Synthetic aperture radar (SAR), allowing for all-weather and all-day imaging, can provide essential data for large-scale flood inundation monitoring. However, limitations such as the revisit period of SAR images make it challenging for single-source SAR data to meet the high temporal requirements for dynamic flood inundation monitoring, which is crucial for disaster relief and decision-making support. Combining multi-temporal SAR data for dynamic flood inundation monitoring is of significant practical value. Nevertheless, SAR images from different sensors exhibit significant spatiotemporal heterogeneity, rendering direct comparisons difficult. Additionally, previous studies frequently extracted flood inundation extents using single-pixel or local spatial neighborhood features while neglecting the application of spatiotemporal non-local features pre- and post-flooding. Therefore, this study first proposed a feature space alignment method for multi-source SAR data based on backscatter characteristics. Then, differential information pre- and post-flooding was extracted using the progressive non-local theory, and flood inundation maps were prepared. Finally, dynamic flood inundation monitoring results were obtained through logical operations of the time-series flood inundation maps. This method was validated using the flood disaster in August 2023 in Zhuozhou, during which five multi-source SAR datasets were acquired from Sentinel-1, Gaofen-3 (GF-3), and Fucheng-1. The results indicate that compared to six commonly used flood monitoring methods, the proposed method exhibited the optimal performance, yielding a Kappa coefficient and F1 score of 0.85 and 0.88, respectively. The dynamic monitoring results of the flood inundation in Zhuozhou reveal that the floodwater in the main urban area largely receded by August 3, and the water levels then gradually decreased, with the inundated areas shifting to the Baigou River in the lower reaches.