Articles | Volume 23, issue 3
https://doi.org/10.5194/bg-23-1043-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-1043-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Feb 2026
Research article |
| 04 Feb 2026
| 04 Feb 2026
Machine learning for estimating phytoplankton size structure from satellite ocean color imagery in optically complex Pacific Arctic waters
International Arctic Research Center, University of Alaska Fairbanks, Alaska, USA
National Institute of Polar Research, Tokyo, Japan
Amane Fujiwara
Institute of Arctic Climate and Environment Research, Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan
Wesley J. Moses
Remote Sensing Division, Naval Research Laboratory, Washington, D.C., USA
Steven G. Ackleson
Remote Sensing Division, Naval Research Laboratory, Washington, D.C., USA
Daniel Koestner
Department of Physics and Technology, University of Bergen, Bergen, Norway
Maria Tzortziou
Earth and Atmospheric Sciences, City College of New York, New York, USA
Kyle Turner
Earth and Atmospheric Sciences, City College of New York, New York, USA
Alana Menendez
Earth and Atmospheric Sciences, City College of New York, New York, USA
Toru Hirawake
National Institute of Polar Research, Tokyo, Japan
Koji Suzuki
Faculty of Environmental Earth Science, Hokkaido University, Hokkaido, Japan
Sei-Ichi Saitoh
Arctic Research Center, Hokkaido University, Hokkaido, Japan
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Short summary
The present study developed a satellite remote sensing algorithm for estimating phytoplankton size structure from space using machine learning approaches in optically complex Pacific Arctic waters. One of the key findings is that more complex machine learning approaches do not always produce more effective performance compared with the simple ones. This study demonstrated the benefits of utilizing machine learning approaches for developing satellite remote sensing algorithms.
The present study developed a satellite remote sensing algorithm for estimating phytoplankton...
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