Assessment of modernized GPS L5 SNR for ground-based multipath reflectometry applications

Abstract

Global Positioning System (GPS) signal-to-noise ratio (SNR) measurements can be employed to retrieve environmental variables in multipath reception conditions, whereby direct or line-of-sight transmission is received simultaneously with coherent reflections thereof. Previous GPS SNR multipath studies of soil moisture and snow depth have focused on the legacy GPS L1 and L2 bands. In the present paper, short-delay, near-grazing incidence multipath from the L5-band GPS SNR is assessed for its value in detecting soil moisture and snow depth. The L5 signal will become more important in the future because of compatibility and interoperability among the different global satellite navigation systems. The L5 results are compared with L2C estimates to determine whether the L2C-L5 differences (given their differing power budgets and their modulation properties) are significant. To address these questions, measurements and simulations were employed. A physically-based multipath simulator was enhanced to investigate the differences between parameter retrievals for the L2C and the L5 GPS signals. Parameter retrievals from synthetic observations for different scenarios were compared. Comparisons included varying reflector height, surface material, and surface roughness. Measurements from two GPS stations in Colorado, USA, were used to retrieve soil moisture and snow depth conditions. Over a 153-day period that encompassed the catastrophic 2013 Colorado flooding event, L2-derived volumetric soil moisture had an RMS difference of 0.042 cm 3 /cm 3 while the L5 RMS difference was 0.034 cm 3 / cm 3 with respect to in-situ data (values of volumetric soil moisture range between 0.04 and 0.34 cm 3 /cm 3 ). In a separate 483-day campaign, L5-derived snow depth estimates were compared to L2C-derived values and found strongly correlated, deviating from a one-toone relationship by only 0.00001 ± 0.0064 cm/cm. These results indicate the absence of any detectable biases in L5 as compared to L2C for retrieving soil moisture and snow depth from GPS SNR multipath observations.

Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa

“…Since the L2C signal provides the highest signal strength and precision, previous studies focused mainly on L2C signals or the upcoming L5 signal (Larson et al 2010;Tabibi et al 2015). However, the SNR of the L1 and L2P signal also shows interference patterns (Fig.…”

Derivation of the Cramér-Rao Bound in the GNSS-Reflectometry Context for Static, Ground-Based Receivers in Scenarios with Coherent Reflection

“…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]. …”

Monitoring Bare Soil Freeze–Thaw Process Using GPS-Interferometric Reflectometry: Simulation and Validation

“…In this section, effects of triple-frequencies (L1 carrier (f1 = 1.57542 GHz), L2 carrier (f2 = 1.22760 GHz) and L5) on GPS multipath observables are evaluated. The forward GPS multipath simulator developed by Nievinski and Larson [37,38] can simulate L1 and L2 carrier frequencies, while Tabibi et al [39] have developed a simulator that supports the third frequency, L5. For the simulator, we set the top of the surface material "air", while the bottom part "wet soil fixed".…”