# Airborne GNSS Reflectometry for Water Body Detection
## 1 Introduction  
Soil moisture is one of the key parameters in the hydrological cycle, i.e., the continuous circulation of water between oceans, atmosphere and land in a neverening process [1, 2]. It directly influences the amount of evaporation, infiltration, and the amount of water uptake by plants, and was recognized as an Essential Climate Variable (ECV) [3, 4]. In this context, measuring the soil moisture content can be of a great benefit in a large number of applications. Disciplines such as hydrology, climatology, and agriculture, require estimating the soil moisture content for prediction of potential flood and drought hazards, understanding land-atmosphere energy balance, and crop yield expectation [5, 6]. The study of soil moisture content can be applied to observe the distribution of in-land water body surfaces. Flaodplains and in-land water body surfaces cover at least \(~{}12.1\times~{}10^{6}\) km\({}^{2}\) (8%) of landscapes on Earth [7, 8]. They play a significant role in the water cycle through river flow variability, flood mitigation, groundwater recharge and water quality improvement [9]. Despite its important role, little knowledge has been acquired concerning the water stored in floodplains and wetlands as well as its temporal variations from regional to global scales until remote sensing techniques emerged as potential instruments for soil moisture and water body detection. In this regard, soil moisture and water content remote sensing on a global and regional scale has been an active area of research over the past few decades. It has been proven that the microwave band that is optimal for soil moisture remote sensinglies within the L-band [10]. It is shown that the soil dielectric constant value that governs the surface reflectivity can be determined from only the top 0-5 cm of soil [10]. Global Navigation Satellite Systems-Reflectometry (GNSS-R) is an emerging bistatic remote sensing technique that uses the GNSS signals (mainly GPS signals) as sources of opportunity to characterize Earth surface. GNSS systems continuously transmit signals to Earth surface at different L-bands ranging between 1 and 2 GHz. A GNSS-R sensor receives the direct GNSS signals from the satellites as well as those reflected from the Earth's surface. The reflected signals carry information about the reflecting surface, such as its height, shape, and moisture content. The reflecting surface can be an ocean, in which case, the sea level altimetry [11; 12], wind speed [13; 14], and ocean salinity [15] can be estimated, or land for studying the cryosphere including snow depth estimation [16; 17] and sea ice detection [18; 19], or for estimating the soil moisture content [10; 20] and vegetation biomass [21]. GNSS-R platforms offer dedicated applications for Earth surface remote sensing on a local scale using ground-based experiments, a regional scale using airborne campaigns, and recently extending to a global scale using GNSS-R based spaceborne missions. Ground-based experiments have been proposed to prove the GNSS-R sensitivity to soil moisture [22; 23; 24; 25; 26; 27; 28; 29]. Many studies have employed information on multipath effects from ground-based GNSS receivers to retrieve soil moisture content and biomass content sensing [24; 25]. In [23; 28], the Interference Pattern Technique (IPT) was employed to estimate the soil moisture content. The literature in [27] conducted the LEiMON experiment, a ground-based experimental campaign that is based on continuous polarimetric measurements of GNSS scattered signals in order to improve the accuracy of the reflectivity measurements. Several airborne campaigns have shown the feasibility of retrieving soil moisture using reflected GNSS signals [5; 10; 30; 31; 32; 33; 34; 35; 36]. [PERSON] et al. [5] conducted an airborne soil moisture remote sensing experiment in order to extract the soil moisture content from the measurements of the surface reflectivity by deriving the relation between the power of the GNSS signals, the Fresnel coefficient, and the soil moisture content.