Characterizing winter landfast sea-ice surface roughness in the Canadian Arctic Archipelago using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging SpectroRadiometer

Abstract

<jats:title>Abstract</jats:title> <jats:p>Two satellite datasets are used to characterize winter landfast first-year sea-ice (FYI), deformed FYI (DFYI) and multiyear sea-ice (MYI) roughness in the Canadian Arctic Archipelago (CAA): (1) optical Multi-angle Imaging SpectroRadiometer (MISR) and (2) synthetic aperture radar Sentinel-1. The Normalized Difference Angular Index (NDAI) roughness proxy derived from MISR, and backscatter from Sentinel-1 are intercompared. NDAI and backscatter are also compared to surface roughness derived from an airborne LiDAR track covering a subset of FYI and MYI (no DFYI). Overall, NDAI and backscatter are significantly positively correlated when all ice type samples are considered. When individual ice types are evaluated, NDAI and backscatter are only significantly correlated for DFYI. Both NDAI and backscatter are correlated with LiDAR-derived roughness (<jats:italic>r</jats:italic> = 0.71 and <jats:italic>r</jats:italic> = 0.74, respectively). The relationship between NDAI and roughness is greater for MYI than FYI, whereas for backscatter and ice roughness, the relationship is greater for FYI than MYI. Linear regression models are created for the estimation of FYI and MYI roughness from NDAI, and FYI roughness from backscatter. Results suggest that using a combination of Sentinel-1 backscatter for FYI and MISR NDAI for MYI may be optimal for mapping winter sea-ice roughness in the CAA.</jats:p>

The Best of Both Worlds: Connecting Remote Sensing and Arctic Communities for Safe Sea Ice Travel

“…However, while both greyscale and classified SAR-based maps show good agreement with local understanding and observations of sea ice roughness, a decorrelation was observed by interviewees for 1) areas of freshened ice influence (i.e., MYI and riverine output areas), or 2) areas of heavy snow. The first discrepancy is due to volume scattering in low-salinity ice and the second is because C-band SAR penetrates through cold, dry snow to the ice surface (Segal et al, 2020).…”

“…Thresholds were initially determined by manual selection, based on results showing a linear relationship between C-band SAR backscatter intensity and root mean square sea ice surface roughness for FYI in the CAA (Cafarella et al, 2019;Segal et al, 2020). Domains were color-coded into classes representing sea ice snowmobile travel that is 1) easy (teal; < −20 dB), 2) slow but passable with caution (orange; ≥ −20 and ≤ −18 dB), and 3) impassable or takes considerable effort (red; > −18 dB).…”

“…SAR-derived products showing surface characteristics were shared with the communities over the course of their research project. For the Victoria Strait region of the Canadian Arctic Archipelago (CAA), detection of sea ice roughness using Sentinel-1 and the optical Multi-angle Imaging SpectroRadiometer (MISR) was evaluated using airborne-derived LiDAR data of sea ice surface heights (Segal et al, 2020). Segal et al (2020) found a strong correlation between roughness and Sentinel-1 backscatter in areas of FYI (r = 0.76) and a strong correlation between roughness and a MISR-derived roughness index in areas of MYI (r = 0.68).…”

“…For the Victoria Strait region of the Canadian Arctic Archipelago (CAA), detection of sea ice roughness using Sentinel-1 and the optical Multi-angle Imaging SpectroRadiometer (MISR) was evaluated using airborne-derived LiDAR data of sea ice surface heights (Segal et al, 2020). Segal et al (2020) found a strong correlation between roughness and Sentinel-1 backscatter in areas of FYI (r = 0.76) and a strong correlation between roughness and a MISR-derived roughness index in areas of MYI (r = 0.68). They suggest that both satellite products be integrated to produce community-relevant roughness information including FYI and MYI.…”

Cross-platform application of a sea ice classification method considering incident angle dependency of backscatter intensity and its use in separating level and deformed ice

“…Studies have isolated deformed ice using the classification of airborne and fully polarimetric, high-resolution satellite SAR data (e.g. Casey et al, 2014;Herzfeld et al, 2015), and linked sea ice roughness to wide-swath SAR backscatter intensities through correlation analyses, thus mapping sea ice deformation (Cafarella et al, 2019;Segal et al, 2020;Toyota et al, 2020). Gegiuc et al, (2018) estimated the degree of ice ridging from the classification of texture features from segmented RS2 ScanSAR Wide A (SCWA) data.…”