Ocean Science
Ocean Science
OS
Articles | Volume 18, issue 2
Articles | Volume 18, issue 2
https://doi.org/10.5194/os-18-361-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-361-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 2
Research article
 | 
24 Mar 2022
Research article |  | 24 Mar 2022

The capabilities of Sentinel-MSI (2A/2B) and Landsat-OLI (8/9) in seagrass and algae species differentiation using spectral reflectance

Abderrazak Bannari, Thamer Salim Ali, and Asma Abahussain

Viewed

Total article views: 4,557 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,217 1,220 120 4,557 98 150
  • HTML: 3,217
  • PDF: 1,220
  • XML: 120
  • Total: 4,557
  • BibTeX: 98
  • EndNote: 150
Views and downloads (calculated since 25 Nov 2021)
Cumulative views and downloads (calculated since 25 Nov 2021)

Viewed (geographical distribution)

Total article views: 4,557 (including HTML, PDF, and XML) Thereof 4,311 with geography defined and 246 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 05 Apr 2026
Download
Short summary
Spectral analyses showed the importance of the blue, green, and NIR wavelengths for submerged aquatic vegetation (SAV) discrimination. Moreover, the integration of the blue or the green bands in water vegetation indices (WVIs) increases their discriminating power of SAV. Statistical fits between homologous bands of Sentinel-SMI and Landsat-OLI revealed excellent linear relationships (R2 of 0.999) with insignificant RMSD (≤ 0.0015). Accordingly, MSI and OLI sensors are spectrally similar.
Read more
Share