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A Passive Reflectometry and Interferometry System (PARIS): Application to ocean altimetry
Abstract
To date, altimetry from space has mainly been limited to nadir-looking-type instruments because of the difficulty of realising a precise wide-swath radar altimeter. A concept is presented that can be used to perform altimetry measurements over points along directions other than nadir, by making use of a passive instrument. The method is applied here to ocean altimetry in particular. This concept, based on the existence of sources of opportunity, consists of combining the direct signal and the signal that is reflected by the Earth's surface to obtain the desired measurement. It can be regarded as a multistatic radar for which the transmitters and the receivers belong to different systems. Because of the combination of direct and reflected signals, this concept has been called the "Passive Reflectometry and Interferometric System' (PARIS). -Author
... Particularly, GNSS-Reflectometry (GNSS-R) is an innovative remote sensing technique that uses the GNSS signals reflected from the Earth's surface to derive a variety of geophysical parameters [1,2]. The application of GNSS reflectometry data for the ocean altimetry was first proposed by Martin-Neira [3], and the global positioning system (GPS) reflected signals were used to sense the roughness of the reflecting surface, which was demonstrated by Garrison et al. [4]. At the same time, the first space-borne observation of an Earth-reflected GPS signal, which was obtained by the space shuttle spaceborne imaging radar-C (SIR-C) at an altitude of 200 km, was an initial measurement to scale GNSS-R measurements to obtain the expected signal-to-noise ratio (SNR) for estimating geophysical parameters [5]. ...
... The development of different global navigation satellite systems including GPS, GLONASS, the Chinese BeiDou navigation system (BDS), and Galileo, have made the number of available satellites that can be used as platforms for GNSS-R signal sources increasing continuously in recent years, which gives potential for the use of multiple GNSS signals for GNSS-R. A Spanish experimental nanosatellite for both GNSS-R and GNSS Radio Occultation (RO) called 3 Cat-2 was successfully launched in 2016, and this experimental satellite investigated the feasibility of the use of multi-GNSS signals for GNSS-R in the future [13]. ...
Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global Positioning System (GPS), Galileo, GLONASS, and BeiDou. On the other hand, to facilitate the operational capability for sensing ocean, land, and ice features globally, multi-satellite low Earth orbit (LEO) constellations with global coverage and high spatio-temporal resolutions should be considered in the design of the follow-on GNSS-R constellation. In the present study, the particle swarm optimization (PSO) algorithm was applied to seek the optimal configuration parameters of 2D-lattice flower constellations (2D-LFCs) composed of 8, 24, 60, and 120 satellites, respectively, for global GNSS-R observations, and the fitness function was defined as the length of the time for the percentage coverage of the reflection observations reaches 90% of the globe. The configuration parameters for the optimal constellations are presented, and the performances of the optimal constellations for GNSS-R observations including the visited and the revisited coverages, and the spatial and temporal distributions of the reflections were further compared. Although the results showed that all four optimized constellations could observe GNSS reflections with proper temporal and spatial distributions, we recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of the GNSS-R missions.
... The subject about the determination of the reflection point has been investigated in the past. In [12], the spherical Earth is assumed and it is shown that by solving a quartic (4-th order) polynomial equation, the specular reflection can be computed. As the Earth is often modeled as an ellipsoid, an analytic treatment of the specular reflection point on the ellipsoidal Earth has not been discussed in the past, to our knowledge. ...
... In summary, the specular reflection point s is governed by (10), (11), and the minimal distance in (12). ...
In applying GNSS-R (Global Navigation Satellite System Reflectometry) techniques for the remote sensing of surface properties of the Earth, it is imperative to determine the specular reflection point and provide a quantitative characterization of the associated errors. In the paper, a rigorous formulation of the problem with respect to ellipsoidal Earth is provided for the determination and error analysis of the specular reflection point. A polynomial equation approach is developed to characterize the specular reflection point. This explicit characterization is beneficial in the GNSS-R receiver operation. The sensitivity analysis is further performed to assess the errors in the presence of uncertainties.
... It has been applied to sea surface monitoring (Balasubramaniam and Ruf 2020;Geremia-Nievinski et al. 2020), detecting vegetation coverage (Pan et al. 2020), detecting changes in soil moisture (Yueh et al. 2020) and detecting snow thickness Munozmartin et al. 2020;Steiner et al. 2020). It was first proposed by Martin Neira in 1993(Martin-Neira 1993 to put the GPS system into normal operation in 1994 (Interface Control and Document 2012). Subsequently, many scientists have studied and field tested the GNSS-R method.This includes deformation monitoring using GNSS passive radars. ...
