Flood damage assessment is often necessary for early flood management. To this end, this paper provides a framework of rapid estimation of flood damage and identification the flooded areas in March 2020 using Sentinel-1 satellite data. To this end, in the present study, after applying the necessary pre-processing in SNAP6 software, the backscattering coefficient, or sigma naught for two images related to before and after the flood occurrence was extracted. The backscattering coefficient histogram was used to separate the image into two classes including water and non-water and the threshold of 0.01 was obtained based on it. Then, by applying mathematical operations on both backscattering images, the binary image of water and non-water was prepared and the flooded areas were determined based on the difference between the two images. After detecting the flooded areas, Sentinel images were classified into three classes including waterbody before flood, flooded area and other lands using supervised classification algorithms. The results indicated the high accuracy of the Random Forest algorithm with kappa of 0.92 compared to other algorithms. By overlaying the land use and flooded areas maps, the inundation percentage for each land use was determined. According to the results, bare lands with 27.9 percent, residential land with 16 percent and rangelands with 12 percent had the highest inundation percentage, respectively.