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A systematic approach for identifying optimal azimuth and elevation angle masks in GNSS-IR: validation through a sea level experiment

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Abstract

GNSS-IR is a method that enables retrieving characteristics of the reflection surface by analyzing the interference between direct and coherently reflected signals transmitted from satellites. The selection of azimuth and elevation angles is of great importance in this method. Traditional methods for determining azimuth and elevation angle masks may be insufficient due to irregularities and time-dependent changes on the reflection surface. In this study, we propose a novel empirical approach that allows determination of optimal azimuth and elevation angle masks. The approach is based on the RMSE and correlation values of estimates obtained by directly evaluating the SNR data with in situ measurements. The proposed approach was trained with four different strategies (subsequently three additional strategies) using 1-week GPS L1C SNR data of the GADA station located in the Aegean Sea and co-located with a tide gauge covering the period December 13–19, 2020. After identifying the angle masks, GNSS-IR analysis of 6-month data from the same station between January 1 and June 30, 2021 was performed using the angle masks found for each strategy combination, and then daily and sub-daily (for 12-h, 6-h, 4-h, 3-h, 2-h, and 1-h intervals) sea level estimations were obtained. Compared with tide gauge measurements, daily estimations showed a correlation of over 97% and an RMSE below 3.2 cm. For sub-daily estimations, a correlation of over 85% and an RMSE below 10 cm were obtained even for the 1-h estimation interval. The results show that the approach proposed here can be used to determine optimal azimuth and elevation angle masks in GNSS-IR analysis without requiring any data other than in situ measurements during the pre-analysis period.

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Data used and analyzed in this study can be obtained by contacting the corresponding author upon a request.

References

  • Altuntas C, Tunalioglu N (2020) Estimation performance of soil moisture with GPS-IR method. Sigma J Eng Nat Sci 38(4):2217–2230

    Google Scholar 

  • Altuntas C, Tunalioglu N (2022) GIRAS: an open-source MATLAB-based software for GNSS-IR analysis. GPS Solut 26(1):16

    Article  Google Scholar 

  • Anderson KD (2000) Determination of water level and tides using interferometric observations of GPS signals. J Atmos Oceanic Tech 17(8):1118–1127. https://doi.org/10.1175/1520-0426(2000)017%3c1118:DOWLAT%3e2.0.CO;2

    Article  Google Scholar 

  • Bilich A, Larson KM, Axelrad P (2008) Modeling GPS phase multipath with SNR: case study from the Salar de Uyuni, Boliva. J Geophys Res: Solid Earth 113(B4):1–12

  • Geremia-Nievinski F, Hobiger T, Haas R, Liu W, Strandberg J, Tabibi S, Vey S, Wickert J, Williams S (2020) SNR-based GNSS reflectometry for coastal sea-level altimetry: results from the first IAG inter-comparison campaign. J Geodesy 94(8):70

    Article  Google Scholar 

  • Han M, Zhu Y, Yang D, Chang Q, Hong X, Song S (2020) Soil moisture monitoring using GNSS interference signal: proposing a signal reconstruction method. Remote Sens Lett 11(4):373–382

  • Hoffmann-Wellenhof B, Lichtenegger H, Wasle E (2007) GNSS–global navigation satellite systems: GPS, GLONASS, Galileo, and more. Springer Science & Business Media

  • Larson KM, Gutmann ED, Zavorotny VU, Braun JJ, Williams MW, Nievinski FG (2009) Can we measure snow depth with GPS receivers? Geophys Res Lett 36(17):1–5

    Article  Google Scholar 

  • Larson KM, Nievinski FG (2013) GPS snow sensing: results from the EarthScope plate boundary observatory. GPS Solut 17(1):41–52. https://doi.org/10.1007/s10291-012-0259-7

    Article  Google Scholar 

  • Larson KM, Small EE, Gutmann ED, Bilich AL, Axelrad A, Braun JJ (2008) Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solut 12(3):173–177

    Article  Google Scholar 

  • Lomb NR (1976) Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci 39(2):447–462

    Article  Google Scholar 

  • Ran Q, Zhang B, Yao Y, Yan X, Li J (2022) Editing arcs to improve the capacity of GNSS-IR for soil moisture retrieval in undulating terrains. GPS Solut 26:1–11

