Airborne SAR/Interferometric Radar Altimeter System (ASIRAS) - Kalibration, Validation and Interpretation of measuring results
Bibliographic record
Abstract
As part of the CryoSat Calibration and Validation Experiment (CryoVEx) this work is related to the calibration and validation of ESA's AirborneSAR/Interferometric Radar Altimeter System (ASIRAS). ASIRAS was designed to simulate the CryoSat SAR Interferometric Radar Altimeter (SIRAL) for reasons of pre-launch and simultaneous accuracy and backscatter studies by using a similar instrument. The purpose of the ASIRAS calibration and data validation is to provide confidence in estimates of the uncertainty of the ASIRAS data products such as surface elevation and phase difference. Error estimates of the ASIRAS surface elevation in different snow zones were assessed using simultaneously acquired reference surfaceheights from an airborne laser scanner (reference digital elevation model). The phase difference and coherence tests were performed using data acquired over corner reflectors. The data were collected over the main validation sites in Greenland (EGIG line), Austfonna (Svalbard) and Devon Ice Cap (Canadian Arctic) during five CryoVEx sea- and land icecampaigns in 2004, 2005, 2006 and 2007. Prior to the validation of the ASIRAS data, a calibration and dating concept was designed and implemented. The latter was necessary in order to align all instruments (GPS, INS, laser scanner and ASIRAS) exactly to one reference time(GPS-UTC). Several dating and instrument uncertainties (e.g. systematic errors in the laser scanner raw data and ASIRAS time shifts) could be solved and corrected. In order to derive the surface elevation, processing software for the determination of laser squinting angles, the geocoding of the laser heights, the interferometric processing, and the retracking of the ASIRAS echoes was developed. The accuracy of the ASIRAS surface elevation was examined along selected \mbox{2 km to 3 km} long profiles for four different surface types (runway, dry snow and percolation zone of Greenland, and accumulation zone of Austfonna) byusing five different retracker algorithms. An overall rms accuracy of 0.05~m was found, however with a varying offset to the reference DEM reaching from 0.0~m for the runway to 1.50~m for the dry snow zone. The findings are related to roughness and backscatter variations along the chosen profiles. In general, higher accuracy could be found in regions of lower radar wavelength related surface roughness. The large offset in the dry snow zone is most likely not caused by surface roughness but related to volume scattering within the snowpack. Furthermore, in\citet{helm_grl_2007} the feasibility was demonstrated to derive the winter snow accumulation in the percolation zone of Greenland based on ASIRAS data. In the high altitude mode, ASIRAS allows the determination of across track surface slopes by means of interferometric processing.The analysis showed a difference of -0.43 /- 0.81 m to the reference DEM with high rms values related to low coherence and unwrapping errors. Additional measurements, possibly at higher flight levels, are needed to investigate the overall low coherence. In summary, this comprehensive study proves the concept of ASIRAS as the core instrument for the Cryosat satellite validation. In addition, ASIRAS on its own may contribute significantly to a fundamental understanding of microwave signatures over land and sea ice. In this context, a forward model which simulates the ASIRAS response over different layered and non layered media will help to better understand and exploit the presented findings.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".