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Global navigation satellite system reflectometry (GNSS-R) is a group of techniques that uses satellite navigation signals as signals of opportunity for remote sensing applications. In GNSS-R, large amounts of data are acquired and have to be processed. Computation time is typically the bottleneck for ground and airborne experiments. This article presents an efficient solution for off-line GNSS-R processing data taking advantage of graphics processing units (GPUs). After comparing to the typically used CPU languages, such as MATLAB and C++, the advantage of using parallel processing on the GPU is clear. GPU-based computation can reduce the processing time by as much as 95% of the acquisition time of the data. An implementation taking advantage of a home-use GPU is proposed for the data processing units. Thanks to its efficiency, even real-time processing experiments are feasible.

This work presents the first RFI detection and mitigation algorithm for the interferometric radiometers that will be implemented in its correlator unit. The algorithm operates in the time and frequency domains, applying polarimetric and statistical tests in both domains, and exhibiting a tunable and arbitrary low probability of false alarm. It is scalable to a configurable number of receivers, and it is optimized in terms of quantization bits and the implementation of the cross-correlations in the time or frequency domains for hardware resource saving. New features of this algorithm are the computation of the Stokes parameters per frequency bin in the Short-Time Fourier Transform and a new parameter called Polarimetric Kurtosis. If RFI is detected in one domain or in both, it is removed using the calculated blanking masks. The optimum algorithm parameters are computed, such as length of the FFTs, the threshold selection for a given probability of false alarm, and the selection of the blanking masks. Last, an important result refers to the application of Parseval’s theorem for the computation of the cross-correlations in the frequency domain, instead of in the time domain, which is more efficient and leads to smaller errors even when using moderate quantization levels. The algorithm has been developed in the framework of the ESA’s technology preparation for a potential L-band radiometer mission beyond SMOS. However, it is also applicable to (polarimetric) real aperture radiometers, and its performance would improve if more than one bit is used in the signal quantization.

Altimetric performance of Global Navigation Satellite System - Reflectometry (GNSS-R) instruments depends on receiver’s bandwidth and signal-to-noise ratio (SNR). The altimetric delay is usually computed from the time difference between the peak of the direct signal waveform and the maximum of the derivative of the reflected signal waveform. Dual-frequency data gathered by the airborne Microwave Interferometric Reflectometer (MIR) in the Bass Strait, between Australia and Tasmania, suggest that this approach is only valid for flat surfaces and large bandwidth receivers. This work analyses different methods to compute the altimetric observables using GNSS-R. A proposed novel method, the Peak-to-Minimum of the 3rd Derivative (P-Min3D) for narrow-band codes (e.g., L1 C/A), and the Peak-to-Half Power (P-HP) for large bandwidth codes (e.g., L5 or E5a codes) show improved performance when using real data. Both methods are also compared to the Peak-to-Peak (P-P) and Peak-to-Maximum of the 1st Derivative (P-Max1D) methods. The key difference between these methods is the determination of the delay position in the reflected signal waveform in order to compute the altimetric observable. Airborne experimental results comparing the different methods, bands and GNSS-R processing techniques show that centimeter level accuracy can be achieved.

Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature.

At L-band (1–2 GHz), and particularly in microwave radiometry (1.413 GHz), vegetation has been traditionally modeled with the τ-ω model. This model has also been used to compensate for vegetation effects in Global Navigation Satellite Systems-Reflectometry (GNSS-R) with modest success. This manuscript presents an analysis of the vegetation impact on GPS L1 C/A (coarse acquisition code) signals in terms of attenuation and depolarization. A dual polarized instrument with commercial off-the-shelf (COTS) GPS receivers as back-ends was installed for more than a year under a beech forest collecting carrier-to-noise (C/N0) data. These data were compared to different ground-truth datasets (greenness, blueness, and redness indices, sky cover index, rain data, leaf area index or LAI, and normalized difference vegetation index (NDVI)). The highest correlation observed is between C/N0 and NDVI data, obtaining R2 coefficients larger than 0.85 independently from the elevation angle, suggesting that for beech forest, NDVI is a good descriptor of signal attenuation at L-band, which is known to be related to the vegetation optical depth (VOD). Depolarization effects were also studied, and were found to be significant at elevation angles as large as ~50°. Data were also fit to a simple τ-ω model to estimate a single scattering albedo parameter (ω) to try to compensate for vegetation scattering effects in soil moisture retrieval algorithms using GNSS-R. It is found that, even including dependence on the elevation angle (ω(θe)), at elevation angles smaller than ~67°, the ω(θe) model is not related to the NDVI. This limits the range of elevation angles that can be used for soil moisture retrievals using GNSS-R. Finally, errors of the GPS-derived position were computed over time to assess vegetation impact on the accuracy of the positioning.

As compared to GPS L1C/A signals, L5/E5a Global Navigation Satellite System-Reflectometry (GNSS-R) improves the spatial resolution due to the narrower auto-correlation function. Furthermore, the larger transmitted power (+3 dB), and correlation gain (+10 dB) allow the reception of weaker reflected signals. If directive antennas are used, very short incoherent integration times are enough to achieve good signal-to-noise ratios, allowing the reception of multiple specular reflection points without the blurring induced by long incoherent integration times. This study presents for the first time experimental evidence of the wind and swell waves signatures in the GNSS-R waveforms, and it performs a statistical analysis, a time-domain analysis, and a frequency-domain analysis for a unique data set of waveforms collected by the UPC MIR instrument during a series of flights over the Bass Strait, Australia.

