Chapitre D'ouvrage Année : 2026

Distribution of Phytoplankton Assemblages Accross Fine-Scale Structures Revealed by Earth Observation Data: A Mediterranean Sea Case Study

Théo Garcia
  • Fonction : Auteur
  • PersonId : 1219173
  • IdHAL : theo-garcia
Laurina Oms
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  • PersonId : 1179946
  • IdHAL : laurina-oms

Résumé

Fine scales (1 – 100 km, day to weeks) are ephemeral physical structures that drive the ocean dynamics. Understanding the link between the spatial distribution of phytoplankton diversity and fine-scale structures is a key concern. Satellite-based observations facilitate the investigation of the spatial distribution of phytoplankton (ocean colour data) according to ocean physics (altimetry data) on a global scale. The recent launch of the SWOT (Surface Water and Ocean Topography) satellite has enabled the study of smaller oceanic structures, which were previously invisible to conventional altimetry. In this study, we propose a parametric approach for the analysis of a satellite-based dataset, which characterises oceanic fine-scale structures and phytoplankton communities in the western Mediterranean Sea. Gaussian mixture models were used to characterise phytoplankton communities based on the estimation of relative biomass for five phytoplankton functional types. Considering the entire community, rather than just the dominant phytoplankton type, provides new insights into phytoplankton distribution studies. A good match was found between the spatial distribution of phytoplankton assemblages and some fine-scale structures. However, local mismatches highlight regions where other processes (e.g. vertical mixing and zooplankton grazing) dominate over horizontal stirring. Subsequent research will be conducted to investigate the phytoplankton communities within the fine-scale structures on a global dataset.

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Théo Garcia, Louise Rousselet, Laurina Oms, Xavier Milhaud, Andrea Doglioli, et al.. Distribution of Phytoplankton Assemblages Accross Fine-Scale Structures Revealed by Earth Observation Data: A Mediterranean Sea Case Study. Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2843, Springer Nature Switzerland, pp.279-286, 2026, Communications in Computer and Information Science, 978-3-032-19107-6. ⟨10.1007/978-3-032-19108-3_20⟩. ⟨hal-05622939⟩
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