## 1 Introduction
GNSS-R sensors have been deployed onboard spaceborne platforms, for example the GPS reflectometry experiment onboard the UK-Disaster Monitoring Mission (UK-DMC) [33], the Cyclone Global Navigation Satellite System (CYGNSS) [34], the International Space Station (ISS) [35] and the UK TechDemoSat-1 mission [36]. They proved to be a valuable source of remote sensing data for both land [37; 38; 39; 40] and ocean [36] remote sensing. However, space-borne GNSS-R have few limitations. As of today they provide 0.5-1.0 km resolution which is not satisfactory for detailed inundation maps. Also, due to the nature of the flight trajectories of satellites, the measurements they produce are in form of surface tracks rather than instantaneous images [17]. These two limitations can be overcome by air-borne GNSS-R, and they are addressed in this paper by flying a GNSS-R onboard a UAV. The diffusion of Unmanned Aerial Vehicles today is a result of the great positive impact it has proven in modern societies [41]. UAVs are offering solutions for a broad range of applications including support after natural disasters operations [42] such as floods (e.g., [43]), monitoring of environmental pollution, security and surveillance, and critical infrastructures diagnostic. UAVs can be equipped with several types of remote sensing sensors like hyperspectral and optical cameras (e.g., [44]), Synthetic Aperture Radar (SAR) (e.g., [45; 46; 47]), and GNSS-R sensors. GNSS-based passive radars on-board commercial UAVs introduce constraints on the size, weight and power consumption of the GNSS-R sensor, especially if a small commercial quad- or hex- copter carries out the experiment. Many designs for airborne GNSS-R sensors have been presented in the literature. For example the sensor presented by [PERSON] et al. [48] implements a SDR based GNSS-R on an ODROID-X2 microprocessor able of receiving both the left-hand circular polarized (LHCP) and the right-hand circular polarized (RHCP) reflected signals using two front-ends streams. [PERSON] and [PERSON] [49] also presented a miniaturized receiver based on two GPS L1 front ends and a Nano-ITX Single Board Computer (SBC) to store raw signal samples, which were analyzed in post-processing [50]. [PERSON] et al. [51] on the other hand, designed an FPGA-based GNSS reflectometery that is capable of computing the Delay Doppler Maps (DDMs), with update rate of 1 ms. The majority of the GNSS-based passive radar sensors in the literature are composed of (1) a zenith pointing RHCP antenna for the reception of the direct GNSS signals, and (2) a nadir-pointing antenna to receive the ground reflected GNSS signals. The latter can be LHCP, assuming the reflected signal underwent a complete polarization flip, or dual polarized. In this article, we present the results of a preliminary study we did under the I-REACT project [52]. This project investigates solutions that can help improving the response of decision makers and also rescuers to extreme events. The overall objective of this study is to investigate the feasibility of using data collected by UAV-based GNSS-R sensors to support flood monitoring operations. In particular, in this paper we show the ability to detect the presence of various water bodies on ground using a custom made UAV-based GNSS-R sensor. In this paper, we extend the preliminary results presented in [PERSON] et al. [53] with twofold objective:  
1. Investigate the possibility to equip small UAVs with GNSS-based passive radar capabilities, to be used for water detection in post-mission assessments. 2. Estimate the performance of the GNSS-based passive radar developed by [PERSON] et al. [48] in monitoring water surfaces on ground, when it is mounted on board a small UAV. This assessment could set-up a further source of geospatial data for the system developed in the I-REACT project [52]. The rest of the paper is organized as follows. In Section 2 we describe the data collection campaign, giving brief description of the sensor mounted on the UAV, and the methodology followed in post processing analysis.