Abstract
Abstract. 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 of SAR data. Also, 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 C-band sensors. We use a novel sea ice classification method developed based on Arctic-wide S1 EW training, which considers per-ice-type incident angle (IA) dependency of backscatter intensity. This study focuses on the region near Fram Strait north of Svalbard to utilize expert knowledge of ice conditions during the Norwegian young sea ICE (N-ICE2015) expedition. Manually drawn polygons of different ice types for S1 EW, RS2 SCWA and RS2 FQ data are used to retrain the classifier. Different training sets yield similar classification results and IA slopes, with the exception of leads with calm open water, nilas or newly formed ice (the “leads” class). This is caused by different noise floor configurations of S1 and RS2 data, which interact differently with leads, necessitating dataset-specific retraining for this class. SAR scenes are then classified based on the classifier retrained for each dataset, with the classification scheme altered to separate level from 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 areas of ice divergence occupied by leads, young ice and level first-year ice (LFYI). However, it has limited capacity in delineating areas of ice deformation due to ambiguities between ice types with higher backscatter intensities. This study provides reference to future studies seeking cross-platform application of training sets so they are fully utilized, and we expect further development of the classifier and the inclusion of other SAR datasets to enable image-classification-based ice deformation detection using only satellite SAR.
Highlights
The general thinning of Arctic sea ice in recent decades has led to reduced internal strength (Landrum and Holland, 2020), which together with increased wind forcing has caused accelerated ice drift speed (Spreen et al, 2011) and increased ice deformation (Rampal et al, 2009, 2011; Itkin et al, 2017)
Regional training sets (Fig. 5a–c, columns S1, RS2 ScanSAR Wide A (SCWA) and RS2 fine quad-polarimetric (FQ)) yield average CAs significantly higher than the original Gaussian incident angle (GIA) classifier (Fig. 5a– c, columns O), which is expected as regional validation is used
The original GIA classifier yields classification maps dominated by deformed FYI (DFYI) and multi-year ice (MYI), while visual inspection of the synthetic aperture radar (SAR) RGB composites (Fig. 6a1–c1) indicates significantly more prominent existence of level first-year ice (LFYI), rough young ice and leads
Summary
The general thinning of Arctic sea ice in recent decades has led to reduced internal strength (Landrum and Holland, 2020), which together with increased wind forcing (as indicated by atmospheric reanalyses) has caused accelerated ice drift speed (Spreen et al, 2011) and increased ice deformation (Rampal et al, 2009, 2011; Itkin et al, 2017). Ice deformation influences ice surface and bottom roughness and affects the transfer of momentum between the atmosphere and the ocean (Cole et al, 2017; Martin et al, 2016), preconditions the ice layer for more lateral melt (Arntsen et al, 2015; Hwang et al, 2017; Graham et al, 2019), and increases ice drift speed due to reduced floe sizes following ice breakups (Toyota et al, 2006; Steer et al, 2008; Asplin et al, 2012). W. Guo et al.: Cross-platform sea ice classification considering per-class IA effect impact on ice primary productivity, as it provides a sheltered growth environment for ice flora and fauna in deformed ice (Gradinger et al, 2010; Fernández-Méndez et al, 2018; Graham et al, 2019) and favorable light conditions under lead ice (Assmy et al, 2017), creating biological hotspots. Reliable examination of sea ice deformation is crucial for the evaluation and modeling of Arctic sea ice changes
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