## 4 Results  
In this manuscript, we have derived SMC estimates from Fresnel reflection coefficients measured by GNSS-R, specifically, from the CYGNSS mission during April 2019 for the eastern region of China. In summary, the conversion of GNSS-R data into SMC values has been carried out according to the methodology specified in Section 3 and summarized in Figure 6. In brief, first, CYGNSS reflectivity measurements (\(\Gamma_{\rm{I\!I}}\)) have been corrected from BSR and VOD (equivalent to VWC) effects by implementing a probability function into the bistatic radar model. Afterwards, the Fresnel reflectivity (\(R_{tilt}\)) has been converted into dielectric permittivity (\(\varepsilon_{r}\)) and, subsequently, into volumetric SMC. Two different approaches are employed for the SMC estimation, one with BSR estimates from ICESat-2 and the other with BSR estimates from SMAP. While ICESat-2 BSR estimates appear to have a better spatial resolution, their measurement footprints are sparsely distributed on both sides along the satellite ground tracks. In turn, it results in large data gaps between orbit legs that can only be resolved by employing the precession of the satellite orbit during a large enough period of time (see Figure 2b). Despite the ICESat-2 lack of coverage, an attempt has been made to verify the suitability of the BSR data provided by such a system for its application in the bistatic SMC retrieval model, which will also help to understand the behavior of SMAP BSR estimates. Subsequently, in order to evaluate the suitability of ICESTA2 BSR estimates, we perform a sensitivity analysis of the surface reflectivity (Equation (4)) to this variable. In the following subsections, we present our analysis and validation test results for the suggested GNSS-R SMC retrieval. SMC Sensitivity Analysis to GNSS-R Reflectivity \(\Gamma\)Ir, Incidence Angle \(\theta\), BSR, and VOD Input Parameters  
Figure 7 shows the SMC model sensitivity analysis to incidence angles (\(\theta\)) of 5\({}^{\circ}\), 15\({}^{\circ}\), 25\({}^{\circ}\), and 35\({}^{\circ}\) under different BSR and VOD conditions, for uncorrected GNSS-R reflectivity measurements (\(\Gamma_{\rm{I\!I}}\)) from 0.1 to 0.5 at 0.1 intervals. From this figure, in the top panels (Figure 7a,b), we can observe that SMC values are strongly dependent on a VOD increase from (a) 0.1 to (b) 0.3. In addition, under these conditions and for a similar SMC state, the dependence on incidence angle variations is more pronounced for the lower VOD case (Figure 7a). This can also be observed in the lower panels of Figure 7, whereas SMC converges for higher VOD values. Moreover, in Figure 7a, the incidence angle shows to be a key parameter for SMC estimation at SPs with high reflectivity (\(\Gamma_{\rm{I\!I}}\)). On the other hand, at the lower panels (Figure 7b,c), SMC values show a similar response to BSR variations from 0.1 m to 0.4 m for VOD cases between 0 and 0.3, suggesting the lack of SMC dependence to BSR values below 0.4. From Figures 7 and 8, we can observe that for a given VOD coverage, surface reflectivity and SMC estimates are not sensitive to BSR values below 0.2 m. These results imply that small variations in surface reflectivity will not influence the SMC retrieval below BSR values of \(\sim\)0.2 m. On the contrary, Figure 8 clearly shows that GNSS-R reflectivity appears to be more sensitive to BSR values above 0.2 m, which supports the findings of [70] for this variable. Since the maximum value of SMAP BSR for the data sets of this study is not higher than 0.18 m, we can foresee that SMAP BSR values will have a small influence on SMC retrieval, as the permittivity is directly related to the surface reflectivity. However, from Figure 2b, we can observe that for ICESat-2 data, BSR values exhibit a higher variability within the same SMAP areas, which would imply better surface reflectivity estimation. Unfortunately, ICESat-2 values are not available over the complete study area but show the convenience of such types of values for the pursued objective.