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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Asta C. Heidemann, Alexander Hayward, Philip Assmy, Atreya Basu, Astrid Bracher, Giulia Castellani, Giacomo Ditullio, Katarzyna Dragańska-Deja, Amane Fujiwara, Glaucia Moreira Fragoso, Jacob Høyer, Jiwoon Hwang, Morten Iversen, Anabel von Jackowski, Thomas Juul-Pedersen, Piotr Kowalczuk, Youngju Lee, Atsushi Matsuoka, Alenya Merz, C. J. Mundy, Else Ostermann, Ilka Peeken, Matt Pinkerton, Joanna Stoń-Egiert, Antonia U. Thielecke, Gaëlle Veyssiere, Hongyan Xi, Eun Jin Yang, and Rafael Gonçalves-Araujo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-144, https://doi.org/10.5194/essd-2026-144, 2026
Preprint under review for ESSD
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Algal pigments are widely used to describe and quantify algal biomass and composition. These organisms represent the basis of the marine food web and are vital components for the wider ecosystem. Here, we present a pan-Arctic dataset on algal pigments that has been compiled through an international collaboration between 19 institutions.
Yuzo Miyazaki, Yunhan Wang, Eri Tachibana, Koji Suzuki, Youhei Yamashita, and Jun Nishioka
Atmos. Chem. Phys., 25, 18325–18340, https://doi.org/10.5194/acp-25-18325-2025, https://doi.org/10.5194/acp-25-18325-2025, 2025
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It is essential to understand how biologically productive oceanic regions during spring phytoplankton blooms after sea ice melting contribute to the sea-to-air emission flux of atmospheric organic aerosols (OAs) in the subarctic oceans. Our shipboard measurements highlight the preferential formation of N-containing secondary water-soluble OAs associated with the predominant diatoms including ice algae during the bloom after sea ice melting/retreat in the subarctic ocean.
Huailin Deng, Koji Suzuki, Ichiro Yasuda, Hiroshi Ogawa, and Jun Nishioka
Biogeosciences, 22, 1495–1508, https://doi.org/10.5194/bg-22-1495-2025, https://doi.org/10.5194/bg-22-1495-2025, 2025
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Iron (Fe) and nitrate are vital for primary production in the North Pacific. Sedimentary Fe is carried by North Pacific Intermediate Water to the North Pacific, but the nutrient return path and its effect on phytoplankton are unclear. By combining Fe and macronutrient fluxes with phytoplankton composition, this study firstly revealed that Fe supply from the subsurface greatly controls diatom abundance and identified the nutrient return path in the subarctic gyre and Kuroshio–Oyashio transition area.
Patrick J. Neale, J. Patrick Megonigal, Maria Tzortziou, Elizabeth A. Canuel, Christina R. Pondell, and Hannah Morrissette
Biogeosciences, 21, 2599–2620, https://doi.org/10.5194/bg-21-2599-2024, https://doi.org/10.5194/bg-21-2599-2024, 2024
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Adsorption/desorption incubations were conducted with tidal marsh soils to understand the differential sorption behavior of colored vs. noncolored dissolved organic carbon. The wetland soils varied in organic content, and a range of salinities of fresh to 35 was used. Soils primarily adsorbed colored organic carbon and desorbed noncolored organic carbon. Sorption capacity increased with salinity, implying that salinity variations may shift composition of dissolved carbon in tidal marsh waters.
Zhibo Shao, Yangchun Xu, Hua Wang, Weicheng Luo, Lice Wang, Yuhong Huang, Nona Sheila R. Agawin, Ayaz Ahmed, Mar Benavides, Mikkel Bentzon-Tilia, Ilana Berman-Frank, Hugo Berthelot, Isabelle C. Biegala, Mariana B. Bif, Antonio Bode, Sophie Bonnet, Deborah A. Bronk, Mark V. Brown, Lisa Campbell, Douglas G. Capone, Edward J. Carpenter, Nicolas Cassar, Bonnie X. Chang, Dreux Chappell, Yuh-ling Lee Chen, Matthew J. Church, Francisco M. Cornejo-Castillo, Amália Maria Sacilotto Detoni, Scott C. Doney, Cecile Dupouy, Marta Estrada, Camila Fernandez, Bieito Fernández-Castro, Debany Fonseca-Batista, Rachel A. Foster, Ken Furuya, Nicole Garcia, Kanji Goto, Jesús Gago, Mary R. Gradoville, M. Robert Hamersley, Britt A. Henke, Cora Hörstmann, Amal Jayakumar, Zhibing Jiang, Shuh-Ji Kao, David M. Karl, Leila R. Kittu, Angela N. Knapp, Sanjeev Kumar, Julie LaRoche, Hongbin Liu, Jiaxing Liu, Caroline Lory, Carolin R. Löscher, Emilio Marañón, Lauren F. Messer, Matthew M. Mills, Wiebke Mohr, Pia H. Moisander, Claire Mahaffey, Robert Moore, Beatriz Mouriño-Carballido, Margaret R. Mulholland, Shin-ichiro Nakaoka, Joseph A. Needoba, Eric J. Raes, Eyal Rahav, Teodoro Ramírez-Cárdenas, Christian Furbo Reeder, Lasse Riemann, Virginie Riou, Julie C. Robidart, Vedula V. S. S. Sarma, Takuya Sato, Himanshu Saxena, Corday Selden, Justin R. Seymour, Dalin Shi, Takuhei Shiozaki, Arvind Singh, Rachel E. Sipler, Jun Sun, Koji Suzuki, Kazutaka Takahashi, Yehui Tan, Weiyi Tang, Jean-Éric Tremblay, Kendra Turk-Kubo, Zuozhu Wen, Angelicque E. White, Samuel T. Wilson, Takashi Yoshida, Jonathan P. Zehr, Run Zhang, Yao Zhang, and Ya-Wei Luo
Earth Syst. Sci. Data, 15, 3673–3709, https://doi.org/10.5194/essd-15-3673-2023, https://doi.org/10.5194/essd-15-3673-2023, 2023
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N2 fixation by marine diazotrophs is an important bioavailable N source to the global ocean. This updated global oceanic diazotroph database increases the number of in situ measurements of N2 fixation rates, diazotrophic cell abundances, and nifH gene copy abundances by 184 %, 86 %, and 809 %, respectively. Using the updated database, the global marine N2 fixation rate is estimated at 223 ± 30 Tg N yr−1, which triplicates that using the original database.
Maria Tzortziou, Charlotte F. Kwong, Daniel Goldberg, Luke Schiferl, Róisín Commane, Nader Abuhassan, James J. Szykman, and Lukas C. Valin
Atmos. Chem. Phys., 22, 2399–2417, https://doi.org/10.5194/acp-22-2399-2022, https://doi.org/10.5194/acp-22-2399-2022, 2022
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The COVID-19 pandemic created an extreme natural experiment in which sudden changes in human behavior significantly impacted urban air quality. Using a combination of model, satellite, and ground-based data, we examine the impact of multiple waves and phases of the pandemic on atmospheric nitrogen pollution in the New York metropolitan area, and address the role of weather as a key driver of high pollution episodes observed even during – and despite – the stringent early lockdowns.
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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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