This dissertation discusses the cyanobacterial bloom phenology in three anthropogenically impacted regions of the Great Lakes: western Lake Erie, Saginaw Bay, and Green Bay. A detection algorithm was applied to ocean color satellite imagery, and a timeseries was constricted from each of the basins using either data from the MODIS sensor (Saginaw Bay), the MERIS sensor (Green Bay), or a combination of the two (western Lake Erie). The sensors have a high temporal resolution, collecting imagery several times a week. The algorithm used, the Cyanobacterial Index (CI), was applied to the imagery. The CI imagery was then sampled into fifteen 10-day composites throughout the bloom season (defined here as June 1 – October 31). Each of the five months will have three composites (each spanning ~10 days). From this point the bloom climatology is shown and the variability of each region is addressed. The interannual variability of the cyanobacterial blooms can be low (factor of ~2 in Saginaw Bay) or high (differing by a factor of ~20 in Green Bay and western Lake Erie). Various ancillary datasets describing the physical environment of each region were assembled including: field data, modeled data, remotely sensed data, or some combination therein. Impacts of associated cyanobacterial biotoxins were addressed and statistical models were formulated to explain any variability. The dissertation will also cross compare the three basins with one another in an effort to determine the similarities as well as differences among the regions. Management recommendations are given at the end of each of the three subsequent chapters to deter potential detrimental impacts of the blooms and their associated toxins.