## 1 Introduction
In the same frequency domain, during the last few decades, an emerging and challenging technology based on the opportunity signal, i.e., receiver devices that take the advantage of employing existing signals from other systems, is being exploited for specific Earth observation applications such as, e.g., geoid determination, sea surface wind speed, and surface SMC [19]. Among these, the technology which employs Global Navigation Satellite Systems (GNSS) is referred as GNSS Reflectometry (GNSS-R). Current GNSS include the constellations of the United States (GPS NAVSTAR), Europe (Galileo), Russia (GLONASS), and China (BeiDou). These systems were originally designed for navigation, positioning and synchronization of time [20]. GNSS-R is based on measurements reflected by GNSS signals on the Earth's surface, which are employed to estimate geophysical parameters [21; 22; 23]. Although the current GNSS-R missions were initially designed and conceived to observe ocean altitudes, winds, and tropical cyclones, recent studies have exhibited the capabilities for sensing land surface attributes [24; 25; 26; 27; 28; 29; 30; 31; 32]. The first missions carrying GNSS-R instruments started in 2003, with the UK Disaster Monitoring Constellation (UK-DMC) mission [33]. Currently, this technology is developing rapidly with the launch of TechDemoSat-1 in 2014, the current NASA's (National Aeronautics and Space Administration) Cyclone GNSS (CYGNSS) since 2012, the 3 CAT-2 of Polytechnic University of Catalonia (UPC), and the GEROS (GNSS Reflectometry, Radio Occultation, and Scatterometry) experiment of the European Space Agency (ESA), among others [34]. It is clear that GNSS-R arouses a growing interest among the scientific community. A GNSS-R system holds the same geometrical configuration properties of a bistatic radar [35]. The principle of measurement is based on observing with a GNSS-R receiver the quasi-specular Left-Hand Circularly Polarized (LHCP) reflection of a Right-Hand Circularly Polarized (RHCP) GNSS signal [36; 31]. In this case, a perfectly smooth surface will produce a near specular reflection,while a rough surface will spread the transmitted signal over a larger area producing a scattered signal. The scattered signal is sampled over the illuminated zone in delay and frequency, creating the so-called Delay Doppler Map (DDM), which is the basic product containing physical information of a surface [34; 36]. One important application of GNSS-R is the measurement of surface reflectivity, which allows the assessment of soil dielectric properties and to derive SMC [35; 37]. An important property of the returned GNSS signals is the coherency parameter, being high for nearly smooth surfaces, and low for very rough surfaces. This parameter, namely called BSR, has an important implication for the modeling of signals when geophysical land surface properties need to be estimated [31; 37]. In addition to BSR, when retrieving SMC over vegetated land, an additional parameter related to the specific vegetation canopy must be taken into account [38]. Therefore, since current GNSS-R missions does not provide BSR nor vegetation canopy characteristics yet, this information need to be accessed from other existing sources, such as NASA's Ice, Cloud, and land Elevation Satellites 2 (ICESat-2) [39], providing BSR estimates, or NASA's Soil Moisture Active Passive (SMAP) [40], providing BSR and vegetation biophysical variables such as Vegetation Optical Depth (VOD) or Vegetation Water Content (VWC). According to previous work, and taking into account the aforementioned analyses, in this paper, our study is motivated by the potential capability of CYGNSS for estimating SMC. In this field, studies have mainly focused their efforts in deriving and comparing CYGNSS reflectivity values with external sources of SMC such as SMAP SMC estimates, either by direct comparison, statistical regressions, or time series analyses [19; 27; 37; 41; 42; 43; 44].