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Applications of GNSS Reflectometry for Earth Observation II
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Applications of GNSS Reflectometry for Earth Observation II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 64540

Special Issue Editors

Dr. Nereida Rodriguez-Alvarez

E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: GNSS-reflectometry; polarimetric GNSS-Reflectometry; ocean, land and cryosphere applications
Special Issues, Collections and Topics in MDPI journals
Dr. Mary Morris

E-Mail 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 Issues, Collections and Topics in MDPI journals
Dr. Joan Francesc Munoz-Martin

E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: GNSS-R; microwave radiometry; nano-satellites; CubeSats; soil moisture; sea-ice; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

      The availability of data from missions such as the CYclone Global Navigation Satellite System (CYGNSS) and TechDemoSat-1 (TDS-1) has had 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 spacecraft, although limited to the tropics (±37° latitude), provides 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 to this second edition of our first Special Issue, “Applications of GNSS Reflectometry for Earth Observation”. As in the previous edition, 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. Topics suggested include, but are not limited to:

  • 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
Dr. Joan Francesc Munoz-Martin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

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

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Published Papers (17 papers)

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Research

18 pages, 8218 KiB  
Article
Impacts of Assimilating CYGNSS Satellite Ocean-Surface Wind on Prediction of Landfalling Hurricanes with the HWRF Model
by Zhaoxia Pu, Ying Wang, Xin Li, Christopher Ruf, Li Bi and Avichal Mehra
Remote Sens. 2022, 14(9), 2118; https://doi.org/10.3390/rs14092118 - 28 Apr 2022
Cited by 8 | Viewed by 2716
Abstract
This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP operational Hurricane Weather Research and Forecasting (HWRF) model. A series [...] Read more.
This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP operational Hurricane Weather Research and Forecasting (HWRF) model. A series of data assimilation experiments are performed using HWRF and a Gridpoint Statistical Interpolation (GSI)-based hybrid 3-dimensional ensemble-variational (3DEnVar) data assimilation system. The influence of CYGNSS data on hurricane forecasts is compared with that of Advanced Scatterometer (ASCAT) wind products that have already been assimilated into the HWRF forecast system in a series of assimilation experiments. The effects of different versions of CYGNSS data (V2.1 vs. V3.0) on hurricane forecasts are evaluated. The results indicate that CYGNSS ocean-surface wind can lead to improved numerical simulations and forecasts of hurricane track and intensity, asymmetric wind structure, and precipitation. The impacts of CYGNSS on hurricane forecasts are comparable and complementary to the operational use of ASCAT satellite data products. The dependence of the relative impacts of different versions of CYGNSS data on optimal thinning distances is evident. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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27 pages, 6927 KiB  
Article
Investigation on Geometry Computation of Spaceborne GNSS-R Altimetry over Topography: Modeling and Validation
by Minfeng Song, Xiufeng He, Milad Asgarimehr, Weiqiang Li, Ruya Xiao, Dongzhen Jia, Xiaolei Wang and Jens Wickert
Remote Sens. 2022, 14(9), 2105; https://doi.org/10.3390/rs14092105 - 27 Apr 2022
Cited by 6 | Viewed by 2513
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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24 pages, 2916 KiB  
Article
Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications
by Weiqiang Li, Estel Cardellach, Serni Ribó, Santi Oliveras and Antonio Rius
Remote Sens. 2022, 14(6), 1344; https://doi.org/10.3390/rs14061344 - 10 Mar 2022
Cited by 26 | Viewed by 5203
Abstract
Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) [...] Read more.
Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) of the power of the reflected GNSS signals as their main data products. In addition to the power DDM products, some of these missions have collected a large amount of raw intermediate frequency (IF) data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications and future spaceborne missions. To facilitate such explorations, the raw IF data sets from different missions have been processed by Institute of Space Sciences (ICE-CSIC, IEEC), and the corresponding data products, i.e., the complex waveform of the reflected signal, have been generated and released through our public open-data server. These complex waveform data products provide the measurements from different GNSS constellations (e.g., GPS, Galileo and BeiDou), and include both the amplitude and carrier phase information of the reflected GNSS signal at higher sampling rate (e.g., 1000 Hz). To demonstrate these advanced features of the data products, different applications, e.g., inland water detection and surface altimetry, are introduced in this paper. By making these complex waveform data products publicly available, new EO capability of the GNSS-R technique can be further explored by the community. Such early explorations are also relevant to ESA’s next GNSS-R mission, HydroGNSS, which will provide similar complex observations operationally and continuously in the future. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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17 pages, 34465 KiB  
Article
Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data
by Yun Zhang, Ziyu Yan, Shuhu Yang, Wanting Meng, Siqi Gu, Jin Qin, Yanling Han and Zhonghua Hong
Remote Sens. 2022, 14(5), 1170; https://doi.org/10.3390/rs14051170 - 26 Feb 2022
Cited by 7 | Viewed by 2356
Abstract
Global navigation satellite system reflectometry technology (GNSS-R) is rarely used for river flow velocity inversion, and in particular, there is currently no research using the BeiDou Navigation Satellite System reflectometry technology (BDS-R) for river flow velocity inversion. In this paper, a carrier phase [...] Read more.
Global navigation satellite system reflectometry technology (GNSS-R) is rarely used for river flow velocity inversion, and in particular, there is currently no research using the BeiDou Navigation Satellite System reflectometry technology (BDS-R) for river flow velocity inversion. In this paper, a carrier phase observation of river flow velocity inversion model is proposed. The interference phase is the integral of the Doppler frequency. The raw intermediate frequency (IF) data sets are processed through an open-loop method to obtain the Doppler frequency observation generated by river flow and then realize velocity inversion. The shore-based river current measurement was conducted on the south bank of Dashengguan Yangtze River in Nanjing city, Jiangsu Province, for nearly two hours on 22 April 2021. After realizing the inversion of river flow velocity in GPS L1, the combined inversion of BDS B1I GEO satellite and IGSO satellite is realized for the first time, which demonstrates the feasibility of river flow velocity inversion using BDS reflected signals. Compared with the real river flow velocity, the GPS L1 PRN 4 (1st period) inversion precision reaches up to 0.028 m/s (mean absolute error, MAE) and 0.036 m/s (root mean square error, RMSE). In parallel, BDS GEO 2 inversion precision can reach 0.048 m/s (MAE) and 0.063 m/s (RMSE), and BDS IGSO 10 inversion precision is 0.061 m/s (MAE) and 0.073 m/s (RMSE). These results illustrate that satellite elevation change rate and distance between specular points and current meter may have a negative effect on the accuracy of river flow velocity inversion. Specular points obstructed by obstacles or too far from the velocity meter may introduce uncertain error in both MAE and RMSE. Neither the satellite elevation nor the signal strength has an obvious correlation with inversion precision, which is consistent with the theoretical principle. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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11 pages, 9944 KiB  
Communication
FY3E GNOS II GNSS Reflectometry: Mission Review and First Results
by Guanglin Yang, Weihua Bai, Jinsong Wang, Xiuqing Hu, Peng Zhang, Yueqiang Sun, Na Xu, Xiaochun Zhai, Xianjun Xiao, Junming Xia, Feixiong Huang, Cong Yin, Qifei Du, Xianyi Wang, Yuerong Cai, Xiangguang Meng, Guangyuan Tan, Peng Hu and Congliang Liu
Remote Sens. 2022, 14(4), 988; https://doi.org/10.3390/rs14040988 - 17 Feb 2022
Cited by 39 | Viewed by 4221
Abstract
FengYun-3E (FY3E), launched on 5 July 2021, is one of China’s polar-orbiting meteorological satellite series. The GNOS II onboard FY3E is an operational GNSS remote sensor that for the first time combines GNSS radio occultation (GNSS RO) and GNSS reflectometry (GNSS-R). It has [...] Read more.
