Special Issue "Applications of GNSS Reflectometry for Earth Observation"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Dr. Nereida Rodriguez-Alvarez
Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: bistatic radar; GNSS-R; freeze/thaw; vegetation characterization; sea ice; wetlands; urban/agricultural flooding
Dr. Mary Morris
Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: atmospheric & hydrologic science; geophysical remote sensing; passive microwave radiometry; GNSS-Reflectometry; inversion techniques; multi-sensor data assimilation

Special Issue Information

Dear Colleagues,

The availability of data from missions such as CYclone Global Navigation Satellite System (CYGNSS) and TechDemoSat-1 (TDS-1) has made a significant impact on the scientific return of the Global Navigation Satellite System–Reflectometry (GNSS-R) measurements. Data from these missions demonstrate the capabilities of GNSS-R and build on many applications that relate the properties of scattered GNSS signals to geophysical parameters. TDS-1 provides global data coverage, while the constellation of CYGNSS spacecrafts, although limited to the tropics (±37° latitude), provide observations on rapid timescales with high spatial resolution. Equally important are measurements from airborne and ground-based instruments; these data enable investigations of the sensitivity of GNSS-R measurements to different phenomena and their use in new applications at a local/regional scale.

We invite authors to submit their work on applications that use GNSS-R data for Earth science. We encourage the submission of works related to the synergistic use of GNSS-R data with data from other sensors at the same or different frequency of operations, enhancing spatial resolution and/or temporal sampling to improve estimates of geophysical parameters. Topics considered for this Special Issue should emphasize practical applications and reach beyond theoretical and model-based studies. Suggested topics include, but are not limited to, the following:

  • Ocean, land, or cryosphere applications using GNSS-R;
  • Applications using GNSS-R ground-based or airborne measurements;
  • Applications using GNSS-R satellite measurements;
  • GNSS-R based neural networks for specific applications;
  • GNSS-R based classification algorithms for targeted applications;
  • GNSS-R and SAR/Radiometer/Optical combined products;
  • Downscaling or enhancement methods employing GNSS-R.

Dr. Nereida Rodriguez-Alvarez
Dr. Mary Morris
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GNSS-R
  • Cryosphere
  • Near-surface ocean wind vector
  • Soil moisture
  • Terrestrial hydrology
  • Biomass
  • Ship detection
  • Oil slick detection
  • Neural networks
  • Classification
  • Downscaling

Published Papers (15 papers)

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Open AccessArticle
Neural Network Based Quality Control of CYGNSS Wind Retrieval
Remote Sens. 2020, 12(17), 2859; https://doi.org/10.3390/rs12172859 - 03 Sep 2020
Abstract
Global Navigation Satellite System – Reflectometry (GNSS-R) is a relatively new field in remote sensing that uses reflected GPS signals from the Earth’s surface to study the state of the surface geophysical parameters under observation. The CYGNSS is a first of its kind [...] Read more.
Global Navigation Satellite System – Reflectometry (GNSS-R) is a relatively new field in remote sensing that uses reflected GPS signals from the Earth’s surface to study the state of the surface geophysical parameters under observation. The CYGNSS is a first of its kind GNSS-R constellation mission launched in December 2016. It aims at providing high quality global scale GNSS-R measurements that can reliably be used for ocean science applications such as the study of ocean wind speed dynamics, tropical cyclone genesis, coupled ocean wave modelling, and assimilation into Numerical Weather Prediction models. To achieve this goal, strong quality control filters are needed to detect and remove outlier measurements. Currently, quality control of CYGNSS data products are based on fixed thresholds on various engineering, instrument, and measurement conditions. In this work we develop a Neural Network based quality control filter for automated outlier detection of CYGNSS retrieved winds. The primary merit of the proposed ML filter is its ability to better account for interactions between the individual engineering, instrument and measurement conditions than can separate thresholded flags for each one. Use of Machine Learning capabilities to capture inherent patterns in the data can create an efficient and effective mechanism to detect and remove outlier measurements. The resulting filter has a probability of outlier detection (PD) >75% and False Alarm Rate (FAR) < 20% for a wind speed range of 5 to 18 m/s. At least 75% of the outliers with wind speed errors of at least 5 m/s are removed while ~100% of the outliers with wind speed errors of at least 10 m/s are removed. This filter significantly improves data quality. The standard deviation of wind speed retrieval error is reduced from 2.6 m/s without the filter to 1.7 m/s with it over a wind speed range of 0 to 25 m/s. The design space for this filter is also analyzed in this work to characterize trade-offs between PD and FAR. Currently the filter performance is applicable only up to moderate wind speeds, as sufficient data is available only in this range to train the filter, as a way forward, more data over time can help expand the usability of this filter to higher wind speed ranges as well. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
Retrieval of Ocean Surface Wind Speed Using Reflected BPSK/BOC Signals
Remote Sens. 2020, 12(17), 2698; https://doi.org/10.3390/rs12172698 - 20 Aug 2020
Cited by 1
Abstract
The Global Navigation Satellite System (GNSS) has become a valuable resource as a remote sensing technique. In the past decade, the use of reflected GNSS signals for sensing the Earth, also known as GNSS reflectometry (GNSS-R), has grown rapidly. On the other hand, [...] Read more.
