# Impact of the Elevation Angle on CYGNSS GNSS-R
## 5 Global Scale Reflectivity over Land and Ocean
Additionally it is worth noting that the Brewster angle could potentially appear in the range \(\theta_{\rm e}\approx\) [20,40]\({}^{\circ}\). In this situation, the power of the RHCP-reflected GNSS signals would dominate that of LHCP-reflected ones. \(\Gamma_{\rm LHCP,CyGNSS}\) was higher at \(\theta_{\rm e}\approx\) [20,40]\({}^{\circ}\) than at \(\theta_{\rm e}\approx\) [70,90]\({}^{\circ}\) (Figure 2), although \(\Gamma_{\rm LHCP,CyGNSS}\) would decrease for lower \(\theta_{\rm e}<\) 20\({}^{\circ}\) especially over vegetated surfaces. \begin{table}
\begin{tabular}{c c c c} \hline
**[dB]** & \(\Gamma^{\rm 20^{\circ}<\theta_{\rm e}<40^{\circ}}\) & \(\Gamma^{\rm 45^{\circ}<\theta_{\rm e}<65^{\circ}}\) & \(\Gamma^{\rm 70^{\circ}<\theta_{\rm e}<90^{\circ}}\) \\ \hline Land & \(-12.1\) & \(-17.7\) & \(-15.8\) \\ Ocean & \(-14.1\) & \(-20.3\) & \(-19.4\) \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the impact of the elevation angle \(\theta_{\rm e}\) on \(\Gamma_{\rm LHCP,CyGNSS}\) over land and ocean. Figure 2: One-month (from 20 September to 20 October 2017) averaged CyGNSS bistatic reflectivity \(\Gamma_{\rm LHCP,CyGNSS}\) values for elevation angles \(\theta_{\rm e}\approx\) [70, 90]\({}^{\circ}\) [(**a**) land, (**b**) ocean], \(\theta_{\rm e}\approx\) [45, 65]\({}^{\circ}\) [(**c**) land, (**d**) ocean] and \(\theta_{\rm e}\approx\) [20, 40]\({}^{\circ}\) [(**e**) land, (**f**) ocean]. CyGNSS coherent integration time \(T_{\rm c}=1\) ms, incoherently averaged samples \(N_{\rm inc}=1000\). Earth’s surface coverage is in the latitude range \(\approx\) [\(-40\),40]\({}^{\circ}\) because of the orbital inclination \(\approx 35^{\circ}\) of the CyGNSS satellites. Thus, only nearly equatorial areas are studied. GNSS signals were attenuated and depolarized by the vegetation cover. This attenuation can be modeled as \(\mathrm{L}^{\mathrm{anoppy}}=\mathrm{e}^{\tau^{\mathrm{canopy}}/\sin\theta_{e}}\), where \(\tau^{\mathrm{canopy}}\) is the vegetation optical depth (VOD) of the vegetation layer. The attenuation is due to scattering and absorption when GNSS signals propagates through the vegetation, and thus it increases for lower \(\theta_{e}\). The vertical stalks scatter more the vertically polarized waves than the horizontal ones. This geometrical effect contributes to the depolarization of the GNSS signals. Here, two land-cover types are selected to show different scenarios:  
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2. Croplands (target area at Lat = [\(-\)37,30]\({}^{\circ}\), Lon = [\(-\)65,\(-\)60]\({}^{\circ}\)): This target area was sparsely vegetated and the surface roughness levels were relatively homogeneous due to agricultural activities. The mean \(\Gamma_{\mathrm{LHCP,CyGNSS}}\) increased by \(\approx\)4.2 dB from \(\theta_{e}\approx\) [70,90]\({}^{\circ}\) to \(\theta_{e}\approx\) [20,40]\({}^{\circ}\) as an indication of a higher signal coherence for lower \(\theta_{e}\) (Figure 3a). This \(\Gamma_{\mathrm{LHCP,CyGNSS}}\) gradient could be only attributed to angular changes on the surface scattering mechanisms because the signal attenuation through the vegetation could be assumed to be negligible in this target area. 3. Amazonian rainforests (target area at Lat = [\(-\)8,1]\({}^{\circ}\), Lon = [\(-\)75, \(-\)65]\({}^{\circ}\)): This target area was characterized by wet biomass with high AGB \(\approx\) 350 tons/ha that strongly attenuated GNSS signals, so that the remaining reflected power could be interpreted as a noise power floor in \(\mathrm{Y}_{\mathrm{r,Peak,LHCP}}\) (areas in \"white\" in Figure 3b correspond to SNR lower than 3 dB). However, the increment due to the coherent scattering \(\mathrm{\sigma}^{0}_{\mathrm{LHCP,coh}}\) over soil and inland water bodies compensated this attenuation, and as a consequence, the mean \(\Gamma_{\mathrm{LHCP,CyGNSS}}\) increased by \(\approx\)3.4 dB for decreasing \(\theta_{e}\), allowing for the detection of the hydrosphere, even at grazing angles (Figure 3b).