Investigating Features of a Permafrost Landscape With Multi-Frequency Airborne SAR Tomography
Bibliographic record
Abstract
Permafrost landscapes in the Arctic are changing due to rapidly rising temperatures in the context of climate change. Monitoring permafrost landscapes is difficult because these zones are remote and affected by polar night, and also because the permafrost is itself under a vegetation cover in summer and snow in winter. Therefore Synthetic Aperture Radar (SAR) remote sensing is particularly interesting to monitor permafrost characteristics. SAR Tomography (TomoSAR) is an established technique to reconstruct the vertical reflectivity profile at a given pixel in the scene, which corresponds to the distribution of scatterers in the vertical direction at this point, as seen by the radar. TomoSAR has been previously used to investigate scattering profiles in the case of forest [1], agriculture fields [2] as well as ice [3] scenarios, but this technique has never been applied to analyze the vertical profiles of permafrost landscapes. In this study, we use the SAR airborne dataset collected by the German Aerospace Center (DLR) during a campaign in Canada extending over two seasons: summer 2018 and winter 2019 [4]. We focus in particular on the test site located at Trail Valley Creek (68.7 °N, 133.5 °W), which is in the continuous permafrost zone in the Canadian low Arctic. The site features gently rolling hills covered with tundra vegetation [5]. DLR’s airborne SAR sensor (FSAR) was operated at several frequencies (X, C and L-band), on several across-track baselines and in fully polarimetric mode, which makes the dataset suitable for a TomoSAR analysis. In summer, the vegetation-covered-ground is thawed up to a depth of several dm, while in winter, the ground is completely frozen and covered with several dm of snow. This last property is particularly interesting in the case of SAR as the radar waves are expected to penetrate to a certain extent within the frozen soil, therefore modifying the vertical reflectivity profile. Consequently, we will focus on the winter acquisitions to investigate different features of the landscape with TomoSAR profiles, and use the summer acquisitions as a reference in this new and challenging scenario. Up-to-date results of the tomographic analysis and the penetration capability over a selected permafrost region will be presented at the ESA Living Planet Symposium. [1] V. Cazcarra-Bes, M. Pardini, M. Tello, K. P. Papathanassiou, “Comparison of Tomographic SAR Reflectivity Reconstruction Algorithms for Forest Applications at L-band” 2020-01, IEEE Transactions on Geoscience and Remote Sensing, 58(1), 147-164, 2020 [2] H. Joerg, M. Pardini, I. Hajnsek, K.P. Papathanassiou, “3-D Scattering Characterization of Agricultural Crops at C-Band Using SAR Tomography”, IEEE Transactions on Geoscience and Remote Sensing, 56(7), 3976-3989, 2018 [3] F. Banda, J. Dall, S. Tebaldini, “Single and Multipolarimetric P-Band SAR Tomography of Subsurface Ice Structure”, IEEE Transactions on Geoscience and Remote Sensing, 54(5), 2832-2845, 2016 [4] I. Hajnsek, H. Joerg, R. Horn, M. Keller, D. Gesswein, M. Jaeger, R. Scheiber, P. Bernhard, S. Zwieback, “DLR Airborne SAR Campaign on Permafrost Soils and Boreal Forests in the Canadian Northwest Territories, Yukon and Saskatchewan: PermASAR”, POLINSAR 2019; 9th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, 2019 [5] I. Grünberg, E.J. Wilcox, S. Zwieback, P. Marsh, and J. Boike, “Linking tundra vegetation, snow, soil temperature, and permafrost”, Biogeosciences, 17(16), 4261-4279, 2020
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".