Due to the high spatial heterogeneity and temporal variability, wetlands are one of the most difficult ecosystems to observe using remote sensing data. With the additional Sentinel-2 vegetation red-edge bands, an improvement of the vegetated classes classification is expected. In order to investigate the influence of the Sentinel-2 red-edge bands, in this paper, we use one Sentinel-2 satellite image acquired in the summer period, in August, and we evaluate two classification scenarios over wetland classes. As a study area, the Central Anatolian region in Turkey has been selected. The first scenario excludes the red-edge bands, while in the second scenario are included all red-edge bands in the classification dataset where two different wetland classes, intensive vegetated wetland classes such as swamps and partially decayed vegetated wetland areas such as bogs, have been classified using Support Vector Machines (SVMs) learning classifier. The classes were defined using high-resolution images from Unmanned Aerial Vehicle (UAV) obtained on the same date with the overpass of the Sentinel-2 satellite over the study area. As expected, the results showed significant improvement of the intensive vegetated wetlands, with more than 30% in both user and producer accuracy, while no significant changes have been noticed in the partially decayed vegetated wetlands. For future studies, we recommend evaluating the influence of the Sentinel radar data over wetland areas.