GNSS-IR Model of Sea Level Height Estimation Combining Variational Mode Decomposition

The Global Navigation Satellite System-Reflections (GNSS-R) signal has been confirmed to be used to retrieve sea level height. At present, the GNSS-Interferometric Reflectometry (GNSS-IR) technology based on the least square method to process signal-to-noise ratio (SNR) data is restricted by the satellite elevation angle in terms of accuracy and stability. This paper proposes a new GNSS-IR model combining variational mode decomposition (VMD) for sea level height estimation. VMD is used to decompose the SNR data into intrinsic mode functions (IMF) of layers with different frequencies, remove the IMF representing the trend item of the SNR data, and reconstruct the remaining IMF components to obtain the SNR oscillation item. In order to verify the validity of the new GNSS-IR model, the measurement data provided by the Onsala Space Observatory in Sweden is used to evaluate the performance of the algorithm and its stability in high elevation range. The experimental results show that the VMD method has good results in terms of accuracy and stability, and has advantages compared to other methods. For the half-year GNSS SNR data, the root mean square error (RMSE) and correlation coefficient of the new model based on the VMD method are 4.86 cm and 0.97, respectively

Python software tools for GNSS interferometric reflectometry (GNSS-IR)

[EN] Global Navigation Satellite System (GNSS) interferometric reflectometry, also known as the GNSS-IR, uses data from geodetic-quality GNSS antennas to extract information about the environment surrounding the antenna. Soil moisture moni-toring is one of the most important applications of the GNSS-IR technique. This manuscript presents the main ideas and implementation decisions needed to write the Python code for software tools that transform RINEX format observation and navigation files into an appropriate format for GNSS-IR (which includes the SNR observations and the azimuth and elevation of the satellites) and to determine the reflection height and the adjusted phase and amplitude values of the interferometric wave for each individual satellite track. The main goal of the manuscript is to share the software with the scientific com-munity to introduce new users to the GNSS-IR technique.The authors want to thank the staff of the Cajamar Center of Experiences, and especially Carlos Baixauli, for their support and collaboration in the Paiporta experiment. The authors also want to thank Alfred Leick and Steve Hilla for their valuable comments and suggestions.Martín Furones, ÁE.; Luján García Muñoz, R.; Anquela Julián, AB. (2020). Python software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solutions. 24(4):1-7. https://doi.org/10.1007/s10291-020-01010-0S17244Chen Q, Won D, Akos DM, Small EE (2016) Vegetation using GPS interferometric reflectometry: experimental results with a horizontal polarized antenna. IEEE J Select Top Appl Earth Obs Rem Sens 9(10):4771–4780. https://doi.org/10.1109/JSTARS.2016.2565687Chew CC, Small EE, Larson KM, Zavorotny VU (2014) Effects of near-surface soil moisture on GPS SNR data: development and retrieval algorithm for soil moisture. IEEE T Geosci Rem Sens 52(1):537–543. https://doi.org/10.1109/TGRS.2013.2242332Chew CC, Small EE, Larson KM, Zavorotny UZ (2015) Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data. IEEE T Geosci Rem Sens 53(5):2755–2764. https://doi.org/10.1109/TGRS.2014.2364513Chew CC, Small EE, Larson KM (2016) An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil. GPS Solut 20(3):525–537. https://doi.org/10.1007/s10291-015-0462-4Gurtner W, Estey L (2015) RINEX: the receiver independent exchange format version 3.03. ftp://igs.org/pub/data/format/rinex303.pdfLarson KM, Small EE, Gutmann ED, Bilich AL, Axelrad A, Braun JJ (2008a) Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solut 12(3):173–177. https://doi.org/10.1007/s10291-007-0076-6Larson KM, Small EE, Gutmann ED, Bilich AL, Braun JJ, Zavorotny VU (2008b) Use of GPS receivers as a soil moisture network for water cycle studies. Geophys Res Lett 35:L24405. https://doi.org/10.1029/2008GL036013Larson KM, Gutmann E, Zavorotny VU, Braun J, Williams M, Nievinski FG (2009) Can we measure snow depth with GPS receivers? Geophys Res Lett 36:L17502. https://doi.org/10.1029/2009GL039430Larson