Ocean Science
Ocean Science
OS
Articles | Volume 16, issue 2
Articles | Volume 16, issue 2
https://doi.org/10.5194/os-16-513-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-513-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Research article
 | 
24 Apr 2020
Research article |  | 24 Apr 2020

Estimation of phytoplankton pigments from ocean-color satellite observations in the Senegalo–Mauritanian region by using an advanced neural classifier

Khalil Yala, N'Dèye Niang, Julien Brajard, Carlos Mejia, Mory Ouattara, Roy El Hourany, Michel Crépon, and Sylvie Thiria

Viewed

Total article views: 3,699 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,524 1,024 151 3,699 185 201
  • HTML: 2,524
  • PDF: 1,024
  • XML: 151
  • Total: 3,699
  • BibTeX: 185
  • EndNote: 201
Views and downloads (calculated since 20 Mar 2019)
Cumulative views and downloads (calculated since 20 Mar 2019)

Viewed (geographical distribution)

Total article views: 3,699 (including HTML, PDF, and XML) Thereof 3,337 with geography defined and 362 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 17 Jun 2026
Download
Short summary
The paper is a contribution to the study of phytoplankton pigment climatology from satellite ocean-color observations in the Senegalo–Mauritanian upwelling, which is a very productive region where in situ observations are lacking. We processed the satellite data with an efficient new neural network classifier. We were able to provide the climatological cycle of diatoms. This study may have an economic impact on fisheries thanks to better knowledge of phytoplankton dynamics.
Read more
Share