There are many approaches to detecting in-water constituents, like color producing agents, in the field of remote sensing. Previously, harmful algal bloom (HAB) monitoring practices via satellite imagery analysis have held a similar goal of identifying a single constituent associated with HAB’s, particularly chlorophyll. Recently, the Kent State University Spectral Decomposition Method has been developed to better distinguish multiple water constituents, such as phylum level Cyanobacteria, Chlorophyta, Bacillariophyta, and Ochrophyta, as well as constituents of HAB’s, color dissolved organic matter (CDOM), and sediment within large water bodies. Using this technique, we can more effectively monitor HAB’s by separating mixed water signals using a varimax-rotated principal component analysis to remotely detect in-water constituents including HAB-causing cyanobacteria. The KSU Spectral Decomposition Method has been successful using sensors such as the Malvern Panalytical Fieldspec HH2, the NASA Glenn second-generation hyperspectral imagery (HSI2), MODIS, Landsat 8 OLI, and Sentinel 3A/B OLCI. It is apparent that better monitoring practices make better management practices possible, and our goal is to provide a method that will trailblaze the path to better water management practices globally. Case studies in Guantanamo Bay, Cuba and Lake Okeechobee, Florida are presented to document the success of the KSU Spectral Decomposition Method.