Floods are the most common type of natural disaster worldwide, causing thousands of deaths every year. Timely and accurate flood forecasts are crucial for evacuation plans and mitigation strategies. Hydrological models used in forecasting depend on high-resolution, spatially continuous soil moisture data. However, existing remote sensing-based techniques for soil moisture estimation lack the spatial resolution needed for accurate flood modeling. In this study, we present an approach for soil moisture estimation using Sentinel-1 C-band synthetic aperture radar (SAR) data. SAR offers high spatial resolution, day/night operation, and operation in adverse weather conditions, making it advantageous for flood mapping.  Furthermore, Sentinel-1's frequent revisit cycle allows for relatively continuous monitoring of soil moisture, which is essential for flash flood modeling. Analyses are performed for flooding following Hurricane Helene in North Carolina and Tennessee from September 25 to 30th of 2024. We gathered Sentinel-1 data using the Alaska Satellite Facility’s Vertex Data Search and streamflow data using the USGS National Water Information System (NWIS) Mapper. Three backscatter parameters --- VV reflectance, VH reflectance, and the VV/VH ratio --- are selected for analysis. An initial soil moisture model is obtained by analyzing the relationship between the selected backscatter parameters and streamflow. Then, flood extent is mapped using thresholding of Sentinel-1 data and verified using NDWI calculated from Sentinel-2 multispectral data. Using the generated flood maps and ancillary data such as precipitation, soil type, and vegetation cover, we create a predictive soil moisture-based flood model. We verify the model using streamflow data gathered using the NWIS Mapper. This approach is promising for improving the accuracy and temporal range of flood prediction, especially in areas with limited ground-based soil moisture monitoring networks.