Study of the S6-MF capability for estimating the Lake Ice Thickness
Lake ice thickness (LIT) is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). LIT is a sensitive indicator of weather and climate conditions through its dependency on changes in air temperature and on-ice snow depth. The monitoring of seasonal variations and trends in ice thickness is not only important from a climate change perspective, but it is also relevant for the operation of winter ice roads that northern communities rely on.
Yet, field measurements tend to be sparse in both space and time, and many northern countries have seen an erosion of in situ observational networks over the last three decades. Therefore, there is a pressing need to develop retrieval algorithms from satellite remote sensing to provide consistent, broad-scale and regular monitoring of LIT at northern high latitudes in the face of climate change.
To date, few studies have investigated the potential of radar altimetry data for the estimation of LIT, e.g. Beckers et al 2017 (CryoSat 2 data), Yang et al 2020 (Jason data, Lake Water Level study). These are empirical methods based on thresholds, that rely on in situ validation (which is not always possible and difficult to compare) and hard to generalize to different targets. Mangilli et al 2022 developed a novel and efficient retracking approach, the LRM_LIT retracker, specific to LIT retrieval from conventional altimetry Low Resolution Mode (LRM) data. The method is based on the physical and analytical modelling of the radar waveforms that show a specific signature related to the ice and due to the double scattering of the radar wave at the snow-ice interface and at the ice-water interface. The LRM_LIT retracker has been validated on thermodynamical lake ice simulations (CLIMo, Duguay et al 2003) and in-situ data. The LRM_LIT analysis performed on Jason-2 and Jason-3 data over the Great Slave lake (GSL) in Canada yields robust and consistent LIT estimations over different winter seasons, capturing the LIT seasonal and inter-seasonal LIT variations.
The Sentinel-6 mission offers an unique opportunity to ensure the continuity of the LIT observations between the LRM data set and the current and future SAR altimetry missions. This continuity is crucial in order to ensure the scientific exploitation of long time series for LIT trends and climatological studies. Within this context, the main goal of the project is to develop and validate a physical based LIT retracker for S6-SAR data and to assess the accuracy of the LIT estimation with both S6 LRM and SAR data.
This activity aims to:
- Characterize the LIT signature on SAR S6 data over a target lake (e.g. the Great Slave Lake in Canada)
- Develop of a new LIT modelling specific to SARDevelopment of the SAR_LIT retrackerAssessment of the accuracy of the SAR LIT estimation
- Compare of LIT LRM (J3 and S6) and LIT SAR retrievals over a target lake
REFERENCES
J. F. Beckers, J. Alec Casey and C. Haas, “Retrievals of Lake Ice Thickness From Great Slave Lake and Great Bear Lake Using CryoSat-2,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3708-3720, July 2017, doi: 10.1109/TGRS.2017.2677583.
Duguay, C.R., Flato, G.M., Jeffries, M.O., Ménard, P., Morris, K. and Rouse, W.R. (2003), Ice-cover variability on shallow lakes at high latitudes: model simulations and observations. Hydrol. Process., 17: 3465-3483. https://doi.org/10.1002/hyp.1394.
A. Mangilli, P. Thibaut, C. Duguay, J. Murfitt, “A New Approach for the Estimation of Lake Ice Thickness From Conventional Radar Altimetry,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 4305515, doi: 10.1109/TGRS.2022.3186253.
Y. Yang, P. Moore, Z. Li and F. Li, “Lake Level Change From Satellite Altimetry Over Seasonally Ice-Covered Lakes in the Mackenzie River Basin,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 10, pp. 8143-8152, Oct. 2021, doi: 10.1109/TGRS.2020.3040853.