Comparison of glacier velocity maps determined over Axel Heiberg Island, Canadian high Arctic derived from differing SAR sensors
Bibliographic record
Abstract
Rates of atmospheric warming in the Canadian Arctic (CA) are twice the global average and are impacting glacier behavior: causing longer and more intense melt seasons; modifying glacier motion; and leading to persistent glacier mass loss. This study aims to further understand the impact of this warming through the investigation of a decadal record of glacier velocities for Thompson and White Glaciers (Axel Heiberg Island) in the CA. This study creates a dense time series of glacier motion from 2008-2020, by applying an offset tracking algorithm to data acquired by two different SAR sensors: RADARSAT-2 (R2) and TerraSAR-X (TSX). Openly available velocity maps, that are pre-derived from Sentinel-1 (S1) SAR imagery, are also used to augment the record we have created. A comparison of the glacier velocity maps produced from each of these sensors indicates that the TSX data provides more velocity maps with lower error than both R2 and S1 data, both of which contain more variability due to noise. Over non-moving bedrock outcrops, initial tests indicate that TSX data provide an overall error of 3.74 m yr-1 with a standard deviation of 2.39 for the period of March 2020. Over the same period and spatial area, pre-derived S1 products yielded an overall error of 9.81 m yr-1 with a standard deviation of 7.45. Previous studies that have utilized R2 data, provided an error of 4.81 m yr-1 and a standard deviation of 3.15 for the ice caps on AHI for 2019-2020. We suggest that the TSX results are likely due to shorter temporal resolution of an 11 day repeat pass compared to 24 days for R2. S1 has either a 6 or 12 day repeat pass but poorer resolution of the data likely that degrades the velocity results. Differing image pixel resolutions could be another cause of improved results from TSX data. To further characterize and constrain the uncertainty of each of these datasets, all the SAR based velocities are compared with in situ dGPS observation for White Glacier. Overall, this work ‘rescues’ a large catalogue of SAR imagery collected over the last decade in order to derive a comprehensive record of glacier velocities on Axel Heiberg Island which can be used to further quantify variations in glacier flow and understand their associated drivers.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".