Consistent long-term observations of surface phytoplankton functional types from space

Xi, Hongyan; Bretagnon, Marine; Mehdipour, Ehsan; Demaria, Julien; Mangin, Antoine; Bracher, Astrid

doi:https://doi.org/10.5194/sp-6-osr9-7-2025

Articles | Volume 6-osr9

https://doi.org/10.5194/sp-6-osr9-7-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Report:

9th edition of the Copernicus Ocean State Report (OSR9)

https://doi.org/10.5194/sp-6-osr9-7-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 6-osr9

30 Sep 2025

| OSR9 | Chapter 2.4

| 30 Sep 2025 | OSR9 | Chapter 2.4

Consistent long-term observations of surface phytoplankton functional types from space

Hongyan Xi, Marine Bretagnon, Ehsan Mehdipour, Julien Demaria, Antoine Mangin, and Astrid Bracher

Supplement

https://doi.org/10.5194/sp-6-osr9-7-2025-supplement

Data sets

A validation data set of phytoplankton pigment concentrations and phytoplankton groups measured on water samples collected from various expeditions Hongyan Xi et al. https://doi.org/10.1594/PANGAEA.982433

Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing) EU Copernicus Marine Service Product https://doi.org/10.48670/moi-00280

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Short summary

To better understand the marine phytoplankton variability on different scales in both space and time, this study proposes a machine-learning-based scheme to provide continuous and consistent long-term observations of various phytoplankton groups from space on a global scale, which enables time series analysis for further trend and anomaly investigations. This study provides an essential ocean variable to help assess the ocean health in the biogeochemical aspect.