Algal blooms are a recurrent problem globally, with a number of multispectral ocean colour satellites designed to enable monitoring and capture critical spectral information. However, monitoring using satellite-borne hyperspectral sensors is limited due to their low revisit period, smaller spatial extents, and capture-to-order systems. Recent and upcoming satellite missions, including EnMAP, PRISMA, Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR), will broaden the opportunity to apply hyperspectral data to monitoring campaigns of algal blooms. Simulated ground truth models are generated using in situ hyperspectral spectra collated in the Global Reflectance community dataset for Imaging and optical sensing in Aquatic environments (GLORIA) database, which includes hyperspectral signatures of water quality parameters in coastal and inland water bodies, including chlorophyll-a. Simulated hyperspectral images, also known as hyperspectral data cubes, are generated from these ground truth models using the versatile hyperspectral imaging simulator HYSIMU. Parameters of the simulated ground truth models tested using HYSIMU include spatial and spectral resolution, spatial heterogeneity in the distribution of spectral signatures, and complexity of different spectra used to generate the models. The ability to generate a high volume of simulated ground truth models, and derived hyperspectral imagery for particular platform-sensor systems, has been demonstrated as essential for assessing a hyperspectral mission’s ability to detect and characterize algal bloom features, enabling a better understanding of potential applications and limitations of established, new, and upcoming missions. The workflow for the simulations and applications for algal blooms in Lake Erie will be presented.