Hidden Image of Thawing Permafrost Project: observing seasonal variabilities of a permafrost landscape with multi-modal Synthetic Aperture Radar data
Bibliographic record
Abstract
This contribution was developed in the frame of the Hidden Image of Thawing (HIT) Permafrost Project, which is a project supported by the Helmholtz Imaging Platform. The HIT Permafrost project combines the knowledge and the project-dedicated datasets of two Helmholtz institutes, namely the Alfred Wegener Institute (AWI) and the German Aerospace Center (DLR). Its goal is to retrieve parameters of a common permafrost super-site by analyzing DLR’s multi-modal airborne Synthetic Aperture Radar (SAR) dataset in combination with AWI’s Lidar products on the same region. This poster will show the results of our common study, which were published recently as [1]. 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 DLR in the Canadian low Arctic, more specifically at Trail Valley Creek (Northwest Territories). 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: SAR frequency band (X-, C-, and L-band), season (summer and winter) and vegetation class. Winter and summer observables are compared, the influence of vegetation type is assessed, and differences between results obtained at different radar frequencies are shown. These results are a step toward the retrieval of soil and vegetation parameters in permafrost tundra environments using multimodal SAR techniques. [1] P. Saporta, A. Alonso-González, J. Hammar, I. Grünberg, J. Boike and I. Hajnsek, "Observing Seasonal Variabilities of a Permafrost Landscape With PolSAR, InSAR and Pol-InSAR," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 10733-10748, 2025, doi: 10.1109/JSTARS.2025.3551422
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".