Extraction of water body information from synthetic aperture radar (SAR) images plays a crucial role in urban flood monitoring. Traditional threshold segmentation methods are commonly employed in water body extraction due to the advantages of not requiring labeled samples and high computational efficiency. However, in complex urban terrains, the optimal threshold may be offset by data quality. To address this challenge, we introduce an adaptive iterative thresholding segmentation method guided by optical prior water body information. First, optical images captured before the disaster are used to identify inherent water body areas within the city. Second, the SAR data coverage areas corresponding to the inherent areas are taken as prior information on water bodies during the disaster. Finally, an adaptive iterative thresholding segmentation method based on prior water body information is constructed to automatically extract urban water bodies from SAR images. To validate the effectiveness of this approach, water body extraction experiments in urban inundation zones are conducted using Sentinel-1 and GF-3 SAR data in Beijing, Shijiazhuang, and Zhengzhou. The results show that the overall accuracy of the method used in this study is 79%, 95%, and 97.8% on Sentinel-1 images in Beijing, Shijiazhuang, and Zhengzhou, respectively, and 91.1% on GF-3 images in Zhengzhou. The experimental results in different regions are favorable and have a certain universality. Meanwhile, compared with traditional threshold segmentation methods, this method improves the accuracy of urban water extraction by at least 2% on Sentinel-1 and GF-3 SAR images, providing a more effective technical means for water extraction in urban flood inundation areas.