FengYun-3E (FY3E), launched on 5 July 2021, is one of China’s polar-orbiting meteorological satellite series. The GNOS II onboard FY3E is an operational GNSS remote sensor that for the first time combines GNSS radio occultation (GNSS RO) and GNSS reflectometry (GNSS-R). It has eight reflection channels that can track eight specular points at the same time, receiving reflected signals from multiple GNSS systems, including GPS, BeiDou and Galileo. The basic GNSS-R output generated by GNOS II is a 122 × 20 non-uniform delay-Doppler map whose high resolution portion captures more information near the specular point. This paper introduces the GNSS-R aspect of the FengYun-3E GNOS II, including the instrument, power calibration and wind speed retrieval algorithm. Preliminary validation results for its first four months of data are also presented. After preliminary quality control, the overall wind speed error is less than 2 m/s at wind speeds below 20 m/s for data from both GPS satellites and BeiDou satellites when compared to the ECMWF reanalysis winds. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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16 pages, 1826 KiB  
Article
Signal-to-Noise Ratio Analyses of Spaceborne GNSS-Reflectometry from Galileo and BeiDou Satellites
by Yang Nan, Shirong Ye, Jingnan Liu, Bofeng Guo, Shuangcheng Zhang and Weiqiang Li
Remote Sens. 2022, 14(1), 35; https://doi.org/10.3390/rs14010035 - 22 Dec 2021
Cited by 11 | Viewed by 4431
Abstract
In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) technology has made considerable progress with the increasing of GNSS-R satellites in orbit, the improvements of GNSS-R data processing technology, and the expansion of its geophysical applications. Meanwhile, with the modernization and evolution of [...] Read more.
In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) technology has made considerable progress with the increasing of GNSS-R satellites in orbit, the improvements of GNSS-R data processing technology, and the expansion of its geophysical applications. Meanwhile, with the modernization and evolution of GNSS systems, more signal sources and signal modulation modes are available. The effective use of the signals at different frequencies or from new GNSS systems can improve the accuracy, reliability, and resolution of the GNSS-R data products. This paper analyses the signal-to-noise ratio (SNR) of the GNSS-R measurements from Galileo and BeiDou-3 (BDS-3) systems, which is one of the important indicators to measure the quality of GNSS-R data. The multi-GNSS (GPS, Galileo and BDS-3) complex waveform products generated from the raw intermediate frequency data from TechDemoSat-1 (TDS-1) satellite and Cyclone Global Navigation Satellite System (CYGNSS) constellation are used for such analyses. The SNR and normalized SNR (NSNR) of the reflected signals from Galileo and BDS-3 satellites are compared to these from GPS. Preliminary results show that the GNSS-R SNRs from Galileo and BDS-3 are ∼1–2 dB lower than the GNSS-R measurements from GPS, which could be due to the power of the transmitted power and the bandwidth of the receiver. In addition, the effect of coherent integration time on GNSS-R SNR is also assessed for different GNSS signals. It is shown that the SNR of the reflected signals can be improved by using longer coherent integration time (∼0.4–0.8 dB with 2 ms coherent integration and ∼0.6–1.2 dB with 4 ms coherent integration). In addition, it is also shown that the SNR can be improved more efficiently (∼0.2–0.4 dB) for reflected BDS-3 and Galileo signals than for GPS. These results can provide useful references for the design of future spaceborne GNSS-R instrument compatible with reflections from multi-GNSS constellations. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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29 pages, 14889 KiB  
Article
Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
by Lucrezia Ricciardulli, Carl Mears, Andrew Manaster and Thomas Meissner
Remote Sens. 2021, 13(24), 5110; https://doi.org/10.3390/rs13245110 - 16 Dec 2021
Cited by 16 | Viewed by 3874
Abstract
The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data [...] Read more.
The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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18 pages, 5100 KiB  
Article
Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data
by Yun Zhang, Xu Chen, Wanting Meng, Jiwei Yin, Yanling Han, Zhonghua Hong and Shuhu Yang
Remote Sens. 2021, 13(21), 4451; https://doi.org/10.3390/rs13214451 - 5 Nov 2021
Cited by 4 | Viewed by 2447
Abstract
In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses [...] Read more.
