Global Navigation Satellite System Reflectometry Observations Research Articles

Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations

ABSTRACT The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China

Drought is a disaster that seriously constrains economic development and endangers human life. This paper explores the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for drought monitoring, using Cyclone Global Navigation Satellite System (CYGNSS) data to monitor drought in Jiangxi and Hunan Provinces, China, in 2022. This study applies the Random Under-sampling Boosting (RUSBoost) algorithm to detect waterbodies and linear regression to retrieve soil moisture (SM). Result shows that drought in September was heaviest, with the area of Poyang Lake in Jiangxi and Dongting Lake in Hunan decreasing by 70.2% and 76.9%, respectively, compared to that in June. The variation in retrieved SM shows that the Poyang Lake Plain and Jitai Basin in Jiangxi and the Dongting Lake, Yuanjiang River, and Xiangjiang River basins in Hunan suffered from the most serious drought. The variation in retrievals shows high consistency with various reference datasets, including Soil Moisture Active Passive (SMAP) SM data and vegetation condition index (VCI). The correlation coefficient between retrieved SM and VCI is 0.93 in Jiangxi and 0.94 in Hunan.

Evaluation of GNSS reflectometry method for sea level estimation in Indonesia

Over the last decade, GNSS-R (Global Navigation Satellite System reflectometry) has emerged as a technique for observing sea level height using data from GNSS observations. GNSS-R estimates sea level height from the phase center antenna using the reflection of the extracted GNSS signal at sea level. With a large number of tidal stations equipped with GNSS antennas, GNSS-R has the potential to be implemented in Indonesia. GNSS-R observations can also cover sea surface areas tens to hundreds of kilometres away from where the antenna is located over the coast. Furthermore, the installation of a GNSS antenna at a safe height on land allows for the observation of sea levels under extreme conditions such as during storms and cyclones. To employ GNSS-R effectively, several factors must be considered, including signal-to-noise ratio (SNR) data analysis, data processing, filtering control variables, and increased frequency extraction. This study will focus on trying to use GNSS-R method by identifying the best control variables for each study area and evaluating the results. This study used three months of GPS and GLONASS satellite SNR data from two stations, Barus and Morotai. The separated multipath data was then analyzed using the Lomb-Scargle Periodogram (LSP) based on its frequency. The results of GNSS-R sea surface height observations were validated using tide gauge data at both stations. Based on the test results, the RMSE values were 8.7 cm and 8.4 cm at Barus and Morotai stations, respectively. GNSS-R results strongly correlate with tide gauge data, with correlation coefficients of 95% and 98% at Barus and Morotai stations, respectively. Based on these findings, the GNSS-R method can be used to complement tidal sensor data by applying proper quality control.

Improving Spaceborne GNSS-R Algal Bloom Detection with Meteorological Data

Algal bloom has become a serious environmental problem caused by the overgrowth of plankton in many waterbodies, and effective remote sensing methods for monitoring it are urgently needed. Global navigation satellite system-reflectometry (GNSS-R) has been developed rapidly in recent years, which offers a new perspective on algal bloom detection. When algal bloom emerges, the water surface will turn smoother, which can be detected by GNSS-R. In addition, meteorological parameters, such as temperature, wind speed and solar radiation, are generally regarded as the key factors in the formation of algal bloom. In this article, a new algal bloom detection method aided by machine learning and auxiliary meteorological data is established. This work employs the Cyclone GNSS (CYGNSS) data and the fifth generation European Reanalysis (ERA-5) data with the application of the random under sampling boost (RUSBoost) algorithm. Experiments were carried out for Taihu Lake, China, over the period of August 2018 to May 2022. During the evaluation stage, the test true positive rate (TPR) of 81.9%, true negative rate (TNR) of 82.9%, overall accuracy (OA) of 82.9% and the area under (receiver operating characteristic) curve (AUC) of 0.88 were achieved, with all the GNSS-R observables and meteorological factors being involved. Meanwhile, the contribution of each meteorological factor and the error sources were assessed, and the results indicate that temperature and solar radiation play a prominent role among other meteorological factors in this research. This work demonstrates the capability of CYGNSS as an effective tool for algal bloom detection and the inclusion of meteorological data for further enhanced performance.

Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables.

Green algae monitoring via ground-based GNSS-R observations

Outbreaks of harmful algal blooms (HABs) exhibit high frequency, large range and damage aggravation characteristics, but existing monitoring methods, such as artificial and optical near-infrared remote sensing, cannot accommodate these characteristics. We propose a new method for monitoring green algae using Global Navigation Satellite System Reflectometry (GNSS-R) observations. The basic principle states that changes in the seawater dielectric constant and sea surface roughness due to the emergence of green algae lead to an increase in brightness temperature, which can be inverted based on the reflection time delay waveform. Shipboard reflection waveform data collected during an Enteromorpha prolifera outbreak in the Qingdao sea area were used for model development and validation of the detection and estimation performance. The results indicated that the root mean square error of GNSS-R-based inversion of the green algae density was 6.74%, indicating the potential of GNSS-R technology for rapid preliminary monitoring of green algae. Moreover, the advantages of a low cost, short return time and no climatic limitations support GNSS-R technology as a new and efficient means of green algae monitoring.

Detecting fire disturbances in forests by using GNSS reflectometry and machine learning: A case study in Angola

This paper aims at exploiting the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for the detection of forest disturbances due to fires. The study focuses on the forested part of Angola that was largely affected by fires during summer 2019. The data collected in the area by the NASA Cyclone GNSS (CyGNSS) constellation have been downloaded and processed. As reference data for developing and testing the retrieval algorithms, the ESA Climate Change Initiative (CCI) decadal burned areas maps have been considered. After evaluating the sensitivity of the GNSS-R observables, namely Signal to Noise Ratio and Equivalent Reflectivity, to the forest disturbances, some retrieval algorithms based on different machine learning techniques have been implemented, tested, and compared with a simple approach based on the temporal gradient of the GNSS-R observables. In details, classifiers based on Support Vector Machines (SVM) and Random Forests (RF) have been implemented for discriminating burned/not burned pixels of the images and regressors based on Artificial Neural Networks (ANN), RF and Support Vector Regressors (SVR) have been implemented for estimating the fraction of burned areas within a pixel. All these algorithms allowed a satisfactory identification of the burned areas with respect to the reference data, enabling the generation of maps every ten days. These results represent the first observations of forest disturbances due to fires using spaceborne GNSS-Reflectometry.

Significant Wave Height Retrieval Method Based on Spaceborne GNSS Reflectometry

A geophysical model function (GMF) for significant wave height (SWH) retrieval is developed based on the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data measured by the Cyclone GNSS (CYGNSS) satellites. The spreading characteristics of delay-Doppler maps (DDMs) generated by receivers onboard satellites are affected by the surface roughness, which is closely related to the SWH. Four GNSS-R observables [i.e., leading edge slope (LES) of normalized integrated delay waveform (NIDW), LES of normalized central delay waveform (NCDW), trailing edge slope (TES) of NCDW, leading edge waveform summation (LEWS) of NCDW] derived from DDM are first used in this letter to retrieve SWH. Collocated ERA5 SWH data are used as the ground truth to develop and evaluate the SWH models based on the four GNSS-R observables. The results show that there is high consistency between the SWH estimates and the ground truth, with a correlation coefficient of 0.88 and a root mean square error (RMSE) of 0.503 m. This letter demonstrates the feasibility of the spaceborne GNSS-R in SWH retrieval.

