The capabilities of Sentinel-MSI (2A/2B) and Landsat-OLI (8/9) in seagrass and algae species differentiation using spectral reflectance
Authors: Bannari Abderrazak , Ali Thamer Salim , Abahussain Asma 
Published: March 2022

Abstract. This paper assesses the reflectance difference values between the
respective spectral bands in the visible and near-infrared (VNIR) of
Sentinel 2A/2B Multi-Spectral
Instrument (MSI) and Landsat 8/9 Operational Land Imager (OLI) sensors for seagrass, algae, and
mixed species discrimination and monitoring in a shallow marine environment
southeast of Bahrain Island in the Arabian Gulf. To achieve these, a
field survey was conducted to collect samples of seawater, underwater
sediments, seagrass (Halodule uninervis and Halophila stipulacea), and algae (green and brown). In addition, an
experimental mode was established in a goniometric laboratory to simulate
the marine environment, and spectral measurements were performed using an
Analytical Spectral Devices (ASD) spectroradiometer. Measured spectra and their transformation using the
continuum-removed reflectance spectral (CRRS) approach were analyzed 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 (WVIs) to investigate the potential of red, green,
and blue bands for seagrass and algae species discrimination. The results of
spectral and CRRS analyses highlighted the importance of the blue, green,
and near-infrared (NIR) wavelengths for seagrass and algae detection and likely
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. Instead of the red
band, the integration of the blue or the green band in WVI increases their
power to discriminate submerged aquatic vegetation (SAV), particularly the water adjusted vegetation index (WAVI),
water enhanced vegetation index (WEVI), and water transformed difference vegetation index (WTDVI). These results corroborate the spectral and the CRRS
analyses. 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 more scattering in this part of the spectrum. Furthermore,
statistical fits (p<0.05) between the reflectance in the
respective VNIR bands of MSI and OLI revealed excellent linear relationships
(R2 of 0.999) with insignificant root mean square difference (RMSD) (≤ 0.0015). Important
agreement (0.63 ≤ R2 ≤ 0.96) was also obtained between
respective 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
environments, provided that rigorous data pre-processing issues are
addressed.