Improvement of snow physical parameters retrieval using SAR data in the Arctic (Svalbard)
Notice bibliographique
Résumé
Arctic snow cover dynamics offer a changing face in terms of temporal duration and water equivalent, due to recent climate change conditions (Callaghan et al., 2011; Lemke & Jacobi, 2011). In this context, innovative methods are helpful to enhance management of the snow-pack resource for climate research, hydrology and human activities. The characteristics of Arctic snow are different from “temperate” snow (i.e. the Alps), in terms of thickness, internal structure, thermal conductivity, and metamorphism. Ground observation often indicates wind slab at the snow surface, internal rounded grains, depth hoar at the bottom, and often internal ice layer or at the interface with ground surface (Dominé et al., 2016). This work is part of the “Precip-A2” project (OSUG, Grenoble-France), focusing on snow and its interaction with the atmosphere, especially in terms of chemistry, radiative processes and precipitation. The the focused area is Ny-Ålesund, Svalbard, Norway (N 78°55’ / E 11° 55’). One subtask of the project is dedicated to X-band radar measurements (ground and spaceborne) to retrieve physical properties of arctic snow. Active radar (SAR) images are used in this project, as they do not suffer of clouds coverage and polar night, unlike optical sensors. Snow mapping at the melting season is well documented, due to the liquid water content at the snow surface (Nagler et al., 2000), dry snow height retrieval is only possible at the moment under the full polarimetric mode of the Radarsat-2 satellite, Canada (Dedieu et al., 2014; 2017).The aims of our specific task is to improve an innovative and recent method to retrieve snow depth from SAR image decomposition (Leinss, 2014), and to validate the output results with a consistent ground network, including a large international partnership (Fr, De, No, It). A set of 10 SAR images was provided by the German Space Agency (DLR) during winter 2017 from the TerraSAR-X sensor (3.1 cm, 9.6 GHz) in dual co-pol HH, VV (2.5 m resolution). Descending and ascending orbits were combined under 35-38° incidence angles, to avoid topographic constraints. The data were processed with a co-polar phase difference (CPD) set between HH and VV polarization, then projected to ground range by DEM from the Norwegian Polar Institute (5m resolution). A total of 400 ground measurements were used for validation, based on automatic permanent stations or manual collection. Snow height, temperature, density, and some structural information (stratigraphy) were observed on open spaces (herb tundra) and on glaciers. Some places are well documented within 20-year recording, as the Bayelva station (Boike et al., 2018) or the Austre Lovenbreen glacier (Bernard et al., 2015). Results show that the temporal evolution of the CPD values is strongly linked with the in-situ snow evolution: positive values for dry snow, negative values for recrystallization process. The best R2 correlation performances between estimated and measured snow depth are ranging from 0.51 to 0.75, assessing the interest of this method for snow mapping and hydrological application.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».