Abstract
A method for retrieving snow and sea ice thickness is developed and simulations are conducted to evaluate the feasibility of the proposed method. When using a defined envelope amplitude and phase, for the snow-free case, RMSEs of 0.24 and 0.34 m can be obtained; for the snow-covered case, the RMSEs of the retrieved sea ice thickness decrease to 0.45 and 0.48 m, and the RMSEs of the retrieved snow thickness are 0.12 and 0.13 m. A method combining the envelope amplitude and phase is proposed to further improve the retrieval accuracy. The simulated results show that combining these two observables can provide an RMSE of 0.19 m for the snow-free case, and for the snow-covered case, the RMSEs of the retrieved snow and sea ice thicknesses are reduced to 0.09 and 0.42 m, respectively. Furthermore, linear polarization observations are proposed and tested. The vertical polarization shows the best measurement performance. When the envelope amplitude and phase of the vertical polarization carrier to noise ratio (CNR) are combined to retrieve snow and sea ice thicknesses, the RMSEs of the retrieved sea ice thickness for the snow-free case decrease to 0.13, and for the snow-covered case, the RMSEs of the retrieved snow and sea ice thicknesses decrease to 0.07 and 0.17 m, respectively.
Similar content being viewed by others
Data Availability
The datasets for this work were simulated by a GNSS-IR simulator. Datasets are available from the author on reasonable request (E-mail: wangf.19@163.com).
References
Alonso-Arroyo A, Camps A, Park H, Pascual D, Onrubis R, Martin F (2015) Retrieval of significant wave height and mean sea surface level using the GNSS-R interference pattern technique: results from a three-month field campaign. IEEE Trans Geosci Remote Sens 53(6):3198–3209. https://doi.org/10.1109/TGRS.2014.2371540
Alonso-Arroyo A, Zavorotny VU, Camps A (2017) Sea ice detection using UK TDS-1 GNSS-R data. IEEE Trans Geosci Remote Sens 55:3782–3788. https://doi.org/10.1109/TGRS.2017.2699122
Attali JG, Pages G (1997) Approximations of functions by a multilayer perceptron: a new approach. Neural Netw 10(6):1069–1081. https://doi.org/10.1016/S0893-6080(97)00010-5
Beckmann P, Spizzichino A (1963) The scattering of electromagnetic waves from rough surfaces. Pergamon, New York
Camps A, Park H, Pablos M, Foti G, Gommenginger PG, Liu PW, Judge J (2016) Sensitivity of GNSS-R spaceborne observations to soil moisture and vegetation. IEEE J Sel Top Appl Earth Observ Remote Sens 9:4730–4742. https://doi.org/10.1109/JSTARS.2016.2588467
Cardellach E, Fabra F, Rius A, Pettinato S, D’Addio S (2012) Characterization of dry-snow sub-structure using GNSS reflected signals. Remote Sens Environ 124:122–134. https://doi.org/10.1016/j.rse.2012.05.012
Chen Q, Won D, Akos DM (2017) Snow depth estimation accuracy using a dual-interface GPS-IR model with experimental results. GPS Solut 21:211–223. https://doi.org/10.1007/s10291-016-0517-1
Expedition M (2022) MOSAiC expedition web page. https://follow.mosaic-expedition.org/. Accessed on 22 June 2022
Fabra F, Cardellach E, Rius A, Ribo S, Oliveras S, Nogues-Correig O, Rivas MB, Semmling M, D’Addio S (2012) Phase altimetry with dual polarization GNSS-R over sea ice. IEEE Trans Geosci Remote Sens 50:2112–2121. https://doi.org/10.1109/TGRS.2011.2172797
