Articles | Volume 20, issue 1
https://doi.org/10.5194/os-20-217-2024
https://doi.org/10.5194/os-20-217-2024
Research article
 | 
21 Feb 2024
Research article |  | 21 Feb 2024

Linking satellites to genes with machine learning to estimate phytoplankton community structure from space

Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler

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Cited articles

ACRI-ST: The European Service for Ocean Colour – GlobColour, [data set], https://hermes.acri.fr/index.php (last access: 29 January 2024), 2019. a
Agustí, S.: Allometric Scaling of Light Absorption and Scattering by Phytoplankton Cells, Can. J. Fish. Aquat. Sci., 48, 763–767, https://doi.org/10.1139/f91-091, 1991. a
Alvain, S., Moulin, C., Dandonneau, Y., and Bréon, F.: Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery, Deep-Sea Res. Pt. I, 52, 1989–2004, https://doi.org/10.1016/j.dsr.2005.06.015, 2005. a, b
Alvain, S., Moulin, C., Dandonneau, Y., Loisel, H., and Bréon, F. M.: A species-dependent bio-optical model of case I waters for global ocean color processing, Deep-Sea Res. Pt. I, 53, 917–925, https://doi.org/10.1016/j.dsr.2006.01.011, 2006. a
Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H., and Dessailly, D.: Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: Application to the detection of phytoplankton groups in open ocean waters, Remote Sens. Environ., 146, 97–112, https://doi.org/10.1016/j.rse.2013.08.046, 2013. a, b
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Short summary
Satellite observations offer valuable information on phytoplankton abundance and community structure. Here, we employ satellite observations to infer seven phytoplankton groups at a global scale based on a new molecular method from Tara Oceans. The link has been established using machine learning approaches. The output of this work provides excellent tools to collect essential biodiversity variables and a foundation to monitor the evolution of marine biodiversity.