    Article  Google Scholar 

  • Rodriguez-Alvarez N, Bosch-Lluis X, Camps A, Ramos-Perez I, Valencia E, Park H, Vall-llossera M (2012) Vegetation water content estimation using GNSS measurements. IEEE Geosci Remote Sens Lett 9(2):282–286

    Article  Google Scholar 

  • Roussel N, Frappart F, Ramillien G, Darrozes J, Baup F, Lestarquit L, Ha MC (2016) Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2° to 70°. IEEE J Select Top Appl Earth Observ Remote Sens 9(10):4781

    Article  Google Scholar 

  • Scargle JD (1982) Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. Astrophys J 263:835–853

    Article  Google Scholar 

  • Small EE, Larson KM, Braun JJ (2010) Sensing vegetation growth with reflected GPS signals. Geophys Res Lett 37(12):245–269. https://doi.org/10.1029/2010GL042951

    Article  Google Scholar 

  • Strandberg J, Hobiger T, Haas R (2016) Improving GNSS-R sea level determination through inverse modeling of SNR data. Radio Sci 51(8):1286–1296

    Article  Google Scholar 

  • Strandberg J, Hobiger T, Haas R (2019) Real-time sea-level monitoring using Kalman filtering of GNSS-R data. GPS Solut. https://doi.org/10.1007/s10291-019-0851-1

    Article  Google Scholar 

  • Tabibi S, Geremia-Nievinski F, van Dam T (2017) Statistical comparison and combination of GPS, GLONASS, and multi-GNSS multipath reflectometry applied to snow depth retrieval. IEEE Trans Geosci Remote Sens 55(7):3773–3785

  • Tebaldi C, Ranasinghe R, Vousdoukas M, Rasmussen DJ, Vega-Westhoff B, Kirezci E, Kopp RE, Sriver R, Mentaschi L (2021) Extreme sea levels at different global warming levels. Nat Clim Chang 11(9):746–751

    Article  Google Scholar 

  • Wan W, Zhao L, Zhang J, Liang H, Guo Z, Liu B, Ji R (2022) Toward terrain effects on GNSS interferometric reflectometry snow depth retrievals: geometries, modeling, and applications. IEEE Trans Geosci Remote Sens 60:1–14

    Google Scholar 

  • Wang X, Zhang Q, Zhang S (2018) Azimuth selection for sea level measurements using geodetic GPS receivers. Adv Space Res 61(6):1546–1557

    Article  Google Scholar 

  • Wang X, Zhang S, Wang L, He X, Zhang Q (2020) Analysis and combination of multi-GNSS snow depth retrievals in multipath reflectometry. GPS Solut 24:1–13

    Article  Google Scholar 

  • Xi R, Zhou X, Jiang W, Chen Q (2018) Simultaneous estimation of dam displacements and reservoir level variation from GPS measurements. Measurement 122:247–256. https://doi.org/10.1016/j.measurement.2018.03.036

    Article  Google Scholar 

  • Zhang S, Wang X, Zhang Q (2016) Avoiding errors attributable to topography in GPS-IR snow depth retrievals. Adv Space Res 59(6):1663–1669

    Article  Google Scholar 

  • Zhang S, Roussel N, Boniface K, Ha MC, Frappart F, Darrozes J, Baup F, Calvet JC (2017) Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop. Hydrol Earth Syst Sci 21:4767–4784. https://doi.org/10.5194/hess-21-4767-2017

    Article  Google Scholar 

  • Zhou W, Liu Y, Huang L, Ji B, Liu L, Bian S (2022) Multi-constellation GNSS interferometric reflectometry for the correction of long-term snow height retrieval on sloping topography. GPS Solut 26(4):140

    Article  Google Scholar 

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Acknowledgements

We would like to thank General Directorate of Mapping of Türkiye for providing GNSS and tide gauge data, as well as station photo.

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C.A. and N.T. developed the methodology and wrote the main manuscript. C.A. wrote the required scripts for analyses and prepared the figures. N.T. conducted the review, editing, and supervision of the work.

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Correspondence to Cemali Altuntas.

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Altuntas, C., Tunalioglu, N. A systematic approach for identifying optimal azimuth and elevation angle masks in GNSS-IR: validation through a sea level experiment. GPS Solut 27, 198 (2023). https://doi.org/10.1007/s10291-023-01535-0

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