There is a strong need to possess quality information on the global distribution of different phytoplankton functional types. Numerous attempts to obtain this information have been conducted using both datasets from various multispectral satellites and datasets collected from different hyperspectral satellites. In both cases, the results obtained had some strengths but also some defect aspects: in the case of the maps obtained through the use of multispectral instruments, the strengths lay in the great spatial resolution shown by the maps, while the defect aspects consisted of their low spectral resolution. Conversely, the maps obtained through the use of the datasets formed through the activity of hyperspectral instruments represented more accurate PFT concentration values but related to much larger pixels. Thus, these were maps with high spectral resolution but low spatial resolution. In recent years an approach, called synergistic, has been experimented with, which aims at obtaining maps that possess the best characteristics of both types of instruments. Through data assimilation techniques, datasets from hyperspectral and multispectral instruments are fused in such a way that maps that have both good spectral resolution and good spatial resolution can be produced. An example of this type of approach can be found in the work published in 2017 by Losa and colleagues, in which an algorithm was developed, called SynSenPFT, that allowed them to fuse data from OC-CCI and SCIAMACHY instruments to obtain maps with the above characteristics. This thesis work is in the vein of studies such as the one just mentioned and aims to produce a synergistic method that can be applied to datasets from instruments of more recent conception than those considered by Losa and colleagues, namely, the OLCI and TROPOMI instruments of the European Space Agency’s Copernicus system. Such a method can be used to produce a set of datasets representative of a time span spanning several months, on which time series analyses can be conducted to understand spatiotemporal variations in diatoms and cyanobacteria distributions. In addition, such datasets can be used to deepen the understanding of important biological information about these two PFTs, such as, for example, their phenology.