Firstly, we re-tune an algorithm based on empirical orthogonal functions (EOF) for globally retrieving the chlorophyll a concentration (Chl-a) of phytoplankton functional types (PFTs) from multi-sensor merged ocean color (OC) products. The re-tuned algorithm, namely EOF-SST hybrid algorithm, is improved by: (i) using 30% more matchups between the updated global in situ pigment database and satellite remote sensing reflectance (Rrs) products, and (ii) including sea surface temperature (SST) as an additional input parameter. In addition to the Chl-a of the six PFTs (diatoms, haptophytes, dinoflagellates, green algae, prokaryotes and Prochlorococcus), the fractions of prokaryotes and Prochlorococcus Chl-a to total Chl-a (TChl-a), are also retrieved by the EOF-SST hybrid algorithm. Matchup data are further separated for low and high temperature regimes based on different PFT dependences on SST, to establish the SST-separated hybrid algorithms which further shows improved performance as compared to the EOF-SST hybrid algorithm. The per-pixel uncertainty of the retrieved TChl-a and PFT products is estimated by taking into account the uncertainties from both input data and model parameters through Monte Carlo simulations and analytical error propagation. The uncertainty assessment provided within this study sets the ground to extend the long-term continuous satellite observations of global PFT products by transferring the algorithm and its method to determine uncertainties to similar OC products until today. Satellite PFT uncertainty is also essential to evaluate and improve coupled ecosystem-ocean models which simulate PFTs, and furthermore can be used to directly improve these models via data assimilation.

Gaussian decomposition and Singular Value Decomposition combined with Non-Negative Least Squares (SVD-NNLS) method are compared and combined to estimate the concentration of 18 phytoplankton pigments (names and abbreviations are displayed in Table 2) from phytoplankton absorption spectra. Results show that both methods tend to overestimate pigment concentration. Gaussian decomposition method provides robust estimation of TChl-a, TChl-b, Chl-c1/2, PSC and PPC. The estimates of TChl-a, Fuco, Diato, $\beta$-Caro, Prasino, TChl-b, Zea, Viola and Lut from SVD-NNLS show reasonable estimation accuracy, while the other pigments are subjected to relatively large prediction errors. The estimated pigments concentrations are further exploited based on Diagnostic Pigment Analysis to derive four phytoplankton functional types, i.e. diatoms, prymnesiophytes, green algae and prokaryotes. By the application of these two methods to the particulate absorption spectra collected by underway spectrophotometry during three summer cruises in 2015 - 2017 in the Fram Strait, continuous surface phytoplankton functional types are estimated along the cruise course.