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

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Cited articles

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ASD: Analytical Spectral Devices. Technical Guide, 4th Edn., ASD Inc.: Boulder, CO, USA, available at: (last access: 30 September 2020), 2015. 
Bakirman, T. and Gumusay, M. U.: Assessment of Machine Learning Methods for Seagrass Classification in the Mediterranean, Baltic J. Modern Comput., 8, 315–326,, 2020. 
Bannari, A.: Synergy between Sentinel-MSI and Landsat-OLI to Support High Temporal Frequency for Soil Salinity Monitoring in an Arid Landscape, in: Research Developments in Saline Agriculture, edited by: Dagar, J. C., Yadav, R. K., and Sharma, P. C., Springer Nature Singapore Pte Ltd., 67–93, ISBN 978-981-13-5831-9,, 2019. 
Bannari, A. and Kadhem, G.: MBES-CARIS Data Validation for Bathymetric Mapping of Shallow Water in the Kingdom of Bahrain on the Arabian Gulf, Remote Sens., 9, 385–404, 2017. 
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.