Ocean Science
Ocean Science
OS
Articles | Volume 20, issue 1
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.
https://doi.org/10.5194/os-20-217-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 1
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

Viewed

Total article views: 2,728 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,097 555 76 2,728 57 55 57
  • HTML: 2,097
  • PDF: 555
  • XML: 76
  • Total: 2,728
  • Supplement: 57
  • BibTeX: 55
  • EndNote: 57
Views and downloads (calculated since 14 Dec 2022)
Cumulative views and downloads (calculated since 14 Dec 2022)

Viewed (geographical distribution)

Total article views: 2,728 (including HTML, PDF, and XML) Thereof 2,648 with geography defined and 80 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
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.
Read more