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<p>In this study, we characterized phytoplankton biomass variability in El Rincón, a shallow and biologically productive coastal region in the Patagonian Continental Shelf. We used as a proxy 7&#xa0;years (2017–2024) of high-resolution Sentinel-3 OLCI satellite data to capture the fine scale spatio-temporal variability of Chl-<i>a</i> in this region. To obtain Chl-<i>a</i> concentration, we applied a regionally validated algorithm (MuRB&amp;NDCI) which provides more accurate Chl-<i>a</i> estimates compared to traditional algorithms, particularly in optically complex waters. Self-organizing maps (SOM) neural network was applied to identify four distinct bio-geographical regions with unique bloom dynamics. The phenological estimates revealed that phytoplankton blooms typically initiate in fall (March), peak during winter (May–August), and are more pronounced near the Bahía Blanca Estuary (BBE), where nutrient and sediment inputs drive high productivity. The findings presented in this work highlight the utility of Optical Water Class–based algorithms for capturing the fine-scale spatiotemporal variability of Chl-<i>a</i> in coastal systems. This study advances our understanding of phytoplankton dynamics in El Rincón and underscores the importance of customized satellite-derived approaches for monitoring and managing productivity in shallow coastal environments.</p>

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Full-Resolution Sentinel-3 Satellite Observations of Phytoplankton Phenology in Optically Complex Waters of the Northern Patagonian Shelf

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月27日

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  • Maximiliano Arena,
  • Paula Pratolongo,
  • Hubert Loisel,
  • Manh Duy Tran,
  • Daniel S. F. Jorge,
  • Ana Laura Delgado

摘要

In this study, we characterized phytoplankton biomass variability in El Rincón, a shallow and biologically productive coastal region in the Patagonian Continental Shelf. We used as a proxy 7 years (2017–2024) of high-resolution Sentinel-3 OLCI satellite data to capture the fine scale spatio-temporal variability of Chl-a in this region. To obtain Chl-a concentration, we applied a regionally validated algorithm (MuRB&NDCI) which provides more accurate Chl-a estimates compared to traditional algorithms, particularly in optically complex waters. Self-organizing maps (SOM) neural network was applied to identify four distinct bio-geographical regions with unique bloom dynamics. The phenological estimates revealed that phytoplankton blooms typically initiate in fall (March), peak during winter (May–August), and are more pronounced near the Bahía Blanca Estuary (BBE), where nutrient and sediment inputs drive high productivity. The findings presented in this work highlight the utility of Optical Water Class–based algorithms for capturing the fine-scale spatiotemporal variability of Chl-a in coastal systems. This study advances our understanding of phytoplankton dynamics in El Rincón and underscores the importance of customized satellite-derived approaches for monitoring and managing productivity in shallow coastal environments.