# Airborne GNSS Reflectometry for Water Body Detection
## 6 Data Analysis  
### Radar Signal Segmentation  
Figure 13 presents the GNSS measurements for an area located at (50.888515\({}^{\circ}\)N, 1.871803\({}^{\circ}\)E) along the trajectory. In the upper figure, the traces of the satellites footprints are represented by 20 ms rate localization of the specular points of reflection. The colors of the points represent different reflectivity measurements associated with different kinds of reflecting surfaces. The number in brackets notes the satellite elevation angle. On the lower figures, we show the GNSS reflectivity measurements and the automatic signal segmentation associated to it using our proposed radar technique over 12 s of data from this area.  
We can notice from the graphs that a difference in the mean of the measurements separates different areas of reflection. We remark a significant increase in reflectivity corresponding to water body surfaces. This increase is not constant though and is dependent on the type of land and water that the signals are reflecting from. We can observe from the graphs of Figure 13 that the proposed radar technique detects different water bodies corresponding to different mean reflectivity levels shown in blue segments. These segments are associated with a blue coloring of the specular points on the Google Earth image. The yellow coloring of the specular points is associated to land reflections. We can also observe fluctuations in the mean reflectivity level (blue segments) corresponding to the detected swamp by satellite PRN 7. This is due to the trace of satellite PRN 7 passing through a small green area in the middle of the swamp that can be a very wet piece of land or small set of leaves that have been accumulated over time.  
We show in Figure 14 different types of water bodies detected along the traces of the satellites footprints. We observe that the radar technique detects in-land water bodies with various sizes and shapes and under different environments. It is worth noting that the approximate size is not the same for all the surfaces of the same water body type. We notice that we detect large-size contained water body surfaces such as lakes (Figure 14a) and swamps (Figure 14b). We also differentiate smaller contained water body surfaces such as wetlands (Figure 14e). In our study, we differentiate lakes from ponds based on the size of the water body surface. We differentiate swamps from lakes and ponds based on the characteristics of the water body and the surrounding environment. However, this is not always explicit by manual inspection using Google Earth map imagery data.  
Figure 12: The satellite footprint traces along the flight trajectory imposed on Google Earth software. The traces of very low elevation satellites are not shown in the figure.  
Figure 13: Automatic segmentation of the GNSS measurements by the proposed radar technique for an area located at (50.888515\({}^{\circ}\)N, 1.871803\({}^{\circ}\)E) along the trajectory.  
Rivers of different widths along the trajectory are detected by the traces of all three satellites due to its length. The river shown in Figure 14c has a width of 21 m. We notice in Figure 14d, two small streams of 3.6 m and 5.76 m width, while we notice in Figure 14f that the proposed radar technique was able to detect a small pool of 4.5 m \(\times\) 9 m size along the trace of satellite PRN 5 footprints. This shows the sensitivity of the proposed radar technique to changes in landforms and shows the importance of high spatio-temporal resolution in the detection of in-land water body surfaces using low-altitude airborne carrier.