## 5 Signal Processing and Results of an In-Field Test
### Test Campaign Results  
Some in-flight tests were executed to assess the prototype performance. The sensor was mounted on two different platforms, as shown in Figures 8 and 9: a manned ultra-light aircraft (Digisky's Tecnam P92) and a UAV (Nimbus' CFly). Some results are shown here from a flight test with the P92 aircraft, which flew over a countryside nearby Turin (Italy), also overflying two small lakes (the Avigliana lakes). The overflow area has been chosen because it includes test scenarios of interest. In fact, this area is in the countryside north of Turin, not far from the airport from where the aircraft used for the tests can take off and land, and it includes water basins, such as lakes and rivers. Moreover, a swampy area is present around the lakes, which looks particularly interesting in the framework of this work, since the evaluation of the soil moisture is the main goal. In that region, different cultivated areas are also present, which are interesting from the perspective of a future agriculture application. Forest zones are present, as well, which are characterized by weaker reflections, due to the higher scattering effects; on this topic, several studies have been presented in the literature to address the analysis of the vegetation characteristics through the GNSS reflections, as, for instance, in [33] and later in [39]. Similarly, the inhabited zones, including buildings, roads or bridges, give a different reflection depending on the surface composition, roughness and inclination. However, the detection of these kinds of targets is not the focus of this work. The prototype was used in the advanced mode, as detailed in Section 3.3, collecting data from both the RHCP and the LHCP channels of the nadir-pointing antenna. The collected signals were then processed to get the aircraft and the satellite position and to compute the specular points for each satellite. Then, the direct and reflected SNR were estimated from both the RH and the LH circular polarizations, so as to enable the post-processing algorithms presented in Sections 4.1 and 4.2. It is important to highlight that the data post-processing necessary to convert the reflectometry measurements in Equation (5) or Equation (10) in estimates of the soil moisture is highly sensitive to the accuracy of either the terrain composition model or the terrain scattering model used in the conversion process, as well as to their sensitivity to the signal incidence angle (satellite geometry). Even if the focus of this paper is on the prototype design, both of the hardware and software parts, some results are presented here of the soil parameter retrieval process, in order to validate the system in terms of the final output. Figure 8: Sensor prototype mounted on the manned Digisky Tecnam P92 aircraft ready to take off (**A**) and during the flight (**B**). Figure 9: Sensor prototype mounted on the Nimbus C-Fly UAV during the flight (**A**) and on the ground after landing (**B**). Figures 10 and 11 show the plot on the map of the estimated specular points for two satellites (PRN30 and PRN 13, respectively); the color is proportional to the ratio between the reflected LHCP SNR and the direct GPS signal SNR, which is related to the geophysical quantity of interest, the soil dielectric constant, through Equations (3) and (4). As explained above, if a suitable model of the soil composition is given, then the full soil moisture information can be retrieved through the measurement of the LHCP reflections only, assuming the approximation of a smooth surface. As expected and indicated by the red points in Figures 10 and 11, the reflection from the water surface is much higher than from the terrain [34], where weaker and different values correspond to different lands, such as forest or fields, with different moisture levels. Furthermore, the strength of the LHCP reflections is more intense as the satellite elevation increases.