The Global Navigation Satellite System (GNSS) has become a valuable resource as a remote sensing technique. In the past decade, the use of reflected GNSS signals for sensing the Earth, also known as GNSS reflectometry (GNSS-R), has grown rapidly. On the other hand, with the continuous development of GNSS, multi-frequency multi-modulation signals have been used to enhance not only positioning performance, but also remote sensing applications. It is known that for some constellations, navigation satellites broadcast signals employing BPSK (binary phase-shift keying) modulation and BOC (binary offset carrier) modulation at the same frequency band. This paper proposes a new GNSS-R measurement, called a composite delay-Doppler map (cDDM), by utilizing the received reflected GNSS signals with different modulation techniques for the purpose of retrieving wind speed. The GNSS-R receiver can receive BPSK and BOC signals simultaneously at the same frequency band (e.g., GPS III L1 C/A and L1C or QZSS L1 C/A and L1C) and process the signals to generate GNSS-R measurements. Exploration of the observable features extracted from the composite DDM and the wind speed retrieval algorithm are also provided. The simulation verifies the proposed method under a configuration that is specified for the orbital and instrument specification of the upcoming TRITON mission. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning
Remote Sens. 2020, 12(15), 2352; https://doi.org/10.3390/rs12152352 - 22 Jul 2020
Abstract
At L-band (1–2 GHz), and particularly in microwave radiometry (1.413 GHz), vegetation has been traditionally modeled with the τ-ω model. This model has also been used to compensate for vegetation effects in Global Navigation Satellite Systems-Reflectometry (GNSS-R) with modest success. This manuscript presents [...] Read more.
At L-band (1–2 GHz), and particularly in microwave radiometry (1.413 GHz), vegetation has been traditionally modeled with the τ-ω model. This model has also been used to compensate for vegetation effects in Global Navigation Satellite Systems-Reflectometry (GNSS-R) with modest success. This manuscript presents an analysis of the vegetation impact on GPS L1 C/A (coarse acquisition code) signals in terms of attenuation and depolarization. A dual polarized instrument with commercial off-the-shelf (COTS) GPS receivers as back-ends was installed for more than a year under a beech forest collecting carrier-to-noise (C/N0) data. These data were compared to different ground-truth datasets (greenness, blueness, and redness indices, sky cover index, rain data, leaf area index or LAI, and normalized difference vegetation index (NDVI)). The highest correlation observed is between C/N0 and NDVI data, obtaining R2 coefficients larger than 0.85 independently from the elevation angle, suggesting that for beech forest, NDVI is a good descriptor of signal attenuation at L-band, which is known to be related to the vegetation optical depth (VOD). Depolarization effects were also studied, and were found to be significant at elevation angles as large as ~50°. Data were also fit to a simple τ-ω model to estimate a single scattering albedo parameter (ω) to try to compensate for vegetation scattering effects in soil moisture retrieval algorithms using GNSS-R. It is found that, even including dependence on the elevation angle (ω(θe)), at elevation angles smaller than ~67°, the ω(θe) model is not related to the NDVI. This limits the range of elevation angles that can be used for soil moisture retrievals using GNSS-R. Finally, errors of the GPS-derived position were computed over time to assess vegetation impact on the accuracy of the positioning. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned
Remote Sens. 2020, 12(12), 2064; https://doi.org/10.3390/rs12122064 - 26 Jun 2020
Cited by 1
Abstract
In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil [...] Read more.
