The launch of Sentinel-1A and Sentinel-1B initiated a new age in Synthetic Aperture Radar (SAR) systems for earth observation. For the first time, multitemporal SAR imagery from all over the world is freely available. SAR images are an essential information source for monitoring and mapping wetlands since the SAR signals are able to penetrate through the vegetation and provide information about soil moisture characteristics and above-ground vegetation. However, vegetation type identification in wetlands using high temporal resolution SAR data requires more investigation. In this work, we consider a portion of the Bajo Delta of the Paraná River, a wide coastal freshwater wetland located in Buenos Aires, Argentina. Due to the high amount of biomass in all its extent, mapping and monitoring this area is particularly challenging. The main objective of this work are: to study the potential of multitemporal Sentinel-1 datasets for land cover maps in densely vegetated areas, to classify the study area and compare the performance of the different multitemporal Sentinel-1 datasets. The Sentinel-1 images were processed using SNAP. The classification was done using Python (libraries: sklearn, pandas, numpy, and gdal).