TanDEM-X elevation data for mass balance estimation
Bibliographic record
Abstract
Ongoing global warming leads to dramatic changes in the cryosphere. In view of these rapid changes as well as the large uncertainties regarding forecasts, there is a constantly growing need for reliable and consistent information on the current state and the evolution of the ice sheets, ice caps and glaciers worldwide. In this context, satellite-based remote sensing allows cost-effective data collection even for inaccessible areas. Radar altimetry, gravimetry and laser altimetry have been widely used to detect height and mass changes. However, these systems either offer only point-based measurements or acquire at low spatial resolution. Since 2010, the single-pass SAR (Synthetic Aperture Radar) interferometry mission TanDEM-X provides area-wide information with high spatial resolution of 0.4 arcsec (i.e. about 12 m) at a global scale. The huge amount of globally consistent elevation data could contribute to meet the urgent need for information with high spatial resolution to monitor the dynamics of the cryosphere. However, the data suffers from an elevation bias up to several meters due to signal penetration. The penetration bias mainly depends on snow and ice characteristics as well as on the continuously changing acquisition geometry and underlies inter- and intra-annual variations. In this regard, we quantify the impact of X-band InSAR penetration bias on mass balance estimation based on TanDEM-X digital elevation models (DEM). In detail, a multiple regression model based on interferometric coherence and backscatter intensity is used to correct a time series of TanDEM-X DEMs acquired between 2010 and 2018 over the Devon Ice Cap (Nunavut, Canada), from which changes in elevation and mass are derived. For validation, GPS and laser altimeter measurements are used, which show good agreement between predictions and observations, with mean deviations between 0.01 and 0.20 m. The predictions well reflect the inter- and intra-annual variations, with a mean penetration bias varying between 2.44 and 3.40 m. Regarding mass balance estimation, corrected DEMs yield up to 50% higher mass loss using corrected TanDEM-X DEMs. Our results demonstrate the significant impact of X-band InSAR penetration bias on measurements of elevation change and mass loss and thus, the importance of correcting the TanDEM-X elevation data with respect to cryosphere and climate impact research.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".