In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil Moisture Active and Passive) correction for vegetation opacity (multiplied by two to account for the descending and ascending passes) seems too high. Surface roughness effects cannot be compensated using in situ measurements, as they are not representative. An ad hoc correction for surface roughness, including the dependence with the incidence angle, and the actual reflectivity value is needed. With this correction, reasonable surface soil moisture values are obtained up to approximately a 30° incidence angle, beyond which the GNSS-R retrieved surface soil moisture spreads significantly. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
Experimental Evidence of Swell Signatures in Airborne L5/E5a GNSS-Reflectometry
Remote Sens. 2020, 12(11), 1759; https://doi.org/10.3390/rs12111759 - 29 May 2020
Abstract
As compared to GPS L1C/A signals, L5/E5a Global Navigation Satellite System-Reflectometry (GNSS-R) improves the spatial resolution due to the narrower auto-correlation function. Furthermore, the larger transmitted power (+3 dB), and correlation gain (+10 dB) allow the reception of weaker reflected signals. If directive [...] Read more.
As compared to GPS L1C/A signals, L5/E5a Global Navigation Satellite System-Reflectometry (GNSS-R) improves the spatial resolution due to the narrower auto-correlation function. Furthermore, the larger transmitted power (+3 dB), and correlation gain (+10 dB) allow the reception of weaker reflected signals. If directive antennas are used, very short incoherent integration times are enough to achieve good signal-to-noise ratios, allowing the reception of multiple specular reflection points without the blurring induced by long incoherent integration times. This study presents for the first time experimental evidence of the wind and swell waves signatures in the GNSS-R waveforms, and it performs a statistical analysis, a time-domain analysis, and a frequency-domain analysis for a unique data set of waveforms collected by the UPC MIR instrument during a series of flights over the Bass Strait, Australia. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
Description of the UCAR/CU Soil Moisture Product
Remote Sens. 2020, 12(10), 1558; https://doi.org/10.3390/rs12101558 - 14 May 2020
Cited by 2
Abstract
Currently, the ability to use remotely sensed soil moisture to investigate linkages between the water and energy cycles and for use in data assimilation studies is limited to passive microwave data whose temporal revisit time is 2–3 days or active microwave products with [...] Read more.
Currently, the ability to use remotely sensed soil moisture to investigate linkages between the water and energy cycles and for use in data assimilation studies is limited to passive microwave data whose temporal revisit time is 2–3 days or active microwave products with a much longer (>10 days) revisit time. This paper describes a dataset that provides soil moisture retrievals, which are gridded to 36 km, for the upper 5 cm of the soil surface at sparsely sampled 6-hour intervals for +/− 38 degrees latitude for 2017–present. Retrievals are derived from the Cyclone Global Navigation Satellite System (CYGNSS) constellation, which uses GNSS-Reflectometry to obtain L-band reflectivity observations over the Earth’s surface. The product was developed by calibrating CYGNSS reflectivity observations to soil moisture retrievals from NASA’s Soil Moisture Active Passive (SMAP) mission. Retrievals were validated against observations from 171 in-situ soil moisture probes, with a median unbiased root-mean-square error (ubRMSE) of 0.049 cm3 cm−3 (standard deviation = 0.026 cm3 cm−3) and median correlation coefficient of 0.4 (standard deviation = 0.27). For the same stations, the median ubRMSE between SMAP and in-situ observations was 0.045 cm3 cm−3 (standard deviation = 0.025 cm3 cm−3) and median correlation coefficient was 0.69 (standard deviation = 0.27). The UCAR/CU Soil Moisture Product is thus complementary to SMAP, albeit with a larger random noise component, providing soil moisture retrievals for applications that require faster revisit times than passive microwave remote sensing currently provides. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
Remote Sens. 2020, 12(8), 1317; https://doi.org/10.3390/rs12081317 - 22 Apr 2020
Cited by 1
Abstract
This paper presents the processing algorithms for geolocating and calibration of the Cyclone Global Navigation Satellite System (CYGNSS) level 1 land data products, as well as analysis of the spatial resolution of Global Navigation Satellite System Reflectometry (GNSS-R) coherent reflections. Accurate and robust [...] Read more.
