Spaceborne Global Navigation Satellite System-Reflectometry Research Articles

Characterization of River Width Measurement Capability by Space Borne GNSS-Reflectometry

Characterization of River Width Measurement Capability by Space Borne GNSS-Reflectometry

A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.

Evaluation of Spire GNSS-R reflectivity from multiple GNSS constellations for soil moisture estimation

ABSTRACT Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a pivotal tool with different land applications, prominently encompassing soil moisture estimation. In contrast to conventional radiometer satellites commonly used for this purpose, GNSS-R offers higher spatiotemporal coverage while maintaining cost-effectiveness. The potential of using Global Positioning System (GPS) reflections measured by the Cyclone Global Navigation Satellite System (CYGNSS) mission to retrieve soil moisture has been previously demonstrated. In 2019, Spire Global Inc. launched their first GNSS-R satellites, which now comprise a constellation of four CubeSats. These satellites track reflections from multi-constellation, encompassing GPS, Galileo, BeiDou Navigation Satellite System (BDS), and Quasi-Zenith Satellite System (QZSS). In this study, an analysis and validation of Spire GNSS-R L1B surface reflectivity for soil moisture retrieval within east Australia during an eight-month period in 2021 is presented. A comparison of the estimated Spire surface reflectivity to that of CYGNSS is performed, unveiling analogous behavioural patterns and biases across both missions. Soil moisture is estimated using observations from Spire GPS-only, Spire multi-constellation, and CYGNSS. The Soil Moisture Active and Passive (SMAP) retrievals are used as the reference, presuming a linear relationship between changes in soil moisture and changes in reflectivity. Our results indicate that the Spire GNSS-R mission can detect variations in soil moisture with a performance comparable to that of CYGNSS. A median unbiased root-mean-square difference (ubRMSD) of 0.062 m3.m−3 is found for both Spire GPS and multi-constellation when using 9-km products and SMAP as the reference.

An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring.

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

Improvement of CYGNSS soil moisture retrieval model considering water and surface temperature

The key to retrieving soil moisture (SM) with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data is to correct the effects of soil surface temperature (SST), roughness, water, and vegetation. In this study, Cyclone Global Navigation Satellite System (CYGNSS) data are used to calculate surface reflectivity and surface roughness is characterized based on the statistical moments of Delay-Doppler Maps (DDM). After removing open water, a multiple linear regression model is created to retrieve SM by combining the SST and vegetation optical depth (VOD) parameters provided by Soil Moisture Active Passive (SMAP) data. With a correlation coefficient of 0.815 and a root-mean-square error of 0.066 cm3cm−3, an experimental analysis reveals decent consistency between the CYGNSS SM and the referenced SMAP SM. In addition, a time-series analysis between the CYGNSS SM and the International Soil Moisture Network (ISMN) referenced SM data shows a good correlation. Since surface reflectivity is significantly affected by water and because there is a coupling relationship between SST and SM, the differences of CYGNSS SM when the two factors are simultaneously considered or ignored are also analyzed. The experimental results show that after removing water and incorporating SST into the linear regression model, the accuracy of CYGNSS SM has been improved significantly, with the root mean square error, mean absolute error, and Bias increasing by 6%, 7%, and 11.3%, respectively. This study demonstrates the necessity of considering water and SST in SM retrieval and provides a novel approach for SM retrieval using high accuracy and high spatial and temporal resolution data.

Joint Retrieval of Sea Surface Rainfall Intensity, Wind Speed, and Wave Height Based on Spaceborne GNSS-R: A Case Study of the Oceans near China

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology.

Enhancing GNSS-R Soil Moisture Accuracy with Vegetation and Roughness Correction

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm3cm−3, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stable.

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

Evaluation of spaceborne GNSS-R based sea surface altimetry using multiple constellation signals

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.

