## 1 Introduction  
During the past two decades, researchers have been studying the use of the reflected Global Positioning System (GPS) and other Global Navigation Satellite Systems' (GNSS) radio signals for remote sensing purposes [1]. This is commonly referred to as GNSS reflectometry (GNSS-R). The GNSS signals are transmitted at the L-band frequencies, which can penetrate cloud cover and are particularly sensitive to soil moisture, snow water content and sea-ice salinity. The GNSS-R concept was first proposed by [PERSON] in [2], in 1993, for its application on ocean altimetry from airborne and spaceborne platforms. Since then, the use of GNSS-R for ocean altimetry, wind speed and sea state determination has gained considerable traction. Several works have been published describing specific GNSS-R instruments and architectures [3; 4; 5], retrieval techniques and algorithms [1; 6], theoretical signal models [7; 8; 9; 10], simulators [11; 12; 13] and several measurement campaigns carried out from different platforms [1; 14; 15]. Even specific GNSS-R space-based missions have been planned by different space agencies [3; 16; 17]. Furthermore, more remote sensing applications have been also proposed and validated through experimental campaigns, such as: soil moisture monitoring [7; 18; 19; 20; 21; 22; 23; 24; 25], snow monitoring [26; 27; 28; 29; 30; 31; 32], sea-ice monitoring [33; 34], ground and calm water altimetry [35; 36; 37; 38; 39; 40; 41; 42; 43] and biomass estimation [44; 45]. Why has GNSS-R gained this much attention? When compared to traditional radars used in remote sensing applications, which are typically monostatic and that make use of the backscattered signal, the GNSS-R receivers represent a non-cooperative passive bistatic (or multi-static) radar opportunity that makes use of the forward-scattered signals. Being a passive system, only the receiving part is required within a GNSS-R instrument, which implies lower cost, complexity and power consumption requirements than traditional active radars. Moreover, the high number of GNSS satellites, which is only expected to grow in the near future, enables global coverage from a network of transmitters whose location is very accurately known at any time instant. For air-borne and space-borne applications, the multi-static configuration allows for simultaneous surface sensing tracks, sensing more surface area within the same time and lower revisit times. However, the GNSS-R has some significant drawbacks when compared to traditional radars, mainly due to the GNSS signals' features, i.e., their low power and relatively low bandwidth. Furthermore, we have no control over the transmitted signal features (e.g., modulation or code length). The theoretical estimation precision that can be achieved using the different GNSS-R techniques is still under study. Early work on the statistical properties of the scattered GNSS signals and their connection to ocean altimetry and wind speed retrievals was described in [46; 47]. This work adapted the classical approach described by [PERSON] et al. in [48] used to characterize the precision of radar altimeters and scatterometers. Later on, a similar approach was used again in [3], in an attempt to predict the precision of the PARISocean altimeter. Another attempt was put forward in [49], where the estimated delay difference error was computed using the approach described in [50], commonly used to characterize the delay error in traditional GPS with an early-prompt-late correlator tracking scheme. It is in [51] where the use of the Cramer-Rao bound (CRB) was firstly proposed in the GNSS-R ocean altimetry context. In [52], the authors computed the CRB using real measurements to estimate the scattered signal statistical properties, while later in [53; 54], the authors used the CRB in an attempt to quantify the impact of the signal's incoherent integration and the receiver's front-end bandwidth, respectively, over the achievable precision.