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

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Cited articles

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung: Polar Research and Supply Vessel POLARSTERN Operated by the Alfred-Wegener-Institute, Journal Of Large-Scale Research Facilities, 3, A119, https://doi.org/10.17815/jlsrf-3-163, 2017. 
Antoine, D., Morel, A., Gordon, H. R., Banzon, V. F., and Evans, R. H.: Bridging ocean color observations of the 1980s and 2000s in search of long-term trends, J. Geophys. Res.-Oceans, 110, C06009, https://doi.org/10.1029/2004JC002620, 2005. 
Behrenfeld, M. J., O'Malley R. T., Boss, E. S., Westberry, T. K., Graff, J. R., Halsey, K. H., Milligan, A. J., Siegel, D. A., and Brown, M. B.: Revaluating ocean warming impacts on global phytoplankton, Nat. Clim. Change, 6, 3223–3330, https://doi.org/10.1038/nclimate2838, 2016. 
Breiman, L: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
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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.
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