Foti G, Gommenginger C, Jales P, Unwin M, Shaw A, Robertson C, Rosello J (2015) Spaceborne GNSS reflectometry for ocean winds: first results from the UK TechDemoSat-1 mission. Geophys Res Lett 43:767–774. https://doi.org/10.1002/2015GL064204
Gao F, Wang X, Gao Y, Dong J (2019) Sea ice change detection in SAR images based on convolutional-wavelet neural network. IEEE Geosci Remote Sens Lett 16:1240–1244. https://doi.org/10.1109/LGRS.2019.2895656
Ghiasi Y, Duguay CR, Murfitt J, Sanden JJ, Thompson A, Drouin H, Prevost C (2020) Application of GNSS interferometric reflectometry for the estimation of lake ice thickness. Remote Sens 12:2721. https://doi.org/10.3390/rs12172721
Hallikainen M, Winebrenner DP (1992) The physical basis for sea ice remote sensing. In: Carsey FD (ed) Microwave remote sensing of sea ice. Wiley, Hoboken, NJ. https://doi.org/10.1029/GM068p0029
Haupt SE, Pasini A, Marzban C (2009) Artificial intelligence methods in the environmental science. Springer, Berlin, pp 3–13. https://doi.org/10.1007/978-1-4020-9119-3
Jacobson MD (2009) Snow-covered Lake ice in GPS multipath reception-theory and measurement. Adv Space Res 46:221–227. https://doi.org/10.1016/j.asr.2009.10.013
Jacobson MD (2015) Potential for estimating the thickness of freshwater lake ice by GPS interferometric reflectometry. J Geogr Geol 7:10–19. https://doi.org/10.5539/jgg.v7n1p10
Klein L, Swift CT (1977) An improved model for the dielectric constant of sea water at microwave frequencies. IEEE Trans Antennas Propag 2:104–111. https://doi.org/10.1109/TAP.1977.1141539
Komjathy A, Maslanik J, Zavorotny VU, Axelrad P, Katzberg SJ (2000) Sea ice remote sensing using surface reflected GPS signals. IGARSS 2000:2855–2857. https://doi.org/10.1109/IGARSS.2000.860270
Larson K, Nievinski F (2013) GPS snow sensing: results from the Earthscope Plate Boundary Observatory. GPS Solut 17:41–52. https://doi.org/10.1007/s10291-012-0259-7
Laxon SW et al (2013) CryoSat-2 estimates of Arctic Sea ice thickness and volume. Geophys Res Lett 40:732–737. https://doi.org/10.1002/grl.50193
Li W, Rius A, Fabra F, Cardellach E, Ribo S, Martin-Neira M (2018) Revisiting the GNSS-R waveform statistics and its impact on altimetric retrievals. IEEE Trans Geosci Remote Sens 56:2854–2871. https://doi.org/10.1109/TGRS.2017.2785343
Munoz-Martin JF et al (2020) Snow and ice thickness retrievals using GNSS-R: preliminary results of the MOSAiC experiment. Remote Sens 12:4038. https://doi.org/10.3390/rs12244038
Nievinski FG, Larson KM (2014a) Forward modeling of GPS multipath for near-surface reflectometry and positioning applications. GPS Solut 18:309–322. https://doi.org/10.1007/s10291-013-0331-y
Nievinski FG, Larson KM (2014b) Inverse modeling of GPS multipath for snow depth estimation-part: formulation and simulations. IEEE Trans Geosci Remote Sens 52:6555–6563. https://doi.org/10.1109/TGRS.2013.2297681
Piizzlato L, Howell SEL, Derksen C, Dawson J, Copland L (2014) Changing sea ice conditions and marine transportation activity in Canadian Arctic waters between 1990 and 2012. Clim Change 123(2):161–173. https://doi.org/10.1007/s10584-013-1038-3
Rivas MB, Maslanik JA, Axelrad P (2010) Bistatic scattering of GPS signals off Arctic Sea ice. IEEE Trans Geosci Remote Sens 48:1548–1556. https://doi.org/10.1109/TGRS.2009.2029342
Rodriguez-Alvarez N et al (2011) Land geophysical parameters retrieval using the interference pattern GNSS-R technique. IEEE Trans Geosci Remote Sens 49(1):71–84. https://doi.org/10.1109/TGRS.2010.2049023
