Objective: The flood disaster is a great threat to the national economy and people"s property along the lakes and rivers in China. Synthetic Aperture Radar (SAR) adopts active imaging methods which can realize all-weather imaging and ensure continuous observation of flood disaster areas under severe weather such as heavy rains and clouds. The current flood monitoring methods based on SAR images often have problems such as difficulty in threshold selection, high computational cost, or inefficient use of time series information. Aiming at above problems, this paper makes full use of information from time series SAR image sequence in order to design an effective and stable method for flood monitoring which can be adapted to complex areas. Method: Through preprocessing and statistical analysis of the image sequence, two normalized difference indexes, including Submerged Range Extraction Index (SREI) and Submerged Range in Vegetation Area Extraction Index (SRVEI), are designed and applied to calculate the candidate area of flood inundation. Then, the adaptive selection method of threshold for flood extraction is given based on the stability assumption of vegetation seasonal distribution in the same area. Finally, considering the characteristics of the surrounding features of the lakes in China, a post-processing process is designed, including removing spots and holes, excluding areas with large slopes, and filtering out fragmented areas with large rectangular degrees. The post-processing process is used to optimize the extraction area for the final results of flood inundation range. Result: In the experiment, this paper takes the East Dongting Lake basin as the main research area to verify the effectiveness of the proposed method by comparing the extraction accuracy with other four methods. Experimental results prove that the overall extraction accuracy of the proposed method is higher than that of all the comparative methods. To achieve the purpose of flood disaster monitoring and evaluation, an analysis of the flood disaster situation in the East Dongting Lake Basin in 2020 and an analysis of flood submerged land cover types are carried out. In addition, the method has also been successfully applied to the data of the East Dongting Lake basin and the Poyang Lake basin in previous years to verify that the method can be applied across time and space. Conclusion: Based on the time-series SAR image sequence, this paper proposes an effective method, forms a detailed process flow, and constructs a general framework for flood monitoring.The proposed method has advantages of simple parameter setting and low user dependence on threshold determination, Experiments show that the method has high extraction accuracy of submerged areas with good robustness and versatility. The proposed method can be applied to different flood monitoring scenarios across time and space, and can preliminarily distinguish different attributes of submerged areas, which also provides a certain reference for the flood disaster monitoring, assessment, and early warning in other regions.