## 1 Introduction  
Satellite remote sensing techniques are very useful and powerful tools that have helped us to acquire in-depth knowledge of the Earth's surface environment and its interior over the past few decades. These techniques play important roles in research fields including hydrology, ecology, oceanography, glaciology and geology. Among the current spaceborne techniques, global navigation satellite system reflectometry (GNSS-R) is an effective and innovative remote sensing technique for the ocean, land and cryosphere [1; 2]. It can be used to derive geophysical parameters based on the GNSS L-band signals that are reflected by the Earth's surface under all weather conditions [3; 4; 5; 6; 7]. Accurate sea surface heights and wind direction/speeds have been retrieved based on GNSS-R observations from different ground-based, airborne and spaceborne platforms [8; 9; 10; 11; 12]. The sea/icetransition was observed using GNSS-R bi-static radar images generated from TechDemoSat-1 (TDS-1) Delay Doppler Maps (DDMs) [13]. Phase altimetry over sea ice was performed using high-precision Global Positioning System (GPS) carrier phase measurements extracted from TDS-1 coherent GNSS reflections [14]. The surface's reflectivity and variability of snow and ice surfaces interacting with GPS L1 and L2 signals were investigated using the direct and reflected polarizations of each signal [15]. An overview of the challenges and status of the determination of soil moisture and snow properties in alpine environments was provided by [PERSON] et al. [16]. The accuracy of GPS interferometric reflectometry for soil moisture and snow depth was evaluated by [PERSON] [17]. An algorithm of sea target detection from spaceborne GNSS-R DDMs was tested and validated using TDS-1 GNSS-R data [18]. Besides, GNSS-R experiments for various land remote sensing applications, such as soil moisture, agriculture and forest elements, have been successfully tested [7; 19; 20]. Thus, it can be seen that previous experimental and theoretical works show that GNSS-R are very useful. In addition, some studies for simulated scenario had been performed. [PERSON] et al. [21] performed a comprehensive simulation to assess the impact of the observation geometry on the GNSS-R observable directly used to describe ocean surface's roughness. An effective technique to reconstruct the normalized radar cross-section image from GNSS-R DDM had been applied to simulated noise DDMs [22]. However, this method provides a wide swath and improved spatiotemporal sampling when compared with other spaceborne missions. Since a few spaceborne GNSS-R missions have been realized to date and several GNSS systems are under construction at present, there is a lack of datasets obtained from real space-based measurements covering dual-band signals from multiple constellations (e.g., GPS, Global Navigation Satellite System (GLONASS), the Galileo satellite navigation system (Galileo), and the BeiDou Navigation Satellite System (BDS)). In this paper, we perform simulations and analyses to evaluate the characteristics of ground-based reflected signals in terms of longitudinal/latitudinal variability and revisit time in the presence of future multiple constellations of GNSS satellites with nearly 120. The spaceborne GNSS-R payload generally consists of one or two nadir-oriented left-hand circularly polarized (LHCP) antenna, a zenith-oriented right-hand circularly polarized (RHCP) antenna, and a specialized GNSS-R receiver [23; 24; 25]. Because the payload does not contain a transmitter and is small enough to be installed on a micro-satellite, GNSS-R is a low-cost passive remote sensing space technique with low power consumption. Consequently, it is possible to launch several LEO (low Earth orbit) micro-satellites into orbit at altitudes of approximately 500 km using a single rocket to form a GNSS-R constellation.