SAR Enabled Glacier Monitoring Within Canada
Bibliographic record
Abstract
Data from Synthetic Aperture Radar sensors and, in particular, the Radarsat missions, have been important for resolving glacier motion within Canada, necessary to determine the impact of changing climate on ice dynamics. For example, offset tracking of data collected by Radarsat-1 provided some of the first evidence that glacier motion could be reliably determined, while offset tracking of Radarsat-2 data enabled the first systematic annual monitoring of glacial motion across Northern Canada and the associated spatial and temporal variations in glacier motion. Offset tracking of data collected by the Radarsat Constellation Mission (RCM) is now being utilized to continue the foundational work from the earlier missions. Here we provide an assessment of the quality of the offset tracking results derived from High Resolution (5 m) RCM imagery acquired over the St. Elias Mountains in SW Yukon in the winters of 2022 and 2023. Our comparisons between remote sensing derived displacements and 50 unique in situ dGPS displacements indicate an average agreement within 6.6 m/year, while off-ice displacements indicate a median error <~8 m/yr. We also present examples of how temporally dense records of glacier velocities derived from both RCM and TerraSAR-X data are being leveraged to better characterize acceleration and deceleration processes related to glacier surging as well as tidewater glacier behaviour. This includes the identification of two surges (Lowell Glacier in winter 2022 and Chitina Glacier in 2023) from RCM data and the description of seasonality of fast flowing glaciers in the Canadian Arctic (Belcher, Trinity and Wykeham Glaciers) from TSX imagery. Finally, we provide a discussion of the future work that will be possible with the fusion of multiple catalogues of glacier motion derived from a multitude of SAR sensors within Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".