Flood disasters are a major factor threatening agriculture, human life, and property safety. Suppose the areas affected by flood disasters can be effectively delineated and reasonably predicted. In that case, it will not only be beneficial for agricultural production but also provide convenience for disaster prevention and relief. The Segment Anything Model (SAM) emerged, providing innovative ideas for many visual tasks. SAM has excellent feature extraction ability in network models, allowing it to adapt to different scenes and effectively segment various objects, so it also has great application prospects in remote sensing images. Therefore, this article utilizes the excellent feature extraction ability of the SAM to enable the model to adapt to downstream tasks of remote sensing image segmentation. This article will use the decoder structure of CycleGAN as the decoder. Due to the large proportion of background in remote sensing images, this article also enhances the loss function to suit remote sensing image tasks better. The MMFlood dataset in this article consists of Synthetic Aperture Radar (SAR) images combined with a digital elevation model (DEM). The experimental results demonstrate improved performance with the assistance of SAM compared to Unet++.