Johansen RA, Beck R, Stumpf R, Lekki J, Tokars R, Tolbert C, McGhan C, Black T, Ma O, Xu M, Liu H, Reif M, Emery E. 2019. HABSat-1: assessing the feasibility of using CubeSats for the detection of cyanobacterial harmful algal blooms in inland lakes and reservoirs. Lake Reserv. Manage. 35:193–207.The detection of cyanobacterial harmful algal blooms (CHABs) in freshwater lakes and reservoirs via satellite remote sensing remains a challenge. This is partially due to the spectral, spatial, and temporal configurations of most satellite imagers, which are designed for large terrestrial applications. This research evaluated the prelaunched performance of HABSat-1 for the detection of CHABs in inland waters. This study used the CASI hyperspectral airborne imager to mimic the potential configurations of the imager for HABSat-1. Synthetic HABSat-1 imagery was atmospherically corrected to reflectance, then used to evaluate the performance of 14 reflectance algorithms to estimate chlorophyll a (Chl-a), phycocyanin (PC), and the sum of pheophytin-corrected chlorophyll a and pheophytin a (SUMReCHL) concentrations. All algorithms use narrow spectral bands centered at 620 nm, 650 nm, 680 nm, and 708 nm, corresponding to important spectral reflectance features associated with CHABs. Eleven Chl-a and 10 PC algorithms performed well (r2 > 0.7), indicating the configuration of HABSat-1 is well suited to the detection of CHABs in cyanobacteria-dominated waterbodies. The highest performing algorithms were the CI324 algorithm (ρ(680) – ρ(650) – (ρ(708) – ρ(650)) with an r2 of 0.81, the 2B4D1 algorithm (ρ(650)/ρ(620)) with an r2 of 0.844, and the NDCI41 algorithm (ρ(708) – ρ(620))/(ρ(708) + ρ(620)) with an r2 of 0.755 for Chl-a, PC, and SUMReCHL. These promising results demonstrate that the use of relatively inexpensive customizable CubeSats coupled with simple reflectance-based algorithms is likely to be sufficient for the detection and estimation of CHABs in inland lakes and reservoirs.