Predicting blooms of noxious cyanobacteria is critical for the protection and management of our freshwater resources. Although many tools and approaches exist for water quality monitoring, the integration of unoccupied aerial systems (i.e., drones) with geographical information systems (GIS) has opened the doors for relatively large-scale and rapid detection of cyanobacteria of small waterbodies when compared to standard lakeside sampling and satellite-based remote sensing methods. To evaluate the utility of using drones to monitor water quality in hypereutrophic ponds where phytoplankton are generally abundant and cyanobacteria dominate during the warm growing season, we conducted missions with an eBee Ag drone with a Duet M camera over 19 aquaculture ponds at one farm each month from June 2023 to August 2024 while simultaneously collecting water quality data for algal pigments (chlorophyll; all phytoplankton) and phycocyanin (cyanobacteria) and two measurements water transparency (Secchi depth and total suspended solids). Our results show strong positive correlations between the normalized difference vegetation index (NDVI) derived from drone imagery and all studied water quality parameters despite fairly large seasonal variation in slopes between NDVI and each studied water quality parameter that is likely mediated by the presence of phytoplankton deeper than the drone cameras are capable of measuring. Our results highlight the value of using drones to monitor surface water quality in aquatic systems for algal blooms that range widely in productivity but show that seasonal variation should not be overlooked considering that phytoplankton communities change in abundance and species composition over time.