Preprints
https://doi.org/10.5194/tc-2021-85
https://doi.org/10.5194/tc-2021-85

  30 Mar 2021

30 Mar 2021

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

Sea ice and water classification on dual-polarized Sentinel-1 imagery during melting season

Yu Zhang1,3, Tingting Zhu2,3, Gunnar Spreen3, Christian Melsheimer3, Marcus Huntemann3, Nick Hughes4, Shengkai Zhang1, and Fei Li1 Yu Zhang et al.
  • 1Wuhan University, Chinese Antarctic Center of Surveying and Mapping, China
  • 2Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, China
  • 3University of Bremen, Institute of Environmental Physics, Germany
  • 4Norwegian Meteorological Institute - Ice Service, Tromsø, Norway

Abstract. We provide a new sea ice and water classification product with high spatial and high temporal coverage using Sentinel-1 Synthetic Aperture Radar (SAR) data. The classification is applied in the Fram Strait region in the Arctic during melting seasons, when the contrast between backscatter intensities of different ice types observed by SAR is reduced due to the melted ice surface and wet snow on sea ice. The wet or melted snow strongly reduces the SAR penetration depth and thus suppresses the volume scattering contribution of sea ice. Furthermore, within the marginal sea ice zone (MIZ)
ambiguities between ice and water can result from the effects of winds and ocean currents on the ocean SAR backscatter. 
On the other hand, under calm conditions the contrast between thin ice and flat open water can be reduced, and thus
decrease the separability of some ice. In summary, the melting season represents the most challenging time of the year for
reliable ice-water classification from SAR data. We propose here a new approach to overcome these problems by using a
mixture statistical distribution based conditional random fields (MSTA-CRF) model. To obtain reliable ice-water
classification whilst maintaining a fast computation time suitable for operational applications, the MSTA-CRF adopts a 
superpixel approach in the fully connected CRF model. The MSTA-CRF is a semantic model, which integrates statistical
distributions (Gamma, Weibull, Alpha-Stable, etc.) to model the backscatters of ice and water and overcome the effects of
speckle noise and wind-roughened water. Dual-polarization Extended Wide (EW) mode Sentinel-1A/1B SAR data with
40 m spatial resolution is available several times per day within the Fram Strait region. Observations from June to
September during the six years 2015–2020 are collected and classified into ice and water categories. The classification 
performance of algorithm is evaluated using ice charts from the Ice Service at the Norwegian Meteorological Institute
(MET Norway). The methods of training sample selection, and their application to processing large data volumes and
automatic classification of ice-water are discussed. In the experiment part, we demonstrate that the MSTA-CRF can provide
a good performance with about 90 % accuracy for ice-water classification, which is better than most of other state-of-the
art algorithms. Compared with the 89 GHz microwave radiometer ASI sea ice concentration product, the sea ice extent in 
Fram Strait derived from MSTA-CRF algorithm is lower during melting seasons from 2015 to 2020, and the monthly June
to September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.

Yu Zhang 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-2021-85', Anonymous Referee #1, 05 May 2021
    • AC1: 'Reply on RC1', Yu Zhang, 28 Jun 2021
  • RC2: 'Comment on tc-2021-85', Anonymous Referee #2, 13 May 2021
    • AC5: 'Reply on RC2', Yu Zhang, 01 Jul 2021
  • RC3: 'Comment on tc-2021-85', Anonymous Referee #3, 22 May 2021
    • AC3: 'Reply on RC3', Yu Zhang, 28 Jun 2021

Yu Zhang et al.

Yu Zhang et al.

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Short summary
We developed an algorithm for ice-water classification using Sentinel-1 data during melting seasons in the Fram Strait. The proposed algorithm has the OA of nearly 90 % with STD less than 10 %. The comparison of sea ice concentration demonstrate that it can provide detailed information of sea ice with the spatial resolution of 1km. The time series shows the average June to September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.