Comparison of Surface Subsidence Measured by Airborne and Satellite InSAR Over Permafrost Areas Near Yellowknife Canada
Bibliographic record
Abstract
In addition to spaceborne Interferometric Synthetic Aperture Radar (InSAR), airborne data such as those obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) have also been utilized to measure surface subsidence in permafrost areas in recent years. Motivated by the integration of multiplatform InSAR data, we generated two UAVSAR interferograms and one Advanced Land Observing Satellite (ALOS)-2 L-band interferogram over a permafrost area near Yellowknife, Canada, then compared the surface subsidence in the thaw seasons of 2017. The correlation coefficient and the root mean square error (RMSE) of subsidence difference are calculated to compare the airborne and spaceborne InSAR measurements. The results demonstrate that the two UAVSAR measurements are self-consistent, with the correlation coefficient between independent airborne measurements ∼0.7. While the RMSE of the difference between surface subsidence measured by UAVSAR and ALOS2 is ∼2.0 cm, and the correlation coefficients are less than 0.41, that is, a noticeable deviation exists between the UAVSAR and ALOS2 results possibly due to different spatial resolution and the calibration processing of airborne and spaceborne InSAR data. In addition, both UAVSAR and ALOS2 interferograms show larger surface subsidence within taiga needleleaf forest regions than in regions of other biome types (including needleleaf forest, shrubland, and grassland). The results demonstrate that a scheme for the elimination of systematic differences needs to be developed before merging multisource InSAR results. This intercomparison will provide valuable insights for narrowing the gap between radar-based measurements and planning the integration of airborne and satellite InSAR measurements in permafrost environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".