Global Navigation Satellite System Reflectometry Technique Research Articles

Retrieval of sea ice thickness using FY-3E/GNOS-II data

Sea ice, a significant component in polar regions, plays a crucial role in climate change through its varying conditions. In Global Navigation Satellite System-Reflectometry (GNSS-R) studies, the observed surface reflectivity Γ serves as a tool to examine the physical characteristics of sea ice covers. This facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium model. However, it is important to note that when Sea Ice Thickness (SIT) becomes thicker, the accuracy of SIT retrieval via this two-layer model begins to decline. In this paper, we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E (FY-3E). Soil Moisture Ocean Salinity (SMOS) data are treated as the reference. The performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT retrieval. The analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 m. By comparing the retrieved SITs against reference values, the three-layer model achieved a Root Mean Square Error (RMSE) of 0.149 m and Correlation Coefficient (r) of 0.830, while the two-layer model reported the RMSE of 0.162 m and r value of 0.789. A scheme incorporating both models yielded superior results than either individual model, with the RMSE of 0.137 m and r reaching up to 0.852. This study is the first application of FY-3E for GNSS-R SIT retrieval, combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 m. This provides a good reference for the future studies on GNSS-R SIT retrieval.

Advancing Accuracy in Sea Level Estimation with GNSS-R: A Fusion of LSTM-DNN-Based Deep Learning and SNR Residual Sequences

The global navigation satellite system reflectometry (GNSS-R) technique has shown promise in retrieving sea levels using signal-to-noise ratio (SNR) data. However, its accuracy and performance are often limited compared to conventional tide gauges, particularly due to constraints in satellite elevation angles. To address these limitations, we propose a methodology integrating Long Short-Term Memory Deep Neural Networks (LSTM-DNN) models, utilising SNR residual sequences as key feature inputs. Our study focuses on the SC02 station, examining elevation angles ranging from 5° to 10°, 5° to 15°, and 5° to 20°. Results reveal notable reductions in root mean square errors (RMSE) of 2.855%, 17.519%, and 15.756%, respectively, showcasing improvements in accuracy across varying elevation angles. Of particular significance is the enhancement in precision observed at higher elevation angles. This underscores the valuable contribution of our approach to nearshore sea level wave height retrieval, promising advancements in the GNSS-R technique.

Enhancing Spatial Resolution of GNSS-R Soil Moisture Retrieval through XGBoost Algorithm-Based Downscaling Approach: A Case Study in the Southern United States

The retrieval of soil moisture (SM) using the Global Navigation Satellite System-Reflectometry (GNSS-R) technique has become a prominent topic in recent years. Although prior research has reached a spatial resolution of up to 9 km through the Cyclone Global Navigation Satellite System (CYGNSS), it is insufficient to meet the requirements of higher spatial resolutions for hydrological or agricultural applications. In this paper, we present an SM downscaling method that fuses CYGNSS and SMAP SM. This method aims to construct a dataset of CYGNSS observables, auxiliary variables, and SMAP SM (36 km) products. It then establishes their nonlinear relationship at the same scale and finally builds a downscale retrieval model of SM using the eXtreme Gradient Boosting (XGBoost) algorithm. Focusing on the southern United States, the results indicate that the SM downscaling method exhibits robust performance during both the training and testing processes, enabling the generation of a CYGNSS SM product with a 1 day/3 km resolution. Compared to existing methods, the spatial resolution is increased threefold. Furthermore, in situ sites are utilized to validate the downscaled SM, and spatial correlation analysis is conducted using MODIS EVI and MODIS ET products. The CYGNSS SM obtained by the downscaling model exhibits favorable correlations. The high temporal and spatial resolution characteristics of GNSS-R are fully leveraged through the downscaled method proposed. Furthermore, this work provides a new perspective for enhancing the spatial resolution of SM retrieval using the GNSS-R technique.

Multi- GNSS reflectometry performance evaluation for coastal sea level monitoring: A case study in Antarctic Peninsula

Antarctica is a continent that crucial for studying climate change and its progression across time, as well as analyzing and forecasting local and global change. In this environment, due to the challenges caused by sea-level rise, storm surges, and tsunamis, sustainability is a critical concern, particularly for coastal regions. As a result, the long-term observations that will be conducted in Antarctica are critical for monitoring the adverse impacts of climate change. In recent years, many monitoring approaches, both space, and ground-based are performed to monitor sea/ice level trends in space-based scientific investigations conducted in and around the region. In the study, based on one year of observations from the Palmer GNSS Station, the GNSS Reflectometry technique was used to measure the sea level on the Antarctic Peninsula (PALM). GNSS Station observations were analyzed with a Lomb-Scargle periodogram to monitor sea-level changes, and results were validated with data from a co-located tide gauge (TG). The results show that the correlation between GNSS-R sea-level changes and tidal sea-level changes is found as 0.91.