Ruf CS et al (2016) New ocean winds satellite mission to probe hurricanes and tropical convection. Bull Am Meteorol Soc 97(3):385–395. https://doi.org/10.1175/BAMS-D-14-00218.1
Strandberg J, Hobiger T, Hass R (2017) Coastal sea ice detection using ground-based GNSS-R. IEEE Geosci Remote Sens Lett 14:1552–1556. https://doi.org/10.1109/LGRS.2017.2722041
Tiuri ME, Sihvola A, Nyfors E, Hallikaiken MT (1984) The complex dielectric constant of snow at microwave frequencies. IEEE J Ocean Eng OE-9:377–382. https://doi.org/10.1109/JOE.1984.1145645
Ulaby FT, Moore RK, Fung AK (1981) Microwave remote sensing: active and passive. Artech House, Norwood, MA (ISBN 978-0-89-006193-0)
Unwin M, Jales P, Gommenginger C, Foti G, Rosello J (2016) Spaceborne GNSS-reflectometry on TechDemoSat-1: early mission operations and exploitation. IEEE J Sel Top Appl Earth Observ Remote Sens 9(10):4525–4539. https://doi.org/10.1109/JSTARS.2016.2603846
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 24:77. https://doi.org/10.1007/s10291-020-00990-3
Wiehl W, Legresy B, Dietrich R (2003) Potential of reflected GNSS signals for ice sheet remote sensing. Prog Electromagn Res 40:177–205. https://doi.org/10.2528/PIER02102202
Yan Q, Huang W (2020) Sea ice thickness measurement using spaceborne GNSS-R: first results with TechDemoSat-1 data. IEEE J Sel Top Appl Earth Observ Remote Sens 13:99. https://doi.org/10.1109/JSTARS.2020.2966880
Yan Q, Huang W, Jin S, Jia Y (2020) Pan-tropical soil moisture mapping based on a three-layer model from CYGNSS GNSS-R data. Remote Sens Environ 247:111944. https://doi.org/10.1016/j.rse.2020.111944
Yu K, Ban W, Zhang X, Yu X (2015) Snow depth estimation based on multipath phase combination of GPS triple-frequency signals. IEEE Trans Geosci Remote Sens 53:5100–5109. https://doi.org/10.1109/TGRS.2015.2417214
Yu K, Li Y, Chang X (2019) Snow depth estimation based on combination of pseudorange and carrier phase of GNSS dual-frequency signals. IEEE Trans Geosci Remote Sens 57:1817–1828. https://doi.org/10.1109/TGRS.2018.2869284
Zhang Y, Meng W, Gu Q, Han Y, Hong Z, Cao Y, Xia Q, Wang W (2015) Detection of Bohai bay sea ice using GPS-reflected signals. IEEE J Sel Top Appl Earth Observ Remote Sens 8:39–46. https://doi.org/10.1109/JSTARS.2014.2357894
Zhang Z, Yu Y, Li X, Hui F, Cheng X, Chen Z (2019) Arctic Sea ice classification using microwave scatterometer and radiometer data during 2002–2017. IEEE Trans Geosci Remote Sens 57:5319–5328. https://doi.org/10.1109/TGRS.2019.2898872
Acknowledgements
This study was financially supported by the China Postdoctoral Innovative Talent Support Program (BX20200039), the National Natural Science Foundation of China (42104031), the fellowship of China Postdoctoral Science Foundation (2021M700319) and BeiDou Technology Achievement Transformation and Industrialization of Beihang (BARI2003). The reviewers are acknowledged for their valuable comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing Interests
The authors declare that they have no competing financial interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, F., Yang, D., Zhang, B. et al. Can sea ice thickness be retrieved using GNSS-interferometric reflectometry?. GPS Solut 26, 128 (2022). https://doi.org/10.1007/s10291-022-01309-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10291-022-01309-0