Identification of flooded areas using time series statistical calculations and based on integrating radar and optical data

Natural hazards have always had devastating effects on human life, of which the flood is one of the most serious. Therefore, providing rapid flood identification methods for crisis management is necessary. The purpose of this study is to provide a method with proper accuracy and speed in preparing flood maps. In this study, two time series of Sentinel-1 and Landsat-8 data were used to prepare a flood intensity map by integrating statistical calculation and index extraction. The proposed algorithm is that first the map of permanent water surfaces is automatically prepared by optical images over a period of 5 years. Then, to determine the flood intensity in different regions, statistical calculations are used on the time series of radar images, and finally, using the Normalized difference flood index, which can quickly identify the flood, the final flood map is obtained. The proposed approach has been implemented following the occurrence of the flood of 1398 in two regions of Golestan and Khuzestan, which have different geographical conditions. Assessments performed with the help of ground truth maps and confusion matrices, and in addition, McNemar test was used for more complete analysis. The implementation of the algorithm in the Google-earth-engine environment showed that this method, in addition to having high accuracy, allows the use of hundreds of images without the need for special hardware. The overall accuracy in a period of time in Golestan and Khuzestan was 91.84 and 97.36, which indicates the high generalizability of the algorithm in regions with different extent.