Numerous studies have demonstrated the effectiveness of CYGNSS data for flood detection and mapping. However, the vast majority of the studies only focuses on flood extent detection, ignoring the importance of flood water levels in post-disaster relief. Meanwhile, most of the existing studies on inland water levels altimetry using CYGNSS data are based on the CYGNSS raw IF data using either the time-delay method or the phase method for altimetry. Although high accuracy can be obtained, at present the CYGNSS raw IF data is not a standard data, so it cannot be applied to the study of emergency flooding events. Given the above research gaps, this study proposes an effective method to combine CYGNSS L1 standard data with DTM for rapid estimation of flood water levels. The effectiveness of the proposed methodology is validated using the case of the 2022 Pakistan mega-flood. Comparison with ICESat-2 altimetry data gives encouraging results.