Retrieval of Sea Surface Rainfall Intensity Using Spaceborne GNSS-R Data

Reliable rainfall information is very important for hydro climatology research, industrial and agricultural production, construction, and drought and flood prediction. As a new remote sensing technology, Global Navigation Satellite System-reflectometry (GNSS-R) can be used to retrieve geophysical parameters due to its unique advantages of high spatial and temporal resolution, low cost, large coverage, and all-weather operation. Existing studies have confirmed that GNSS-R technology can detect rainfall on the ocean surface, but there is no further study on the magnitude of rainfall intensity (RI). Therefore, this article aims to study the utilization of GNSS-R in the retrieval of sea surface RI based on the observables derived from GNSS-R delay-Doppler maps (DDMs). First, a wavelet denoising method is proposed for DDM denoising to improve the quality of DDM data. Then, three models were developed based on the four GNSS-R observables, which were divided into three categories according to the number of observables. The performance of RI retrieval based on the three models was evaluated against reference data IMERG-F extensively. The experimental results show that under very low wind speed (WS) (< 5 m/s), the following holds. First, the retrieval performance of the single observable model based on leading-edge slope of the normalized integrated delay waveform (LES-NIDW) proposed in the first category of model is the worst [root mean square error (RMSE) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=3.69$ </tex-math></inline-formula> mm/h and correlation coefficient (CC) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=0.71$ </tex-math></inline-formula> ]. However, right edge waveform summation of NIDW (REWS-NIDW) is the best, and RMSE and CC are better than 3.17 mm/h and 0.79, respectively. Second, compared with LES-NIDW observable, the model retrieval accuracy based on DDM Average (DDMA), leading-edge waveform summations of NIDW (LEWS-NIDW), and REWS-NIDW observables is improved by 7.05%, 7.96%, and 12.29%, respectively. Third, when using the combined model, compared with LES-NIDW single observable model, the retrieval accuracy of the model based on two observables (i.e., LES-NIDW and REWS-NIDW) combination and three observables (i.e., LES-NIDW, LEWS-NIDW, and REWS-NIDW) combination is improved by 10.34% and 13.60%, respectively. Therefore, considerable performance gain is obtained.

Arctic TEC Mapping Using Integrated LEO-Based GNSS-R and Ground-Based GNSS Observations: A Simulation Study

Ionospheric total electron content (TEC) maps with high spatial and temporal resolutions are essential for depicting the state of the ionosphere and for performing ionospheric delay corrections associated with satellite navigation applications. Low Earth orbit (LEO) CubeSat-based global navigation satellite system (GNSS) reflectometry (GNSS-R) measurements provide a promising opportunity for retrieval of ionospheric TEC over sea ice and calm waters, which offers a potential new data source to fill the gaps of ground-based GNSS networks. However, the GNSS-R slant TEC (sTEC) estimations include contributions from the incident and reflection ray paths, whose ionospheric piercing points (IPPs) can be separated by hundreds of kilometers. This article presents an algorithm that integrates sTEC measurements from the GNSS-R CubeSats and available ground-based GNSS receivers to derive Arctic vertical TEC (vTEC) maps. A simulation study using the model ionosphere constructed from the NeQuick-2 is conducted to assess the performance of the algorithm. Varying levels of temporal resolutions and solar activities, and the number of CubeSats and the number of maximum simultaneously tracked reflection signals by a CubeSat are implemented in the simulation. The results show that the inclusion of coherent GNSS-R measurements improves the accuracy of the vTEC maps under all levels of solar activities. The RMSE improvement percentage is most obvious when the update interval is the shortest. Increasing the number of CubeSats further improves the accuracy. However, no significant improvement in vTEC map accuracy is observed when the number of maximum simultaneously tracked GNSS-R satellites is higher than 4. Quantitative measures and analyses of the algorithm performances are presented in this article.

Sea Surface Rainfall Detection and Intensity Retrieval Based on GNSS-Reflectometry Data From the CYGNSS Mission

