Monitoring mangrove health and distribution requires reliable methods that can be undertaken rapidly and at a resolution that optimises costs and accuracy. The Landsat record has been used for this purpose, but its application has been limited by the capacity to provide accurate results that distinguish mangrove from adjoining communities. The Australian Geoscience Data Cube provides a framework for exploring the Landsat record from 1987 onwards, and as pre-processing has already been undertaken there are efficiencies gained using this resource. Using the Data Cube, we exploited the differential spectral signature of mangrove under high tide and low tide conditions at Darwin Harbour, Australia, a relatively stable mangrove ecosystem, using image composites that combined imagery corresponding to the highest 10% and lowest 10% of tides. By applying the automated RandomForest classification technique, we demonstrate the capacity to accurately determine the extent of mangrove zones. Classification identified five mangrove zones: (1) seaward margin dominated by Sonneratia alba, (2) Rhizophora zone dominated by Rhizophora stylosa, (3) tidal flat dominated by Ceriops tagal, (4) landward salt flat and (5) marginal hinterland. Image composites that included high and low tide images achieved the best outcomes with kappa co-efficient of 0.81 and overall accuracy of 82%.