This paper presents the processing algorithms for geolocating and calibration of the Cyclone Global Navigation Satellite System (CYGNSS) level 1 land data products, as well as analysis of the spatial resolution of Global Navigation Satellite System Reflectometry (GNSS-R) coherent reflections. Accurate and robust geolocation and calibration of GNSS-R land observations are necessary first steps that enable subsequent geophysical parameter retrievals. The geolocation algorithm starts with an initial specular point location on the Earth’s surface, predicted by modeling the Earth as a smooth ellipsoid (the WGS84 representation) and using the known transmitting and receiving satellite locations. Information on terrain topography is then compiled from the Shuttle Radar Topography Mission (SRTM) generated Digital Elevation Map (DEM) to generate a grid of local surface points surrounding the initial specular point location. The delay and Doppler values for each point in the local grid are computed with respect to the empirically observed location of the Delay Doppler Map (DDM) signal peak. This is combined with local incident and reflection angles across the surface using SRTM estimated terrain heights. The final geolocation confidence is estimated by assessing the agreement of the three geolocation criteria at the estimated surface specular point on the local grid, including: the delay and Doppler values are in agreement with the CYGNSS observed signal peak and the incident and reflection angles are suitable for specular reflection. The resulting geolocation algorithm is first demonstrated using an example GNSS-R reflection track that passes over a variety of terrain conditions. It is then analyzed using a larger set of CYGNSS data to obtain an assessment of geolocation confidence over a wide range of land surface conditions. Following, an algorithm for calibrating land reflected signals is presented that considers the possibility of both coherent and incoherent scattering from land surfaces. Methods for computing both the bistatic radar cross section (BRCS, for incoherent returns) and the surface reflectivity (for coherent returns) are presented. a flag for classifying returns as coherent or incoherent developed in a related paper is recommended for use in selecting whether the BRCS or reflectivity should be used in further analyses for a specific DDM. Finally, a study of the achievable surface feature detection resolution when coherent reflections occur is performed by examining a series of CYGNSS coherent reflections across an example river. Ancillary information on river widths is compared to the observed CYGNSS coherent observations to evaluate the achievable surface feature detection resolution as a function of the DDM non-coherent integration interval. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
Untangling the Incoherent and Coherent Scattering Components in GNSS-R and Novel Applications
Remote Sens. 2020, 12(7), 1208; https://doi.org/10.3390/rs12071208 - 09 Apr 2020
Cited by 2
Abstract
As opposed to monostatic radars where incoherent backscattering dominates, in bistatic radars, such as Global Navigation Satellite Systems Reflectometry (GNSS-R), the forward scattered signals exhibit both an incoherent and a coherent component. Current models assume that either one or the other are dominant, [...] Read more.
As opposed to monostatic radars where incoherent backscattering dominates, in bistatic radars, such as Global Navigation Satellite Systems Reflectometry (GNSS-R), the forward scattered signals exhibit both an incoherent and a coherent component. Current models assume that either one or the other are dominant, and the calibration and geophysical parameter retrieval (e.g., wind speed, soil moisture, etc.) are developed accordingly. Even the presence of the coherent component of a GNSS reflected signal itself has been a matter of discussion in the last years. In this work, a method developed to separate the leakage of the direct signal in the reflected one is applied to a data set of GNSS-R signals collected over the ocean by the Microwave Interferometer Reflectometer (MIR) instrument, an airborne dual-band (L1/E1 and L5/E5a), multi-constellation (GPS and Galileo) GNSS-R instrument with two 19-elements antenna arrays with 4 beam-steered each. The presented results demonstrate the feasibility of the proposed technique to untangle the coherent and incoherent components from the total power waveform in GNSS reflected signals. This technique allows the processing of these components separately, which increases the calibration accuracy (as today both are mixed and processed together), allowing higher resolution applications since the spatial resolution of the coherent component is determined by the size of the first Fresnel zone (300–500 meters from a LEO satellite), and not by the size of the glistening zone (25 km from a LEO satellite). The identification of the coherent component enhances also the location of the specular reflection point by determining the peak maximum from this coherent component rather than the point of maximum derivative of the incoherent one, which is normally noisy and it is blurred by all the glistening zone contributions. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS
Remote Sens. 2020, 12(7), 1168; https://doi.org/10.3390/rs12071168 - 05 Apr 2020
Cited by 3
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
The Polarimetric Sensitivity of SMAP-Reflectometry Signals to Crop Growth in the U.S. Corn Belt
Remote Sens. 2020, 12(6), 1007; https://doi.org/10.3390/rs12061007 - 21 Mar 2020
Abstract
Crop growth is an important parameter to monitor in order to obtain accurate remotely sensed estimates of soil moisture, as well as assessments of crop health, productivity, and quality commonly used in the agricultural industry. The Soil Moisture Active Passive (SMAP) mission has [...] Read more.
