Marine macroalgae fulfil an important role in coastal ecosystems providing food and habitat for wildlife. The impacts of climate change and increasing human encroachment exert significant pressures on coastal ecosystems. Monitoring of marine macroalgae communities provides information on the state of habitats and their structural changes. Remote sensing is an acknowledged tool for the monitoring of coastal vegetation at landscape scale. Imaging spectrometers with their narrow bandwidths enable the detection of characteristic absorption features and thus, the development of new classification methods based on hyperspectral data. In this paper, we adapt an easy to use slope-based classification approach to CHRIS/Proba data. The slope approach has been successfully applied to map intertidal habitats at the rocky shores of Helgoland (North Sea, Germany) using airborne AISA Eagle data. The application using satellite remote sensing has significant advantages for an operational use of mapping approaches, while the generally lower spatial and spectral resolution of satellite based data often require adaptation of the original approach. To test the applicability of the slope approach, we compare the classification results of CHRIS/Proba and AISA Eagle data. Furthermore, we examine the applicability of the slope approach in subtidal areas and present its suitability for intertidal and subtidal macroalgae mapping.