Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate numbers of ground-truth samples and without consideration of the imbalances in the ground-truth datasets. In this study, we aim to address these issues by conducting class-specific predictions using ensemble modelling to map seafloor sediment distributions with minimal ground-truth data combined with hydroacoustic datasets. The resulting class-specific maps were then assembled into a sediment classification map, in which the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. The methods presented can also be used for other underwater exploration studies with minimal ground-truth data. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the marine conservation area during two different short timescales: 2016–2018 (17 months) and 2018–2019 (4 months). The analyses of the sediment shifts showed that the western area of the Sylt Outer Reef experienced sediment fluctuations but the morphology of the bedform features was relatively stable. The results provided information on the seafloor dynamics, which can assist in the management of the marine conservation area.