Évaluation du potentiel de la méthode par différence de phase copolaire de l’onde radar (CPD) en bande-X pour l’extraction de l’épaisseur du couvert nival arctique

Les changements d'état du manteau neigeux dans le contexte de réchauffement global observé doivent être pris en compte pour améliorer notre compréhension empirique des processus régissant les interactions thermiques et radiatives au sein de la cryosphère. La variabilité spatio-temporelle du couvert nival fait en sorte que l’approche d’acquisition de données par les techniques traditionnelles, telle que la prise de mesure ponctuelle sur le terrain, ne peut répondre en totalité aux questions de recherche à l’échelle globale (Bokhorst et al., 2016). Cette variabilité est une des contraintes principales dans le développement de modèles auxquelles les micro-ondes actives (radar à ouverture synthétique - RSO) peuvent répondre en surpassant les méthodes utilisant les micro-ondes passives en termes de résolution spatiale. Le suivi à haute résolution spatiale de l'épaisseur de la neige (SD) permettrait une meilleure paramétrisation des processus locaux qui dirigent la variabilité spatiale de la neige, qui est une limitation connue pour le développement de modèles dans ces régions (Domine et al., 2018; King et al., 2018; Meloche et al., 2020). L’objectif général de l’étude est donc d’évaluer le potentiel du capteur TerraSAR-X (TSX) avec la méthode de changement de phase copolaire de l’onde (CPD) pour la caractérisation du couvert nival à haute résolution spatiale. Pour l’atteinte de cet objectif, les étapes suivantes ont été réalisées : (i) Quantifier la variabilité spatio-temporelle des propriétés géophysiques et de l’épaisseur du manteau neige dans un bassin versant arctique; (iii) Quantifier l’évolution de la neige en fonction de la couverture du sol et (iii) Corréler le signal du satellite TSX à l’épaisseur de neige en fonction des informations issues des propriétés nivales mesurée en (i) et les liens avec la couverture du sol quantifié en (ii). Cette étude a été la première à effectuer une caractérisation complète de la neige couvrant l'île Herschel, combinée à des données SAR. Grâce à l’utilisation d’une carte à haute résolution spatiale du couvert végétal disponible au projet, nous avons pu quantifier la variabilité de l’épaisseur de neige ainsi que l’index topographique d’humidité du sol (TWI). Le TWI a permis de mieux comprendre l’interaction onde-sol où un angle d'incidence élevé avec un TWI élevé (>7,0) permet d'extraire une corrélation entre l’épaisseur de neige et le CPD. Les travaux futurs devraient porter sur le développement d’un seuil de sensibilité du CPD au TWI et à l'angle d'incidence afin de cartographier l'épaisseur de la neige dans de tels environnements et d'évaluer le potentiel de l'utilisation d'outils d'interpolation pour compléter les cartes d’épaisseur de neige où l’approche par CPD n’est pas possible.Abstract : Changes in the state of the snowpack in the context of observed global warming must be considered to improve our empirical understanding of the processes governing thermal and radiative interactions within the cryosphere. The spatiotemporal variability of the snowpack means that the data acquisition approach using traditional techniques, such as point measurements in the field, cannot fully address global-scale research questions (Bokhorst et al., 2016). This variability is one of the primary constraints in model development that active microwaves (synthetic aperture radar - SAR) can address by outperforming methods using passive microwaves in terms of spatial resolution. High spatial resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow, which is a known limitation for model development in these regions (Domine et al., 2018; King et al., 2018; Meloche et al., 2020). The overall objective of the study is therefore to evaluate the potential of the TerraSAR-X (TSX) sensor with the wave copolar phase difference (CPD) method for characterizing snow cover at high spatial resolution. To achieve this objective, the following steps were performed: (i) Quantify the spatio-temporal variability of geophysical properties and snowpack depth in an Arctic watershed; (ii) Quantify the evolution of snow as a function of land cover; and (iii) Correlate the TSX satellite signal to snow depth based on information from snow properties measured in (i) and the links to land cover quantified in (ii). This study was the first to perform a complete characterization of the snow covering Herschel Island, combined with SAR data. Using a high spatial resolution vegetation classification available to the project, we were able to quantify the variability of snow depth as well as the topographic soil wetness index (TWI). The TWI provided a better understanding of the electromagnetic wave-ground interaction where a high incidence angle with a high TWI (>7.0) allows us to extract a correlation between snow depth and CPD. Future work should focus on developing a threshold for the sensitivity of CPD to TWI and incidence angle to map snow depth in such environments and to evaluate the potential of using interpolation tools to supplement snow depth maps where the CPD approach is not possible

TerraSAR-X Time Series Fill a Gap in Spaceborne Snowmelt Monitoring of Small Arctic Catchments—A Case Study on Qikiqtaruk(Herschel Island), Canada

The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt

Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic

The applicability of optical satellite data to quantify coastal erosion across the Arctic is limited due to frequent cloud cover. Synthetic Aperture Radar (SAR) may provide an alternative. The interpretation of SAR data for coastal erosion monitoring in Arctic regions is, however, challenging due to issues of viewing geometry, ambiguities in scattering behavior and inconsistencies in acquisition strategies. In order to assess SAR applicability, we have investigated data acquired at three different wavelengths (X-, C-, L-band; TerraSAR-X, Sentinel-1, ALOS PALSAR 1/2). In a first step we developed a pre-processing workflow which considers viewing geometry issues (shoreline orientation, incidence angle relationships with respect to different landcover types). We distinguish between areas with foreshortening along cliffs facing the sensor, radar shadow along cliffs facing away and traditional land-water boundary discrimination. Results are compared to retrievals from Landsat trends. Four regions which feature high erosion rates have been selected. All three wavelengths have been investigated for Kay Point (Canadian Beaufort Sea Coast). C- and L-band have been studied at all sites, including also Herschel Island (Canadian Beaufort Sea Coast), Varandai (Barents Sea Coast, Russia), and Bykovsky Peninsula (Laptev Sea coast, Russia). Erosion rates have been derived for a 1-year period (2017–2018) and in case of L-band also over 11 years (2007–2018). Results indicate applicability of all wavelengths, but acquisitions need to be selected with care to deal with potential ambiguities in scattering behavior. Furthermore, incidence angle dependencies need to be considered for discrimination of the land-water boundary in case of L- and C-band. However, L-band has the lowest sensitivity to wave action and relevant future missions are expected to be of value for coastal erosion monitoring. The utilization of trends derived from Landsat is also promising for efficient long-term trend retrieval. The high spatial resolution of TerraSAR-X staring spot light mode (<1 m) also allows the use of radar shadow for cliff-top monitoring in all seasons. Derived retreat rates agree with rates available from other data sources, but the applicability for automatic retrieval is partially limited. The derived rates suggest an increase of erosion at all four sites in recent years, but uncertainties are also high