Preprints
https://doi.org/10.5194/egusphere-2024-178
https://doi.org/10.5194/egusphere-2024-178
07 Feb 2024
 | 07 Feb 2024

Pan-Arctic Sea Ice Concentration from SAR and Passive Microwave

Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner

Abstract. Arctic sea ice monitoring is a fundamental prerequisite for anticipating and mitigating the impacts of climate change. Satellite-based sea ice observations have been subject to intense attention over the last few decades, with passive microwave (PMW) radiometers being the primary sensors for retrieving pan-Arctic sea ice concentration, albeit with coarse spatial resolutions of a few or even tens of kilometers. Space-borne Synthetic Aperture Radar (SAR) missions, such as Sentinel-1, provide dual-polarized C-band images with <100 meter spatial resolution, which are particularly well-suited for retrieving high-resolution sea ice information. In recent years, deep learning-based vision methodologies have emerged with promising results for SAR-based sea ice concentration retrievals. Despite recent advancements, most contributions focus on regional or local applications without empirical studies on the generalization of the algorithms to the pan-Arctic region. Furthermore, many contributions omit uncertainty quantification from the retrieval methodologies, which is a prerequisite for the integration of automated SAR-based sea ice products into the workflows of the national ice services, or for the assimilation into numerical ocean-sea-ice coupled forecast models. Here, we present ASIP (Automated Sea Ice Products): a new and comprehensive deep learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from Sentinel-1 SAR and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave observations at a pan-Arctic scale for all seasons. We compiled a vast matched dataset of Sentinel-1 HH/HV imagery and AMSR2 brightness temperatures to train ASIP with regional ice charts as labels. ASIP achieves an R2-score of 95 % against a held-out test dataset of regional ice charts. In a comparative study against pan-Arctic ice charts and PMW-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region. Additionally, the comparison reveals that ASIP consistently produces relatively higher sea ice concentration than the PMW-based sea ice product, with mean biases ranging from 1.45 % to 8.55 %, and that the discrepancies are primarily attributed to disparities in the marginal ice zone.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-178', Anonymous Referee #1, 27 Mar 2024
    • AC2: 'Reply on RC1', Tore Wulf, 24 Jun 2024
  • RC2: 'Comment on egusphere-2024-178', Anonymous Referee #2, 24 Apr 2024
    • AC1: 'Reply on RC2', Tore Wulf, 24 Jun 2024
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner

Viewed

Total article views: 447 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
311 113 23 447 15 10
  • HTML: 311
  • PDF: 113
  • XML: 23
  • Total: 447
  • BibTeX: 15
  • EndNote: 10
Views and downloads (calculated since 07 Feb 2024)
Cumulative views and downloads (calculated since 07 Feb 2024)

Viewed (geographical distribution)

Total article views: 454 (including HTML, PDF, and XML) Thereof 454 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Jul 2024
Download
Short summary
Here, we present ASIP (Automated Sea Ice Products): a new and comprehensive deep learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from Sentinel-1 SAR and AMSR2 passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and passive microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.