GNSS interferometric reflectometry as a passive remote sensing method for studying environmental phenomena
-
Mohamed Abdelhamid
Abstract
Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a versatile remote sensing technique that utilizes reflected GNSS signals for environmental monitoring. By analyzing interference patterns between direct and reflected GNSS signals, GNSS-IR enables the estimation of surface characteristics such as soil moisture, water level, and snow depth. This study investigates snow accumulation, soil moisture, and water level measurements using GNSS-IR over extended periods, providing insights into climate-related trends. Data from GNSS stations in both the northern and southern hemispheres were analyzed. Snow depth was monitored at two stations over a decade, revealing fluctuations in accumulation and highlighting potential impacts of climate change. Soil moisture was analyzed at a grass-covered site, with comparisons before and after vegetation correction. Water levels were measured using GNSS-IR data near a coastal stream, capturing clear tidal signatures. Results show consistent reflector height variations corresponding to snow depth changes, soil moisture dynamics, and water level oscillations (including tides), demonstrating the robustness of GNSS-IR for environmental monitoring of snow, soil moisture, and sea level.
Acknowledgments
I would like to express my gratitude to Prof. Larson and her research team for providing the Python-based code (gnssrefl). I also extend my thanks to the editors and the anonymous reviewers for their valuable feedback on this work.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
-
Conflict of interest: The author states no conflict of interest.
-
Research funding: This work was funded by research subvention 16.16.150.545 at AGH University of Krakow.
-
Data availability: Raw GNSS RINEX data were downloaded from the website: (https://cddis.nasa.gov).
References
1. Barnett, TP, Adam, JC, Lettenmaier, DP. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 2005;438:303–9. https://doi.org/10.1038/nature04141.Suche in Google Scholar PubMed
2. Musselman, KN, Addor, N, Vano, JA, Molotch, NP. Winter melt trends portend widespread declines in snow water resources. Nat Clim Change 2021;11:418–24. https://doi.org/10.1038/s41558-021-01014-9.Suche in Google Scholar PubMed PubMed Central
3. Kudrys, J. Spectral analysis of multi-year GNSS code multipath time-series. Bud Archit 2019;18:015–22. https://doi.org/10.35784/BUD-ARCH.1319.Suche in Google Scholar
4. Larson, KM, Small, EE, Gutmann, ED, Bilich, AL, Braun, JJ, Zavorotny, VU. Use of GPS receivers as a soil moisture network for water cycle studies. Geophys Res Lett 2008;35:1–5. https://doi.org/10.1029/2008GL036013.Suche in Google Scholar
5. McCreight, JL, Small, EE, Larson, KM. Snow depth, density, and SWE estimates derived from GPS reflection data: validation in the western U. S. Water Resour Res 2014;50:5375–77. https://doi.org/10.1002/2013WR014979.Reply.Suche in Google Scholar
6. Peng, D, Hill, EM, Li, L, Switzer, AD, Larson, KM. Application of GNSS interferometric reflectometry for detecting storm surges. GPS Solut 2019;23:47. https://doi.org/10.1007/s10291-019-0838-y.Suche in Google Scholar
7. Larson, KM, Löfgren, JS, Haas, R. Coastal sea level measurements using a single geodetic GPS receiver. Adv Space Res 2013a;51:1301–10. https://doi.org/10.1016/j.asr.2012.04.017.Suche in Google Scholar
8. Löfgren, JS. Local sea level observations using reflected GNSS signals [Ph.D. thesis]. Chalmers University of Technology. 2014.Suche in Google Scholar
9. Löfgren, JS, Haas, R, Scherneck, HG, Bos, MS. Three months of local sea level derived from reflected GNSS signals. Radio Sci 2011;46:1–2. https://doi.org/10.1029/2011RS004693.Suche in Google Scholar
10. Larson, KM, Ray, RD, Williams, SDP. A 10-year comparison of water levels measured with a geodetic GPS receiver versus a conventional tide gauge. J Atmos Ocean Technol 2017;34:295–307. https://doi.org/10.1175/JTECH-D-16-0101.1.Suche in Google Scholar