In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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21 pages, 5435 KiB  
Article
Improved CYGNSS Wind Speed Retrieval Using Significant Wave Height Correction
by Daniel Pascual, Maria Paola Clarizia and Christopher S. Ruf
Remote Sens. 2021, 13(21), 4313; https://doi.org/10.3390/rs13214313 - 27 Oct 2021
Cited by 23 | Viewed by 5778
Abstract
This article presents the methodology for an improved estimation of the sea surface wind speed measured by the cyclone global navigation satellite system (CYGNSS) constellation of satellites using significant wave height (SWH) information as external reference data. The methodology consists of a correcting [...] Read more.
This article presents the methodology for an improved estimation of the sea surface wind speed measured by the cyclone global navigation satellite system (CYGNSS) constellation of satellites using significant wave height (SWH) information as external reference data. The methodology consists of a correcting 2D look-up table (LUT) with inputs: (1) the CYGNSS wind speed given by the geophysical model function (GMF); and (2) the collocated reference SWH given by the WW3 model, which is forced by winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) organization. In particular, the analyzed CYGNSS wind speeds are the fully developed seas (FDS) obtained with the GMF 3.0, and the forcing winds are the ECMWF forecast winds. Results show an increase in sensitivity to large winds speeds and an overall reduction in the root mean square difference (RMSD) with respect to the ECMWF winds from 2.05 m/s to 1.74 m/s. The possible influence of the ECWMF winds on the corrected winds (due to their use in the WW3 model) is analyzed by considering the correlation between: (1) the difference between the ECMWF winds and those from another reference; and (2) the difference between the corrected CYGNSS winds and those from the same reference. Results using ASCAT, WindSat, Jason, and AltiKa as references show no significant influence. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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19 pages, 9904 KiB  
Article
Improved GNSS-R Altimetry Methods: Theory and Experimental Demonstration Using Airborne Dual Frequency Data from the Microwave Interferometric Reflectometer (MIR)
by Oriol Cervelló i Nogués, Joan Francesc Munoz-Martin, Hyuk Park, Adriano Camps, Raul Onrubia, Daniel Pascual, Christoph Rüdiger, Jeffrey P. Walker and Alessandra Monerris
Remote Sens. 2021, 13(20), 4186; https://doi.org/10.3390/rs13204186 - 19 Oct 2021
Cited by 5 | Viewed by 3882
Abstract
Altimetric performance of Global Navigation Satellite System - Reflectometry (GNSS-R) instruments depends on receiver’s bandwidth and signal-to-noise ratio (SNR). The altimetric delay is usually computed from the time difference between the peak of the direct signal waveform and the maximum of the derivative [...] Read more.
Altimetric performance of Global Navigation Satellite System - Reflectometry (GNSS-R) instruments depends on receiver’s bandwidth and signal-to-noise ratio (SNR). The altimetric delay is usually computed from the time difference between the peak of the direct signal waveform and the maximum of the derivative of the reflected signal waveform. Dual-frequency data gathered by the airborne Microwave Interferometric Reflectometer (MIR) in the Bass Strait, between Australia and Tasmania, suggest that this approach is only valid for flat surfaces and large bandwidth receivers. This work analyses different methods to compute the altimetric observables using GNSS-R. A proposed novel method, the Peak-to-Minimum of the 3rd Derivative (P-Min3D) for narrow-band codes (e.g., L1 C/A), and the Peak-to-Half Power (P-HP) for large bandwidth codes (e.g., L5 or E5a codes) show improved performance when using real data. Both methods are also compared to the Peak-to-Peak (P-P) and Peak-to-Maximum of the 1st Derivative (P-Max1D) methods. The key difference between these methods is the determination of the delay position in the reflected signal waveform in order to compute the altimetric observable. Airborne experimental results comparing the different methods, bands and GNSS-R processing techniques show that centimeter level accuracy can be achieved. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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20 pages, 3080 KiB  
Article
Sea Surface Salinity and Wind Speed Retrievals Using GNSS-R and L-Band Microwave Radiometry Data from FMPL-2 Onboard the FSSCat Mission
by Joan Francesc Munoz-Martin and Adriano Camps
Remote Sens. 2021, 13(16), 3224; https://doi.org/10.3390/rs13163224 - 13 Aug 2021
Cited by 18 | Viewed by 3364
Abstract
The Federated Satellite System mission (FSSCat), winner of the 2017 Copernicus Masters Competition and the first ESA third-party mission based on CubeSats, aimed to provide coarse-resolution soil moisture estimations and sea ice concentration maps by means of the passive microwave measurements collected by [...] Read more.
