## 3 Data Analysis
### Relationships between GNSS-R Observables and Soil Moisture  
To minimize the influence of heterogeneity in the vegetation characteristics between the reference fields, the sensitivity of the GNSS-R observables to soil moisture were analyzed for two classes of LAI. The first of these was defined by LAI \(<\) 1, corresponding to sets of 22 and 19 GNSS-R measurements over the reference fields with bare or poorly vegetation-covered soils for the two elevation angle intervals (50-70\({}^{\circ}\)) and (70-90\({}^{\circ}\)). The second class is defined for LAI \(>\) 1, corresponding to sets of 14 and 25 GNSS-R measurements over the reference fields with medium and high vegetation densities for the two elevation angle intervals (50-70\({}^{\circ}\)) and (70-90\({}^{\circ}\)), respectively. Figures 3 and 4 show \(\varGamma_{RL}\), the reflected SNR and PR as a function of soil moisture, for the two LAI classes. Each plotted point represents a GNSS-R mean measurement on a reference field at a given date. The number of samples is different from that of the reference fields; it varies from one measurement configuration to another. The sensitivity of GNSS-R observables to soil moisture can be seen to generally decrease with increasing LAI. The vegetation component, which attenuates the signal scattered by the soil, thus influenced the GNSS-R signal sensitivity to variations in soil moisture. As an example, for the 70-90\({}^{\circ}\) elevation interval, the \(\varGamma_{RL}\) sensitivity to soil moisture (S) decreased from 45.7 dB/(m\({}^{3}\)/m\({}^{3}\)) for the weak LAI values (LAI \(<\) 1) to 28.4 dB/(m\({}^{3}\)/m\({}^{3}\)) for the highest LAI values (LAI \(>\) 1). The same behavior was observed for reflected signal SNR, with the decreasing of sensitivity from 39 to 24.1 dB/(m\({}^{3}\)/m\({}^{3}\)), from weak LAI values to high LAI values, for the same elevation angle interval (70-90\({}^{\circ}\)). The dynamic range of the GNSS-R signals was greater than 10 dB for reflectivity and SNR. For the case of \(\varGamma_{RL}\) and reflected signal SNR, the retrieved sensitivity was greater than that in the case of the Leimon and GRASS campaigns, which reported values of \(\sim\)20 dB/(m\({}^{3}\)/m\({}^{3}\)) [25; 26]. They were in agreement with values retrieved in [19] using TechDemosat data (38 dB/(m\({}^{3}\)/m\({}^{3}\))). Figure 3: \(Cont.\)Figure 4: \(Cont\). Figure 3: Global Navigation Satellite System reflectometry (GNSS-R) observables at 50–70\({}^{\circ}\) elevation angles as a function of soil moisture: (**a**) cross-polarization (right-left) reflectivity (\(\Gamma_{RL}\)) for leaf area index (LAI) \(<\) 1; (**b**) signal-to-noise ratio (SNR) for LAI \(<\) 1; (**c**) polarimetric ratio (PR) for LAI \(<\) 1; (**d**) \(\Gamma_{RL}\) for LAI \(>\) 1; (**e**) SNR for LAI \(>\) 1; (**f**) PR for LAI \(>\) 1. With the decrease of S with increasing LAI (i.e., vegetation cover development), we also observed a decrease in the correlation coefficient between GNSS-R observables (\(\Gamma_{RL}\) or reflected SNR) and soil moisture. For example, the correlation between \(\Gamma_{RL}\) and the moisture decreased for the 70-90\({}^{\circ}\) elevation angle range from R\({}^{2}\) = 0.54 for LAI < 1 data to R\({}^{2}\) = 0.14 for LAI > 1 data. This result clearly illustrates the effect of noise generated by the vegetation contributing to the GNSS-R observables. This relatively low correlation value could also be attributed to differences in the scattering behavior between the various types of vegetation cover observed during the campaign (i.e., different crop species, cultivars, or development stages). For the case of PR, we observed a low correlation with soil moisture (R\({}^{2}\) < 0.2, even for low LAI values). This correlation suggests that, in airborne scenarios, the polarimetric ratio has a lower sensitivity to soil moisture, contrary to the findings reported for the example in [26].