Correcting atmospheric effects on InSAR with MERIS water vapour data and elevation-dependent interpolation model
Bibliographic record
Abstract
The propagation delay when radar signals travel from the troposphere has been one of the major limitations for the applications of high precision repeat-pass Interferometric Synthetic Aperture Radar (InSAR). In this paper, we first present an elevation-dependent atmospheric correction model for Advanced Synthetic Aperture Radar (ASAR—the instrument aboard the ENVISAT satellite) interferograms with Medium Resolution Imaging Spectrometer (MERIS) integrated water vapour (IWV) data. Then, using four ASAR interferometric pairs over Southern California as examples, we conduct the atmospheric correction experiments with cloud-free MERIS IWV data. The results show that after the correction the rms differences between InSAR and GPS have reduced by 69.6 per cent, 29 per cent, 31.8 per cent and 23.3 per cent, respectively for the four selected interferograms, with an average improvement of 38.4 per cent. Most importantly, after the correction, six distinct deformation areas have been identified, that is, Long Beach–Santa Ana Basin, Pomona–Ontario, San Bernardino and Elsinore basin, with the deformation velocities along the radar line-of-sight (LOS) direction ranging from −20 mm yr−1 to −30 mm yr−1 and on average around −25 mm yr−1, and Santa Fe Springs and Wilmington, with a slightly low deformation rate of about −10 mm yr−1 along LOS. Finally, through the method of stacking, we generate a mean deformation velocity map of Los Angeles over a period of 5 yr. The deformation is quite consistent with the historical deformation of the area. Thus, using the cloud-free MERIS IWV data correcting synchronized ASAR interferograms can significantly reduce the atmospheric effects in the interferograms and further better capture the ground deformation and other geophysical signals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".