Aiming at the issues of over-fitting and limited generalization ability of classical U-Net model in extracting the flood retention area in the eastern foot of Helan Mountain, a new convolutional neural network model (WEU-Net) for the water body information extraction was proposed based on images of Sentinel-1 synthetic aperture radar(SAR) satellite and Sentinel-2 multispectral instrument(MSI) satellite.This model simplifies the network structure by reducing the number of convolutional kernels and the skip connection levels between encoder and decoder, and introduces residual blocks to enhance the feature extraction ability, which makes up the loss of image features due to the simplified model.In terms of data set, the Sentinel-1 water index was constructed by stepwise regression method combined with modified normalized difference water index (MNDWI), and the feature richness of data set from Sentinel-1 satellite was optimized.The main conclusions are as follows: the overall accuracy of the WEU-Net is 98.19% and the F1 score is 0.946 9, which are 0.357 7% and 0.948 8% higher than the classical U-Net model, respectively, and the training time is shortened by 49.30%; after fusing the sentinel-1 water index, the overall accuracy and F1 score were improved by 0.51% and 3.16%, respectively.