Remote sensing technology has played a significant role in the dynamic information extraction of crop information and mapping. The accuracy of crop type information using this technology needed ground truth data and high-resolution data set. However, accurate crop classification remains challenging in both the same crop with different spectral and different crops with same spectrum phenomenon in the field. Now days, Unmanned Aerial Vehicle (UAV)-based high-resolution images gets the popularity for its high spatial resolution and applicability to solve scientific problems. Therefore, this study aims to evaluate the potential of UAV images for crop field classification blending with Sentinel 2A satellite images. In this study, Crop types was identified such as Teff, Wheat, Faba bean, Barley and Sorghum. UAV data, sentinel 2A and fieldwork data were acquired. The UAV data was preprocessed like camera calibration, photo alignment, dense point cloud generation based on the estimated camera positioning of scouting crop types. Then, orthomosaic UAV image was generated from single dense point cloud. UAV data was fused with Sentinel 2A (the medium resolution) satellite data using Gram Schmidt pan sharpening method to improve the spectral variability and evaluating the accuracy of crop type classification. For crop classification, machine-learning algorithm on R software was applied using the Random forest (RF) and Maximum likelihood. The vegetation indices of the NDVI for UAV and S2A was carried out and correlation was performed. The results show that RF classifier algorithm classifies the crop types with 94% overall accuracy whereas the Maximum likelihood classified with 90% overall accuracy. The correlation between the vegetation indices shows the fusion of UAV and S2A for crop type classification was 0.57. This indicates that how much the fusion of the sensors was fine for classifications. It is mostly significant that if the area of interest enhanced then easily detect the regions stressed, monitor and crop type mapping of Ethiopian Agricultural practices using blending of UAV and newly satellite launched by Ethiopian.