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Article

Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data

1
CLS, 11 Rue Hermes, 31520 Ramonville, France
2
Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
3
H2O Geomatics, Kitchener, ON N2L 1S7, Canada
4
Bioceanor, 06560 Nice, France
5
European Space Agency/European Space Research and Technology Centre, 2201 AZ Noordwijk, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(14), 2510; https://doi.org/10.3390/rs16142510
Submission received: 23 April 2024 / Revised: 17 June 2024 / Accepted: 25 June 2024 / Published: 9 July 2024
(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

Abstract

Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions.
Keywords: radar altimetry; ice thickness; unfocused SAR; fully focused SAR; Sentinel-6 Michael Freilich; Jason-3; lake ice; cryosphere; hydrology radar altimetry; ice thickness; unfocused SAR; fully focused SAR; Sentinel-6 Michael Freilich; Jason-3; lake ice; cryosphere; hydrology

Share and Cite

MDPI and ACS Style

Mangilli, A.; Duguay, C.R.; Murfitt, J.; Moreau, T.; Amraoui, S.; Mugunthan, J.S.; Thibaut, P.; Donlon, C. Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data. Remote Sens. 2024, 16, 2510. https://doi.org/10.3390/rs16142510

AMA Style

Mangilli A, Duguay CR, Murfitt J, Moreau T, Amraoui S, Mugunthan JS, Thibaut P, Donlon C. Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data. Remote Sensing. 2024; 16(14):2510. https://doi.org/10.3390/rs16142510

Chicago/Turabian Style

Mangilli, Anna, Claude R. Duguay, Justin Murfitt, Thomas Moreau, Samira Amraoui, Jaya Sree Mugunthan, Pierre Thibaut, and Craig Donlon. 2024. "Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data" Remote Sensing 16, no. 14: 2510. https://doi.org/10.3390/rs16142510

APA Style

Mangilli, A., Duguay, C. R., Murfitt, J., Moreau, T., Amraoui, S., Mugunthan, J. S., Thibaut, P., & Donlon, C. (2024). Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data. Remote Sensing, 16(14), 2510. https://doi.org/10.3390/rs16142510

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