Located along the east coast of Florida, the Indian River Lagoon (IRL) is a shallow-marine estuary that extends along 240 km of coastline. Historically, freshwater flowing into the IRL has transported high concentrations of nitrogen and phosphorus runoff from agricultural fertilizers and septic systems. As a result, eutrophic waters have driven the growth of various types of harmful algal blooms (HABs). Previous remote sensing research has focused on monitoring water quality by identifying the spectral characteristics of color producing agents (CPAs) associated with HABs through the use of ocean color chlorophyll-a algorithms. The ability to reliably distinguish CPAs of HABs, color dissolved organic matter (CDOM), and suspended sediment within water bodies through remote sensing techniques has become critically important for monitoring regional water quality. Recent statistical techniques for processing Landsat 8 and Sentinel 3 imagery have expanded retrievals beyond chlorophyll-a and corrected for atmospheric interferences. The Kent State spectral decomposition method, a type of Varimax-rotated Principal Component Analysis (VPCA), is used to process visible reflectance spectra (400-700nm) from multispectral and hyperspectral imaging systems. The VPCA decomposition describes the total percentage of variability of CPAs mixed in the water column and determines the leading spectral components of the satellite image that contribute to the overall signal. We identify these leading spectral components obtained from this analysis with lab measured reflectance spectra, such as brown tide cultures, A. lagunensis, to qualitatively assess areas of the IRL which have relatively high or low proportions of CPAs over time. Results using the VPCA method have identified A. lagunensis constituents within the Banana River region of the IRL and have since been validated with in-situ biovolume and water quality measurements.