Search Results (10)

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. Full article

High temporal and spatial resolutions are the key advantages of the global navigation satellites system-reflectometry (GNSS-R) technique, while low precision and instabilities constrain its development. Compared with conventional Ku/C band nadir-looking radar altimetry, the precision of GNSS-R code-level altimetry is restricted by the smaller bandwidth and the lower transmitted power of the signals. Fortunately, modernized GNSS broadcast new open-available ranging codes with wider bandwidth. The Chinese BDS-3 system was built on 31 July 2020; its inclined geostationary orbit and medium circular orbit satellites provide B1C and B2a public navigation service signals in the two frequency bands of B1 and B2. In order to investigate their performance on GNSS-R code-level altimetry, a coastal experiment was conducted on 5 November 2020 at a trestle of Weihai in the Shandong province of China. The raw intermediate frequency data with a 62 MHz sampling rate were collected and post-processed to solve the sea surface height every second continuously for over eight hours. The precisions were evaluated using the measurements from a 26 GHz radar altimeter mounted on the same trestle near our GNSS-R setup. The results show that a centimeter-level accuracy of GNSS-R altimetry—based on B1C code after the application of the moving average—can be achieved, while for B2a code, the accuracy is about 10 to 20 cm. Full article

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m${}^3/$m${}^3$. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m${}^3/$m${}^3$. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m${}^3/$m${}^3$. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM. Full article

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. Full article

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. Full article

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. Full article

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. Full article

While the synergy between thermal, optical, and passive microwave observations is well known for the estimation of soil moisture and vegetation parameters, the use of remote sensing sources based on the Global Navigation Satellite Systems (GNSS) remains unexplored. During an airborne campaign performed in August 2014, over an agricultural area in the Duero basin (Spain), an innovative sensor developed by the Universitat Politècnica de Catalunya-Barcelona Tech based on GNSS Reflectometry (GNSS-R) was tested for soil moisture estimation. The objective was to evaluate the combined use of GNSS-R observations with a time-collocated Landsat 8 image for soil moisture retrieval under semi-arid climate conditions. As a ground reference dataset, an intensive field campaign was carried out. The Light Airborne Reflectometer for GNSS-R Observations (LARGO) observations, together with optical, infrared, and thermal bands from Landsat 8, were linked through a semi-empirical model to field soil moisture. Different combinations of vegetation and water indices with LARGO subsets were tested and compared to the in situ measurements. Results showed that the joint use of GNSS-R reflectivity, water/vegetation indices and thermal maps from Landsat 8 not only allows capturing soil moisture spatial gradients under very dry soil conditions, but also holds great promise for accurate soil moisture estimation (correlation coefficients greater than 0.5 were obtained from comparison with in situ data). Full article

Lack of frequent and global observations from space is currently a limiting factor in many Earth Observation (EO) missions. Two potential techniques that have been proposed nowadays are: (1) the use of satellite constellations, and (2) the use of Global Navigation Satellite Signals (GNSS) as signals of opportunity (no transmitter required). Reflectometry using GNSS opportunity signals (GNSS-R) was originally proposed in 1993 by Martin-Neira (ESA-ESTEC) for altimetry applications, but later its use for wind speed determination has been proposed, and more recently to perform the sea state correction required in sea surface salinity retrievals by means of L-band microwave radiometry (T_B). At present, two EO space-borne missions are currently planned to be launched in the near future: (1) ESA’s SMOS mission, using a Y-shaped synthetic aperture radiometer, launch date November 2nd, 2009, and (2) NASA-CONAE AQUARIUS/SAC-D mission, using a three beam push-broom radiometer. In the SMOS mission, the multi-angle observation capabilities allow to simultaneously retrieve not only the surface salinity, but also the surface temperature and an “effective” wind speed that minimizes the differences between observations and models. In AQUARIUS, an L-band scatterometer measuring the radar backscatter (σ₀) will be used to perform the necessary sea state corrections. However, none of these approaches are fully satisfactory, since the effective wind speed captures some sea surface roughness effects, at the expense of introducing another variable to be retrieved, and on the other hand the plots (T_B-σ₀) present a large scattering. In 2003, the Passive Advance Unit for ocean monitoring (PAU) project was proposed to the European Science Foundation in the frame of the EUropean Young Investigator Awards (EURYI) to test the feasibility of GNSS-R over the sea surface to make sea state measurements and perform the correction of the L-band brightness temperature. This paper: (1) provides an overview of the Physics of the L-band radiometric and GNSS reflectometric observations over the ocean, (2) describes the instrumentation that has been (is being) developed in the frame of the EURYI-funded PAU project, (3) the ground-based measurements carried out so far, and their interpretation in view of placing a GNSS-reflectometer as secondary payload in future SMOS follow-on missions. Full article

Signals from Global Navigation Satellite Systems (GNSS) were originally conceived for position and speed determination, but they can be used as signals of opportunity as well. The reflection process over a given surface modifies the properties of the scattered signal, and therefore, by processing the reflected signal, relevant geophysical data regarding the surface under study (land, sea, ice…) can be retrieved. In essence, a GNSS-R receiver is a multi-channel GNSS receiver that computes the received power from a given satellite at a number of different delay and Doppler bins of the incoming signal. The first approaches to build such a receiver consisted of sampling and storing the scattered signal for later post-processing. However, a real-time approach to the problem is desirable to obtain immediately useful geophysical variables and reduce the amount of data. The use of FPGA technology makes this possible, while at the same time the system can be easily reconfigured. The signal tracking and processing constraints made necessary to fully design several new blocks. The uniqueness of the implemented system described in this work is the capability to compute in real-time Delay-Doppler maps (DDMs) either for four simultaneous satellites or just one, but with a larger number of bins. The first tests have been conducted from a cliff over the sea and demonstrate the successful performance of the instrument to compute DDMs in real-time from the measured reflected GNSS/R signals. The processing of these measurements shall yield quantitative relationships between the sea state (mainly driven by the surface wind and the swell) and the overall DDM shape. The ultimate goal is to use the DDM shape to correct the sea state influence on the L-band brightness temperature to improve the retrieval of the sea surface salinity (SSS). Full article