KM, Braun JJ, Small EE, Zavorotny VU (2010) GPS multipath and its relation to near-surface soil moisture content. IEEE J Selec Top Appl Earth Obs Rem Sens 3(1):91–99. https://doi.org/10.1109/JSTARS.2009.2033612Larson 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/s10291-012-0259-7Leick A, Rapoport L, Tatarnikov D (2015) GPS satellite surveying, 4th edn. Wiley, Hoboken, p 840Martín A, Ibañez S, Baixauli C, Blanc S, Anquela AB (2020) Multi-constellation interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrol Earth Syst Sci. https://doi.org/10.5194/hess-24-3573-2020Nievinski GG, Larson KM (2014) An open source GPS multipath simulator in Matlab/Octave. GPS Solut 18:473–481. https://doi.org/10.1007/s10291-014-0370-zNischan T (2016) GFZRNX—RINEX GNSS data conversion and manipulation toolbox (Version 1.05). GFZ Data Serv. https://doi.org/10.5880/GFZ.1.1.2016.002Roesler C, Larson KM (2018) Software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solut. https://doi.org/10.1007/s10291-018-0744-8Roussel N, Ramilien G, Frappart F, Darrozes J, Gay A, Biancale R, Striebig N, Hanquiez V, Bertin X, Allain A (2015) Sea level monitoring and sea estimate using a single geodetic receiver. Remote Sens Environ 171:261–277. https://doi.org/10.1016/j.rse.2015.10.011Roussel N, Frappart F, Ramillien G, Darroes J, Baup F, Lestarquit L, Ha MC (2016) Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2º to 70º. IEEE J Selec Top Appl Earth Obs Rem Sens 9(10):4781–4794. https://doi.org/10.1109/JSTARS.2016.2537847Sanz J, Juan JM, Hernández-Pajares M (2013) GNSS data processing. Volume I: fundamentals and algorithms. European Space Agency Communications, 223 ppSmall EE, Larson KM, Chew CC, Dong J, Ochsner TE (2016) Validation of GPS-IR soil moisture retrievals: comparison of different algorithms to remove vegetation effects. IEEE J Selec Top Appl Earth Obs Rem Sens 9(10):4759–4770. https://doi.org/10.1109/JSTARS.2015.2504527Vey S, Güntner A, Wickert J, Blume T, Ramatschi M (2016) Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa. GPS Solut 20:641–654. https://doi.org/10.1007/s10291-015-0474-0Wan W, Larson KM, Small EE, Chew CC, Braun JJ (2015) Using geodetic GPS receivers to measure vegetation water content. GPS Solut 19:237–248. https://doi.org/10.1007/s10291-014-0383-7Zhang 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-21-4767-201

Analyzing the multipath of GPS time series to study snow properties

Thousands of Global Positioning System (GNSS) receivers worldwide record signals sent by satellites to infer how each receiver (and the ground they are attached to) moves over time. The motion of GNSS receivers is used for many purposes, including studying tectonic deformation and changes in Earth\u27s shape caused by surface loading. In this project, reflected wave arrivals contained within the multipath signal of GNSS time series are extracted and analyzed to advance understanding of snow properties in mountainous regions of Montana/Idaho, USA. Analyzing reflected signals in GNSS series has the potential to reveal properties of local snowpack, such as height, water content, snow surface temperature, dielectric properties, and density. Improving our ability to monitor the physical characteristics of snowpack and how they evolve over space and time is essential as properties of snow are key to understanding the slippage of one layer on another, which impacts avalanche hazard. Moreover, snowpack monitoring provides information about the availability of water resources and snow hydrology. This project focuses on analyzing the ray paths and attenuation of reflected GNSS signals, also using reflections to infer properties of snow. Traditionally, to study snow properties, one must manually dig a snow pit to study the snowpack and/or use expensive remote-sensing technologies (e.g. InSAR). However, digging snow pits can be dangerous due to avalanche risk as well as costly and time inefficient. Relatively low-cost GNSS stations that are now widely deployed worldwide present new opportunities to study snow properties, including in developing nations with fewer financial resources. We will use GNSS interferometric reflectometry (GNSS-IR) software developed by Kristine Larson (CCAR) to infer snow depth data from GNSS multipath. Results will be validated with snow-height data from nearby Snow Telemetry (SNOTEL) stations

Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring

[EN] Per capita arable land is decreasing due to the rapidly increasing population, and fresh water is becoming scarce and more expensive. Therefore, farmers should continue to use technology and innovative solutions to improve efficiency, save input costs, and optimise environmental resources (such as water). In the case study presented in this paper, the Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique was used to monitor soil moisture during 66¿d, from 3 December 2018 to 6 February 2019, in the installations of the Cajamar Centre of Experiences, Paiporta, Valencia, Spain. Two main objectives were pursued. The first was the extension of the technique to a multi-constellation solution using GPS, GLONASS, and GALILEO satellites, and the second was to test whether mass-market sensors could be used for this technique. Both objectives were achieved. At the same time that the GNSS observations were made, soil samples taken at 5¿cm depth were used for soil moisture determination to establish a reference data set. Based on a comparison with that reference data set, all GNSS solutions, including the three constellations and the two sensors (geodetic and mass market), were highly correlated, with a correlation coefficient between 0.7 and 0.85.Martín Furones, ÁE.; Ibañez Asensio, S.; Baixauli, C.; Blanc Clavero, S.; Anquela Julián, AB. (2020). Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrology and Earth System Sciences. 24(7):3573-3582. https://doi.org/10.5194/hess-24-3573-2020S35733582247Chan, S. K., Bindlish, R., O'Neill, P. E., Njoku, E., Jackson, T., Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S., Entekhabi, D., Cosh, M. H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martiìnez-Fernaìndez, J., Gonzaìlez-Zamora, A., Seyfried, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Palecki, M., Small, E. E., Zreda, M., Calvet, J.-C., Crow, W., and Kerr, Y.: Assessment of the SMAP passive soil moisture product, IEEE T. Geosci. Remote, 54, 4994–5007, 2016.Chen, Q., Won, D., Akos, D. M., and Small, E. E.: Vegetation using GPS interferometric reflectometry: experimental results with a horizontal polarized antenna, IEEE J. Sel. Top. Appl., 9, 4771–4780, 2016.Chew, C. C., Small, E. E., Larson, K. M., and Zavorotny, V. U.: Effects of near-surface soil moisture on GPS SNR data: development and retrieval algorithm for soil moisture, IEEE T. Geosci. Remote, 52, 537–543, 2014.Chew, C. C., Small, E. E., Larson, K. M., and Zavorotny, U.Z.: Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data, IEEE T. Geosci. Remote, 53, 2755–2764, 2015.Chew, C. C., Small, E. E., and Larson, K. M.: An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil, GPS Solut., 20, 525–537, 2016.Hofmann-Wellenhof, B., Lichtenegger, H., and Wasle, E.: GNSS Global Navigation Satellite Systems, GPS, GLONASS, GALILEO and more, Springer, Vienna, Austria, New York, USA, 2008.Katzberg, S. J., Torres, O., Grant, M. S., and Masters, D.: Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: results from SMEX02, Remote Sens. Environ., 100, 17–28, 2005.Kerr, Y., Waldteufel, P., Wigneron, J., Martinuzzi, J., Font, J., and Berger, M.: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission, IEEE T. Geosci. Remote, 39, 1729–1735, 2001.Larson, K. M. and Nievinski, F. G.: GPS snow sensing: results from the EarthScope plate boundary observatory, GPS Solut., 17, 41–52, 2013.Larson, K. M., Small, E. E., Gutmann, E. D., Bilich, A. L., Axelrad, A., and