A new era of high spatial resolution imaging has just emerged with the launch of CubeSat satellites. CubeSat constellations overcome the traditional trade-off between high spatial and high temporal resolutions of spaceborne imagers. This new imaging technology can potentially contribute to monitoring the dynamics of inland and nearshore waters with an unprecedented combination of daily revisits and meter-scale spatial resolutions. For instance, estimation of water quality parameters can benefit from the very high spatiotemporal resolution of CubeSat imagery for monitoring subtle variations and identification of hazardous events like algal blooms.

In this talk, I will focus on the potentials and limitations of CubeSat imagery in terms of aquatic remote sensing. This will be demonstrated by presenting several case studies with applications ranging from water quality to bathymetry. Physics-based and machine learning approaches will be discussed for retrieving biophysical parameters in inland waters (rivers and lakes) exploiting CubeSat imagery