## 5 Discussion  
Recent advances on GNSS-R [19; 21; 34] have shown extraordinary capabilities to sense SMC [23; 24; 25; 26; 27; 28; 29; 30; 31; 32] with high spatial and temporal coverage (Figure 12), low cost, and all-weather conditions, far to be  
Figure 12: In (**a**) is shown SMC from CYGNSS for 1 April 2019, for \(\theta<40^{\circ}\) and using SMAP VOD \(<0.5\) estimates from Figure 3 and ICESat-2 BSR estimates from Figure 2b. In (**b**) is shown SMC from CYGNSS for the same day, for \(\theta<40^{\circ}\) and using SMAP VOD \(<0.5\) estimates from Figure 3 and SMAP BSR estimates from Figure 2a. In (**c**) is shown SMC from SMAP for the same day at 500-sample intervals. achieved by using conventional active/passive microwave instruments onboard satellites (e.g., SMAP, SMOS). However, the research community has been facing difficulties to define a reliable benchmarking GNSS-R SMC retrieval method (e.g., [27; 41; 42; 43; 44]) due to its inherent multidisciplinary character (GNSS technology, microwave remote sensing, measurement geometry and errors, spatial and temporal sampling, computation load, etc.). In response to this situation, this work presents a new methodology for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates. This methodology differs from other studies where SMC was inappropriately related to GNSS-R reflectivity estimates and without physical basis, as for example, by deriving large time time-series of data, or doing a regression analysis to reliable SMC datasets (e.g., [19; 27; 41; 42; 43; 44]). Instead, it has been proven that under some geometrical circumstances, i.e., for incidence angles below \(30^{\circ}\), the inversion of the Fresnel reflectivity equations for linear polarizations is possible [31; 45; 47; 51]. This can be achieved (makes sense) since most of CYGNSS' SPs are observed under these conditions (Figure 10). According to the presented results, this strategy has some weaknesses that must be reviewed and solved in future work. First, improvements must be made at the level of the SMC estimation procedure. It is important to extend the SMC estimation to greater angles of incidence, not limited to \(35^{\circ}\), and thus to increase the number of SPs. This implies reviewing methods such as those proposed in [72], in which an inversion method based on the two linear Fresnel coefficients for GNSS-R data is undertaken. The authors based their work on the coherent component of the signal without considering the incoherent BSR and VOD effects. However, these effects can be studied using bistatic radar models such as the one proposed by [63]. Regarding, the use of external data such as BSR and VOD, SMAP/Sentinel 1 L2 product appears to be suitable for agricultural areas. However, for forest or woodland areas additional efforts should be taken to supply more realistic estimates, e.g., ICESat-2 BSR. In this work, we derive CYGNSS SMC from different land cover types such as woodland, grassland and agricultural areas (Figure 4), and test two data sources for BSR estimates. On the other hand, BSR estimates from ICESat-2 are more sensitive to land surface microrelief, and the measurements are given along orbital paths, though generating large gaps between these tracks if no large time-series of data is employed. For instance, Figure 2b provides a one-month data set. However, since the BSR state might be considered stable along time, a sufficiently dense data set can be interpolated and employed for SMC retrieval. In this manuscript, our tests carried out with one month ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values. In fact, since SMAP SMC estimates employed in our validation test are derived from SMAP BSR data, the high sensitivity of ICESat-2 to land surface microrelief has provided a promising research gap for future studies on GNSS-R SMC retrieval. On the one hand, we employ SMAP SMC products to validate our new methodology, so it makes sense to employ SMAP BSR and VOD products for the validation test.