## 4 Conclusions  
This paper presents the preliminary results of investigating the feasibility of using data from UAV-based GNSS-R sensors for water detection and with a potential application in supporting flood monitoring operations. The GNSS-R sensor used in this paper is a custom made sensor, built using low cost commercial of the shelf components. It allowed for collecting samples of ground-reflected GNSS signals, which revealed the presence of water surfaces on ground when post processed.  
Three different case studies were investigated through the processing of two different data sets collected in different seasons. The case studies are: a lake, a river and artificial water basins. These cases were selected because they challenged the sensor in detecting flood-like water presence. Performing the data collections over the lakes (and in general over large water surfaces), we were able to detect the boundaries between ground and water with few tens of meters accuracy and to estimate the extension of the water surface. Moreover, It was shown that the multi-GNSS approach (i.e.: processing of GPS, Galileo, GLONASS and Beidou signals) could even improve the estimation accuracy, without extra costs on the sensor hardware, but at the expenses of a moderate increase in the complexity of the software used in the off-line analysis. The data collections from the UAV based GNSS-R and the consequent signal processing demonstrated to be effective also for the detection of narrower water surfaces, like river stretches and water channels. In addition to that, small unexpected water  
Figure 13: Example of the detection of an backyard pond taken from data set (2).  
presence on the ground were localized including an artificial pond in a golf court and a backyard pond. The experiments reported in this paper confirmed that the use of GNSS-R onboard UAVs is a valid remote sensing tool to be used for water detection, and potentially for flood monitoring operations. However, to extend these results to flood monitoring, weather conditions affecting both the UAV operations and the water surface roughness need to be further analyzed. The UAV stability, attitude and orientation are to be considered because the geo-referencing of the measurements is affected when the RHCP and the LHCP antennas might be no longer pointing at Zenith and Nadir respectively. Moreover, investigation of the roughness and wind speed from small and narrow water bodies could be of interest, in a similar way as GNSS-R has been used for ocean surface roughness and wind speed measurements e.g., in [18; 19; 21; 22]. Furthermore, since flight trajectory optimization is a common practice in UAV-based data collections, we recommend investigating how GNSS-R measurements will perform in an optimized data collection scenario. Also investigating the optimization of the UAV trajectory and the data collection time of the day to achieve optimum multi-GNSS reflectometry measurements is suggested. Finally, for flood detection in the future, we recommend also investigating the possibility to integrate data from the digital elevation model of the terrain with data coming from GNSS-R sensors. Indeed, GNSS-R should be considered as a valuable source of geospatial data after floods, for UAV-based reconnaissance of remote areas and for environmental monitoring.  
Conceptualization, M.P., F.D. ([PERSON]) and F.D. ([PERSON]); methodology, R.I. and M.P.; software, R.I.; validation, M.P. and F.D. ([PERSON]); formal analysis, R.I. and M.P.; investigation, G.M.; resources, G.M.; data curation, R.I.; writing--original draft preparation, R.I., M.P. and G.M.; writing--review and editing, F.D. ([PERSON]); visualization, R.I.; supervision, F.D. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.