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.
the Creative Commons Attribution 4.0 License.
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.
the Creative Commons Attribution 4.0 License.
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
Alfred Wegener Institute, Helmholtz-Centre for Polar and Marine Research, 27570 Bremerhaven, Germany
Marine Bretagnon
ACRI-ST, Sophia Antipolis CEDEX, France
Ehsan Mehdipour
Alfred Wegener Institute, Helmholtz-Centre for Polar and Marine Research, 27570 Bremerhaven, Germany
School of Business, Social & Decision Sciences, Constructor University, Bremen, Germany
Julien Demaria
ACRI-ST, Sophia Antipolis CEDEX, France
Antoine Mangin
ACRI-ST, Sophia Antipolis CEDEX, France
Astrid Bracher
Alfred Wegener Institute, Helmholtz-Centre for Polar and Marine Research, 27570 Bremerhaven, Germany
Institute of Environmental Physics, University of Bremen, 28359 Bremen, Germany
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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.
To better understand the marine phytoplankton variability on different scales in both space and...
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