The utilisation of global navigation satellite system reflectometry (GNSS-R) signals in remote sensing of land surface parameters has undergone significant advancements over the years. However, a paucity of analysis exists regarding the vegetation-transmitted GNSS signal, which represents an avenue for further research. In this study, an empirical investigation was conducted to ascertain the statistical characteristics of the power associated with GNSS signals emitted from vegetation, and the most appropriate distribution function model was obtained by a combinatorial test. The experimental results indicate that the vegetation-transmitted GNSS signal continues to conform to the characteristics of a Normal (right-hand circular polarization, RHCP) and Weibull (left-hand circular polarization, LHCP) distribution; however, significant variations are observed in the distribution parameters and the parameter value ranges. Furthermore, the results suggest a positive correlation between the k-order (k= 1, 2, 3, 4) moment order and the discrepancy in signals obtained by disparate GNSS antennas. Both antenna elevation angle and vegetation type exert an influence on moments of all orders, and the influence of the latter is more pronounced, thereby enabling the differentiation of vegetation types.

A novel basic observable, termed the alternating-current texture (ACT), is defined from the original linear-polarization carrier-to-noise ratio (CNR) of Global Navigation Satellite System (GNSS) for monitoring wind vector. The amplitude distribution (AD), autocorrelation function (ACF), power spectral density (PSD), and fractal dimension (FD) of the ACT are explored. The results show that these statistical characteristics derived from the GNSS-IR CNR resemble those directly extracted from the reflected signal. Twelve statistics from the AD, ACF, PSD, and FD, as observables sensitive to wind speed, are analyzed. The findings suggest that these statistics exhibit geometric dependence, especially at the low elevation angle. Furthermore, the skewness of the AD, the correlation time of the ACF, PSD peak, PSD width, and fractal dimension respond more effectively to wind speed than other statistics, and are used to assess the capability of retrieving wind speed. Kernel principal component analysis (KPCA) is employed to fuse these statistics to produce a new sensitive observable to wind speed. When elevation and azimuth angles are confined to optimal regions with minimal interference, a root mean square error (RMSE) of 1.57 m/s is obtained with a minute-level temporal resolution. In contrast, a geodetic GNSS receiver provides an RMSE of 2.55 m/s. Additionally, based on the anisotropy of sea surface, the feasibility of these statistics in retrieving wind direction is investigated. Novel sensitive observables to wind direction, derived from the best-fit ellipse to the spatial distributions of the statistics, are defined. The fusion using KPCA achieves the best determination coefficient (DC) of 0.50 with wind direction, while the geodetic receiver yields a DC of only 0.16. These results conclude that low-cost GNSS sensors can be utilized for retrieving wind vector, and the linear-polarization GNSS-Interferometric Reflectometry (GNSS-IR) outperforms its right-handed circular polarization (RHCP) counterpart.

This paper revisits the Interferometric Complex Field (ICF) concept in coastal Global Navigation Satellite System-Reflectometry (GNSS-R), and proposes a novel baseband signal processing scheme for monitoring sea states. The scheme utilizes incoherent averaging to reduce hardware complexity while maintaining accuracy comparable to correlation-based methods. This eliminates requirements for the RHCP antenna, RF front-end, and baseband processor of the direct signal. Compared to the existing schemes, it has lower computational complexity, power, and cost. The primary observable, termed the alternating-current incoherent average power (aIAP), is derived by detrending a stable baseline component from the output of the proposed scheme. The coherence time and spectral width from the aIAP time series are defined as the observables of retrieving sea state, specifically wind speed and significant wave height (SWH). Three experimental data sets are used to demonstrate and assess the proposed scheme. Results indicate that the coherence time and spectral width of aIAP exhibit sea-state dependencies similar to those of alternating ICF within a wind speed range of 0 ∼ 15 m/s, and thus can be used to retrieve sea state. Spectral width retrieves sea state more effectively than coherence time. Coherence time and spectral width weakly depend on elevation angle so that an elevation correlation is unneeded. The proposed scheme, as a cost-efficient alternative, has the potential for operational sea state monitoring.

This study provides statistical characteristics of the power of the reflected GNSS signals and establishes associations between probability density function (PDF) characteristics and different land cover situations. The feasibility of utilizing the PDF of signal power for determining the presence of vegetation cover and inverting associated parameters has been demonstrated. The simulation demonstrates that the signal-to-noise ratio (SNR) of land surface-reflected signals from various reflectors follows a Weibull distribution for the dual-antenna model, and an F distribution for the single-antenna model. The moment-generating function is used for the calculation of 1st-4thorder moments to study the characteristics of the PDF. Furthermore, the distribution parameters and moments are influenced by both the land-cover type and the reflector’s physical parameters. Experiments were conducted on mud flats and farmland for four months to validate the simulation model utilized in this study. Moreover, the simulation and experimental results demonstrate a mathematical correlation between the moments of the PDF and land surface parameters such as soil moisture content (SMC), soil roughness, and vegetation density.

This article presents the statistical analysis of the reflected global navigation satellite system-reflectometry (GNSS-R) signal from a coastal experiment, including the non-Gaussianity, probability distribution functions, autocorrelations, and fractal dimensions of the speckle and texture components. The results clearly show that the amplitude distribution is modeled well by a Weibull model. The texture component of the reflected GNSS signal has a log-normal distribution. Due to the presence of the coherent and non-coherent components, the phase of the reflected signal is not uniformly distributed with$\left [{{-\pi, \pi }}\right]$. The autocorrelation functions (ACFs) of the speckle and texture components both are Gaussian-shaped, with the correlation times on the order of hundreds of milliseconds and a few seconds, respectively. Some statistical properties of the reflected GNSS signal depend on GNSS-R geometry and sea state; therefore, once the influence of GNSS-R geometry is corrected, they can be used to determine sea state. The speckle and texture correlation times of the reflected GNSS signal, as an example, are used to retrieve wind speed. The speckle and texture correlation times provide retrieved wind speeds with root mean square errors (RMSEs) of 1.66 and 1.75 m/s. When a minimum variance estimator is used to fuse two retrieved wind speeds, the RMSE is reduced to 1.46 m/s. The work is helpful for developing a GNSS signal scattering model over the sea surface and further studies on coastal GNSS-R to monitor sea state and maritime target.