Due to the need to understand marine processes and changes in coastal environments, several
algorithms and satellites have been developed in recent years.
Currently, ocean color sensors are widely used to monitor, map and detect the smallest
variations in phytoplankton populations using measurable parameters. Chlorophyll is known to
be the main parameter and indicator for assessing water quality and biochemistry.
This study focuses on chlorophyll mapping and the identification of phytoplankton groups in
the Algerian basin using a satellite approach. First of all, a performance comparison was made
between four images from four different sensors acquired in the same day (MODIS, OLI, MSI,
and OLCI) in order to identify the best in terms of spatial and spectral resolution. To do so,
MODIS level 2 data and OLI, MSI and OLCI level 1 data were used. The C2RCC model was
applied to calculate reflectance and chlorophyll concentrations for the level 1 images.
The second part of the study is devoted to the realization of an annual cycle of Chl-a and for
the different phytoplankton groups, by applying empirical abundance-based algorithms (Hirata
et al., 2011) on Sentinel-3 Level 1 images from 2019.
Discussion of the results of the first part revealed that for our case study, images from Sentinel3 are more relevant compared to other sensors. The second part of the study showed that the
distribution of phytoplankton groups and chlorophyll concentration are variable depending on
the region and its climatology.
Key words: Annual cycle, , C2RCC, Chlorophyll, phytoplankton, Ocean color.