CIRFA seminar 15 April: Arttu Jutila (AWI) and Wenkai Guo (UiT)

On the consistency of airborne sea ice thickness, freeboard, and snow depth measurements in the late-winter Arctic: application to sea ice bulk density
Arttu Jutila (AWI), 20 minutes

Basin-scale sea ice thickness retrieval by the means of satellite radar and laser altimetry suffers from significant uncertainty resulting from imprecise knowledge of snow depth and sea ice density. Both are needed to accurately convert the observed freeboard to sea ice thickness based on the assumption of isostatic equilibrium. Due to the lack of year-round, Arctic-wide observations of snow depth and sea ice density, these parameters have to be often assumed. Most widely used values today are based on field measurements collected in the 1980s and before. However, Arctic sea ice has undergone rapid change since then due to the warming climate resulting in a thinner and younger ice cover.
The airborne AWI IceBird sea-ice campaign series carries a unique instrumentation suite allowing simultaneous, high-resolution measurements of total (ice+snow) thickness, snow depth, and snow freeboard over the ice-covered Arctic Ocean. Observing the air-snow, snow-ice, and ice-water interfaces along the survey trajectories enables not only accurate retrievals of snow depth and ice thickness on regional scales but also deriving estimates of sea ice density in the so-called new Arctic. This presentation reviews the 2017 and 2019 winter campaigns and their results.

Cross-platform application of a sea ice classification method and its use in separating level and deformed ice
Wenkai Guo (AWI), 20 minutes

Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features created by sea ice deformation using ice type classification based on several different wide-swath C-band SAR. We investigate the cross-platform transferability between training sets derived from Sentinel-1 Extra Wide (S1 EW) and RADARSAT-2 (RS2) ScanSAR Wide A (SCWA) and Fine Quad-polarimetric (FQ) data, as the same radiometrically calibrated backscatter coefficients are expected from the two SAR platforms. As a starting point, we use a novel sea ice classification method developed based on Arctic-wide S1 EW training, which considers the ice-type-dependent change of SAR backscatter intensity with incident angle (IA). This study focuses on the region near Fram Strait north of Svalbard to utilize expert knowledge of ice conditions from co-authors who participated in the Norwegian young sea ICE (N-ICE2015) expedition in the region. Separate training sets for S1 EW, RS2 SCWA and RS2 FQ data are derived using manually drawn polygons of different ice types, and are used to re-train the classifier. Results show that although the best classification accuracy is achieved for each dataset using its own training, different training sets yield similar results and IA slopes, with the exception of leads with open water, nilas or newly formed ice (the ‘leads’ class). This is found to be caused by different noise floor configurations across the IAs for S1 and RS2 data, which affects the IA slope of this class with low backscatter intensities. This indicates that dataset-specific re-training is needed for leads in the cross-platform application of the classifier. Following the local re-training, the classification scheme is altered to target the separation of level and deformed ice to enable direct comparison with independently derived sea ice deformation maps. The comparisons show that the classification of C-band SAR can be used to distinguish leads, rough young ice and level first-year ice. However, it has limited capacity in precisely delineating deformed ice and multiyear ice, likely due to the ambiguity between contribution from surface deformation and volume scattering to the higher backscatter intensities typical for both.