Biogeosciences
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Articles | Volume 23, issue 3
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.
https://doi.org/10.5194/bg-23-1043-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 3
Research article
 | 
04 Feb 2026
Research article |  | 04 Feb 2026

Machine learning for estimating phytoplankton size structure from satellite ocean color imagery in optically complex Pacific Arctic waters

Hisatomo Waga, Amane Fujiwara, Wesley J. Moses, Steven G. Ackleson, Daniel Koestner, Maria Tzortziou, Kyle Turner, Alana Menendez, Toru Hirawake, Koji Suzuki, and Sei-Ichi Saitoh

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Latest update: 03 Jul 2026
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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.
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