... It has been applied to sea surface monitoring (Balasubramaniam and Ruf 2020;Geremia-Nievinski et al. 2020), detecting vegetation coverage (Pan et al. 2020), detecting changes in soil moisture (Yueh et al. 2020) and detecting snow thickness Munozmartin et al. 2020;Steiner et al. 2020). It was first proposed by Martin Neira in 1993(Martin-Neira 1993 to put the GPS system into normal operation in 1994 (Interface Control and Document 2012). Subsequently, many scientists have studied and field tested the GNSS-R method.This includes deformation monitoring using GNSS passive radars. ...
China’s BeiDou navigation system has been completed and is currently in operation. As the newest constellation, BD-3 is composed of 30 satellite configurations. The application of BD-3 has improved rapidly, however, using the BD-3 signal as a signal resource in the global navigation satellite system (GNSS) passive radar domain to monitor deformation has not been proven to be feasible. The authors of his paper designed a BD-3 passive radar model and carried out a proof-of-concept experimental demonstration for deformation monitoring. As a result, a method based on the B3I signal as a signal resource for GNSS radar (GNSS-R) deformation monitoring is proven to be feasible. The experimental scenarios include laboratory simulation and proof of concept field displacement deformation. The test error reaches a value of 0.023 m for a simulation scenario, 0.0435 m for a field scene of deformation based on a target translation measurement, and 0.1011 m for a field scene of deformation based on a target rotation measurement.
... In 1993, Martin-Neira first proposed the concept of sea surface altimetry using Global Positioning System (GPS) direct and reflected signals (Martin-Neira, 1993). In 2000, a theoretical framework was developed to establish the relationship between the properties of reflected GPS signal and the sea surface roughness (Zavorotny & Voronovich, 2000). ...
In this paper, the effect of geographical location on Cyclone Global Navigation Satellite System (CYGNSS) observables is demonstrated for the first time. It is found that the observables corresponding to the same wind speed vary with geographic location regularly. Although latitude and longitude information is included in the conventional method, it cannot effectively reduce the errors caused by geographic differences due to the non-monotonic changes of observables with respect to latitude and longitude. Thus, an improved method for Global Navigation Satellite System Reflectometry (GNSS-R) wind speed retrieval that takes geographical differences into account is proposed. The sea surface is divided into different areas for independent wind speed retrieval, and the training set is resampled by considering high wind speed. To balance between the retrieval accuracies of high and low wind speeds, the results with the random training samples and the resampling samples are fused. Compared with the conventional method, in the range of 0–20 m/s, the improved method reduces the Root Mean Square Error (RMSE) of retrieved wind speeds from 1.52 to 1.34 m/s, and enhances the correlation coefficient from 0.86 to 0.90; while in the range of 20–30 m/s, the RMSE decreases from 8.07 to 4.06 m/s, and the correlation coefficient increases from 0.04 to 0.45. Interestingly, the SNR observations are moderately correlated with marine gravities, showing correlation coefficients of 0.5–0.6, which may provide a useful reference for marine gravity retrieval using GNSS-R in the future.
... It is worth noting that the specular point prediction algorithm applied in TDS-1 and CYGNSS is called the quasi-spherical Earth (QSE) approach [21], and it brings about a three-kilometer-level position error [22], which may cause trouble in DDM data processing and blur the boundary of the land-sea interface. Another conventional specular point prediction method named the minimum path length (MPL) [23,24], however, has a huge computation burden, and its accuracy varies with latitude. ...
The vigorous development of the global navigation satellite system (GNSS) has led to a boom in GNSS radio occultation (GNSS RO) and GNSS reflectometry (GNSS-R) techniques. Consequently, we have proposed an innovative signal processing scheme for spaceborne integrated GNSS remote sensors (SIGRS), combining a GNSS RO and a GNSS-R module. In the SIGRS, the GNSS-R module shares one precise orbit determination (POD) module with the GNSS RO module, and the GNSS-R module first achieves compatibility with GPS, BDS, and Galileo. Moreover, the programmable non-uniform delay resolution was introduced and first used by the SIGRS to generate the output DDM, which achieves a high delay resolution in the DDM central region around the specular point to improve the accuracy of basic observables but requires fewer delay bins than the conventional DDM with uniform delay resolution. The SIGRS has been successfully used to design the GNOS II onboard the Chinese FY-3E satellite, and the results of in-orbit operation validate the performance of the SIGRS, which means the SIGRS is an economically and technically efficient design and has become the first successful signal processing scheme for spaceborne integrated GNSS remote sensors around the world.
