Exploring the Potential of SWOT Altimetry for Retrieving Lake Ice Thickness
Bibliographic record
Abstract
Lakes play a critical role as climate change proxies and cover a significant portion of the northern latitude landscape. Lake ice phenology offers valuable insight into changing climate patterns, yet in situ observations of lake ice have declined substantially in recent decades (Li et al., 2023). This observational gap highlights the growing importance of remote sensing as a tool for understanding and monitoring lake ice (Tang et al., 2023). Northern and remote communities particularly rely on lake ice quality, quantity, and thickness for transportation on ice roads, subsistence activities, and recreational use (Knopp et al., 2022). There has been limited research exploring the use of satellite altimetry for the retrieval and estimation of lake ice thickness (LIT), however its efficacy and utility has been highlighted in recent studies (Beckers et al., 2017; Mayers et al., 2018; Li et al., 2023; Mangilli et al., 2024). Ku-band SWOT (Surface Water and Ocean Topography) altimetry presents an opportunity to retrieve ice properties and directly measure ice thickness. This study assesses the retrieval of LIT from SAR altimeters aboard legacy sensors Sentinel-3 and Sentinel-6 over the ice seasons from 2019 to 2024 on Kluane Lake, Yukon and compares it to the estimated LIT acquired from the SWOT altimeter analysis. LIT can be determined using Ku-band altimetry through the analysis of double-peaked waveforms characteristic to lake ice formed by the interaction of the radar signal with the ice interfaces (Beckers et al., 2017). The utilization of SWOT altimetry has the potential to advance the understanding of lake ice processes and provide valuable datasets for climate and hydrological models as well as overall resource management. This presentation discusses the potential applications of SWOT altimetry in lake ice thickness retrieval, emphasizing its capacity to fill critical data gaps and contribute to our understanding of lakes as dynamic systems in a changing climate.Beckers, J. F., Casey, J. A., & Haas, C. (2017). Retrievals of lake ice thickness from great slave lake and great bear lake using CryoSat-2. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3708-3720. Knopp, J. A., Levenstein, B., Watson, A., Ivanova, I., & Lento, J. (2022). Systematic review of documented Indigenous Knowledge of freshwater biodiversity in the circumpolar Arctic. Freshwater Biology, 67(1), 194–209.Li, X., Long, D., Cui, Y., Liu, T., Lu, J., Hamouda, M. A., & Mohamed, M. M. (2023). Ice thickness and water level estimation for ice-covered lakes with satellite altimetry waveforms and backscattering coefficients. Cryosphere, 17(1), 349–369.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.Mayers, D., & Ruf, C. (2018, July). Measuring ice thickness with CYGNSS altimetry. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 8535-8538). IEEE. Tang, F., Chen, P., An, Z., Xiong, M., Chen, H., & Qiu, L. (2023). A Dual-Threshold Algorithm for Ice-Covered Lake Water Level Retrieval Using Sentinel-3 SAR Altimetry Waveforms. Sensors, 23(24), Article 24.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".