Articles | Volume 22, issue 17
https://doi.org/10.5194/bg-22-4309-2025
© 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/bg-22-4309-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Sep 2025
Research article |
| 01 Sep 2025
| 01 Sep 2025
Estimating the variability of deep-ocean particle flux collected by sediment traps using satellite data and machine learning
Univ Brest, CNRS, IRD, Ifremer, Laboratoire des Sciences de l'Environnement Marin (LEMAR), IUEM, Plouzané, France
Chelsey A. Baker
National Oceanography Centre, Southampton, UK
Jonathan Gula
Univ Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Plouzané, France
Institut Universitaire de France (IUF), Paris, France
Ronan Fablet
IMT Atlantique, Lab-STICC, Plouzané, France
Laurent Mémery
Univ Brest, CNRS, IRD, Ifremer, Laboratoire des Sciences de l'Environnement Marin (LEMAR), IUEM, Plouzané, France
Richard Lampitt
National Oceanography Centre, Southampton, UK
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Short summary
Ocean sediment traps measure the sequestrated sinking organic carbon. While sinking, the particles are affected by local currents, which presents a challenge with regard to linking the deep flux with the surface. We present a machine learning tool that predicts the source location of the sinking particles based on satellite data. The predictions demonstrate a stronger correlation between surface and deep carbon fluxes, allowing a more comprehensive understanding of the deep carbon sequestration drivers.
Ocean sediment traps measure the sequestrated sinking organic carbon. While sinking, the...
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