Observing Seasonal Variabilities of a Permafrost Landscape With PolSAR, InSAR and Pol-InSAR
Bibliographic record
Abstract
Synthetic aperture radar (SAR) remote sensing is an established approach for observing Earth processes. The combination of different types of SAR acquisitions in polarimetric, interferometric, and polarimetric-interferometric frameworks is well studied for retrieving parameters of certain landscape features, such as forests and glaciers. These frameworks have only been rarely applied to permafrost regions, characterized by particular dielectric and structural properties, in particular frozen ground. Here, we investigate the effect of permafrost characteristics on the different SAR imaging modes. This study performs an analysis of the SAR data retrieved during an airborne campaign conducted by the German Aerospace Center (DLR) in the Canadian low Arctic. Established polarimetric SAR, SAR interferometry, and polarimetric SAR interferometry techniques are applied on the region of interest. For each of these techniques, results are analyzed in several dimensions. Winter and summer observables are compared, the influence of vegetation type is assessed, and differences between results obtained at different radar frequencies are shown. This study leads the path toward the retrieval of soil and vegetation parameters in permafrost tundra environments using multimodal SAR techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".