###### Abstract  
Flooding is one of the most frequently occurring meteorological disasters nowadays, and its occurrence can cause significant socio-economic losses. Aiming at the problem that traditional optical remote sensing makes it difficult to monitor floods, this paper designs a scheme to jointly extract the scope of the affected area by using heterogeneous satellite-based SAR images acquired at different times within the flood period. This paper takes the \"7-20\" rainstorm in Zhengzhou City as an example and uses two kinds of heterogeneous SAR images, Sentinel-1A and GF-3, to extract the flooding damage in the main urban area. In addition, combinations with the vector data of the Gaode road network were performed to further monitor and analyze the \"7-20\" rainstorm damage in the main urban area of Zhengzhou. The results showed that the main urban area of Zhengzhou City was affected by the \"7-20\" rainstorm. The roads in the main urban area of Zhengzhou were seriously affected, and the total length of the affected roads reached 1324.63 km. The monitoring scheme for flooding road network damage using Sentinel-1A and GF-3 heterogeneous SAR images has certain feasibility. rainstorm monitoring; threshold method; road damage area; heterogeneous SAR +
Footnote †: journal: Medical  
vegetation information [7]. The NDWI can hide the vegetation information and suppress the influence of soil, buildings, and shadows to highlight the water body information. This method is simple, fast, efficient, and widely used in water body extraction. In addition, it can achieve better classification results in plain areas with slight variations in topographic relief [8; 9]. The object-oriented method takes the image object as the primary processing unit. It makes homogeneous image elements into objects of different sizes by various segmentation algorithms, thus realizing the extraction of image information by an object as a unit [10]. With the increasing spatial resolution of remote sensing images, the classification methods to achieve target feature extraction using the images' rich spectral and complex texture features are becoming increasingly sophisticated [11]. [PERSON] et al. introduced multiple SAR image texture features for water body information extraction and compared them with the traditional classification method to improve water body extraction accuracy [12]. Foroughnia uses supervised and unsupervised classification methods, combined with multispectral and SAR data, to assess the precision and accuracy of flood extraction [13]. Deep learning has been widely used in remote sensing water extraction with its unique advantages, such as powerful feature representation and automatic feature learning from data through deep neural network structures [14], commonly used deep learning image segmentation algorithms FCN (full convolutional networks) [15] and U-Net networks [16]. [PERSON] et al. combined multi-temporal TerraSAR-X data and interferometric coherence as training samples to propose an active self-learning time-integrated convolutional neural network framework (A-SL CNN) [17]. [PERSON] presents a novel Synthetic Aperture Radar (SAR) image change detection method that integrates effective image preprocessing and Convolutional Neural Network (CNN) classification [18]. Results show that the proposed method has higher accuracy in comparison with traditional change-detection methods. [PERSON] et al. applied the constructed full convolutional neural network model for water body extraction experiments. The results showed that the fully convolutional neural network model is more automated, better applicable, and has higher extraction accuracy than the traditional threshold method for water body extraction [19]. Since floods are mostly accompanied by various types of cloudy and rainy weather extremes, traditional optical remote sensing images cannot obtain ground information in a timely and accurate manner.