## 3 Results and Discussion  
### TDS-1 Data Analysis on a Global Scale  
After the TDS-1 data has been quality screened and compensated for all the above effects, different GNSS-R observables are first analyzed. Figure 11 shows the scatter plots of the: (a) SNR; (b) DDM peak; and (c) reflectivity compensated by the distance factor corresponding to incidence angles up to 30\({}^{\circ}\) only, vs. the collocated SMOS SM (low-resolution, L3 product, descending pass (no differences have been found if using the ascending or the descending SMOS passes). Note that units are arbitrary (in dB), as it aims only to see which parameters exhibit the largest sensitivity and the lowest error. The color scale shows the VOD in Nepers (Nep). In these scatter plots, the largest values (1.6 Nep) correspond to one-way attenuations of up to \(\sim\) 7 dB. Note that the pixels with the highest VOD are well above the robust fit [27], which suggests that the vegetation scattering effects must be properly taken into account, and that the VOD compensation of Equation (2) may be overcompensating these effects [8]. This effect is more noticeable in the SNR and DDM peak plots, than in the reflectivity ones.  
As it can be appreciated, the SNR exhibits the largest uncertainty (\(\sigma\) = 7.1 dB) even after the linear robust fit, but also the largest sensitivity to soil moisture 22.75 dB/100%. The peaks of the DDM (not compensated for the background noise) exhibit a very weak sensitivity to SM, as the signal is masked by the speckle noise (dominant when coherent scattering is present). Reflectivity scatter plot (Figure 11c) exhibits a sensitivity to SM of 9.11 dB/100% and an uncertainty of \(\sigma\) = 3.17 dB/100%.  
From now on, only the reflectivity will be analyzed (i.e., neither the SNR, nor the DDMpeak). Figure 12 shows the scatter plots of the (calibrated) reflectivity for incidence angles (a) from 0\({}^{\circ}\) to 30\({}^{\circ}\), (b) from 30\({}^{\circ}\) to 60\({}^{\circ}\), and (c) from 60\({}^{\circ}\) to 90\({}^{\circ}\) vs. the collocated SMOS SM.  
Figure 10: Monthly average spatial-temporal distribution of soil moisture (mm) derived from MUSA model during 2015 year (January to November) and TDS-1 GNSS-R reflectivity (all incidence angles).  
As it can be appreciated in Figure 12, as the incidence angles increases, the sensitivity to SM decreases: 8.7 dB/100% from 0\({}^{\circ}\) to 30\({}^{\circ}\), 5.7 dB/100% from 30\({}^{\circ}\) to 60\({}^{\circ}\), and 2.8 dB/100% from 60\({}^{\circ}\) to 90\({}^{\circ}\), while the pixels more affected by dense vegetation (large VOD) depart more from the linear robust fit as vegetation effects (multiple scattering and increased attenuation) become more important. Table 2 shows the sensitivities and associated uncertainties estimated using different corrections.  
While the obtained values are lower than the ones obtained in previous studies [1; 2], they are still significant \(\sim\) 8.75 dB/100% (\(\sim\) 0.0875 dB/%). However, the large scattering that remains in the data prevents (so far) from performing an accurate SM retrieval. Part of this scattering is due to the lack of an accurate characterization of the platform attitude an antenna pattern variations in the estimated \(P_{dir}\), part to the different spatial resolution of the different variables used, part to the pixel inhomogeneity, and part to speckle and thermal noises.  
Trying to partially address these issues in the next section a study is conducted to assess the sensitivity to SM using downscaled SMOS products [13; 14] and in situ data from CEMADEM (Brazil) [15].