The Federated Satellite System mission (FSSCat), winner of the 2017 Copernicus Masters Competition and the first ESA third-party mission based on CubeSats, aimed to provide coarse-resolution soil moisture estimations and sea ice concentration maps by means of the passive microwave measurements collected by the Flexible Microwave Payload-2 (FMPL-2). The mission was successfully launched on 3 September 2020. In addition to the primary scientific objectives, FMPL-2 data are used in this study to estimate sea surface salinity (SSS), correcting for the sea surface roughness using a wind speed estimate from the L-band microwave radiometer and GNSS-R data themselves. FMPL-2 was executed over the Arctic and Antarctic oceans on a weekly schedule. Different artificial neural network algorithms have been implemented, combining FMPL-2 data with the sea surface temperature, showing a root-mean-square error (RMSE) down to 1.68 m/s in the case of the wind speed (WS) retrieval algorithms, and RMSE down to 0.43 psu for the sea surface salinity algorithm in one single pass. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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19 pages, 71161 KiB  
Article
Evaluation and Correction of Elevation Angle Influence for Coastal GNSS-R Ocean Altimetry
by Guodong Zhang, Zhichao Xu, Feng Wang, Dongkai Yang and Jin Xing
Remote Sens. 2021, 13(15), 2978; https://doi.org/10.3390/rs13152978 - 28 Jul 2021
Cited by 9 | Viewed by 2786
Abstract
The elevation angle influence on coastal GNSS-R ocean code-based altimetry for GPS signals (L1 C/A and L5) and BDS B1 signals is investigated, and the corresponding correction method is presented. The study first focuses on the coastal ocean altimetry method, including the general [...] Read more.
The elevation angle influence on coastal GNSS-R ocean code-based altimetry for GPS signals (L1 C/A and L5) and BDS B1 signals is investigated, and the corresponding correction method is presented. The study first focuses on the coastal ocean altimetry method, including the general experiment geometry and the code delay estimation using the single-point tracking algorithm. The peak power and the maximum first derivative are used as the location of the specular point. Then, the sensitivity of the height retrieved using the above coastal ocean altimetry method to elevation angle is analyzed based on the Z-V model. It can be seen that the elevation angle has a significant influence on the height retrieval, which will affect the precision of the coastal GNSS-R ocean altimetry. Finally, two correction methods, the model-driven method and the data-driven method, are proposed. The coastal altimetry experiments demonstrate that the correction methods can correct the elevation angle influence, and the data-driven method is more effective. The experimental results show that, after correcting the elevation angle influence, the code-based altimetry precision of the GPS L1 C/A signal, L5 signal, and BDS B1 signal can be up to the meter level, decimeter level (less than 4 decimeters), and meter level with respect to a reference tide gauge (TG) data set, respectively, without smoothing over time. These results provide information to guide the sea surface height retrieval using coastal GNSS-R, especially multi-satellite observation and GNSS signal with a higher chipping rate. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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21 pages, 48099 KiB  
Article
The Bistatic Radar as an Effective Tool for Detecting and Monitoring the Presence of Phytoplankton on the Ocean Surface
by Nereida Rodriguez-Alvarez and Kamal Oudrhiri
Remote Sens. 2021, 13(12), 2248; https://doi.org/10.3390/rs13122248 - 9 Jun 2021
Cited by 18 | Viewed by 3269
Abstract
A massive dust storm formed over the Sahara Desert in June 2020. The African dust cloud, which traveled over the tropical Atlantic’s main development region for hurricanes, resulted in the highest aerosol optical thickness (AOT) for the past two decades. Dust particles contained [...] Read more.