Braun, J. J.: Using GPS multipath to measure soil moisture fluctuations: initial results, GPS Solut., 12, 173–177, 2008a.Larson, K. M., Small, E. E., Gutmann, E. D., Bilich, A. L., Braun, J. J., and Zavorotny, V. U.: Use of GPS receivers as a soil moisture network for water cycle studies, Geophys. Res. Lett., 35, L24405, https://doi.org/10.1029/2008GL036013, 2008b.Larson, K. M., Braun, J. J., Small, E. E., and Zavorotny, V. U.: GPS multipath and its relation to near-surface soil moisture content, IEEE J. Sel. Top. Appl., 3, 91–99, 2010.Leick, A., Rapoport, L., and Tatarnikov, D.: GPS satellite surveying, 4th edn., John Wiley & Sons, Hoboken, New Jersey, USA, 840 pp., 2015.Li, G. and Geng, J.: Characteristics of raw multi-GNSS measurement error from Google Android smart devices, GPS Solut., 23, 1–5, https://doi.org/10.1007/s10291-019-0885-4, 2019.Lomb, N. R.: Least-squares frequency – Analysis of unequally spaced data, Astrophys. Space Sci., 39, 447–462, 1976.Masters, D., Axelrad, P., and Katzberg, S.: Initial results of land-reflected GPS bistatic radar measurements in SMEX02, Remote Sens. Environ., 92, 507–520, 2002.Mattia, F., Balenzano, A., Satalino, G., Lovergine, F., Peng, J., Wegmuller, U., Cartus, O., Davidson, M. W. J., Ki<span id="page3582"/>m S., Johnson, J., Walker, J., Wu, X., Pauwels, V. R. N., McNairn, H., Caldwell, T., Cosh, M., and Jackson, T.: Sentinel-1 & Sentinel-2 for SOIL Moisture Retrieval at Field Scale, IGARSS 2018–2018, IEEE I. Geosci. Rem. Sens. Symposium, 22–27 July 2018, Valencia, Spain, 6143–6146, https://doi.org/10.1109/IGARSS.2018.8518170, 2018.Press, W. H., Teukolsky, S. S., Vetterling, W. T., and Flannery, B. P.: Numerical recipes in Fortran 77, vol. 1, 2nd edn., Cambirdge University Press, New York, USA, 569–573, 1992.Roesler, C. and Larson, K. M.: Software tools for GNSS interferometric reflectometry (GNSS-IR), GPS Solut., 22, 80, https://doi.org/10.1007/s10291-018-0744-8, 2018.Roussel, N., Frappart, F., Ramillien, G., Darroes, J., Baup, F., Lestarquit, L., and Ha, M. C.: Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2∘ to 70∘, IEEE J. Sel. Top. Appl., 9, 4781–4794, 2016.Small, E. E., Larson, K. M., Chew, C. C., Dong, J., and Ochsner, T. E.: Validation of GPS-IR soil moisture retrievals: comparison of different algorithms to remove vegetation effects, IEEE J. Sel. Top. Appl., 9, 4759–4770, 2016.Strang, G. and Borre, K.: Linear algebra, Geodesy and GPS, Wellesley-Cambride Press, 624 pp., available at: https://www.unavco.org/data/gps-gnss/derived-products/pbo-h2o/documentation/documentation.html#soil (last access: 18 December 2019), 1997.Vey, S., Güntner, A., Wickert, J., Blume, T., and Ramatschi, M.: Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa, GPS Solut., 20, 641–654, https://doi.org/10.1007/s10291-015-0474-0, 2016.Wan, W., Larson, K. M., Small, E. E., Chew, C. C., and Braun, J. J.: Using geodetic GPS receivers to measure vegetation water content, GPS Solut., 19, 237–248, 2015.Zavorotny, V. U., Masters, D., Gasiewski, A., Bartram, B., Katzberg, S., Aselrad, P., and Zamora, R.: Seasonal polarimetric measurements of soil moisture using tower-based GPS bistatic radar, IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (IEEE Cat. No.03CH37477), Toulouse, France, 2003, vol. 2, 781–783, https://doi.org/10.1109/IGARSS.2003.1293916, 2003.Zhang, S., Roussel, N., Boniface, K., Ha, M. C., Frappart, F., Darrozes, J., Baup, F., and Calvet, J.-C.: 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, 2017

