Assessment of a TanDEM-X Digital Elevation Model of the Greenland Ice Sheet and its Zonation for Winter 2015/16
Bibliographic record
Abstract
The Greenland Ice Sheet (GIS) was the largest contributor to global sea level rise in the 2005 to 2016 period (Meredith et al. in press). Therefore, it is one of the biggest players influencing our climate and monitoring and understanding of its mechanisms and development are of highest relevance. Means to observe and measure such large areas are remote sensing. The Tandem-X mission of DLR and Airbus consists of two satellites (TerraSAR-X and TanDEM-X) that are flying in single pass formation, mapping the Earth in interferometric SAR X-band with a resolution of 12m (Zink et al. 2014). The mission has been flying in this constellation since 2010. Due to the satellite constellation and the SAR system, digital elevation models (DEMs) can be created in high resolution, unaffected by the availability of daylight and the presence of clouds. All data acquired between 2010 to 2014 (Rizzoli et al. 2017) were compled to a global elevation model. Besides this global product, several time slices were created for the GIS (Wohlfart et al. 2018). In this project, we created a DSM mosaic from winter 2015/16 acquisitions, more precisely using more than 2000 DEM scenes (Fritz at al. 2011) from end of October 2015 to beginning of February 2016. One issue of a SAR system is the penetration of the signal into snow. Additionally, water surfaces appear dark in the images due to low backscatter towards the sensor. Therefore, we used winter scenes to minimize the height error. We created an almost seamless DSM out of these scenes for 2015/16. Second, we used SAR features to delineate different snow zones. For this purpose, we used the amplitude, the height error map, and additionally ICESat and ICE Bridge data. References Fritz, T.; Rossi, C.; Yague-Martinez, N.; Rodriguez Gonzalez, F.; Lachaise, M.; Breit H. Interferometric processing of TanDEM-X data, IGARSS 2011, Vancouver, July 2011 Meredith, M.; Sommerkorn M.; Cassotta S.; Derksen C.; Ekaykin A.; Hollowed A.; Kofinas G.; Mackintosh A.; Melbourne-Thomas J.; Muelbert M.M.C.; Ottersen G.; Pritchard H.; and Schuur E.A.G.; 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press. Rizzoli, P.; Martone, M.; Gonzalez, C.; Wecklich, C.; Tridon, D.B.; Bräutigam, B.; Bachmann, M.; Schulze, D.; Fritz, T.; Huber, M.; et al. Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS J. Photogramm. Remote Sens. 2017, 132, 119–139. Wohlfart, C.; Wessel, B.; Huber, M.; Leichtle, T.; Abdullahi, S.; Kerkhoff, S.; Roth, A. TanDEM-X DEM derived elevation changes of the Greenland Ice Sheet. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 22–27 July 2018. Zink, M.; Bachmann, M.; Bräutigam, B.; Fritz, T.; Hajnsek, I.; Krieger, G.; Moreira, A.; Wessel, B. TanDEM-X: The New Global DEM Takes Shape. IEEE GRSM 2014, 2, 8–23.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".