An Improved Method for Water Body Removal in Spaceborne GNSS-R Soil Moisture Retrieval

The global soil moisture (SM) retrievals by the spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) are significantly influenced by the presence of water bodies. The traditional method is to build a grid based on the location of satellite sampling points and determine the presence or absence of water bodies. In this paper, we propose a water body removal method for global spaceborne GNSS-R SM retrievals that combines water bodies and buffers derived from the marginal areas around water bodies as mask data, thus achieving accurate removal of the water body and avoiding margin effects. To verify the effectiveness of the proposed method, the Cyclone GNSS (CYGNSS) data with two different spatial resolutions (36 km and 3 km) were used for SM retrieval, and the Soil Moisture Active and Passive (SMAP) Radiometer SM as well as the International Soil Moisture Network (ISMN) were used as references. Results show that the correlation coefficient (R) and root mean square error (RMSE) of the 36 km grid are 0.50 and 0.057 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively, while the R and RMSE of the 3 km grid are 0.68 and 0.041 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively. Such performances are better than the traditional method. Moreover, the method proposed in this paper preserves more grids. Take the 3 km spatial resolution for example, it preserves 2.2 fold grids more than the traditional water body removal method. In the comparison with SMAP SM, the overall improvement of RMSE by using the water body removal method proposed in this paper is 16.3% (8.2% for the traditional method). In the in-situ validation, the overall improvement of RMSE is 19.4% (-1.2% for the traditional method). Therefore, in the future high spatial resolution SM retrieval, the water body removal method proposed in this paper can preserve the maximum area and effectively eliminate the influence of water bodies on SM retrieval.

Target Detection and Location by Fusing Delay-Doppler Maps

The use of delay-Doppler maps (DDMs) measured by Global Navigation Satellite System Reflectometry (GNSS-R) for target detection and location is a hot issue because of the potentially global coverage and short revisit periods of GNSS-R satellite missions. Existing researches have explored the detection of oil rigs, oil spills, sea ice, and other targets with spaceborne GNSS-R. However, these researches may not fully consider the advantages of multiple GNSS-R satellites, for example, Cyclone Global Navigation Satellite System (CYGNSS) which has eight satellites. Two problems of using multiple GNSS-R satellites for detection and location are how to estimate the sea clutter that is used to cancel the clutter in DDMs and how to remove location ambiguity caused by the transformation from the delay-Doppler (DD) domain to the spatial domain. In this article, an oil rig is taken as an example for detection and location with multiple GNSS-R satellites. To distinguish the oil rig from sea clutter, this article uses subspace projection to estimate the clutter and subtract the estimation from the DDMs. Moreover, after the clutter cancellation, this article projects the potential oil rig coordinates in the DD domain into a uniform coordinate system and removes the location ambiguity based on multisatellites fusion. The measured DDMs collected by CYGNSS are employed to conduct experiments to validate the feasibility of the proposed methods for detection and location with DDMs. By subtracting the estimated sea clutter from DDMs, the potential oil rig can be indicated in the DD domain. Two location experiments can calculate the unambiguous coordinate of the oil rig with a deviation less than 4 km.

Spaceborne GNSS Reflectometry With Galileo Signals on FY-3E/GNOS-II: Measurements, Calibration, and Wind Speed Retrieval

Reflected global navigation satellite system (GNSS) signals from Earth surface can be received by receivers at low Earth orbit for the remote sensing of geophysical parameters. While the technique has been studied for around 30 years, most early spaceborne GNSS reflectometry missions only adapted to receive GPS signals and the studies of reflected Galileo (GAL) signals in space are limited. The Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the FY-3E satellite is the first mission that can operationally receive reflected GPS, BeiDou (BDS), and GAL signals at the same time. This letter presents the GAL reflectometry measurements from GNOS-II together with their calibration and wind speed (WS) retrieval methods. Results show that while GAL has a different signal modulation, the observables can be used to retrieve WSs using the same geophysical model functions (GMFs) of GPS after a dedicated calibration. The retrieved WSs from GAL also have a comparable accuracy as those from GPS and BDS.