Determination of weather-induced short-term sea level variations by GNSS reflectometry

We propose to derive local sea level variations by using the Signal-to-Noise Ratio (SNR) of the GNSS reflected signals at four GNSS single antenna sites (ILDG, TAR0, FFT2, LYTT) located at different latitudes. For these sites representing various ocean conditions (waves, tides, storm surges, etc…), tides estimates by SNR are highly consistent to tide gauges records as highlighted by tidal harmonic analysis, with a Root-Sum-Square (RSS) ranging from few centimeter in micro-tidal environment to near a decimeter in macro-tidal environment.SNR non-tidal residuals (NTR) are compared to two modelled sea level responses to meteorological forcing, namely the analytical Local Inverse Barometer (LIB) model and the numerical Dynamic Atmospheric Correction (DAC). Both DAC and LIB models are coherent with this non-tidal SNR residual in a 2-days to 2-months time window with correlations reaching 0.7 and high coherences. Several noteworthy atmospheric events are observed in the time series of about one year. During intense events, SNR and models are highly correlated. According to the results obtained in this study, we conclude that the GNSS reflectometry technique is relevant to derive sea level variations at tidal periods but also for studying the behaviour of the sea surface in response to atmospheric forcing at short-term scales.

THE STUDY ON THE SEMI-EMPIRICAL SOIL MODELS FOR IMPROVING THE GNSS-R SM RETRIEVAL

Abstract. Soil moisture (SM) is one of the important physical properties of soil and plays an important role in agricultural production. Global Navigation Satellite System Reflectometry (GNSS-R) SM retrieval is based on the concept of receiving GPS signals reflected by the ground using a passive receiver. Although many research has been reported showing the capability of the GNSS-R technique on SM application. Due to the diversity of soil types and the complexity of soil composition, the study of soil permittivity is still attracting many interesting. This paper presented the investigations on characteristic and performances for different semi-empirical soil models, to reveal and verify the relationship between dielectric constant and SM under different realistic soil compositions. This study is helpful to improve the accuracy for the GNSS-R SM retrieval and can be used to other SM related studies.

Evaluation of SNR-based GNSS-reflectometry altimetric precision by a height displacement tool

Global Navigation Satellite System Reflectometry (GNSS-R) technique is an approach that has been used in the last few years and produces effective results to estimate important climate change parameters such as snow thickness, sea level, forest biomass, growth of vegetation. In this study, the GNSS-R technique height determination accuracy was evaluated by using a vertical displacement tool that allows shifting the receiver height and measure within 1 mm accuracy. The main purpose is to investigate the accuracy of the GNSS-R technique to monitor vertical displacements of buildings which surfaces is smooth and the signal strength is high. For this purpose, height of the GNSS antenna was set to 3 heights with a 5 cm interval on the displacement tool. The signals was analyzed based on dominant signal reflection effect frequency of Signal to Noise Ratio (SNR) data and heights were determined by Lomb-Scargle Periodogram (LSP) method. According to the results, GNSS-R technique provide sufficient accuracy with a 0.51 cm RMSE for smooth surfaces.

Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications

Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) of the power of the reflected GNSS signals as their main data products. In addition to the power DDM products, some of these missions have collected a large amount of raw intermediate frequency (IF) data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications and future spaceborne missions. To facilitate such explorations, the raw IF data sets from different missions have been processed by Institute of Space Sciences (ICE-CSIC, IEEC), and the corresponding data products, i.e., the complex waveform of the reflected signal, have been generated and released through our public open-data server. These complex waveform data products provide the measurements from different GNSS constellations (e.g., GPS, Galileo and BeiDou), and include both the amplitude and carrier phase information of the reflected GNSS signal at higher sampling rate (e.g., 1000 Hz). To demonstrate these advanced features of the data products, different applications, e.g., inland water detection and surface altimetry, are introduced in this paper. By making these complex waveform data products publicly available, new EO capability of the GNSS-R technique can be further explored by the community. Such early explorations are also relevant to ESA’s next GNSS-R mission, HydroGNSS, which will provide similar complex observations operationally and continuously in the future.