GNSS-IR Measurements of Inter Annual Sea Level Variations in Thule, Greenland from 2008–2019

Studies of global sea level often exclude Tide Gauges (TGs) in glaciated regions due to vertical land movement. Recent studies show that geodetic GNSS stations can be used to estimate sea level by taking advantage of the reflections from the ocean surface using GNSS Interferometric Reflectometry (GNSS-IR). This method has the immediate benefit that one can directly correct for bedrock movements as measured by the GNSS station. Here we test whether GNSS-IR can be used for measurements of inter annual sea level variations in Thule, Greenland, which is affected by sea ice and icebergs during much of the year. We do this by comparing annual average sea level variations using the two methods from 2008–2019. Comparing the individual sea level measurements over short timescales we find a root mean square deviation (RMSD) of 13 cm, which is similar to other studies using spectral methods. The RMSD for the annual average sea level variations between TG and GNSS-IR is large (18 mm) compared to the estimated uncertainties concerning the measurements. We expect that this is in part due to the TG not being datum controlled. We find sea level trends from GNSS-IR and TG of −4 and −7 mm/year, respectively. The negative trend can be partly explained by a gravimetric decrease in sea level as a result of ice mass changes. We model the gravimetric sea level from 2008–2017 and find a trend of −3 mm/year

GNSS interferometric reflectometry for station location suitability analysis

National geodetic reference systems can be continuously monitored using applications of Global Navigation Satellite Systems (GNSS). Within these reference systems, Continuously Operating GNSS Reference Stations (CORSs) are often employed to provide 24/7 satellite tracking data. Understanding the influence of the surroundings of a CORS on the recorded satellite tracking data is indispensable for quality analysis of both acquired data and station location suitability. One of the main sources of inaccurate tracking data is the result of the combined reception of direct as well as indirect, environment-reflected satellite signals by the CORS, in which the latter can be considered interference compromising the signal’s accuracy. The magnitude of this interference is usually evaluated by the Signal-to-Noise Ratio (SNR), a parameter stored by default in the RINEX interchange format for raw GNSS data. The technique of GNSS Interferometric Reflectometry (GNSS-IR) exploits the availability of the SNR data and has been frequently used for applications such as soil moisture monitoring, detection of vegetation water content, measuring snowfall or determining water levels. In this research, we propose to employ GNSS-IR to investigate the effect of the surrounding on a CORS in order to evaluate station location suitability. More specifically, this will be done by using the signal to estimate the Reflector Height (RH), which depends on the reflector roughness (i.e. the roughness of the surface surrounding the CORS). The quality of this estimation will be validated by comparing with the actual measurement of the RH of the CORS on site. In our approach, a statistically sound method is developed quantifying the stability of the RH determination. The proposed methodology consists of using Lomb-Scargle periodograms to select the dominant oscillation frequency of each satellite track SNR data, followed by an analysis and filtering of the peak amplitudes. This leads to the analysis product: number of significant peak amplitudes for an individual CORS over (sub-)daily timeframes. With historical data covering long time periods, statistical analysis of the (sub-)daily timeseries allows for reviewing the station location suitability. In Belgium, CORS are located on two typical positions: in Flanders, the 32 antennas are mainly installed on rooftops of buildings; in Wallonia, the 23 antennas are installed on a concrete pole next to highways. There is no evidence of one choice of station position being more suitable than the other. However, cars are known to be an important factor in signal reflections. In our analysis of station suitability, the effect of cars passing by on the highway near a Walloon CORS, but also movements on, e.g., parking lots next to buildings with a rooftop CORS, will be investigated. With the developed methodology, guidelines for station location selection could be further developed, together with a system to continuously monitor CORS position suitability using GNSS-IR, triggering a warning when significant changes in the environment changes the local reflectometry fingerprint

An open‑source low‑cost sensor for SNR‑based GNSS reflectometry : design and long‑term validation towards sea‑level altimetry

