Estimation of phytoplankton pigments from ocean-color satellite observations in the Senegalo–Mauritanian region by using an advanced neural classifier

Abstract

We processed daily ocean-color satellite observations to construct a monthly climatology of phytoplankton pigment concentrations in the Senegalo–Mauritanian region. Our proposed new method primarily consists of associating, in well-identified clusters, similar pixels in terms of ocean-color parameters and in situ pigment concentrations taken from a global ocean database. The association is carried out using a new self-organizing map (2S-SOM). Its major advantage is allowing the specificity of the optical properties of the water to be taken into account by adding specific weights to the different ocean-color parameters and the in situ measurements. In the retrieval phase, the pigment concentration of a pixel is estimated by taking the pigment concentration values associated with the 2S-SOM cluster presenting the ocean-color satellite spectral measurements that are the closest to those of the pixel under study according to some distance. The method was validated by using a cross-validation procedure. We focused our study on the fucoxanthin concentration, which is related to the abundance of diatoms. We showed that the fucoxanthin starts to develop in December, presents its maximum intensity in March when the upwelling intensity is maximum, extends up to the coast of Guinea in April and begins to decrease in May. The results are in agreement with previous observations and recent in situ measurements. The method is very general and can be applied in every oceanic region.