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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1421', Anonymous Referee #1, 11 Feb 2023
    • AC1: 'Reply on RC1', Roy El Hourany, 10 Jun 2023
  • RC2: 'Comment on egusphere-2022-1421', Anonymous Referee #2, 19 Mar 2023
    • AC2: 'Reply on RC2', Roy El Hourany, 10 Jun 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Roy El Hourany on behalf of the Authors (11 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (27 Jul 2023) by Jochen Wollschlaeger
RR by Anonymous Referee #3 (17 Sep 2023)
RR by Alison Chase (04 Oct 2023)
ED: Reconsider after major revisions (11 Oct 2023) by Jochen Wollschlaeger
AR by Roy El Hourany on behalf of the Authors (09 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (10 Nov 2023) by Jochen Wollschlaeger
RR by Anonymous Referee #3 (29 Nov 2023)
RR by Alison Chase (29 Nov 2023)
ED: Publish subject to technical corrections (05 Dec 2023) by Jochen Wollschlaeger
AR by Roy El Hourany on behalf of the Authors (15 Dec 2023)  Author's response   Manuscript 
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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.