An Improved Method for Ionospheric TEC Estimation Using the Spaceborne GNSS-R Observations

Ionospheric monitoring and modeling have been difficult for a long time over the data-void or data-sparse oceans. As an emerging remote sensing technique, GNSS reflectometry (GNSS-R) has presented great potential in ionosphere sounding over these regions. However, the conventional approach to generate delay-Doppler-map (DDM) involved in the GNSS-R total electron content (TEC) retrieval process ignores the effects of tropospheric delay and the topside ionospheric delay above the GNSS-R receiver. This would cause certain errors in retrieved TEC results. In this contribution, an improved method to estimate ionospheric TEC over oceans using the GNSS-R technique is proposed, which considers the influence of the tropospheric delays and the topside ionospheric delays above the spaceborne GNSS-R receiver. To achieve the best matching between measured and simulated DDM, this paper employs the least squares (LS) fitting method for elastic matching. Additionally, the assessment was performed in May 2015 and 2017 at different solar activities, by comparing the ionospheric TEC derived from our proposed method with that from two ionospheric empirical models (NeQuick2 and IRI-2016), the Global Ionospheric Maps (GIMs) final products, as well as the measured GNSS TEC. The results show good consistency between these models. Meanwhile, when considering the topside ionospheric delay and tropospheric delay in DDM, the TEC accuracy has significantly improved. Especially, the improvements of root mean square error (RMS) can reach 5.3% and 23.5% during high and low solar activities, respectively, versus GNSS TEC. It is expected to benefit the application of GNSS-R in ionospheric modeling and application over the ocean area.

Significant Wave Height Estimation Using Multi-Satellite Observations from GNSS-R

This study proposes a significant wave height (SWH) retrieval method based on multi-satellite observations using Global Navigation Satellite System Reflectometry (GNSS-R). The algorithm obtains the signal-to-noise ratio (SNR) through delay-Doppler maps (DDMs) and introduces an offset correction that combines the differences between the elevation angles of multi-satellites. The correction improves the correlation between SNR and SWH and thus enables the estimation of SWH. The feasibility of the proposed algorithm was verified by experiments and a performance comparison with the traditional estimation method based on SNR. The estimation results of the proposed algorithm, with a mean absolute percentage error of 8.26%, a root mean square error of 0.1671 m, and a maximum error of 0.32 m, better matched the true values than the traditional SNR-based method. The method is intended to improve the retrieval accuracy of SWH based on the GNSS-R technique, and to provide services for sea-state information monitoring and ship navigation.

Sea-level variations from co-located tide gauge and GNSS stations using GNSS-Reflectometry in Indonesia

Sea level variation is an important key for hazard mitigation in the coastal region to study climate change. In the last decade, reflected GNSS signals have been used in many applications, such as snow, sea level, and ocean storm monitoring. In this research, we use GNSS-Reflectometry co-located with a tide gauge station to monitor sea-level variations. We use the Signal Noise to Ratio (SNR) data to derive sea-level height from GNSS. One-month comparison of sea-level variations has been made from a tide gauge and a GNSS measurement in Barus, North Sumatra Indonesia. The results have a mean bias of about 2 cm with a root mean square error of 11 cm and a correlation coefficient value of 0.90. Our result demonstrates that the GNSS-Reflectometry technique is potential to provide a new approach to monitor sea-level changes that complement the existing tide-gauge networks in Indonesia.

Feasibility Analysis of GNSS-Reflectometry for Monitoring Coastal Hazards

Coastal hazards, such as a tsunamis and storm surges, are a critical threat to coastal communities that lead to significant loss of lives and properties. To mitigate their impact, event-driven water level changes should be properly monitored. A tide gauge is one of the conventional water level measurement sensors. Still, alternative measurement systems can be needed to compensate for the role of tide gauge for contingency (e.g., broken and absence, etc.). Global Navigation Satellite System (GNSS) is an emerging water level measurement sensor that processes multipath signals reflected by the water surface that is referred to as GNSS-Reflectometry (GNSS-R). In this study, we adopted the GNSS-R technique to monitor tsunamis and storm surges by analyzing event-driven water level changes. To detect the extreme change of water level, enhanced GNSS-R data processing methods were applied which included the utilization of multi-band GNSS signals, determination of optimal processing window, and Kalman filtering for height rate determination. The impact of coastal hazards on water level retrievals was assessed by computing the confidence level of retrieval (CLR) that was computed based on probability of dominant peak representing the roughness of the water surface. The proposed approach was validated by two tsunami events, induced by 2012 Haida Gwaii earthquake and 2015 Chile earthquake, and two storm surge events, induced by 2017 Hurricane Harvey and occurred in Alaska in 2019. The proposed method successfully retrieved the water levels during the storm surge in both cases with the high correlation coefficients with the nearby tide gauge, 0.944, 0.933, 0.987, and 0.957, respectively. In addition, CLRs of four events are distinctive to the type of coastal events. It is confirmed that the tsunami causes the CLR deduction, while for the storm surges, GNSS-R keep high CLR during the event. These results are possibly used as an indicator of each event in terms of storm surge level and tsunami arrival time. This study shows that the proposed approach of GNSS-R based water level retrieval is feasible to monitor coastal hazards that are tsunamis and storm surges, and it can be a promising tool for investigating the coastal hazards to mitigate their impact and for a better decision making.