11. Larson, KM, Ray, RD, Nievinski, FG, Freymueller, JT. The accidental tide gauge: a GPS reflection case study from Kachemak Bay, Alaska. IEEE Geosci Rem Sens Lett 2013b;10:1200–4. https://doi.org/10.1109/LGRS.2012.2236075.Suche in Google Scholar
12. Larson, KM, Small, EE. Using GPS to study the terrestrial water cycle. Eos, Trans Am Geophys Union 2013;94. https://doi.org/10.1029/2010GL042951.Water.Suche in Google Scholar
13. Larson, KM. GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States. Wiley Interdiscip Rev Water 2016;3:775–87. https://doi.org/10.1002/wat2.1167.Suche in Google Scholar
14. Tu, J, Wei, H, Zhang, R, Yang, L, Lv, J, Li, X, et al.. Gnss‐ir snow depth retrieval from multi‐gnss and multi‐ frequency data. Remote Sens 2021;13:4311. https://doi.org/10.3390/rs13214311.Suche in Google Scholar
15. Wang, X, Zhang, S, Wang, L, He, X, Zhang, Q. Analysis and combination of multi-GNSS snow depth retrievals in multipath reflectometry. GPS Solut 2020;24:1–3. https://doi.org/10.1007/s10291-020-00990-3.Suche in Google Scholar
16. Lei, J, Li, W, Zhang, S. Improving consistency of GNSS-IR reflector height estimates between different frequencies using multichannel singular spectrum analysis. Remote Sens 2023;15:1779. https://doi.org/10.3390/rs15071779.Suche in Google Scholar
17. Li, Y, Yu, K, Jin, T, Chang, X, Wang, Q, Li, J. Development of a GNSS-IR instrument based on low-cost positioning chips and its performance evaluation for estimating the reflector height. GPS Solut 2021;25:1–2. https://doi.org/10.1007/s10291-021-01163-6.Suche in Google Scholar
18. Wei, H, Yang, X, Pan, Y, Shen, F. GNSS-IR soil moisture inversion derived from multi-GNSS and multi-frequency data accounting for vegetation effects. Remote Sens 2023;15:5381. https://doi.org/10.3390/rs15225381.Suche in Google Scholar
19. Zhang, J, Liu, S, Liang, H, Wan, W, Guo, Z, Liu, B. Using GNSS-IR snow depth estimation to monitor the 2022 early February snowstorm over Southern China. Remote Sens 2022;14:1–9. https://doi.org/10.3390/rs14184530.Suche in Google Scholar
20. Chen, P, Li, Z, Zheng, N. A new approach for GNSS-IR snow depth monitoring with slope correction and error prediction; 1–16 pp. https://doi.org/10.21203/rs.3.rs-150402/v1.Suche in Google Scholar
21. Larson, KM. Gnssrefl: an open source software package in python for GNSS interferometric reflectometry applications. GPS Solut 2024;28:1–6. https://doi.org/10.1007/s10291-024-01694-8.Suche in Google Scholar
22. Chew, C, Small, EE, Larson, KM. An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil. GPS Solut 2016;20:525–37. https://doi.org/10.1007/s10291-015-0462-4.Suche in Google Scholar
23. Sturm, M, Goldstein, MA, Parr, C. Water and life from snow: a trillion dollar science question. Water Resour Res 2017;53:3534–44. https://doi.org/10.1002/2017WR020840.Suche in Google Scholar
24. Zhan, J, Zhang, R, Tu, J, Lv, J, Bao, X, Xie, L, et al.. GNSS-IR snow depth retrieval based on the fusion of multi-satellite SNR data by the BP neural network. Remote Sens 2022;14:1395. https://doi.org/10.3390/rs14061395.Suche in Google Scholar
25. Ryan, WA, Doesken, NJ, Fassnacht, SR. Evaluation of ultrasonic snow depth sensors for U.S. snow measurements. J Atmos Ocean Technol 2008;25:667–84. https://doi.org/10.1175/2007JTECHA947.1.Suche in Google Scholar
26. Larson, KM, Braun, JJ, Small, EE, Zavorotny, VU, Gutmann, ED, Bilich, AL. GPS multipath and its relation to near-surface soil moisture content. IEEE J Sel Top Appl Earth Obs Rem Sens 2010;3:91–9. https://doi.org/10.1109/JSTARS.2009.2033612.Suche in Google Scholar
27. Chen, K, Cao, X, Shen, F, Ge, Y. An improved method of soil moisture retrieval using multi-frequency SNR data. Remote Sens 2021;13:3725. https://doi.org/10.3390/rs13183725.Suche in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
