Abstract: Satellite-based Global Navigation Satellite System Reflectometry (GNSS-R) technology has become an effective tool for large-scale monitoring of soil moisture. The Cyclone Global Navigation Satellite System (CYGNSS), with its high temporal and spatial resolution, is widely applied in soil moisture inversion research. In September 2024, the Soil Moisture Active Passive (SMAP) satellite publicly released its GNSS-R reflectivity data for the first time. This study first performs spatiotemporal matching of multi-source GNSS-R surface reflectivity data, SMAP soil moisture products, and the ECMWF Reanalysis V5 (ERA5) soil moisture dataset. It then discusses the global differences in surface reflectivity of CYGNSS and SMAP GNSS-R under varying geographical latitudes, land types, and vegetation optical thicknesses. An empirical formula based on a power-law function is proposed to correct the differences between them. Finally, the response of satellite-based GNSS-R surface reflectivity to soil moisture is analyzed. The results show that CYGNSS data is abundant and evenly distributed between 38°S and 38°N, making it favorable for surface parameter inversion, while SMAP data is relatively sparse but covers middle to high latitudes, indicating their complementarity. Under different geographical latitudes, land types, and vegetation optical thicknesses, CYGNSS and SMAP exhibit nonlinear differences in surface reflectivity. These differences are closely related to the signal frequency and polarization modes received by the two systems. CYGNSS receives left-hand circularly polarized signals in the GPS L1 band, while SMAP receives horizontal and vertical linearly polarized signals in the GPS L2C band, and a power-law function can effectively correct the differences between them. Overall, the surface reflectivity from both CYGNSS and SMAP shows a good correlation with soil moisture. The findings of this study are beneficial for future multi-source satellite-based GNSS-R reflectivity joint inversions of surface environmental parameters.