Preprints
https://doi.org/10.5194/os-2021-81
https://doi.org/10.5194/os-2021-81

  25 Nov 2021

25 Nov 2021

Review status: this preprint is currently under review for the journal OS.

The Capability of Sentinel-MSI (2A/2B) and Landsat-OLI (8/9) for Seagrass and Algae Species Differentiation using Spectral Reflectance

Abderrazak Bannari1, Thamer Salim Ali2, and Asma Abahussain2 Abderrazak Bannari et al.
  • 1Space Pix-Map International Inc., Gatineau (Québec) J8R 3R7, Canada
  • 2Department of Natural Resources and Environment, College of Graduate Studies, Arabian Gulf University, Manama, Kingdom of Bahrain, P.O. Box: 26671

Abstract. This paper assesses the reflectance difference values between the homologous visible and near-infrared (VNIR) spectral bands of Sentinel-MSI-2A/2B and Landsat-OLI-8/9 sensors for seagrass, algae, and mixed species discrimination and monitoring in a shallow marine environment southeastern of Bahrain in the Arabian Gulf. To achieve these, a field survey was conducted to collect samples of seawater, underwater sediments, seagrass (Halodule uninebell.netrvis and Halophila stipulacea) and algae (green and brown). As well, an experimental mode was established in a Goniometric-Laboratory to simulate the marine environment, and spectral measurements were performed using an ASD spectroradiometer over each separate and different case of seagrass and algae mixed species at different coverage rate (0, 10, 30, 75, and 100 %) considering the bottom sediments with clear and dark colors. All measured spectra were analyzed and transformed using continuum-removed reflectance spectral (CRRS) approach to assess spectral separability among separate or mixed species at varying coverage rates. Afterward, the spectra were resampled and convolved in the solar-reflective spectral bands of MSI and OLI sensors and converted into water vegetation indices (WVI) to investigate the potential of red, green, and blue bands for seagrass and algae species discrimination. For comparison and sensor differences quantification, statistical fits (p < 0.05) were conducted between reflectances in homologous bands and also between homologous WVI; as well as the coefficient of determination (R2) and root mean square difference (RMSD) were calculated. The results of spectral and CRRS analyses highlighted the importance of the blue, green, and NIR wavelengths for seagrass and algae detection and probable discrimination based on hyperspectral measurements. However, when resampled and convolved in MSI and OLI bands, spectral information loses the specific and unique absorption features and becomes more generalized and less precise. Therefore, relying on the multispectral bandwidth of MSI and OLI sensors, it is difficult or even impossible to differentiate or to map seagrass and algae individually at the species level. Additionally, instead of the red band, the integration of the blue or the green bands in WVI increases their discriminating power of submerged aquatic vegetation (SAV), particularly Water Adjusted Vegetation Index (WAVI), Water Enhance Vegetation Index (WEVI), and Water Transformed Vegetation Index (WTDVI) indices. These results corroborate the spectral analysis and the CRRS transformations that the blue and green electromagnetic radiation allows better marine vegetation differentiation. However, despite the power of blue wavelength to penetrate deeper into the water, it also leads to a relative overestimation of dense SAV coverage due to the higher scattering in this part of the spectrum. Furthermore, statistical fits between the reflectance in the VNIR homologous bands of SMI and OLI revealed excellent linear relationships (R2 of 0.999) with insignificant RMSD (≤ 0.0015). Important agreements (0.63 ≤ R2 ≤ 0.96) were also obtained between homologous WVI regardless of the integrated spectral bands (i.e., red, green, and blue), yielding insignificant RMSD (≤ 0.01). Accordingly, these results pointed out that MSI and OLI sensors are spectrally similar, and their data can be used jointly to monitor accurately the spatial distribution of SAV and its dynamic in time and space in shallow marine environment, provided that rigorous data pre-processing issues are addressed.

Abderrazak Bannari et al.

Status: open (until 20 Jan 2022)

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Abderrazak Bannari et al.

Abderrazak Bannari et al.

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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 (WVI) 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.