Abstract: Harmful algal blooms degrade water quality and can adversely impact human and wildlife health. Monitoring these at scale is difficult due to the lack of coincident data. Additionally, traditional field collection methods are labor- and cost-prohibitive, resulting in disparate data collection in capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to alleviate this by leveraging large, public, water quality databases and open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usability of four common chlorophyll a algorithms as a proxy for detecting and mapping algal blooms nationwide. Chlorophyll a data were aggregated from spatially diverse sites across the continental US between 2019 and 2022. The 2BDA and the NDCI algorithms were the most viable for broadscale mapping of chlorophyll a, which performed moderately well, encompassing highly diverse spatial, temporal, and physical conditions. The most compatible field data acquisition method was the chlorophyll a, water, trichromatic method, uncorrected. Resulting data indicate the feasibility of utilizing band ratio algorithms for broadscale detection and mapping of chlorophyll a as a proxy for HABs, which is valuable when coincident data are unavailable or limited.