# Airborne GNSS Reflectometry for Water Body Detection
## 1 Introduction
In 2016, [PERSON] et al. [36] introduced the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and in-land water bodies from airborne flights. This instrument used two hemispherical GPS dual-frequency (L1 and L2) dual-polarization active antennas and mainly relied on Delay Doppler Maps (DDM) with a total integration time of 1 s to obtain measurements of surface reflectivity. At the beginning of 2021, a study [31] performed a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument. In this work, the effective integration time was set to 5 s to neglect surface roughness effects. The use of such integration time led to a decrease in the spatial resolution of the application. Few studies [7; 37; 38] have been conducted concerning the detection of in-land water body surfaces using airborne GNSS-R techniques. These studies also lacked quantitative analysis concerning the localization accuracy of the detected water body surfaces and the detection capacity of the proposed techniques over large flight trajectories and mostly recurred to very few number of test areas. In 2015, [PERSON] et al. [37] developed a GNSS-based sensor for UAVs and small manned aircraft, used to classify lands according to their water content. The classification is based on the Signal-to-Noise (SNR) ratio between the direct and reflected signals. This experiment used a dual antenna configuration with one up-looking and two down-looking antennas. The sensitivity of the approach was tested on an in-land water body surface. Its main limitations arise from (1) the use of different components in the receiving channels, leading to power and phase variations between the LHCP and RHCP channels, and (2) the instrument antennas, which are affected by cross-polarization isolation issues, thus preventing reliable polarimetric measurements. Most recently, exhaustive demonstration of the sensitivity of spaceborne reflected GPS signals to changes in the soil moisture has been carried out using data from TechDemoSat-1(TDS-1) [39; 40] and data from the Cyclone GNSS (CYGNSS) missions [41; 42]. GNSS-R spaceborne missions promise enhanced resolution compared to passive satellite missions for soil moisture remote sensing. Currently, there are two L-band passive microwave spaceborne missions specifically devoted to measuring soil moisture: the Soil Moisture and Ocean Salinity mission (SMOS) and the Soil Moisture Active and Passive mission (SMAP). Both SMOS and SMAP derive global maps of soil moisture from brightness temperature measurements at a spatial resolution of 35 to 36 km with a 3-day revisit time. On the other hand, CYGNSS provides observation of soil moisture at a spatial resolution of \(\sim\)500 m determined by the major-axis size of the first Fresnel zone for flat surfaces with coherent bistatic scattering and up to \(\sim\)10 km for rough surfaces with incoherent (diffuse) bistatic scattering [43]. The CYGNSS constellation consists of 8 observatories in order to provide measurements as frequently as possible. This results in a mean revisit time of 6 to 7 h. The primary GNSS-R spaceborne missions employ conventional GNSS-R (cGNSS-R) signal processing techniques to obtain DDM observables, which is the basic product containing physical information of a surface. In this regard, CYGNSS satellites generate on-board DDM products with an integration time of 1 s.  
However, GNSS-R spaceborne missions still suffer from a relatively low spatial and temporal resolution compared to ground-based and airborne experiments. The spatial and temporal resolution of such GNSS-R applications depend on the receiver height as well as the constraints and requirements of the application. A minimal height implies a maximized spatial resolution but with less coverage.