Analysis of a topographic-based InSAR SWE estimation technique for low-land permafrost terrain north of Inuvik, Northwest Territories
Bibliographic record
Abstract
InSAR (Interferometric Synthetic Aperture Radar) is a well-established method for measuring small-scale surface deformations over large regions; however, contaminating effects of snow cover on the InSAR phase prevents the use of (usually less noisy) winter InSAR data, limiting the accuracy of comprehensive measurement of seasonal dynamics in permafrost terrain.In this study we investigate if a previously developed topography-based approach for estimating the contribution of the Snow Water Equivalent (SWE) from repeat pass InSAR phase is accurate enough to correct the displacement phase of the winter data.We use a stack of TerraSAR-X strip map data covering several winters over a study region located in low-lying permafrost north of Inuvik, Northwest Territories.In the study region several ground truth sites have been instrumented with (1) an inclinometer to measure vertical surface deformation due to active layer dynamics of the permafrost, and (2) an ultra-sonic range finder to measure snow-depth.Our analysis found a high uncertainty in the topographic SWE estimates around our ground truth sites due to insufficient variation in terrain preventing us from evaluating the method directly against the ground truth.Estimates for other areas with higher terrain variability farther away from our ground truth sites, however, showed more promising results in terms of error estimates from the topographic SWE estimation being small enough to correct the phase of winter InSAR data to allow their use for comprehensive permafrost active layer displacement measurements.1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".