An Aircraft Wetland Inundation Experiment Using GNSS Reflectometry

In early May of 2017, a flight campaign was conducted over Caddo Lake, Texas, to test the ability of Global Navigation Satellite System-Reflectometry (GNSS-R) to detect water underlying vegetation canopies. This paper presents data from that campaign and compares them to Sentinel-1 data collected during the same week. The low-altitude measurement allows for a more detailed assessment of the forward-scattering GNSS-R technique, and at a much higher spatial resolution, than is possible using currently available space-based GNSS-R data. Assumptions about the scattering model are verified, as is the assumption that the surface spot size is approximately the Fresnel zone. The results of this experiment indicate GNSS signals reflected from inundated short, thick vegetation, such as the giant Salvinia observed here, results in only a 2.15 dB loss compared to an open water reflection. GNSS reflections off inundated cypress forests show a 9.4 dB loss, but still 4.25 dB above that observed over dry regions. Sentinel-1 data show a 6-dB loss over the inundated giant Salvinia, relative to open water, and are insensitive to standing water beneath the cypress forests, as there is no difference between the signal over inundated cypress forests and that over dry land. These results indicate that, at aircraft altitudes, forward-scattered GNSS signals are able to map inundated regions even in the presence of dense overlying vegetation, whether that vegetation consists of short plants or tall trees.

Sea Ice Remote Sensing Using GNSS-R: A Review

Knowledge of sea ice is critical for offshore oil and gas exploration, global shipping industries, and climate change studies. During recent decades, Global Navigation Satellite System-Reflectometry (GNSS-R) has evolved as an efficient tool for sea ice remote sensing. In particular, thanks to the availability of the TechDemoSat-1 (TDS-1) data over high-latitude regions, remote sensing of sea ice based on spaceborne GNSS-R has been rapidly growing. The goal of this paper is to provide a review of the state-of-the-art methods for sea ice remote sensing offered by the GNSS-R technique. In this review, the fundamentals of these applications are described, and their performances are evaluated. Specifically, recent progress in sea ice sensing using TDS-1 data is highlighted including sea ice detection, sea ice concentration estimation, sea ice type classification, sea ice thickness retrieval, and sea ice altimetry. In addition, studies of sea ice sensing using airborne and ground-based data are also noted. Lastly, applications based on various platforms along with remaining challenges are summarized and possible future trends are explored. In this review, concepts, research methods, and experimental techniques of GNSS-R-based sea ice sensing are delivered, and this can benefit the scientific community by providing insights into this topic to further advance this field or transfer the relevant knowledge and practice to other studies.

Detecting Targets above the Earth’s Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1

Global Navigation Satellite System (GNSS) reflected signals can be used to remotely sense the Earth’s surface, known as GNSS reflectometry (GNSS-R). The GNSS-R technique has been applied to numerous areas, such as the retrieval of wind speed, and the detection of Earth surface objects. This work proposes a new application of GNSS-R, namely to detect objects above the Earth’s surface, such as low Earth orbit (LEO) satellites. To discuss its feasibility, 14 delay Doppler maps (DDMs) are first presented which contain unusually bright reflected signals as delays shorter than the specular reflection point over the Earth’s surface. Then, seven possible causes of these anomalies are analysed, reaching the conclusion that the anomalies are likely due to the signals being reflected from objects above the Earth’s surface. Next, the positions of the objects are calculated using the delay and Doppler information, and an appropriate geometry assumption. After that, suspect satellite objects are searched in the satellite database from Union of Concerned Scientists (UCS). Finally, three objects have been found to match the delay and Doppler conditions. In the absence of other reasons for these anomalies, GNSS-R could potentially be used to detect some objects above the Earth’s surface.