Global Sea Surface Height Measurement From CYGNSS Based on Machine Learning

Cyclone Global Navigation Satellite System (CYGNSS) launched in recent years, provides a large amount of spaceborne GNSS Reflectometry 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 article 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 1.04 m and 0.63 m, 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.

Leveraging the CYGNSS Spaceborne GNSS‐R Observations to Detect Ionospheric Irregularities Over the Oceans: Method and Verification

AbstractThe Global Navigation Satellite System (GNSS) has significantly facilitated the detection of ionospheric irregularities. Nevertheless, the ground‐based GNSS stations are inhomogeneously distributed around the world with large gaps, especially in the vast ocean regions. This adversely affects the interpretation of the spatio‐temporal variation characteristics of the ionospheric irregularities. In this study, ionospheric irregularities over the regions of the equatorial, and low‐latitude ocean during a G4‐class strong geomagnetic storm were investigated by utilizing the spaceborne GNSS reflectometry (GNSS‐R) technique. We derived and validated the ionospheric regularities index S4R from the signal‐to‐noise ratio measurements of the spaceborne GNSS‐R. Meanwhile, we utilized the ionospheric irregularities data from ground‐based (GNSS and ionosonde) and space‐based (Swarm and FORMOSAT‐3/COSMIC) instruments to evaluate and analyze the CYGNSS‐derived S4R. All comparisons and validations conducted within this study illustrate that the spaceborne GNSS‐R measurements provide complementary data and realize the timely observation as well as confirmation of the ionospheric irregularities at the same location over the oceans. It is expected to benefit the application of spaceborne GNSS‐R in ionospheric irregularities monitoring and applications over the ocean area.

The potential of spaceborne GNSS reflectometry for detecting ocean surface currents

The detectability of ocean surface currents in global navigation satellite system reflectometry (GNSS-R) observations is analyzed. We use a large dataset of spaceborne GNSS-R measurements from NASA cyclone GNSS (CYGNSS) mission. The data is collocated with ocean wind and near-surface current measurements. Our analysis reveals clear responses of the GNSS-R σ0 to the presence of currents. The response depends on the wind conditions and is more prominent for wind speeds below 6 m/s. A current velocity of 0.5 m/s under an opposing wind can, on average, suppress the GNSS-R σ0 by 0.8 decibels for low incidence angles. The interaction of the same current with a codirectional wind can enhance σ0 by almost the same amount. This enhancement is most visible at high incidence angles. We develop a model that improves the prediction of the GNSS-R σ0 in the presence of surface currents. The detected signatures of wind–current interactions highlight the potential of GNSS-R sensors onboard small satellites for observing ocean surface currents.

Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites

Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.

Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China

Snow plays an important role in the water cycle and global climate change, and the accurate monitoring of changes in snow depth is an important task. However, monitoring snow properties is still challenging and unclear, particularly in the Tibetan Plateau, which has rough land and uneven terrain. The traditional monitoring methods have some limitations in monitoring snow depth changes, and the Global Navigation Satellite System-Reflectometry (GNSS-R) provides a new opportunity for snow monitoring. This paper employed data from the Cyclone Global Navigation Satellite System (CYGNSS) to discover the effect of snow properties. Firstly, the observations of CYGNSS were used to find the sensitive to snow properties, and the relationships between signal to noise ratio (SNR), leading edge slope (LES), surface reflectivity (SR), and snow depth were studied and analyzed, respectively. It is found that the correlation between the first two parameters and snow depth is poor, while SR can indicate the changes in snow depth, and is proposed as an indicator of SR change, namely, surface reflectivity–difference ratio factor (SR–DR factor). Furthermore, the long-time series data in the Tibetan Plateau (2018–2019) are used to analyze its effects on the time series of the SR–DR factor, while the influences of the soil freeze/thaw (F/T) process and soil moisture are excluded during the analysis. The results indicate that the SR–DR factor can be a good indicator and discriminator for snow depth. Our work shows that space-borne GNSS-R has the potential for the monitoring of snow properties.

