## 4 Discussion and Conclusions  
In this paper, the measurements of soil moisture with two different L-band observation systems are presented. On the one hand, the ESA ELBARA-II passive radiometer measures brightness temperature in horizontal/vertical polarization, from where reflectivity on both linear polarizations may be derived. On the other hand, the ESA Oceanpal GNSS Reflectometry instrument measures soil surface reflectivity on both LHCP/RHCP circular polarizations. GNSS surface-measured reflectivity basically depends on soil moisture and incidence angle. Thus, reflectivity under different incidence angles has been used to obtain soil moisture. GPS signals are transmitted with right hand circular polarization (RHCP), but when reflecting from the earth surface, the electromagnetic waves suffer a polarization rotation, and most of the power is reflected in LHCP. Thus, Rrl is more sensitive to soil moisture variation than Rrr. Although Rrr was less sensitive to soil \begin{table}
\begin{tabular}{c c c} \hline
**Input** & **Regression Coefficient** & **RMSE (m\({}^{3}\)/m\({}^{3}\))** \\ \hline _Rrl_ & 0.88 & 0.020 \\ _Rrr_ & 0.78 & 0.027 \\ _Rrl_ **and Rrr** & 0.93 & 0.016 \\ _Rrl_, _Rrr_, and **EVI** & 0.95 & 0.014 \\ \hline \end{tabular}
\end{table} 
Table 2: Regression coefficient and RMSE of 4 neural networks. Figure 10: GNSS-R soil moisture as compared to ELBARA-II and ThetaProbe soil moisture as reference. moisture, when Rrr is set together with Rrl as input, the regression to obtain soil moisture improves. Moreover, when EVI from MODIS was added as input together with Rrl and Rrr, the regression was further improved to 0.94 and the RMSE was reduced to 0.014 m\({}^{3}\)/m\({}^{3}\), which indicates that the vegetation effect is significant in the L-band soil moisture inversion. This study uses data measured with an Oceanpal GNSS-R system at the Valencia anchor station. Reference soil moisture estimations were obtained from the ELBARA-II radiometer and in situ measurements. There was a strong correlation between the GNSS-R LHCP reflectivity and horizontal/vertical reflectivity calculated from ELBARA-II radiometer measurements. A neural net fitting was used for GNSS-R soil moisture inversion. Daily averaged GNSS LHCP reflectivity was used for 10 different incidence angles as input, and in situ soil moisture measurements as the target. The results clearly show that the algorithm is effective. Going still further, adding Rrr and MODIS EVI as input significantly improved the accuracy of the results. [PERSON], [PERSON], and [PERSON] co-organized the experiment; [PERSON], [PERSON], and [PERSON] performed the experiment. [PERSON] and [PERSON] helped with Oceanpal software and hardware and [PERSON] assisted to identify and control eventual radio frequency interferences (RFI). [PERSON] and [PERSON] supervised the work. [PERSON] wrote the original draft; [PERSON] and [PERSON] contributed to the data analysis and, together with [PERSON], they also revised the final version of the paper. This research has been carried out in the framework of an International Research Fellowship--Hosting Proposal financed by the European Space Agency International Relations for Cong Yin and of the Spanish National Space Research Programme Projects SMOS and Future Missions Advanced Products and Applications (MIDAS-7/UVEG) (February 2013-December 2015) and Validation of Earth Observation Data and Products at the Valencia and the Alacant Anchor Stations (VARIETAL) (January 2016-December 2018). Demonstration application of atmospheric, ocean and space monitoring and early warming based on the Beidou Navigation Satellite System (GFZX03030303). Three-dimensional Sounding of the Ionosphere and the Development of the Radiosonde with GNSS (SYHY201006048). Firstly, the authors would like to extend their sincere gratitude to [PERSON], SMOS Mission Manager, for allowing the use of the ESA SMOS ELBARA-II radiometer also for the SOMOSTA experiment at the Valencia Anchor Station.