Quantifying the impact of X-band InSAR penetration bias on elevation change and mass balance estimation
Bibliographic record
Abstract
Abstract Interferometric synthetic aperture radar (InSAR) data suffer from an elevation bias due to signal penetration into the firn and ice surface, rendering the height information unusable for elevation and mass-change detection. This study estimates the penetration bias in X-band InSAR data to quantify its impact on elevation and mass-change detection and to demonstrate the applicability of TanDEM-X digital elevation models (DEMs) for cryosphere research. To achieve this, a multiple linear regression model is applied to a time series of four TanDEM-X DEMs acquired between 2010 and 2018 over the Sverdrup Glacier basin (SGB), Devon Ice Cap, Canada. The resulting penetration corrected TanDEM-X DEMs agreed to within ±14 cm of spatially and temporally coincident precise in situ kinematic dGPS data (±10 cm RMSE). Additionally, multi-year estimations of mass change for the SGB derived from differencing TanDEM-X DEMs over multi-year periods between 2010 and 2018, showed good agreement with mean deviation of 338 ± 166 mm w.e. with independent measurements of mass change derived from annual in situ surface mass balance over the same time periods. The results show that the penetration bias can vary significantly, leading to random under- and overestimations in the detection of elevation and mass changes.
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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 | 0.000 |
| 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) | 0.000 | 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".