Monitoring sea level is critical due to climate change observed over the years. Global Navigation Satellite System Reflectometry (GNSS-R) has been widely demonstrated for coastal sea-level monitoring. The use of signal-to-noise ratio (SNR) observations from ground-based stations has been especially productive for altimetry applications. SNR records an interference pattern whose oscillation frequency allows retrieving the unknown reflector height. Here we report the development and validation of a complete hardware and software system for SNR-based GNSS-R. We make it available as open source based on the Arduino platform. It costs about US$200 (including solar power supply) and requires minimal assembly of commercial off-the-shelf components. As an initial validation towards applications in coastal regions, we have evaluated the system over approximately 1 year by the Guaíba Lake in Brazil. We have compared water-level altimetry retrievals with independent measurements from a co-located radar tide gauge (within 10 m). The GNSS-R device ran practically uninterruptedly, while the reference radar gauge suffered two malfunctioning periods, resulting in gaps lasting for 44 and 38 days. The stability of GNSS-R altimetry results enabled the detection of miscalibration steps (10 cm and 15 cm) inadvertently introduced in the radar gauge after it underwent maintenance. Excluding the radar gaps and its malfunctioning periods (reducing the time series duration from 317 to 147 days), we have found a correlation of 0.989 and RMSE of 2.9 cm in daily means. To foster open science and lower the barriers for entry in SNR-based GNSS-R research and applications, we make a complete bill of materials and build tutorials freely available on the Internet so that interested researchers can replicate the system

Future efforts to contribute to the International Height Reference System (IHRS)

The Tenth Symposium on Polar Science/Special session: [S] Future plan of Antarctic research: Towards phase X of the Japanese Antarctic Research Project (2022-2028) and beyond, Tue. 3 Dec. / Entrance Hall (1st floor) at National Institute of Polar Research (NIPR

90 years of the Permanent Service for Mean Sea Level (PSMSL)

A short update from the Permanent Service for Mean Sea Level on their recent work from the point of view of their role as a service of the International Association of Geodesy (IAG)

Application of GNSS Interferometric Reflectometry for Lake Ice Studies

This thesis examines the use of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) for the study of lake ice with a particular focus on the estimation of ice thickness. Experiments were conducted in two lake regions: (1) sub-Arctic lakes located near Yellowknife and Inuvik in the Northwest Territories during March 2017 and 2019, and (2) MacDonald Lake, Haliburton, Ontario, which is known as a mid-latitude lake, during the ice season of 2019-2020. For both regions, GNSS-IR results are compared and validated against in-situ ice and on-ice snow measurements, and also with ice thickness derived from thermodynamic lake ice models. In the first experiment, GNSS antennas were installed directly on the ice surface and the ice thickness at each site was estimated by analyzing the signal-to-noise ratio (SNR) of the reflected GNSS signals. The GNSS-IR capability of ice thickness estimation tested on sub-Arctic lakes results in a root mean square error (RMSE) of 0.07 m, a mean bias error (MBE) of -0.01 m, and a correlation of 0.66. At MacDonald Lake, a GNSS antenna was mounted on a 5-m tower on the shore to collect reflected signals from the lake surface. The Least-Squares Harmonic Estimation (LS-HE) method was applied to retrieve higher SNR frequencies in order to estimate the depths of multiple layers within lake ice and the overlaying snowpack. Promising results were obtained from this experiment; however, ice thickness estimation using GNSS-IR at this mid-latitude lake site was found to be highly dependent on the presence or absence of wet layers such as slush at the snow-ice interface and wet snow above that interface. On colder days, when there was a lower chance for the formation of wet layers, ice thickness could be estimated with a correlation of 0.68, RMSE of 0.07 m, and MBE of -0.02 m. In addition, GNSS-IR showed the potential for determining the freeze-up and break-up timing based on the SNR amplitude of reflected signals. The novel work presented in this thesis points to the potential of using reflected signals acquired by recent (e.g. Cyclone Global Navigation Satellite System (CYGNSS) and TechDemoSat-1 (TDS-1)) and future GNSS-R missions for lake ice investigations