络 (C2RCC) 和 OC4ME）结合使用，以最佳地提取 nGoM 中从浑浊河口到清澈海水的水类型中的 Chl a。与 nGoM 的河口-沿海-海洋水域中的任何单一算法相比，这种自适应方法 (Chl a_AD) 在估计 OLCI-Chl a（R2 = 0.84，N = 178）方面表现出更好的性能。接下来，使用基于多回归 (MR) 的 OLCI-Chl a 区域调谐三阶函数和河口到海洋自适应准-分析算法（QAA-AD）。最后，将经验正交函数 (EOF) 算法应用于 OLCI-aphy 以检索以河流为主的 nGoM 区域的浮游植物大小分数 (PSF)。结果表明，与 QAA-AD-EOF 相比，MR-EOF 在估计 fmicro (R2=0.87)、fnano(R2=0.76) 和 fpico(R2=0.64) 方面的性能相对更好。Chl a 时间序列（2016 年 10 月至 2020 年 1 月）在 nGoM 的河口和陆架水域中显示出与河流流量相关的季节性（春季/秋季的高峰/低谷），其中由风暴引起的水动力和风力驱动的变化进一步影响了浮游植物生物量时空分布。微浮游植物主要分布在河口和 nGOM 内陆架水域，包括春季河流流量高峰期的中陆架区域。外陆架中的 PSF 动力学受到环流涡流的强烈影响，其中温暖的近海水域有助于微型浮游植物部分的主导地位。飓风巴里（2019 年）造成的洪水排放和强风将河口微型浮游植物运送到外陆架，同时也增加了微型浮游植物比例。