Nutrient enrichment of lakes and rising global temperatures promote the proliferation of cyanobacteria. Cyanobacterial blooms are becoming more widespread and increasing in frequency, size and duration, causing a cascade of detrimental changes to freshwater ecosystems. Improved lake monitoring techniques are required for early detection of bloom initiation to allow mitigation measures to be better targeted. Recent studies suggest remote sensing of the reflectance spectra of lakes can be a valuable tool to map the distribution of potentially toxic cyanobacteria in near-real time, though as yet, these are not widely used in New Zealand. In this study, three Rotorua and five Waikato Lakes were sampled to determine hyperspectral reflectance, phytoplankton composition and biovolume, chlorophyll a, phycocyanin concentrations, and optically active constituents. The data obtained were used to develop algorithms to examine the utility of reflectance data to aid in estimations of chlorophyll a and phycocyanin concentrations and phytoplankton composition with a particular focus on cyanobacteria. Results show that relationships amongst reflectance-related attributes, including chromophoric dissolved organic material, suspended solids and absorption properties of phytoplankton and pigment concentrations varied among the lakes and, to varying extents, over time. Properties of specific regions of reflectance spectra could be used to predict concentrations of these two pigments, but reflectance was not effective in separating the effects of phytoplankton composition from the other optically active variables. The two algorithms best suited these lakes for estimation of chlorophyll a and phycocyanin showed strong relations with the observed pigments at R2 = 0.83 and R2 = 0.92, respectively. This suggests reflectance spectra and the appropriate algorithm can be used to yield estimations of chlorophyll a and phycocyanin concentrations within inland waters with a wide variation of bio-optical characteristics. Such information could contribute to the development of an early warning system to predict cyanobacterial biovolume in New Zealand lakes using remote sensing.