Unmanned Aerial System (UAS) imagery has enabled very high-resolution multispectral image acquisition. Detection of wet areas and classification of land cover based on these images using the Machine Learning (ML) algorithm named Random Forest (RF) is our main purpose in this paper. Very high-resolution UAS images have been used as inputs for a machine learner in different scenarios. Starting from Random Forest Classifier, 3 different datasets consisting of RGB only, Multispectral only, RGB plus Multispectral for classification of the area have been used in three different test areas in two time epochs. Therefore, another objective of this study is to investigate performance of datasets (number of included bands and related wavelength) in the classification and wet area detection, to probe whether RGB (visible) light provide better results for our goal or multispectral data (Red-Edge and NIR) outperform in this analysis, or whether combining visible and Multispectral data is a superior alternative or not.