A massive dust storm formed over the Sahara Desert in June 2020. The African dust cloud, which traveled over the tropical Atlantic’s main development region for hurricanes, resulted in the highest aerosol optical thickness (AOT) for the past two decades. Dust particles contained in dust clouds are at some point deposited on the ocean surface, impacting the ocean biogeochemistry through the supply of nutrients. Although there are remote sensing systems that can map the AOT, the locations of the aerosol particles deposited on the ocean surface remain unknown quantities with remote sensing measurements. In addition, the supplied nutrients are not static and are displaced by ocean currents. Nutrients trigger the phytoplankton (algae) blooms, which form a film on the ocean surface and affect the ocean surface tension. The change in ocean surface tension causes a local decrease of ocean surface roughness over the areas covered with phytoplankton. Bistatic radar data from the CYclone Global Navigation Satellite System (CYGNSS) mission can detect changes in the ocean surface roughness, expressed as an increase in reflectivity when the surface becomes smoother. Therefore, decreased ocean surface roughness correlated with a recent dust storm represents a key indicator of the presence of phytoplankton. In this paper, we present for the first time the capability of bistatic radar measurements to provide an effective tool to map information of areas covered with phytoplankton, establishing bistatic radar as the most reliable remote sensing tool for detecting phytoplankton blooms and monitoring their presence across the ocean surface. We present the analysis of low ocean roughness signatures in the bistatic radar measurements from the CYGNSS mission observed in the Gulf of Mexico after the Sahara’s dust storm circulation from Africa to the American continent from May to July 2020. CYGNSS data offer an unprecedented spatial and temporal coverage that allows for the analysis of those signatures at time scales of 1-day, robust to the presence of clouds and dust clouds. The described capability benefits the improvement of models, promoting a better constraint of the supply of dust into the ocean surface and a better understanding of the excess of nutrients that triggers the phytoplankton blooms. This new bistatic radar application enhances our understanding on the role of dust storms on ocean biogeochemistry and the global carbon cycle. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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17 pages, 8443 KiB  
Article
An Improved Back-Projection Algorithm for GNSS-R BSAR Imaging Based on CPU and GPU Platform
by Shiyu Wu, Zhichao Xu, Feng Wang, Dongkai Yang and Gongjian Guo
Remote Sens. 2021, 13(11), 2107; https://doi.org/10.3390/rs13112107 - 27 May 2021
Cited by 13 | Viewed by 2864
Abstract
Global Navigation Satellite System Reflectometry Bistatic Synthetic Aperture Radar (GNSS-R BSAR) is becoming more and more important in remote sensing because of its low power, low mass, low cost, and real-time global coverage capability. The Back Projection Algorithm (BPA) was usually selected as [...] Read more.
