The instrument's sensitivity to soil moisture was confirmed to be on the order of 20 dB/(m\({}^{3}\)/m\({}^{3}\)), with a determination coefficient between the ground moisture measurements and the GNSS-R reflectivity polarization ratio equal to 0.86. In the case of forest biomass estimations, the observed sensitivity was approximately 0.015 dB/(Mg\(\cdot\)ha\({}^{-1}\)), with a correlation coefficient (R\({}^{2}\)) of 0.91 between the ground measurements and the cross-polarization reflection coefficient (\(\Gamma_{RL}\)). The system's sensitivity to VWC could not be accurately determined, due to a lack of ground-truth data. [PERSON] et al. [18] have confirmed the potential of GNSS-R reflectivity to retrieve soil moisture, using LARGO airborne measurements over an agricultural area in Spain. A multiple regression model was proposed to link soil moisture to the surface temperature, the vegetation index and the LARGO reflectivity. This study is based on one day measurements with soil moisture limited to low values below 0.15 m\({}^{3}\)/m\({}^{3}\). In the last two years, the use of GNSS-R has gained new momentum with the launch of several spaceborne missions with GNSS-R capabilities, such as TechDemosat in 2014, SMAP in 2015 and CYGNSS in 2016, and various studies have already demonstrated the considerable potential of these instruments for the estimation of land surface parameters [19; 27; 28; 29]. [PERSON] et al. [19] demonstrated the sensitivity of TechDemosat data to soil moisture and vegetation. For nearly bare soils, they retrieved a high sensitivity of GNSS-R scattered power to soil moisture (~38 dB/(m\({}^{3}\)/m\({}^{3}\))). The sensitivity of TechDemosat data to soil moisture has been confirmed by [PERSON] et al. [28] with a 7 dB dynamic due to changes in soil moisture. The GLORI'2015 campaign [30] analyzing agricultural land in the present paper was designed to confirm the results obtained during the aforementioned studies, to generate a dataset allowing some of the limitations encountered in these studies to be avoided and to deepen the understanding of the GNSS-R physical signal, an essential step to propose operational satellite algorithms for land surface properties mapping. Accurate polarimetric GNSS reflected signals were measured over agricultural plots and were accompanied by high temporal and spatial resolution of in situ sampling of the soil and vegetation parameters. In the present analysis, the potential use of three different GNSS-R observables (apparent reflectivity \((\Gamma_{RL})\), reflected signal (SNR), and the reflectivity polarization ratio) for the monitoring of the soil and vegetation is discussed, which was not possible in the context of previous campaigns. A large ground-truth database, including data corresponding to a wide range of soil moisture values and various types of vegetation cover, was collected during the GLORI'2015 campaign to confirm several important results retrieved from smaller databases, such as those reported in [30]. In the second section, we present the study site, the airborne and ground measurements and the GNSS-R data processing. In Section 3, we analyze the correlations observed between in situ data and GNSS-R measurements. Section 4 discusses results obtained for modeling and inversion of GNSS-R reflectivity. Our conclusions are presented in Section 5.