Articles | Volume 20, issue 1
https://doi.org/10.5194/os-20-217-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-20-217-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Feb 2024
Research article |
| 21 Feb 2024
| 21 Feb 2024
Linking satellites to genes with machine learning to estimate phytoplankton community structure from space
Roy El Hourany
CORRESPONDING AUTHOR
Univ. Littoral Côte d’Opale, Univ. Lille, CNRS, IRD, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, 62930 Wimereux, France
Juan Pierella Karlusich
Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
FAS Division of Science, Harvard University, Cambridge, MA, USA
Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016 Paris, France
Lucie Zinger
Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016 Paris, France
Naturalis Biodiversity Center, 2300 RA Leiden, the Netherlands
Hubert Loisel
Univ. Littoral Côte d’Opale, Univ. Lille, CNRS, IRD, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, 62930 Wimereux, France
Sorbonne Université, LOCEAN-IPSL, Laboratoire d'Océanographie et du Climat; Expérimentations et Approches Numériques, CNRS, IRD, MNHN, 75005 Paris, France
Chris Bowler
CORRESPONDING AUTHOR
Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016 Paris, France
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Cited
15 citations as recorded by crossref.
- Shift in phytoplankton community composition over fronts M. Lévy et al. https://doi.org/10.1038/s43247-025-02553-1
- A pan-Arctic pigment database for phytoplankton and sea–ice algae A. Heidemann et al. https://doi.org/10.5194/essd-18-5505-2026
- Machine learning-driven mapping of prokaryotic community diversity in the Mediterranean Sea using omics, earth observation, and model data C. Marchese et al. https://doi.org/10.1016/j.ecoinf.2026.103747
- Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data C. Marchese et al. https://doi.org/10.3389/fmars.2026.1875929
- Satellite-Based Chlorophyll-a Prediction Reveals Salinity-Dominated Regime Shifts in the East China Sea: A 22-Year Multi-Sensor Analysis with Explainable AI S. Liu & Z. Han https://doi.org/10.3390/rs18091392
- Quantifying the relative contributions of forcings to the variability of estuarine surface suspended sediments using a machine learning framework J. Tavora et al. https://doi.org/10.1016/j.csr.2025.105429
- Quantitative metagenomics for marine prokaryotes and photosynthetic eukaryotes Q. Bei et al. https://doi.org/10.1093/ismeco/ycaf131
- Integrating phytoplankton phenology, traits, and model‐data fusion to advance bloom prediction M. Hipsey et al. https://doi.org/10.1002/lol2.70052
- Protists and protistology in the Anthropocene: challenges for a climate and ecological crisis A. Perrin & R. Dorrell https://doi.org/10.1186/s12915-024-02077-8
- AIGD-PFT: the first AI-driven global daily gap-free 4 km phytoplankton functional type data product from 1998 to 2023 Y. Zhang et al. https://doi.org/10.5194/essd-16-4793-2024
- AI in Satellite Remote Sensing of the Ocean X. Li et al. https://doi.org/10.1109/JPROC.2026.3664121
- The Promise and Pitfalls of Machine Learning in Ocean Remote Sensing P. Gray et al. https://doi.org/10.5670/oceanog.2024.511
- Patterns and drivers of diatom diversity and abundance in the global ocean J. Pierella Karlusich et al. https://doi.org/10.1038/s41467-025-58027-7
- Machine Learning Approaches to Phytoplankton Identification and Classification Using GCOM-C/SGLI Imagery D. Candra & E. Siswanto https://doi.org/10.3390/rs17223759
- Science challenges and solutions to support implementation of the Biodiversity Beyond National Jurisdiction Agreement C. Szostek et al. https://doi.org/10.1038/s44183-026-00191-4
15 citations as recorded by crossref.
- Shift in phytoplankton community composition over fronts M. Lévy et al. https://doi.org/10.1038/s43247-025-02553-1
- A pan-Arctic pigment database for phytoplankton and sea–ice algae A. Heidemann et al. https://doi.org/10.5194/essd-18-5505-2026
- Machine learning-driven mapping of prokaryotic community diversity in the Mediterranean Sea using omics, earth observation, and model data C. Marchese et al. https://doi.org/10.1016/j.ecoinf.2026.103747
- Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data C. Marchese et al. https://doi.org/10.3389/fmars.2026.1875929
- Satellite-Based Chlorophyll-a Prediction Reveals Salinity-Dominated Regime Shifts in the East China Sea: A 22-Year Multi-Sensor Analysis with Explainable AI S. Liu & Z. Han https://doi.org/10.3390/rs18091392
- Quantifying the relative contributions of forcings to the variability of estuarine surface suspended sediments using a machine learning framework J. Tavora et al. https://doi.org/10.1016/j.csr.2025.105429
- Quantitative metagenomics for marine prokaryotes and photosynthetic eukaryotes Q. Bei et al. https://doi.org/10.1093/ismeco/ycaf131
- Integrating phytoplankton phenology, traits, and model‐data fusion to advance bloom prediction M. Hipsey et al. https://doi.org/10.1002/lol2.70052
- Protists and protistology in the Anthropocene: challenges for a climate and ecological crisis A. Perrin & R. Dorrell https://doi.org/10.1186/s12915-024-02077-8
- AIGD-PFT: the first AI-driven global daily gap-free 4 km phytoplankton functional type data product from 1998 to 2023 Y. Zhang et al. https://doi.org/10.5194/essd-16-4793-2024
- AI in Satellite Remote Sensing of the Ocean X. Li et al. https://doi.org/10.1109/JPROC.2026.3664121
- The Promise and Pitfalls of Machine Learning in Ocean Remote Sensing P. Gray et al. https://doi.org/10.5670/oceanog.2024.511
- Patterns and drivers of diatom diversity and abundance in the global ocean J. Pierella Karlusich et al. https://doi.org/10.1038/s41467-025-58027-7
- Machine Learning Approaches to Phytoplankton Identification and Classification Using GCOM-C/SGLI Imagery D. Candra & E. Siswanto https://doi.org/10.3390/rs17223759
- Science challenges and solutions to support implementation of the Biodiversity Beyond National Jurisdiction Agreement C. Szostek et al. https://doi.org/10.1038/s44183-026-00191-4
Saved (final revised paper)
Latest update: 02 Aug 2026
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.
Satellite observations offer valuable information on phytoplankton abundance and community...