Global Navigation Satellite System Reflectometry Bistatic Synthetic Aperture Radar (GNSS-R BSAR) is becoming more and more important in remote sensing because of its low power, low mass, low cost, and real-time global coverage capability. The Back Projection Algorithm (BPA) was usually selected as the GNSS-R BSAR imaging algorithm because it can process echo signals of complex geometric configurations. However, the huge computational cost is a challenge for its application in GNSS-R BSAR. Graphics Processing Units (GPU) provides an efficient computing platform for GNSS-R BSAR processing. In this paper, a solution accelerating the BPA of GNSS-R BSAR using GPU is proposed to improve imaging efficiency, and a matching pre-processing program was proposed to synchronize direct and echo signals to improve imaging quality. To process hundreds of gigabytes of data collected by a long-time synthetic aperture in fixed station mode, a stream processing structure was used to process such a large amount of data to solve the problem of limited GPU memory. In the improvement of the imaging efficiency, the imaging task is divided into pre-processing and BPA, which are performed in the Central Processing Unit (CPU) and GPU, respectively, and a pixel-oriented parallel processing method in back projection is adopted to avoid memory access conflicts caused by excessive data volume. The improved BPA with the long synthetic aperture time is verified through the simulation of and experimenting on the GPS-L5 signal. The results show that the proposed accelerating solution is capable of taking approximately 128.04 s, which is 156 times lower than pure CPU framework for producing a size of 600 m × 600 m image with 1800 s synthetic aperture time; in addition, the same imaging quality with the existing processing solution can be retained. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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16 pages, 2922 KiB  
Article
Long-Term Snow Height Variations in Antarctica from GNSS Interferometric Reflectometry
by Elisa Pinat, Pascale Defraigne, Nicolas Bergeot, Jean-Marie Chevalier and Bruno Bertrand
Remote Sens. 2021, 13(6), 1164; https://doi.org/10.3390/rs13061164 - 18 Mar 2021
Cited by 3 | Viewed by 2626
Abstract
Acquiring reliable estimates of the Antarctic Ice Sheet surface mass balance is essential for trustworthy predictions of its evolution and future contribution to sea level rise. Snow height variations, i.e., the net change of the surface elevation resulting from a combination of surface [...] Read more.
Acquiring reliable estimates of the Antarctic Ice Sheet surface mass balance is essential for trustworthy predictions of its evolution and future contribution to sea level rise. Snow height variations, i.e., the net change of the surface elevation resulting from a combination of surface processes such as snowfall, ablation, and wind redistribution, can provide a unique tool to constrain the uncertainty on mass budget estimations. In this study, GNSS Interferometric Reflectometry (GNSS-IR) is exploited to assess the long-term variations of snow accumulation and ablation processes. Eight antennas belonging to the Polar Earth Observing Network (POLENET) network are considered, together with the ROB1 antenna, deployed in the east part of Antarctica by the Royal Observatory of Belgium. For ROB1, which is located on an ice rise, we highlight an annual variation of snow accumulation in April–May (~30–50 cm) and ablation during spring/summer period. A snow surface elevation velocity of +0.08 ± 0.01 ma−1 is observed in the 2013–2016 period, statistically rejecting the “no trend” null hypothesis. As the POLENET stations are all located on moving glaciers, their associated downhill motion must be corrected for using an elevation model. This induces an increased uncertainty on the snow surface elevation change determined from GNSS-IR. Among the eight stations analyzed, only three of them show a long-term snow height variation larger than the uncertainties. One is located on the Flask Galcier in the Antarctic Peninsula, with a decrease of more than 4 m between 2012 and 2014, with an uncertainty of 2.5 m. The second one is located on the Lower Thwaites Glacier where we observe, between 2010 and 2020, a snow surface drop of 10 m, with a conservative uncertainty of 1 m. The third station, located on the West Antarctic Ice Sheet (WAIS) divide, shows on the opposite an upward motion from 2005 to 2019, of 1.2 m with an uncertainty of 0.4 m. The snow surface change of the other POLENET stations analyzed is smaller than the uncertainty associated with the glacier slope. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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19 pages, 13501 KiB  
Article
Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks
by David Llaveria, Juan Francesc Munoz-Martin, Christoph Herbert, Miriam Pablos, Hyuk Park and Adriano Camps
Remote Sens. 2021, 13(6), 1139; https://doi.org/10.3390/rs13061139 - 17 Mar 2021
Cited by 20 | Viewed by 4609
Abstract
CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched [...] Read more.
CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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23 pages, 4575 KiB  
Article
Soil Moisture Estimation Synergy Using GNSS-R and L-Band Microwave Radiometry Data from FSSCat/FMPL-2
by Joan Francesc Munoz-Martin, David Llaveria, Christoph Herbert, Miriam Pablos, Hyuk Park and Adriano Camps
Remote Sens. 2021, 13(5), 994; https://doi.org/10.3390/rs13050994 - 5 Mar 2021
Cited by 25 | Viewed by 4517
Abstract
The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected [...] Read more.
The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)
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