## 3 Earth Surface Effects on GNSS-R Relevant to Land Applications
### Global Scale
The highest power waveforms' peaks are even stronger than over Greenland because of the specular reflection over smooth and calm water. However, the averaged waveform shows a higher peak power over Greenland than over the Amazon region and a dissymmetric shape (Figure 3). In South America, when the specular point moves away from the tropical forests, the spreading of the waveforms increases, also showing detectability to the Amazon river with a higher PR value (Figure 5). Furthermore, it is also observed that the spreading of the trailing edge is larger than the leading edge. This aspect over tropical rainforests will be further evaluated at a regional scale. The topography [11] is a key parameter to take into consideration, in addition to the effects of biomass, because the scattering coefficient \(\sigma^{0}\) is proportional to the probability that a facet could forward scatter the electromagnetic field to the receiver in a specular way. AGB over some areas  
Figure 8: Sample power waveforms [\(\mathrm{T}_{\mathrm{coh}}\) = 5 ms and \(\mathrm{N}_{\mathrm{inc}}\) = 5]: (a) H-Pol Amazon; (b) V-Pol Amazon. Figure 7: (a) Global map of forest height produced from NASA’s ICESAT/GLAS, MODIS, and TRMM sensors. Available online: [[https://www.nasa.gov/topics/earth/features/forest20120217.html](https://www.nasa.gov/topics/earth/features/forest20120217.html)]([https://www.nasa.gov/topics/earth/features/forest20120217.html](https://www.nasa.gov/topics/earth/features/forest20120217.html)). (b) ETOPO1 Global Relief Model. Available online: [[https://www.ngdc.noaa.gov/mgg/global/global.html](https://www.ngdc.noaa.gov/mgg/global/global.html)]([https://www.ngdc.noaa.gov/mgg/global/global.html](https://www.ngdc.noaa.gov/mgg/global/global.html)). of the Himalayas is \(\sim\)200 ton/ha [37], so that both effects (topography and biomass) are merged. Looking at the East mountains of Greenland and the Andes mountains (Figure 7b), neither with appreciable vegetation (Figure 7a), it can be seen that topography contributes to the GNSS signals depolarization (Figure 5c). The effect of topography is further analyzed using the information on the waveforms leading and trailing edges width (Figure 6). Similar measurements of waveform spreading were obtained for H-Pol and V-Pol. At regional scale H-Pol is selected for further study. Similar antenna gain and rate of change of the radar cross-section with the positioning vector, at both polarizations. These observables are used here for the first time (to the authors' knowledge) for land surfaces applications. In particular, the width of both edges is assessed as a function of the surface type. It is shown that there is a dependence on changes in topography but also in dielectric properties. The width is lower for larger values of biomass and surface topography. This result deserves attention since, in this case, a lower spreading of the waveforms does not mean a higher signal coherence. In the latter case, it means that the area that contributes to the signal in the specular direction is reduced. In the former case, the interpretation is that the effects of attenuation are very strong away from the nominal specular reflection point so that the spreading of the waveform is lower as compared to bare soil. Finally, this reconstructed SNR at Left Hand Circular Polarization is also shown here for reference (Figure 9) as [32]:  
\[\text{SNR}_{\text{LHCP}}=\left(\frac{\sqrt{\text{SNR}_{\text{H}}}+\sqrt{ \text{SNR}_{\text{V}}}}{2}\right)^{2} \tag{12}\]  
Figure 9 shows high peaks power associated with bare regions with high soil moisture values, lakes regions and also over the Sahara Desert (black rectangles in Figure 9). On the other hand, lower power levels are observed over tropical or boreal forests due to the larger signal attenuation.