Preprints
https://doi.org/10.5194/tc-2021-119
https://doi.org/10.5194/tc-2021-119

  26 Apr 2021

26 Apr 2021

Review status: this preprint is currently under review for the journal TC.

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

Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris Wenkai Guo et al.
  • Department of Physics and Technology, UiT The Arctic University of Norway

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. To perform this task with extended spatial and temporal coverage, 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 these two C-band SAR platforms. For this, 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 calm open water, nilas or newly formed ice (the “leads”' class). This is found to be caused by different noise floor configurations of S1 and RS2 data, which lead to different IA slopes of this class. This indicates that dataset-specific re-training is needed for leads in the cross-platform application of the classifier. Based on the classifier thus re-trained for each dataset, the classification scheme is altered to target the separation of level and deformed ice, which enables 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 in ice types represented by classes 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 data.

Wenkai Guo et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-119', Anonymous Referee #1, 19 May 2021
    • AC1: 'Reply on RC1', wenkai guo, 11 Jun 2021
  • RC2: 'Comment on tc-2021-119', Anonymous Referee #2, 21 Jun 2021
    • AC2: 'Reply on RC2', wenkai guo, 03 Jul 2021

Wenkai Guo et al.

Wenkai Guo et al.

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Short summary
This study uses radar satellite data categorized to different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors, and show an optimal way to apply cateogrization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.