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In this paper, the potential of Sentinel-1A and Sentinel-2A satellite images for land cover mapping is evaluated at three levels of spatial detail; ex ploratory, reconnaissance, and semi-detailed. To do so, two different image classification approaches are compared: (i) a traditional pix el-wise approach; and (ii) an object- oriented approach. In both cases, the classification task was conducted using the " RandomForest" algorithm. The case study was also intended to identify a set of radar channels, optical bands, and indices that are relevant for classification. The thematic accuracy of the classifications displays the best results for the object-oriented approach to ex ploratory and recognition levels. The results show that the integration of multispectral and radar data as ex planatory variables for classification provides better results than the use of a single data source. [ABSTRACT FROM AUTHOR]