As opposed to monostatic radars where incoherent backscattering dominates, in bistatic radars, such as Global Navigation Satellite Systems Reflectometry (GNSS-R), the forward scattered signals exhibit both an incoherent and a coherent component. Current models assume that either one or the other are dominant, and the calibration and geophysical parameter retrieval (e.g., wind speed, soil moisture, etc.) are developed accordingly. Even the presence of the coherent component of a GNSS reflected signal itself has been a matter of discussion in the last years. In this work, a method developed to separate the leakage of the direct signal in the reflected one is applied to a data set of GNSS-R signals collected over the ocean by the Microwave Interferometer Reflectometer (MIR) instrument, an airborne dual-band (L1/E1 and L5/E5a), multi-constellation (GPS and Galileo) GNSS-R instrument with two 19-elements antenna arrays with 4 beam-steered each. The presented results demonstrate the feasibility of the proposed technique to untangle the coherent and incoherent components from the total power waveform in GNSS reflected signals. This technique allows the processing of these components separately, which increases the calibration accuracy (as today both are mixed and processed together), allowing higher resolution applications since the spatial resolution of the coherent component is determined by the size of the first Fresnel zone (300–500 meters from a LEO satellite), and not by the size of the glistening zone (25 km from a LEO satellite). The identification of the coherent component enhances also the location of the specular reflection point by determining the peak maximum from this coherent component rather than the point of maximum derivative of the incoherent one, which is normally noisy and it is blurred by all the glistening zone contributions.

This work analyzes the satellite cross-talk observed by the microwave interferometric reflectometer (MIR), a new global navigation satellite system (GNSS) reflectometer, during an airborne field campaign in Victoria and New South Wales, Australia. MIR is a GNSS reflectometer with two 19-element, dual-band arrays, each of them having four steerable beams. The data collected during the experiment, the characterization of the arrays, and the global positioning system (GPS) and Galileo ephemeris were used to compute the expected delays and power levels of all incoming signals, and the probability of cross-talk was then evaluated. Despite the MIR highly directive arrays, the largest ever for a GNSS-R instrument, one of the flights was found to be contaminated by cross-talk almost half of the time at the L1/E1 frequency band, and all four flights were contaminated ∼5–10% of the time at the L5/E5a frequency band. The cross-talk introduces an error of up to 40 cm of standard deviation for altimetric applications and about 0.24 dB for scatterometric applications.

This manuscript describes the Microwave Interferometric Reflectometer (MIR) instrument, a multi-beam dual-band GNSS-Reflectometer with beam-steering capabilities built to assess the performance of a PAssive Reflectrometry and Interferometry System—In Orbit Demonstrator (PARIS-IoD) like instrument and to compare the performance of different GNSS-R techniques and signals. The instrument is capable of tracking up to 4 different GNSS satellites, two at L1/E1 band, and two at L5/E5 band. The calibration procedure of the up- and down-looking arrays is presented, the calibration performance is evaluated, and the results of the validation experiments carried out before the field experiments are shown in this paper.

Wind speed and direction are important essential climate variables (ECVs). GNSS-R is an emerging remote sensing technique that can be potentially used to retrieve wind speed from space. However, few studies have addressed the wind direction retrieval from spaceborne GNSS-R observables, namely the Delay Doppler map (DDM). In this study, the feasibility of retrieving wind direction from the synthetic DDMs is analyzed. First, the simulation tool P2EPS is used to generate the DDMs under different geometry configurations, wind speed, and wind direction. Then, DDM changes caused by the wind direction are investigated, and two metrics are proposed to retrieve the wind direction from the DDM shape changes. The influence on wind direction retrieval of the wind speed, receiver’s elevation, and azimuth is further discussed. Finally, the sensitivity of DDM changes to noise is investigated, as well as the impact of noise on these two metrics.

This work addresses the accuracy of the Global Navigation Satellite Systems (GNSS)-Reflectometry (GNSS-R) scatterometric measurements considering the presence of both coherent and incoherent scattered components, for both conventional GNSS-R (cGNSS-R) and interferometric GNSS-R (iGNSS-R) techniques. The coherent component is present for some type of surfaces, and it has been neglected until now because it vanishes for the sea surface scattering case. Taking into account the presence of both scattering components, the estimated Signal-to-Noise Ratio (SNR) for both techniques is computed based on the detectability criterion, as it is done in conventional GNSS applications. The non-coherent averaging operation is considered from a general point of view, taking into account that thermal noise contributions can be reduced by an extra factor of 0.88 dB when using partially overlapped or partially correlated samples. After the SNRs are derived, the received waveform’s peak variability is computed, which determines the system’s capability to measure geophysical parameters. This theoretical derivations are applied to the United Kingdom (UK) TechDemoSat-1 (UK TDS-1) and to the future GNSS REflectometry, Radio Occultation and Scatterometry on board the International Space Station (ISS) (GEROS-ISS) scenarios, in order to estimate the expected scatterometric performance of both missions.