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Optimization and assessment of phytoplankton size class algorithms for ocean color data on the Northeast U.S. continental shelf
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-10-26 , DOI: 10.1016/j.rse.2021.112729
Kyle J. Turner 1 , Colleen B. Mouw 1 , Kimberly J.W. Hyde 2 , Ryan Morse 2 , Audrey B. Ciochetto 1
Affiliation  

The size structure of phytoplankton communities influences important ecological and biogeochemical processes, including the transfer of energy through marine food webs. A variety of algorithms have been developed to estimate phytoplankton size classes (PSCs) from satellite ocean color data. However, many of these algorithms were developed for application to the global ocean, and their performance in more productive, optically complex coastal and continental shelf regions warrants evaluation. In this study, several existing PSC models were applied in the Northeast U.S. continental shelf (NES) region and compared with in situ PSC estimates derived from a local HPLC pigment data set. The effect of regional re-parameterization and incorporation of sea surface temperature (SST) into existing abundance-based model frameworks was investigated and model performance was assessed using an independent data set. Abundance-based model re-parameterization alone did not result in significant improvement in model performance compared with other models. However, the inclusion of SST led to a consistent reduction in model error for all size classes. Of two absorption-based algorithms tested, the best performing approach displayed similar performance metrics to the regional SST-dependent abundance-based model. The SST-dependent model and the absorption-based method were applied to monthly composites of the NES region for April and September 2019 and qualitatively compared. The results highlight the benefit of considering SST in abundance-based models and the applicability of absorption-based PSC methods in optically complex regions.



中文翻译:

美国东北部大陆架海洋颜色数据浮游植物大小分类算法的优化与评估

浮游植物群落的大小结构影响重要的生态和生物地球化学过程,包括通过海洋食物网传递能量。已经开发了多种算法来从卫星海洋颜色数据估计浮游植物大小等级 (PSC)。然而,这些算法中有许多是为应用于全球海洋而开发的,它们在生产力更高、光学复杂的沿海和大陆架区域的性能值得评估。在这项研究中,几个现有的 PSC 模型应用于美国东北部大陆架 (NES) 地区,并与从当地 HPLC 颜料数据集得出的原位PSC 估计值进行比较。区域重新参数化和结合海面温度 ( SST) 的影响) 研究了现有的基于丰度的模型框架,并使用独立数据集评估了模型性能。与其他模型相比,仅基于丰度的模型重新参数化并没有显着提高模型性能。然而,包含SST导致所有尺寸类别的模型误差一致减少。在测试的两种基于吸收的算法中,性能最佳的方法显示出与区域SST 相关丰度模型相似的性能指标。将SST 相关模型和基于吸收的方法应用于 NES 地区 4 月和 2019 年 9 月的月度复合材料,并进行定性比较。结果突出了考虑SST的好处 在基于丰度的模型中以及基于吸收的 PSC 方法在光学复杂区域中的适用性。

更新日期:2021-10-27
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