Rainfall detection (RD) and rainfall intensity (RI) retrieval are hot topics in the field of ocean remote sensing (RS). In the past, the sea surface RD and RI retrieval were usually based on X-band ocean radar image data. In this study, we aim to investigate the potential of global navigation satellite system-reflectometry (GNSS-R) for sea surface RD and RI retrieval based on delay Doppler maps (DDMs) data collected by the cyclone GNSS (CYGNSS) mission. First, the block-matching and 3-D filtering (BM3D) algorithm is proposed to improve the quality of DDM data. In addition, 12 GNSS-R observables derived from DDM are calculated, and an RD method based on the threshold of 12 GNSS-R observables is proposed based on the probability density function (PDF). When rainfall DDM data are detected, these data are used to develop and verify the sea surface RI retrieval model. The integrated multisatellite retrievals of global precipitation measurements (GPM-IMERG) data product are used as reference data to evaluate the performance of RD and RI retrieval model. The experimental results show that under very low wind speed (<5 m/s), the proposed trailing edge waveform summation of normalized integral delay waveform (TEWS-NIDW), TEWS of normalized center delay waveform (TEWS-NCDW), and TEWS of differential delay waveform (TEWS-DDW) observables are the best for RD, and the probability of detection of rainfall (PDr) is better than 75%. In the aspect of model retrieval performance, the root mean square error (RMSE) of the 12 observables is less than 4.66 mm/hr. Among them, the model based on TEWS-NCDW observables has the highest accuracy, better than 3.74 mm/hr.

Ionospheric Total Electron Content and Disturbance Observations From Space-Borne Coherent GNSS-R Measurements

In this article, we investigate coherent global navigation satellite system reflectometry (GNSS-R) measurements obtained at the low earth orbits (LEOs) as a potential new data source for ionospheric total electron content (TEC) and ionospheric disturbance observations. Current global ionospheric TEC maps (GIMs) have limited spatial and temporal resolutions and accuracy due to lack of GNSS observations over oceans, polar caps, and inaccessible terrains. Our analysis of Spire Global’s CubeSat data indicates that coherent GNSS signals reflected off sea ice, inland water bodies, and calm ocean surface can be processed to achieve cm-level precision carrier phase estimations. Signal coherency is especially prevalent over sea ice where 41.7% reflections are coherent, compared to 4.3% in the overall dataset. This article presents the methodology to estimate slant TEC along the reflection signal ray path using coherent dual-frequency GNSS-R pseudoranage and carrier phase estimations obtained from low-cost CubeSats. The methodology is applied to Spire Global’s CubeSat data. The results show that the slant TEC retrieved from GNSS-R measurements and from GIM follow a similar trend. Moreover, the GNSS-R-based TEC time series offer a nearly “frozen in time” observation of the ionospheric structures due to the rapid scan velocity of GNSS-R rays across the ionosphere. The study demonstrates the potential of GNSS-R observations to fill the data gaps over the polar regions where space weather activities and TEC disturbances are most frequent and intense. Potential error sources and mitigation techniques are also discussed.

Detection of Red Tide Over Sea Surface Using GNSS-R Spaceborne Observations

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment.

Characterization and Calibration of Spaceborne GNSS-R Observations Over the Ocean From Different BeiDou Satellite Types

The Global Navigation Satellite System Reflectometry (GNSS-R) technique can measure ocean surface winds and other geophysical parameters from forward scattered GNSS (GPS, BeiDou, Galileo, etc.) signals. However, most early spaceborne missions only captured GPS signals while the study of spaceborne reflectometry using BeiDou signals (BDS-R) is limited. The GNOS-II payload onboard China’s FY-3E satellite has been operationally collecting a large number of BDS-R data since July 10, 2021. BDS is different from GPS in the orbit, signal frequency, chipping rate and Effective Isotropic Radiated Power (EIRP). Furthermore, BDS satellites have different generations and orbits. This paper, for the first time, comprehensively characterizes the spaceborne BDS-R observations over the ocean using the FY-3E GNOS-II data from different BDS satellite types including BDS-2 IGSO, BDS-2 MEO, BDS-3 IGSO and BDS-3 MEO. Their spatial coverage, spatial resolution, effective scattering area, incidence angle, range corrected gain, EIRP and signal-to-noise ratio have been analyzed and compared to those of GPS-R. Spaceborne BDS-R shows a lot of uniquenesses compared to GPS-R. The BDS-R observables are then calibrated separately for each type. An intercalibration is also applied to correct extra calibration errors. After the calibration, BDS-R observables and retrieved winds are consistent between each type and compared to those of GPS-R. Calibrated observables from FY-3E GNOS-II are also evaluated by comparing them to those measured by the Cyclone Global Navigation Satellite System (CYGNSS) mission and a theoretical model. The results of this paper can provide a reference for future BDS-R studies and spaceborne GNSS-R mission design.