Crop growth is an important parameter to monitor in order to obtain accurate remotely sensed estimates of soil moisture, as well as assessments of crop health, productivity, and quality commonly used in the agricultural industry. The Soil Moisture Active Passive (SMAP) mission has been collecting Global Positioning System (GPS) signals as they reflect off the Earth’s surface since August 2015. The L-band dual-polarization reflection measurements enable studies of the evolution of geophysical parameters during seasonal transitions. In this paper, we examine the sensitivity of SMAP-reflectometry signals to agricultural crop growth related characteristics: crop type, vegetation water content (VWC), crop height, and vegetation opacity (VOP). The study presented here focuses on the United States “Corn Belt,” where an extensive area is planted every year with mostly corn, soybean, and wheat. We explore the potential to generate regularly an alternate source of crop growth information independent of the data currently used in the soil moisture (SM) products developed with the SMAP mission. Our analysis explores the variability of the polarimetric ratio (PR), computed from the peak signals at V- and H-polarization, during the United States Corn Belt crop growing season in 2017. The approach facilitates the understanding of the evolution of the observed surfaces from bare soil to peak growth and the maturation of the crops until harvesting. We investigate the impact of SM on PR for low roughness scenes with low variability and considering each crop type independently. We analyze the sensitivity of PR to the selected crop height, VWC, VOP, and Normalized Differential Vegetation Index (NDVI) reference datasets. Finally, we discuss a possible path towards a retrieval algorithm based on Global Navigation Satellite System-Reflectometry (GNSS-R) measurements that could be used in combination with passive SMAP soil moisture algorithms to correct simultaneously for the VWC and SM effects on the electromagnetic signals. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
An Aircraft Wetland Inundation Experiment Using GNSS Reflectometry
Remote Sens. 2020, 12(3), 512; https://doi.org/10.3390/rs12030512 - 05 Feb 2020
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model
Remote Sens. 2019, 11(21), 2556; https://doi.org/10.3390/rs11212556 - 31 Oct 2019
Cited by 4
Abstract
Understanding the effects of Earth’s surface topography on Global Navigation Satellite Systems Reflectometry (GNSS-R) space-borne data is important to calibrate experimental measurements, so as to provide accurate soil moisture content (SMC) retrievals. In this study, several scientific observables obtained from delay-Doppler maps (DDMs) [...] Read more.
Understanding the effects of Earth’s surface topography on Global Navigation Satellite Systems Reflectometry (GNSS-R) space-borne data is important to calibrate experimental measurements, so as to provide accurate soil moisture content (SMC) retrievals. In this study, several scientific observables obtained from delay-Doppler maps (DDMs) | Y r , t o p o ( τ , f ) | 2 generated on board the Cyclone Global Navigation Satellite System (CyGNSS) mission were evaluated as a function of several topographic parameters derived from a digital elevation model (DEM). This assessment was performed as a function of Soil Moisture Active Passive (SMAP)-derived SMC at grazing angles θ e ~ [20,30] ° and in a nadir-looking configuration θ e ~ [80,90] °. Global scale results showed that the width of the trailing edge (TE) was small T E ~ [100, 250] m and the reflectivity was high Γ ~ [–10, –3] dB over flat areas with low topographic heterogeneity, because of an increasing coherence of Earth-reflected Global Positioning System (GPS) signals. However, the strong impact of several topographic features over areas with rough topography provided motivation to perform a parametric analysis. A specific target area with little vegetation, low small-scale surface roughness, and a wide variety of terrains in South Asia was selected. A significant influence of several topographic parameters i.e., surface slopes and curvatures was observed. This triggered our study of the sensitivity of T E and Γ to SMC and topographic wetness index ( T W I ). Regional scale results showed that T E and Γ are strongly correlated with the T W I , while the sensitivity to SMC was almost negligible. The Pearson correlation coefficients of T E and Γ with T W I are r Γ ~ 0.59 and r T E ~−0.63 at θ e ~ [20, 30] ° and r Γ ~ 0.48 and r T E ~ −0.50 at θ e ~ [80, 90] °, respectively. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessArticle
Detecting Targets above the Earth’s Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1
Remote Sens. 2019, 11(19), 2327; https://doi.org/10.3390/rs11192327 - 07 Oct 2019
Cited by 1
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessTechnical Note
Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
Remote Sens. 2020, 12(18), 3026; https://doi.org/10.3390/rs12183026 - 17 Sep 2020
Abstract
The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating [...] Read more.
The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17–19. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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Open AccessLetter
First Evidence of Mesoscale Ocean Eddies Signature in GNSS Reflectometry Measurements
Remote Sens. 2020, 12(3), 542; https://doi.org/10.3390/rs12030542 - 06 Feb 2020
Cited by 1
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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