Software receiver design for GNSS‐R using multiple GNSS satellites as transmitters

A novel global navigation satellite system-reflectometry (GNSS-R) software receiver and its preliminary experimental results are presented in this study. The antenna systems, the multi-channel radio-frequency front end, and the digital signal processing unit of this receiver are described in detail. With this developed software receiver, the signals in the Global Positioning System L1 band (GPS L1), BeiDou-2 B1, GLObal NAvigation Satellite System L1, and Galileo E1 bands can be tracked and used as the transmissions of GNSS-R to obtain more information about the scattering properties of ground objects. The preliminary experimental results show that various GNSS signals can be successfully acquired and tracked by this receiver. Furthermore, when it was applied to the soil moisture estimation with the ground-based GNSS-R technique, a relative measure error of 5.79 and 1.39% can be obtained with GPS L1 and BeiDou-2 B1 signals, respectively, by comparing the retrieval results with the measuring results provided by a moisture metre.

A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry.

Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.

A METHOD USING GNSS LH-REFLECTED SIGNALS FOR SOIL ROUGHNESS ESTIMATION

Abstract. Global Navigation Satellite System Reflectometry (GNSS-R) is based on the concept of receiving GPS signals reflected by the ground using a passive receiver. The receiver can be on the ground or installed on a small aircraft or UAV and collects the electromagnetic field scattered from the surface of the Earth. The received signals are then analyzed to determine the characteristics of the surface. Many research has been reported showing the capability of the GNSS-R technique. However, the roughness of the surface impacts the phase and amplitude of the received signals, which is still a worthwhile study. This paper presented a method can be used by GNSS-R to estimate the surface roughness. First, the data was calculated in the specular reflection with the assumption of a flat surface with different permittivity. Since the power reflectivity can be evaluated as the ratio of left-hand (LH) reflected signal to the direct right-hand (RH) signal. Then a semi-empirical roughness model was applied to the data for testing. The results showed the method can distinguish the water and the soil surface. The sensitivity of the parameters was also analyzed. It indicates this method for soil roughness estimation can be used by GNSS-R LH reflected signals. In the next step, several experiments need to be done for improving the model and exploring the way of the estimation.

Weak Tsunami Detection Using GNSS-R-Based Sea Surface Height Measurement

This paper investigates weak tsunami detection using noisy sea surface height (SSH) measurement data such as that recorded by a satellite-borne receiver and produced by the Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) technique. By studying the patterns of many real tsunamis, a triangle function is proposed to model the shape of tsunami lead waves for theoretical studies. Very similar simulation results are produced when the modeled and real tsunami data are used separately, indicating good modeling accuracy. A bin averaging (BA) technique is proposed for detecting a tsunami, and a hypothesis testing method is developed to decide whether a tsunami is present by examining the BA outputs against a predefined threshold. Mathematical formulas are derived for the probability of detection (PD) and the probability of false alarm (PFA), which can be employed as a guideline in parameter selection and performance evaluation. Simulation results using both modeled and real tsunami data demonstrate that, given a PFA of 10%, the PD can be around 60% when the wave height is 45 cm, and the SSH measurement error standard deviation (STD) is 76 cm. The results also show that, when a suitable bin length is selected, a two-stage hypothesis testing scheme and a one stage one produce very similar results. Based on the methods developed here and elsewhere, the GNSS-R technique has the potential for future tsunami detection.

A first comprehensive evaluation of China’s GNSS-R airborne campaign: part II—river remote sensing

The Global Navigation Satellite System Reflectometry (GNSS-R) has been proven to be a powerful technique for retrieving geophysical parameters of ocean and land. Airborne GNSS-R is an important experimental platform, because it is not only needed as validation for spaceborne application, but also possesses the advantages to be capable of remote sensing of small and medium scale targets, such as rivers and lakes. This paper presents an overview of China’s airborne GNSS-R campaign conducted on May 30, 2014, in Henan. The campaign has two objectives, i.e.: (1) to examine the capability of a GNSS-R payload developed by National Space Science Center, Chinese Academy of Sciences (NSSC, CAS) for airborne observations and (2) to study the algorithms for soil moisture and river remote sensing, including altimetry and flow velocity measurement. A previous paper has presented results of soil moisture retrieval as part I, and in this paper, initial results of the Yellow River remote sensing are presented as part II. This paper presents the river altimetry results and explores a new potential application of GNSS-R technology, which is used to detect the flow velocity of the river. The river surface height results observed by code delay altimetry method were consistent with the height results of GPS dual-frequency differential positioning altimetry. The GNSS-R altimetry results showed that decimeter level heights were achieved in 1-min sliding average. Comparing with in situ measurements, the GNSS-R flow velocity result was reasonable; the error was about 0.027m/s, which indicated the validity and feasibility of using GNSS-R technique to detect river flow velocity.