... Spaceborne GNSS-R technology is an emerging remote sensing technology that uses a satellite platform to receive GNSS satellite signals reflected from the earth surface to analyze and invert the physical information of the reflected surface. Martin-Neira 1993 [1] proposed using GPS reflection signals for sea surface height (SSH) retrieval, which was an earlier attempt of GNSS-R for ocean altimetry. This was followed by new applications in tidal [2], [3], soil moisture [4], [5], sea ice detection [6], [7], [8], and wind speed retrieval [9], [10]. ...
Cyclone Global Navigation Satellite System (CYGNSS) launched in recent years, provides a large amount of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data with all-weather, global coverage, high space-time resolution, and multiple signal sources, which provides new opportunities for the machine learning (ML) study of sea surface height (SSH) inversion. This study proposes for the first time two different CYGNSS SSH inversion models based on two widely used ML methods, back propagation (BP) neural network and convolutional neural network (CNN). The SSH calculated by using Danmarks Tekniske Universitet (DTU) 18 ocean wide mean SSH (MSSH) model (DTU18) with DTU global ocean tide model is used for verification. According to the strategy of independent analysis of data from different signal sources, the mean absolute error (MAE) of the BP and CNN models' inversion specular points' results during 7 days is 1.04m and 0.63m, respectively. The CLS 2015 product and Jason-3 data were also used for further validation. In addition, the generalization ability of the model, for 6 days and 13 days training sets, was also evaluated. For 6 days training set, the prediction results' MAE of the BP model is 11.59 m and 5.90 m for PRN2 and PRN4, and the MAE of the CNN model is 1.37 m and 0.97 m for PRN2 and PRN4, respectively. The results show that BP and CNN inversions are in high agreement with each product, and the CNN model has relatively higher accuracy and better generalization ability.
Comparisons between different global navigation satellite system (GNSS) signals and GNSS-reflectometry (GNSS-R) satellite systems can provide valuable suggestions for future development of the GNSS-R instrument and signal processing method. This article evaluates the ocean altimetry performance of multiple GNSS constellation signals using raw intermediate frequency data collected by Cyclone GNSS (CYGNSS) and TechDemoSat-1 (TDS-1) satellites. Data used for the evaluation include observations of GPS L1, Galileo E1, and BDS B1 band signal. The specular point position and the ground-truth bistatic delay are calculated through the HALF method. After ionospheric, tropospheric, and tide corrections, the sea surface height can be retrieved; and then the height is compared with the DTU18 mean sea surface model derived one. Based on the GNSS-R satellite-collected observations, an optimal incoherent integral duration is determined. By making use of the optimal duration, the CYGNSS-based ranging delay estimating accuracy can reach up to 2.38 m, 1.98 m, and 1.91 m for GPS, Galileo and BDS, respectively; and the TDS-1 based one can reach up to 5.46 m and 3.84 m for GPS and Galileo, respectively. The results can provide suggestion on the strategies of multi-constellation observations fusion to improve the altimetry accuracy.
This study utilizes L1B level data from reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission to estimate sea surface Significant Wave Height (SWH). The Normalized Bistatic Radar Cross Section (NBRCS), Leading Edge Slope (LES), Signal to Noise Ratio (SNR) and Delay-Doppler Map Average (DDMA) are used as the key variables for the SWH retrieval. Eight other parameters including instrument gain and scatter area are also utilized as auxiliary variables to enhance the SWH retrieval performance. A variety of multi-variable regression models are investigated to clarify the relationship between the SWH and the variables by using the following five methods: Stepwise Linear Regression, Gaussian Support Vector Machine, Artificial Neural Network, Sparrow Search Algorithm–Extreme Learning Machine and Bagging Tree. Results show that among the five regression models developed, the Bagging Tree model performs the best with the Root Mean Square Error (RMSE) of 0.48 m and Correlation Coefficient (CC) of 0.82 when testing 1 million sets of data randomly selected, while the RMSE and CC of Bagging Tree model is 0.44 m and 0.73 in 4,500 National Data Buoy Center (NDBC) buoy testing dataset. Meanwhile, the Bagging Tree model also has the best generalization ability, which means it performs well in the practical application. In addition, the impacts of different input variables, the size of the training dataset and sea surface wind speed are also investigated. These findings are anticipated to serve as helpful guides for creating future SWH retrieval algorithms that are more advanced.
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