Investigating Features of a Permafrost Landscape With Multi-Frequency Airborne SAR Tomography
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
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