Investigation of Potential of GNSS-R Polarization: Theoretical Simulations

Global navigation satellite system (GNSS) reflectometry (GNSS-R) developed into a promising remote sensing technique. However, few previous related studies considered the potential of its polarization. Owing to lack of sufficient in situ measurement data to support comprehensive investigation of GNSS-R polarization, this study used theoretical models and reference to our previous work to explore this topic. The commonly used microwave scattering models are employed to get the bare soil or vegetation scattering properties of GNSS-R configurations, i.e., the random surface scattering model and the first-order radiative transfer equation were improved and then employed to obtain the scattering properties of both bare soil and vegetation. Since the final output of the space-borne GNSS-R missions is a delay Doppler map (DDM), a spaceborne (DDM) simulator, oriented for the Chinese FengYun-3E (FY-3E) GNSS-R payload, was utilized to obtain the final output at different polarizations. Using the developed models (such as the bare soil and vegetation scattering models), corresponding polarization simulations were performed. That is to say, not only the commonly used LR (left hand circular polarizations (LHCP) received and the right hand circular polarizations (RHCP) received) can be presented, but also the scattering properties at RR, VR, and HR (the transmitted signals are RHCP, while the received polarizations are RHCP, vertical (V) and horizontal (H) polarizations, respectively) can be predicted by our developed models. Results reveal obvious polarization differences for the bistatic scattering and DDM. Therefore, the use of GNSS-R polarization information has potential to provide competitive and fruitful results in the future detection of land surface geophysical parameters.

The Potential of Spaceborne GNSS Reflectometry for Soil Moisture, Biomass, and Freeze–Thaw Monitoring: Summary of a European Space Agency-funded study

This work presents an overview of the activity developed in the frame of a project funded by the European Space Agency (ESA). The research was focused on the study of the potential applications of GNSS Reflectometry (GNSS-R) over land, with an emphasis on soil moisture (SM) and biomass. A study about the sensitivity with respect to the freeze–thaw dynamics was considered as well. The work started with an analysis of the sensitivity of GNSS-R reflectivity collected by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TechDemoSat-1</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TDS-1</i> ) experimental satellite, although, to a limited extent, the Cyclone GNSS (CyGNSS) constellation was considered as well. The encouraging sensitivity outcomes led to the development of retrieval algorithms: three different approaches for SM and one for biomass based on neural networks.

Investigation on Geometry Computation of Spaceborne GNSS-R Altimetry over Topography: Modeling and Validation

The spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) offers versatile Earth surface observation. While the accuracy of the computed geometry, required for the implementation of the technique, degrades when Earth’s surface topography is complicated, previous studies ignored the effects of the local terrain surrounding the ideal specular point at a suppositional Earth reference surface. The surface slope and its aspect have been confirmed that it can lead to geolocation-related errors in the traditional radar altimetry, which will be even more intensified in tilt observations. In this study, the effect of large-scale slope on the spaceborne GNSS-R technique is investigated. We propose a new geometry computation strategy based on the property of ellipsoid to carry out forward and inverse calculations of path geometries. Moreover, it can be extended to calculate unusual reflected paths over versatile Earth’s topography by taking the surface slope and aspects into account. A simulation considering the slope effects demonstrates potential errors as large as meters to tens kilometers in geolocation and height estimations in the grazing observation condition over slopes. For validation, a single track over the Greenland surface received by the TechDemoSat 1 (TDS-1) satellite with a slope range from 0% to 1% was processed and analyzed. The results show that using the TanDEM-X 90 m Digital Elevation Model (DEM) as a reference, a slope of 0.6% at an elevation angle of 54 degrees can result in a geolocation inaccuracy of 10 km and a height error of 50 m. The proposed method in this study greatly reduces the standard deviation of geolocations of specular points from 4758 m to 367 m, and height retrievals from 28 m to 5.8 m. Applications associated with topography slopes, e.g., cryosphere could benefit from this method.