Raytracing atmospheric delays in ground-based GNSS reflectometry

Several studies have recognized that Global Navigation Satellite System Reflectometry (GNSS-R) is subject to atmospheric propagation delays. Unfortunately, there is little information in the peer-reviewed literature about the methods and algorithms involved in correcting for this effect. We have developed an atmospheric ray-tracing procedure to solve rigorously the three-point boundary value problem of ground-based GNSS-R observations. We defined the reflection-minus-direct or interferometric delay in terms of vacuum distance and radio length. We clarified the roles of linear and angular refraction in splitting the total delay in two components, along-path and geometric. We have introduced for the first time two subcomponents of the atmospheric geometric delay, the geometry shift and the geometric excess. We have defined corresponding atmospheric altimetry corrections necessary for unbiased altimetry retrievals. Using simulations, we examined the interferometric atmospheric delay for a range of typical scenarios, where it attained centimeter-level values at low satellite elevation angles ~ 5° for a 10-m high station. We found a linear and exponential dependence on reflector height and satellite elevation angle, respectively. A similar trend was found for the atmospheric altimetry correction, albeit with an amplified meter-level magnitude. The two delay components were similar near the horizon while the angular one vanished at zenith. For the altimetry correction components, both remained non-zero at zenith. We thus quantified the atmospheric bias in GNSS-R sea level retrievals.

Evaluation of CYGNSS Observations for Flood Detection and Mapping during Sistan and Baluchestan Torrential Rain in 2020

Flood detection and produced maps play essential roles in policymaking, planning, and implementing flood management options. Remote sensing is commonly accepted as a maximum cost-effective technology to obtain detailed information over large areas of lands and oceans. We used remote sensing observations from Global Navigation Satellite System-Reflectometry (GNSS-R) to study the potential of this technique for the retrieval of flood maps over the regions affected by the recent flood in the southeastern part of Iran. The evaluation was made using spaceborne GNSS-R measurements over the Sistan and Baluchestan provinces during torrential rain in January 2020. This area has been at a high risk of flood in recent years and needs to be continuously monitored by means of timely observations. The main dataset was acquired from the level-1 data product of the Cyclone Global Navigation Satellite System (CYGNSS) spaceborne mission. The mission consisted of a constellation of eight microsatellites with GNSS-R sensors onboard to receive forward-scattered GNSS signals from the ocean and land. We first focused on data preparation and eliminating the outliers. Afterward, the reflectivity of the surface was calculated using the bistatic radar equations formula. The flooded areas were then detected based on the analysis of the derived reflectivity. Images from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used for evaluation of the results. The analysis estimated the inundated area of approximately 19,644 km2 (including Jaz-Murian depression) to be affected by the flood in the south and middle parts of the Sistan and Baluchestan province. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we showed the capability of detecting floods in the study area. The sensitivity of the spaceborne GNSS-R observations, together with the relatively short revisit time, highlight the potential of this technique to be used in flood detection. Future GNSS-R missions capable of collecting the reflected signals from all available multi-GNSS constellations would offer even more detailed information from the flood-affected areas.

Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS

Soil moisture (SM) derived from satellite-based remote sensing measurements plays a vital role for understanding Earth’s land and near-surface atmosphere interactions. Bistatic Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) has emerged in recent years as a new domain of microwave remote sensing with great potential for SM retrievals, particularly at high spatio-temporal resolutions. In this work, a machine learning (ML)-based framework is presented for obtaining SM data products over the International Soil Moisture Network (ISMN) sites in the Continental United States (CONUS) by leveraging spaceborne GNSS-R observations provided by NASA’s Cyclone GNSS (CYGNSS) constellation alongside remotely sensed geophysical data products. Three widely-used ML approaches—artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—are compared and analyzed for the SM retrieval through utilizing multiple validation strategies. Specifically, using a 5-fold cross-validation method, overall RMSE values of 0.052, 0.061, and 0.065 cm3/cm3 are achieved for the RF, ANN, and SVM techniques, respectively. In addition, both a site-independent and a year-based validation techniques demonstrate satisfactory accuracy of the proposed ML model, suggesting that this SM approach can be generalized in space and time domains. Moreover, the achieved accuracy can be further improved when the model is trained and tested over individual SM networks as opposed to combining all available SM networks. Additionally, factors including soil type and land cover are analyzed with respect to their impacts on the accuracy of SM retrievals. Overall, the results demonstrated here indicate that the proposed technique can confidently provide SM estimates over lightly-vegetated areas with vegetation water content (VWC) less than 5 kg/m2 and relatively low spatial heterogeneity.

