عنوان مقاله [English]
Flood is known as one of the natural disasters that cause financial and human losses not only in developing countries but also in the developed countries. Synthetic Aperture Radar (SAR) sensors are an essential data source for flood crisis planners and experts, because they have the ability to image the earth's surface independently of weather conditions and time of the day. This advantage coupled with cloud computing platforms such as Google Earth Engine (GEE) provide a tremendous opportunity for the crisis response community for effective management plans. It allows them to quickly access ready data for analysis which is of great importance in case of flooding. The purpose of this research is to quickly monitor the flood of Kashkan river. The algorithm presented in this study uses time series images of Sentinel-1 and Landsat 8 along with other auxiliary data for flood monitoring in the GEE system. This algorithm relies on multi-temporal SAR statistics to identify unexpected floods in near real-time. Additionally, historical Landsat-based surface water class probabilities are used to distinguish unexpected floods from permanent or seasonal surface water. Based on the results of this research, Sentinel-1 images have an acceptable performance for flooded areas detection in Sentinel-1 images. Therefore, managers can use this method to obtain flood information and locations in order to reduce flood damages.