Flooding is a recurrent and dramatic natural disaster that affects several areas in the world, both in tropical and temperate regions, often causing great damages to lives and property, industrial settlements, infrastructures, artistic and historical sites, aside from dramatically affecting local ecosystems. It is expected that the frequency of these phenomena will increase in the next future, due to climate change and the occurrence of several extreme weather events. Accurate knowledge of flood extents is crucial to improve disaster management and to mitigate the impact of flood episodes, during both the emergency phase, when an overall view is needed in order to plan relief efforts, and the aftermath, for the assessment of damaged areas. The addition based on the SAR images collected at regular intervals and the comparison are made and analyzed. Using feature extraction process that creates meaningful sequential time series that can be analyzed and processed for change detection. In addition, the Enhanced Fuzzy C-Means (E-FCM) approach is used to cluster the various types of sub image details. The method was evaluated on real and simulated land cover change examples are and obtained according to more change detection accuracy.