Cyanobacteria harmful algal blooms (cyanoHABs) are a global problem with human health, environmental, and economic concerns. The severity and frequency of toxic cyanoHABs are expected to increase with climate change. Remote sensing has proven to be a useful tool in monitoring cyanoHABs. This study uses remote sensing observations from Sentinel-3Ocean Land Color Imager (OLCI)combined with the Spectral Shape Algorithm (SSA) to detect the presence of cyanobacteria in numerous waterbodies throughout the Northeast United States over 2016to 2020. The ACOLITE processor was used for the atmospheric correction of the Sentinel-3 OLCI data, as it has been shown to provide more accurate results over water bodies. Citizen scientist data from two EPA apps, BloomWatch and CyanoScope, were used to guide the cyanoHAB detection results. This study focuses on three different aspects of using the remote sensing methods to detect cyanobacteria harmful algal blooms: (1) examine two different reflectance center band combinations of the SSA at 665 nm and 681 nm, (2) compare SSA controls for non-bloom periods in winter/spring to the summer bloom periods, and (3) examine bloom areas within waterbodies. The SSA method with the center band at 665 nm was found to perform better than the SSA method with the center band at 681 nm. Additionally, cyanoHAB environmental precursors were determined and their effects on the formation of cyanoHABs were assessed, with maximum air temperature having the greatest impact on cyanoHABs formation.