Vegetation mapping is one of the most critical challenges of remote sensing society in forestry applications. Sentinel-1 dataset has the potential of vegetation mapping, but because of its limited number of polarizations, full polarized vegetation indexes are not accessible. The Sentinel-2 dataset is more suitable for vegetation mapping because a wide variety of vegetation indexes can be extracted from them. Handling this large number of vegetation indexes needs a robust feature extractor. Convolutional Neural Networks (CNN) extract relevant features through their deep feature layers structure and throw out disturbances from small to large scales. Hence, they can be far useful for classifying remote sensing data when the number of input bands is considerable. After pre-processing Sentinel-1 and 2 datasets and extracting the dual-polarized and optical vegetation indexes, we fed the sentinel-1 vegetation indexes alongside the VV and VH sigma Nought bands to a Random Forest (RF) and 1D CNN classifier. Also, 13 spectral features of the Sentinel-2 and the extracted indexes like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR) were imported to a RF and 1D CNN. The classification result of Sentinel-1 data showed that Dual Polarized Soil Vegetation Index (DPSVI) is a good indicator for discriminating vegetation pixels. Also, the experiment on the Sentinel-2 dataset using 1D CNN resulted in True Positive Rate (TPR) and False Positive Rate of 0.839 and 0.034, respectively.