With the extensive use of ocean color (OC) satellite products, diverse algorithms have been developed in the past decades to observe the phytoplankton community structure in terms of functional types, taxonomic groups and size classes. There is a need to combine satellite observations and biogeochemical modelling to enable comprehensive phytoplankton groups time series data and predictions under the changing climate. A prerequisite for this is continuous long-term satellite observations from past and current OC sensors with quantified uncertainties are essential to ensure their application. Previously we have configured an approach, namely OLCI-PFT (v1), to globally retrieve total chlorophyll a concentration (TChl-a), and chlorophyll a concentration (Chl-a) of multiple phytoplankton functional types (PFTs). This algorithm is developed based on empirical orthogonal functions (EOF) using satellite remote sensing reflectance (Rrs) products from the GlobColour archive (https://www.globcolour.info/). The algorithm can be applied to both, merged OC products and Sentinel 3A OLCI data. Global PFT Chl-a products of OLCI-PFT v1 are available on CMEMS under Ocean Products since July 2020. Lately we have updated the approach and established the OLCI-PFT v2 by including sea surface temperature (SST) as input data. The updated version delivers improved global products for the aforementioned PFT quantities. The per-pixel uncertainty of the retrieved TChl-a and PFT Chl-a products is estimated and validated by taking into account the uncertainties from both input data (satellite Rrs and SST) and model parameters through Monte Carlo simulations and analytical error propagation. The uncertainty of the OLCI-PFT products v2 was assessed on a global scale. For PFT Chl-a products this has been done for the first. The uncertainty of OLCI-PFT v2 TChl-a product is in general much lower than that of the TChl-a product generated in the frame of the ESA Ocean Colour Climate Change Initiative project (OC-CCI). The OLCI-PFT algorithm v1 and v2 have also been further adapted to use a merged MODIS-VIRRS input. Good consistency has been found between the OLCI-PFT products derived from using input data from the different OC sensors. This sets the ground to realize long-term continuous satellite global PFT products from OLCI-PFT. Satellite PFT uncertainty, as provided for our products, is essential to evaluate and improve coupled ecosystem-ocean models which simulate PFTs, and furthermore can be used to improve these models directly via data assimilation.