Analysis of signal-to-noise ratio retrieved from multi-GNSS satellite data of land surface reflections

The reflection of Global Navigation Satellite System (GNSS) signals, known as GNSS reflectometry (GNSS-R), has made significant progress in monitoring ocean and land surface geophysical parameters. The main purpose of this investigation is to analyze the impact of antenna gain, land surface properties, and coherent integration time on the signal-to-noise ratio (SNR) of multi-GNSS reflected signals using complex waveform (CW) products generated by the Cyclone Global Navigation Satellite System (CYGNSS) constellation recording multi-GNSS raw intermediate frequency (IF) data. For different GNSS constellations, CYGNSS antenna gain of 6–9 dB can achieve an SNR of 0 dB or more over most pan-tropical regions. The normalized SNR (NSNR) calculated by the calibration procedure is used for such analysis. The results show that the coherent reflection caused the largest NSNR by the inland water body, and NSNR from the GPS reflected signal is about 2 dB and 4 dB larger than Galileo and BDS, respectively, whereas the dense vegetation caused a strong attenuation of the reflected signal and resulted in the smallest NSNR. Different GNSS systems have similar sensitivity to roughness coefficient (RC). In contrast, different GNSS constellations exhibit different sensitivities for moderate soil moisture (SM), the NSNR increases for GPS, BDS, and Galileo are estimated by linear fitting to be 4.85 dB, 7.67 dB, and 8.73 dB, respectively. NSNR gradually increases with above-ground biomass (AGB) when AGB is less than 100 Mg/ha, which seems to be the threshold for signals to penetrate the sparse vegetation and sense SM under the vegetation. However, when AGB is greater than 100 Mg/ha, the opposite is true. Furthermore, NSNR can be improved by longer coherent integration time, the average improvement in NSNR is about 1.5 dB with 2 ms coherent integration time and approximately 3 dB with 4 ms coherent integration time.This work was funded by the National Natural Science Foundation of China (No. 42074041); Shaanxi Province Science and Technology Innovation Team (Ref. 2021TD-51) and the Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team (2022); National Key Research and Development Program of China (No. 2020YFC1512000); and Fundamental Research Funds for the Central Universities, Chang’an University (No. 300102262401). This work of Weiqiang LI is partially supported by Grant RYC2019-027000-I funded by MCIN/AEI/http://dx.doi.org/10.13039/501100011033 and by “European Union Next Generation EU/PRTR,” as is also supported by Grant 20215AT007 funded by Spanish National Research Council. Qi Liu's work was partly supported by the China Scholarship Council (CSC) through a State Scholarship Fund (No. 202106560048).Peer reviewe