GNSS Reflectometry

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Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. Many nations have deployed their own satellite constellations for positioning, and new receivers can use all of them. Currently available constellations are

  • G: GPS, Global Positioning System, United States
  • R: GLONASS, Russia
  • E: Galileo, European Space Agency
  • J: QZSS, Japan
  • C: BDS, Beidou, China
  • I: IRNSS Indian Regional Navigation Satellite System
  • S: SBAS payload
  • M: Mixed

The preceding letters are use as a satellite systems shortcuts in many systems, such as Rinex GNSS data files.

The GNSS signals are affected by the ionosphere [1] and weather conditions in troposhpere. For example rain [2] and other atmospheric water content [3] and other weather conditions cause delay to the GNSS signal propagation and it is then possible to monitor these phenomena by GNSS signals.

Reflectometry

The GNSS radio signals transmitted from satellites are also reflected from the ground and by monitoring these reflections, the properties of the ground and atmosphere can be examined [4]. This kind of analysis is called as GNSS reflectometry (GNSS-R) [5].

Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R backscatter data has been also collected by ESA's TechDemoSat-1 (TDS-1) satellite and with NASA's CYGNSS satellite constellation. Observation data can be downloaded from their portals.

GNSS reflectometry has been used for detecting sea ice thickness [6] [7] [8][9] [10][11] [12][13][14][15] GNSS-R is also popular for other sea target analysis which is not directly related to icing. [4] [16][17]. Many research articles concentrating in method develoment, are also indirectly related to icing [18] [19]

Links

References

  1. ↑ Su, Ke; Jin, Shuanggen; Hoque, M. (2019-01-17). "Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance". Remote Sensing. 11 (2): 171. doi:10.3390/rs11020171. ISSN 2072-4292. Retrieved 2022-02-09.
  2. ↑ Cardellach, Estel; Tomas, Sergio; Oliveras, Santi; Padulles, Ramon; Rius, Antonio; de la Torre-Juarez, Manuel; Turk, Francis Joseph; Ao, Chi O.; Kursinski, E. Robert; Schreiner, Bill; Ector, Dave; Cucurull, Lidia (2015-01). "Sensitivity of PAZ LEO Polarimetric GNSS Radio-Occultation Experiment to Precipitation Events". IEEE Transactions on Geoscience and Remote Sensing. 53 (1): 190–206. doi:10.1109/TGRS.2014.2320309. ISSN 1558-0644 0196-2892, 1558-0644 Check |issn= value (help). Retrieved 2022-02-09. Check date values in: |date= (help)
  3. ↑ Selbesoglu, Mahmut Oguz (2020-10-01). "Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data". Engineering Science and Technology, an International Journal. 23 (5): 967–972. doi:10.1016/j.jestch.2019.11.006. ISSN 2215-0986. Retrieved 2021-02-15.
  4. ↑ 4.0 4.1 King, Lucinda S.; Unwin, Martin; Rawlinson, Jonathan; Guida, Raffaella; Underwood, Craig (2020). "Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 2916–2924. doi:10.1109/JSTARS.2020.2997199. ISSN 2151-1535.
  5. ↑ Jia, Yan; Pei, Yuekun (2018-07-25). Remote Sensing in Land Applications by Using GNSS-Reflectometry. IntechOpen. ISBN 978-1-78923-537-1. Retrieved 2021-12-09.
  6. ↑ Yan, Qingyun; Huang, Weimin; Moloney, Cecilia (2017). "Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10 (8): 3789–3798. doi:10.1109/JSTARS.2017.2689009. ISSN 2151-1535.
  7. ↑ Alonso-Arroyo, Alberto; Zavorotny, Valery U.; Camps, Adriano (2017). "Sea ice detection using U.K. TDS-1 GNSS-R data". IEEE Transactions on Geoscience and Remote Sensing. 55 (9): 4989–5001. doi:10.1109/TGRS.2017.2699122. ISSN 1558-0644.
  8. ↑ Semmling, A. Maximilian; Rösel, Anja; Divine, Dmitry V.; Gerland, Sebastian; Stienne, Georges; Reboul, Serge; Ludwig, Marcel; Wickert, Jens; Schuh, Harald (2019). "Sea-ice concentration derived from GNSS reflection measurements in fram strait". IEEE Transactions on Geoscience and Remote Sensing. 57 (12): 10350–10361. doi:10.1109/TGRS.2019.2933911. ISSN 1558-0644.
  9. ↑ Yan, Qingyun; Huang, Weimin (2016). "Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9 (10): 4795–4801. doi:10.1109/JSTARS.2016.2582690. ISSN 2151-1535.
  10. ↑ Yan, Qingyun; Huang, Weimin (2018). "Sea ice sensing from GNSS-R data using convolutional neural networks". IEEE Geoscience and Remote Sensing Letters. 15 (10): 1510–1514. doi:10.1109/LGRS.2018.2852143. ISSN 1558-0571.
  11. ↑ Southwell, Benjamin J.; Dempster, Andrew G. (2020). "Sea ice transition detection using incoherent integration and deconvolution". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 14–20. doi:10.1109/JSTARS.2019.2943510. ISSN 2151-1535.
  12. ↑ Strandberg, Joakim; Hobiger, Thomas; Haas, Rüdiger (2017). "Coastal sea ice detection using ground-based GNSS-R". IEEE Geoscience and Remote Sensing Letters. 14 (9): 1552–1556. doi:10.1109/LGRS.2017.2722041. ISSN 1558-0571.
  13. ↑ Zhu, Yongchao; Tao, Tingye; Yu, Kegen; Li, Zhenxuan; Qu, Xiaochuan; Ye, Zhourun; Geng, Jun; Zou, Jingui; Semmling, Maximilian; Wickert, Jens (2020). "Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 217–226. doi:10.1109/JSTARS.2019.2955175. ISSN 2151-1535.
  14. ↑ Yan, Qingyun; Huang, Weimin (2020). "Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 577–587. doi:10.1109/JSTARS.2020.2966880. ISSN 2151-1535.
  15. ↑ Southwell, Benjamin J.; Cheong, Joon Wayn; Dempster, Andrew G. (2020). "A matched filter for spaceborne GNSS-R based sea-target detection". IEEE Transactions on Geoscience and Remote Sensing. 58 (8): 5922–5931. doi:10.1109/TGRS.2020.2973142. ISSN 1558-0644.
  16. ↑ Di Simone, Alessio; Park, Hyuk; Riccio, Daniele; Camps, Adriano (2017). "Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10 (9): 4237–4255. doi:10.1109/JSTARS.2017.2705350. ISSN 2151-1535.
  17. ↑ Valencia, Enric; Camps, Adriano; Rodriguez-Alvarez, Nereida; Park, Hyuk; Ramos-Perez, Isaac (2013). "Using GNSS-R imaging of the ocean surface for oil slick detection". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6 (1): 217–223. doi:10.1109/JSTARS.2012.2210392. ISSN 2151-1535.
  18. ↑ Cheong, Joon Wayn; Southwell, Benjamin J.; Dempster, Andrew G. (2019). "Blind sea clutter suppression for spaceborne GNSS-R target detection". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12 (12): 5373–5378. doi:10.1109/JSTARS.2019.2956183. ISSN 2151-1535.
  19. ↑ Li, Bowen; Yang, Lei; Zhang, Bo; Yang, Dongkai; Wu, Di (2020). "Modeling and simulation of GNSS-R observables with effects of swell". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 1833–1841. doi:10.1109/JSTARS.2020.2992037. ISSN 2151-1535.
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