## 5 Discussion and Future Directions  
The IGOT model has been validated with CYGNSS data over three areas: WG, JER, and SLV. In Section 4, the results of WG and JER were presented. Furthermore, the results of SLV were discussed in [20]. These sites cover various topographies from rough (WG) to smooth (JER). The results show that this model can estimate CYGNSS DDMs with good accuracy under most conditions. Both the peak reflectivity and the DDM shape of the IGOT model were close to that of CYGNSS with discrepancies in some of the points that are discussed in this section. The performance assessment criteria we follow are that (1) the shape of the modeled DDM should track the observed one, and (2) the values of reflectivity should be within a few dB of the measured ones. Some of the latter may be accounted for based on the calibration accuracy of CYGNSS land DDMs, and some of it can be accounted for based on the uncertainties of input parameters. The discrepancies of some of the points in the tracks can be attributed to the SRTM noise and vertical resolution, as the model is sensitive to DEMs. In generating the IGOT DDMs, we assumed that both \(\sigma_{L}\) and \(\sigma_{5}\) are fixed for the whole area. Hence, we selected one value for WG sites and another value for JER sites. This assumption was made due to limited information on these parameters. However, in reality, these parameters are not constant. Thus, some of the errors in peak reflectivity are also attributed to this assumption. Using a lidar DEM instead of the SRTM DEM can improve the match between CYGNSS reflectivity and IGOT reflectivity, as proven in [31]. The results of the WG tracks, demonstrated in Figures 6 and 7, show the importance of accounting for VWC in modeling GNSS-R. In this paper, the vegetation modeling is simple and can be applied to low-vegetated terrains. To account for dense vegetation, including forests, an advanced electromagnetic model is required as the scattering from the vegetation components is not negligible. An adaptation of the model presented in [32] to GNSS-R can be used to improve the IGOT model to account for dense vegetation and is the subject of our ongoing work. The results of JER track 2 highlight a limitation of the IGOT model. Specifically, the jump in reflectivity of the DDM located at 32.59\({}^{\circ}\) latitude and \(-\)107.01\({}^{\circ}\) longitude (Figure 9b) is expected to be due to the presence of higher moisture content in subsurface  
Figure 12: DDM of sample 104,027 of JER track 1: **(a)** Normalized CYGNSS DDM; **(b)** Normalized IGOT DDM; **(c)** DEM; **(d)** WAF projection of DDM cell (0, 0). soil, as the dry surface soil allows for deep penetration of the GPS L1 signal. However, the model used in this work only accounts for surface reflection. Thus, a modification to the Fresnel reflection coefficient \(\mathcal{R}\) is needed to model the reflections from a subsurface soil moisture profile. We expect the subsurface soil moisture to affect the DDM reflectivity when the surface soil is very dry. However, the contribution of subsurface soil dielectric constant change to the Fresnel reflection coefficient for the frequency of the GPS L1 signal is expected to be negligible when the surface soil moisture is high. The DDM results, presented in Figures 10-12, give insight into the location and area of the DDM cells. Furthermore, the DDM of Figure 11 shows a case where the SP location was changed due to topography. In this DDM (Second DDM), there are two strong returns at (0, 0) and (\(-\)7, 3). The strongest is at (0, 0). However, it does not correspond to the reported SP location, as shown in Figure 11e. Instead, the scattering is from the high elevation area and its slope. The discrepancies in relative reflectivity between CYGNSS and IGOT in the tail of the first DDM, shown in Figure 10, can be attributed to heterogeneity in the surface roughness of the scattering regions. These scattering regions are valleys as illustrated in Figure 10d,e.