## 2 Materials and Methods
### Soil Moisture Retrieval Algorithms for GNSS-R and ELBARA-II  
Neural net fitting [25] was used for GNSS-R soil moisture inversion. The chosen neural network architecture consisted of one input layer, one hidden layer, and the output layer. The transfer function used was tansig and the number of nodes was 8. In total, 238 samples of GNSS daily measurements were used as input, and 238 samples of soil moisture measurements by in-situ probes were used as target. Randomly, 70% samples were selected for training, 15% were used to measure network generalization and to halt training when generalization stopped improving. The last 15% samples had no effect on training and were used for validation by providing an independent measure of the network performance during and after training. Besides soil moisture, vegetation and soil roughness also have an impact at L-band. In this paper, soil roughness was considered to be constant, and vegetation was assumed to have a homogeneous distribution. 250 m resolution MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) data were used for vegetation estimation. Spline interpolation was used to obtain daily EVI. Figure 5a, b shows LHCP reflectivity variation with azimuth and elevation. The data was measured in August 2014, with high vegetation, without any precipitation, and the soil moisture was constant at 0.10-0.11 m\({}^{3}\)/m\({}^{3}\). The dependence of Rrl(LHCP reflectivity) with elevation is more obvious. So, we considered the azimuthal effect negligible. It was also assumed that the soil moisture diurnal variation was not obvious, so we could take the average over the Oceanpal measured LHCP/RHCP reflectivity with same incidence in one day. ELBARA-II is a dual polarization L-band microwave radiometer which measures TB (brightness temperature) at two channels (1400-1418 MHz and 1409-1427 MHz) [14]. The observed brightness temperature at H or V polarization TB\(p\) (\(p=\) H, V) used in this study are averages of the brightness  
Figure 4: Calibration parameter \(K\) plot over time. Figure 5: LHCP reflectivity vs azimuth angle (**a**) and vs elevation angle (**b**). temperature values measured in both channels, which are part of the protected part of the microwave L-band. The radiometer was placed on a 15 m high platform over the vineyard to measure TB\({}_{\rm V}\) and TB\({}_{\rm H}\) automatically for different observation angles every 30 min. For the soil moisture retrieval, we used measurements at angles = 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), 45\({}^{\circ}\), 50\({}^{\circ}\), and 55\({}^{\circ}\), relative to nadir [26]. ELBARA-II soil moisture retrievals were based on the L-MEB model [26],  
\[TB_{p}(\theta)=T_{GC}\!\left[1-r^{\prime}_{GP}(\theta)exp\!\left(-2\tau_{NAD} \frac{\cos^{2}(\theta)+tt_{p}\sin^{2}(\theta)}{\cos(\theta)}-H_{R}\cos^{N_{RP} }(\theta)\right)\right] \tag{4}\]  
where \(\tau_{NAD}\) is the optical thickness, \(tt_{V}\) and \(tt_{H}\) are parameters used to quantify the dependence of \(\tau_{p}\) on the incidence angle, and \(N_{RP}\) governs the changes in the angular dependence of reflectivity. For the specific case when \(N_{RV}=N_{RH}=-1\), and vegetation is assumed as isotropic \(tt_{H}=tt_{V}=1\), the Equation (4) was simplified as:  
\[TB_{p}(\theta)=T_{GC}\!\left[1-r^{\prime}_{GP}(\theta)exp(-2 TR/\cos(\theta))\right] \tag{5}\]  
where  
\[TR=\tau_{NAD}+H_{R}/2 \tag{6}\]  
In that case, vegetation (through the \(\tau_{NAD}\) parameter) and roughness effects (through the HR parameter) can be combined in the single parameter TR, defined in Equations (5) and (6). \(r^{\prime}_{GP}(\theta)\) is reflectivity of a plane (specular) surface, which was computed from the Fresnel equations as a function of \(\theta\) and of the soil dielectric constant \(\epsilon\), which in turn was computed as a function of soil moisture, soil effective temperature (T\({}_{GC}\)), and soil texture in terms of clay fraction [27]. For the MELBEX-III site, [PERSON] et al.