First Evidence of Mesoscale Ocean Eddies Signature in GNSS Reflectometry Measurements

Feasibility of sensing mesoscale ocean eddies using spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) measurements is demonstrated for the first time. Measurements of Cyclone GNSS (CYGNSS) satellite missions over the eddies, documented in the Aviso eddy trajectory atlas, are studied. The investigation reports on the evidence of normalized bistatic radar cross section ( σ 0 ) responses over the center or the edges of the eddies. A statistical analysis using profiles over eddies in 2017 is carried out. The potential contributing factors leaving the signature in the measurements are discussed. The analysis of GNSS-R observations collocated with ancillary data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis-5 (ERA-5) shows strong inverse correlations of σ 0 with the sensible heat flux and surface stress in certain conditions.

Space-Borne GNSS-R Signal Over a Complex Topography: Modeling and Validation

A significant quantity of space-borne Global Navigation Satellite Systems-Reflectometry (GNSS-R) data over land was made available in the last decade, leading to an increasing interest in the assessment of the potentialities of this new remote sensing technique for land monitoring. In this frame, an electromagnetic simulator, such as the Soil And VEgetation Reflection Simulator (SAVERS), has the key role to support the understanding of the physical mechanism involved in the bistatic scattering and to identify the surface features mainly contributing to the observed signal. Originally developed for ground and airborne GNSS-R observations over homogeneous areas, in this study, SAVERS was upgraded to account for space-borne systems. The new version of SAVERS takes into account the inhomogeneity characterizing the large area observed from space altitudes, due to a variable surface elevation and land cover. Coherent and incoherent scattering and polarization rotation are computed taking into account the local slope and elevation of the surface. The simulator was validated against TechDemoSat-1 observations over a bare surface with a complex topography and over a forested surface with a gentle topography. The validation results show the capability of SAVERS to correctly estimate the effect of the topography, enhancing the understanding of the observations. Moreover, it was found that the sensitivity to soil moisture is independent of the topography (about 1.5 dB for a 10% variation of soil moisture). Whereas a saturation of the GNSS-R reflectivity over a variable topography is reached for lower values of biomass, earlier than in the flat case.

Generic Performance Simulator of Spaceborne GNSS-Reflectometer for Land Applications

Nowadays, the space missions employing the global navigation satellite system reflectometry (GNSS-R) are UK TDS-1, NASA CYGNSS, and the Chinese BuFeng-1A/B twin satellites, part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. They provide delay-Doppler map (DDM) measurements reflected from the land as well as the ocean. Using land reflected DDMs, several studies have been conducted to retrieve land geophysical parameters, such as soil moisture and biomass. Despite the clear dependence on these parameters, many other parameters impact the DDMs as well, such as topography, surface roughness, etc. The impact of these perturbing factors must be analyzed, and modeled in various conditions. This article presents a comprehensive end-to-end simulator that can generate synthetic DDMs reflected over land. It is an extension of a previously developed simulator validated for ocean applications. This simulator is very generic, and it includes numerous configurable parameters such as arbitrary scattering geometry (transmitter and receiver positions and speeds), arbitrary GPS and Galileo transmitted signals and frequencies, GNSS-R instrument antenna and receiver errors, as well as surface topography, roughness, soil moisture, vegetation cover, etc. The sensitivities of GNSS-R observables with respect to soil moisture and vegetation are obtained and compared to previous experimental results, and synthetic DDMs are compared and validated against TDS-1 ones.