Numerous studies have demonstrated the effectiveness of CYGNSS (Cyclone Global Navigation Satellite System) data to detect inland water bodies. However, most of them focus on the detection of surface water extent, rather than the water levels and depths. Most of the existing studies on inland water level altimetry using CYGNSS data are based on the CYGNSS raw IF (raw Intermediate Frequency) data, and use either the time-delay or the phase methods. Although high accuracy can be obtained, raw IF data are currently not a CYGNSS standard data product, and thus it cannot be applied to emergency detection of sudden flood events, or long time-series monitoring of a single large inland water body. Based on these research gaps, this study presents an effective method to estimate the water levels and depths (on a 3 km grid) by combining CYGNSS L1 standard data with DTM (Digital Terrain Model) information. We compared the CYGNSS retrieved water levels with other water level reference data for eight case studies with different characteristics (3 floods, 3 lakes, and 2 reservoirs). The spaceborne LiDAR ICESat-2 and GEDI are used for the flood case studies, and water levels from DAHITI and Hydroweb databases are used for long-term changes of the lakes and reservoirs. All comparative validations yield encouraging results. For flood cases, comparison with ICESat-2 and GEDI showed strong correlation (mean R values for the three case studies were 0.98). The mean bias of CYGNSS retrieved water levels was - 0.26 m, and the mean RMSE was 1.34 m. For lakes and reservoirs, the comparison to DAHITI and Hydroweb showed a mean correlation value of 0.62 (R), while the mean bias and RMSE were - 0.63 m, and 2.61 m, respectively. Finally, the sources of uncertainty are discussed, including the effect of topography discretization, and the effect of uncertainty in the determination of surface water boundaries. This study demonstrates the feasibility of combining CYGNSS L1 standard data with DTM data to measure inland water levels, highlighting its suitability to monitor both flash floods and long-term changes of lake and reservoir water levels. This rapidly updatable water level information will contribute to further comparisons and hydrological researches.