## 3 Results and Discussion
### Case-Study I: Lakes  
The UAV flew over the Avigliana lakes, which are wide water bodies convenient for validating the sensor and the post-processing algorithms. The approximate dimensions of the largest lake is 970 m on the widest east-west direction, and 1200 m on the north-south direction. The sensor passed over the lakes three times: north-south, south-north, and west-east. The average height of the UAV was 450 m over the lake surface with average speed of 50 ms\({}^{-1}\). In data set (1) that originated Figure 4, these passes correspond to epochs 170-230 s, 330-390 s and 1560-1620 s. Reflections were observed, as expected, during these epochs, as shown in Figure 5, which is a zoom view into the epochs corresponding to the passes over the lakes. The figure shows that for some PRNs the values of \(\alpha_{mean}\) increase up to roughly 10 dB and remain constant for many seconds. This trend can be noticed for the satellites that have specular points falling on the lakes and it confirms detecting GNSS signals reflected from the lakes. The periods of these reflections depend on the lake width, the sensor speed and the satellite-sensor geometry. Indeed, the value of \(\alpha_{mean}\) stays high as long as the specular points are on water. It can be noticed also that the reflection value corresponding to water detection differs by some dBs for the different satellites. This is due to the different received power from the different satellites on the first place, which when reflected correspond to different surface properties. The same reflections can be noticed looking at data set (2) where the flight, again, passed over the lakes three times: north-south, south-north and west-east. The corresponding passes are 600-700 s, 800-860 s and 1190-1240 s as reported in Figure 4 and zoom-viewed in Figure 5.  
Figure 4: Peak-to-noise-floor separation \(\alpha_{mean}\) of the reflected signal for a subset of visible GPS satellites during (**a**) flight (1) December 2013 and (**b**) flight (2) May 2014. This concept can be appreciated in Figure 6,b, which show the specular points lines of all the PRNs in view, superimposed into orthophotomaps. These maps correspond to data set (1) and data set (2) respectively. The flight trajectory is represented by the black dots and it is displayed at a rate of 1 Hz. The colored lines are the specular points of the satellites which showed enhanced values of \(\alpha_{mean}\). The colors of the specular points reflect on the values of \(\alpha_{mean}\) associated to them. From Figure 6, it can be noticed that the boundary between land and water is clearly distinguishable and it correlates with the orthophotomap. Concentrating on the area between the two lakes, the steep increments and decays in the values of the peak-to-noise-floor separation along time, that were commented in Figure 5, actually correspond to the water-land boundaries. The resolution of the water edge detection is at the order of tens of meters, as expected and reported in similar investigations available in the scientific literature. This demonstrates the suitability of the GNSS-R onboard UAVs in detecting water at a resolution space-borne GNSS-R are not able to provide at the moment. Note that we processed these measurements at 20 Hz rate, with an integration time of 10 ms only. The measurements rate could be set up to 100 Hz to provide an even higher resolution. Now that we know which reflections were from the lake, we want to have an insight on the reflected power as a function of the satellite elevation. The reflected power depends on many factors: the transmitted power by the satellite, the satellite elevation, the receiving antenna pattern, the properties of the reflecting surface, among others. Figure 7 reports the elevation angle for the reflections observed over the lake. Each point is averaged from five consecutive seconds during which the specular points were falling on the lakes. We used samples from both data sets (1) and (2) to create this graph. We also reported the PRN associated to each of these data points.