Neural-variational algorithm adaptation from SeaWiFS to MODIS sensor for analysis of atmospheric and oceanic parameters

<p>Particularly interesting because of its socio-economic contribution, the Canary upwelling system encompasses a number of regions with very special characteristics.&#160;The wind that blow over this system induces a permanent upwelling off Mauritania and a seasonal upwelling in the south off Senegal, which boosts the development of phytoplankton.&#160;To refine the understanding of the phytoplankton in this region (its distribution, variability, response to physical forcings), we combine a number of tools and methods to arrive at a better estimate, and a better monitoring of the concentration of chlorophyll-a (Chl-a), an input parameter for primary production models.&#160;Remote sensing of ocean color has particularly interesting advantages, both in terms of global sampling and data acquisition frequency.&#160;This method is all the more interesting since ocean color algorithms can be adapted to reduce bias when standard methods have limitations.&#160;The regional ocean color algorithm called SOM-NV (Self-Organized Map-Neuro-variational) offers the advantage of making atmospheric correction in the presence of absorbent aerosols, especially desert dust, which sweeps this area permanently and which compels the standard algorithm to apply a mask when atmospheric optical thickness exceeds a threshold of 0.3.&#160;This contribution of SOM-NV in the process of atmospheric correction allowed us to 1&#160;: obtain a better reflectance spectra, and as a consequence offer a better estimate of the Chl-a concentrations&#160;; 2&#160;: acquire a larger number of pixels by processing pixels with an optical thickness greater than 0.3&#160;; 3&#160;: go beyond the general distribution towards the distribution of dominant groups according to the Physat spectral method.&#160;The synthesis of 16 years of data from the MODIS-Aqua sensor, allowed us to revisit the seasonality of Chl-a distribution and its cross-shore particularityand an extension towards the open sea which differs according to the season.&#160;The highest coastal values are measured in winter and spring, when upwelling intensifies, while the lowest values are measured in summer, when warm, nutrient-poor equatorial waters freplace upwelling waters along the Senegalese coast.&#160;This change in water masses impacts phytoplankton communities. According to the work of some authors, nanoplankton gradually replaces diatoms, known to be present during the upwelling season.&#160;This makes this region a particularly interesting zone for monitoring dominant groups of phytoplankton, knowing that the change in community impacts the upper levels of the marine food chain, with phytoplankton playing a leading role.</p><p>Keywords: Phytoplankton, ocean color, upwelling, atmospheric correction, dust</p>