## 1 Introduction  
Over the past few decades, Global Navigation Satellite System (GNSS) signals have not been used solely for navigation purposes. Indeed, since satellites can be considered a passive source of radiation, GNSS signals have been used for remote sensing applications, which consist of the processing of GNSS signals reflected back from the ground. Such reflected signals can be used to characterize the Earth's surface, because they have different characteristics from those of the signal directly received from the satellite, in terms of delay, Doppler shift, power strength and polarization. These differences depend on the geophysical properties of the scattering surface; therefore, they potentially carry information about the surface geophysics. GNSS signals are broadcast over the L-band, and many experiments have successfully demonstrated GNSS-reflectometry (GNSS-R) for the remote sensing of land and ocean surfaces [1] using the GPS L1 at 1575.42 MHz. Wind retrieval and altimetry, mainly from static platforms [2], are the most consolidated applications, while new employments, such as soil moisture sensing, ice monitoring, water level and snow thickness measurements [3], are continuously emerging and are presently under development. More recently, the joint use of GNSS-R data and other sensors, such as optical, infrared, thermal and microwave radiometers, turns out to be promising for accurate soil moisture estimation [4] and sea surface salinity retrieval [5,6]. In addition to better environmental monitoring, it is expected that new GNSS-R data will represent valuable inputs to numerical weather prediction (NWP) systems. Although today's NWPs are capable of predicting many meteorological events, their accuracy is sometimes poor or they have an insufficient lead-time to initiate actions aimed at protecting life and property. For instance, uncertainty in present meteorological forecasts and the lack of integration of currently scattered monitoring networks represent a bottleneck for flood and drought risk assessment at local and regional scales. GNSS-R can be a means to provide additional data at low cost, mainly if new GNSS-R devices are mounted on-board unmanned aerial vehicles (UAVs). Today, UAVs offer a broad range of solutions for many civilian applications and can be equipped with several types of sensors for environmental monitoring; some UAVs are also more cost effective with respect to manned light aircraft. Until now, many instruments for GNSS-R have been proposed, and several algorithms have been developed for the estimate of geophysical properties of the scattering materials (e.g., [1,7]) and for altimetry (e.g., [2,3,8,9]). The hardware of traditional GNSS-based passive radars consists of custom GNSS receivers, based on application-specific integrated circuits (ASIC) or field programmable gate arrays (FPGA). In most cases, they require a personal computer (PC) and sufficient memory to store a large amount of data [10,11]. GNSS-based passive radars use a right-hand circular polarized (RHCP) antenna pointing toward the zenith for the reception of the direct signals from satellites and a second left-hand circular polarized (LHCP) antenna pointing towards the nadir for the reception of the reflected signals. Some devices (e.g., [12]) have been designed to collect the LHCP-only reflected component, because most of the reflected power has this polarization. More advanced versions (e.g., [13]) enable the reception of both LHCP and RHCP polarizations, because even weak RHCP reflected signals carry valuable information for precise measurements, like for the estimate of the soil moisture. It is worth noticing that other configurations making simultaneous use of horizontally- andvertically-polarized antennas, such as in [14], are possible, but their use on-board a small UAV would pose several mounting and reception issues. The main drawback of many GNSS-based bistatic radars proposed so far is the heavy and bulky set up, which prevents the use of such devices on-board small and light UAVs.