## 5 Conclusions  
The aim of this study was to confirm the sensitivity of airborne GNSS-R measurements to soil moisture and vegetation parameters over agricultural crops. An extensive campaign was conducted with the GLORI instrument over two agricultural sites in the southwest of France, with collocated measurements of soil moisture and vegetation parameters. The sensitivity of the GNSS-R measurements to soil moisture content was evaluated through the interpretation of data corresponding to low and high LAI levels, ranging from bare soil to dense cover, and for two elevation intervals (50-70\({}^{\circ}\) and 70-90\({}^{\circ}\)). Three GNSS-R observables (right-left apparent reflectivity (\(I_{RL}\)), the reflected signal noise ratio (SNR), and the polarimetric ratio (PR)) were found to increase with increasing soil moisture. The sensitivities of reflectivity \(I_{RL}\) and reflected SNR to soil moisture were also found to increase with an increasing elevation angle, equal to 40 dB/(m\({}^{3}\)/m\({}^{3}\)) and 45.7 dB/(m\({}^{3}\)/m\({}^{3}\)) for the case of reflectivity and the two elevation intervals (50-70\({}^{\circ}\) and 70-90\({}^{\circ}\)), respectively. The third observable PR was weakly correlated to the soil moisture. The role of vegetation on the GNSS-R observables dynamic was analyzed using a class of data with approximately the same soil moisture conditions (dry conditions with soil moisture lower than 0.1 m\({}^{3}\)/m\({}^{3}\)), revealing a sensitivity to the vegetation LAI, with a clear decrease in all GNSS-R observables with an increasing vegetation LAI. The correlations between the vegetation LAI and both \(I_{RL}\) and the reflected signal SNR were strongest in the case of low elevation angles. The correlation was very weak for high elevation angles. The sensitivities of these observables were quite similar, equal to \(-\)0.91 dB/(m\({}^{2}\)/m\({}^{2}\)) and \(-\)0.76 dB/(m\({}^{2}\)/m\({}^{2}\)) for the case of elevation angle between 50\({}^{\circ}\) and 70\({}^{\circ}\), respectively. The PR observable illustrates the high potential for vegetation LAI retrieval, probably due to the high effect of vegetation on GNSS data depolarization. A simplified tau-omega model was proposed for modeling the \(I_{RL}\), observed over reference plots as a function of soil moisture, LAI and elevation angle. Although several different types of plants were considered in this study, the proposed model demonstrated good potential for the simulation of \(I_{RL}\) data, with RMSE equal to 2.9 dB and R\({}^{2}\) equal to 0.64 for GNSS-R data with elevation angles  
Figure 8: Comparison between the retrieved soil moisture from \(I_{RL}\) and ground soil moisture measurements. ranging between 50\({}^{\circ}\) and 90\({}^{\circ}\). The validation step applied using a 5-fold cross-validation approach illustrated an RMSE equal to 0.06 m\({}^{3}\)/m\({}^{3}\) between retrieved and ground soil moistures values. As the model was developed using data acquired during an airborne campaign over two study sites and applied to different vegetation covers, it is considered to be reliable and more broadly applicable to the interpretation of satellite GNSS-R data. Future studies will be designed to confirm these results through the measurement and interpretation of satellite GNSS-R signals observed with the TechDemoSat and CYGNSS satellites, and the proposed development of a retrieval algorithm based on the use of additional vegetation information, which is easily measurable from optical satellite sensors, such as the NDVI. [PERSON] and [PERSON] conceived, designed and implemented the research. [PERSON] coded and performed the processing. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] assisted in the data measurements and analysis. [PERSON] and [PERSON] wrote the manuscript. All authors reviewed and improved the manuscript. The study is supervised by [PERSON]. This research was funded by ESA/ESTEC grant number [n.4000120299/17/NL/AF/hh] and TOSCA/CNES GLORI project.