The effective application of future ocean colour data for southern African waters requires an in-depth assessment of bio-optical algorithm performance, given the productive and highly variable nature of the oceanographic region. Phytoplankton degradation products, commonly known as gelbstoff, represent a large potential error source to future remotely sensed chlorophyll data, particularly in highly productive regions, where there can be a dramatic lack of covariance between phytoplankton and gelbstoff concentrations. Data relevant to gelbstoff character in the Agulhas Bank and southern Benguela systems are examined to assess the variability of gelbstoff chromophoric structure in these regions. These take the form of gelbstoff absorption spectra, and fluorescence excitation-emission matrices, performed on filtered seawater samples. Modelled bio-optical data, using conditions typical of high biomass marine environments, are then used to assess the performance of a proposed SeaWiFS combined algorithm with respect to variations in the magnitude and nature of gelbstoff absorption. It is shown that expected algorithm performance can be poor in bloom type scenarios, and that an appreciation of algorithm application is needed for effective interpretation of ocean colour imagery.