HABSat-1: Assessing the feasibility of using CubeSats for the detection of cyanobacterial harmful algal blooms in inland lakes and reservoirs

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 (r(2) > 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 (rho(680) - rho(650) - (rho(708) - rho(650)) with an r(2) of 0.81, the 2B4D1 algorithm (rho(650)/rho(620)) with an r(2) of 0.844, and the NDCI41 algorithm (rho(708) - rho(620))/(rho(708) + rho(620)) with an r(2) 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.