Sea ice and water classification on dual-polarized Sentinel-1 imagery during melting season
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.
Yu Zhang et al.
Yu Zhang et al.
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4 citations as recorded by crossref.
- Deep Learning Based Sea Ice Classification with Gaofen-3 Fully Polarimetric SAR Data T. Zhang et al. 10.3390/rs13081452
- River ice monitoring of the Danube and Tisza rivers using Sentinel-1 radar data L. van et al. 10.5937/gp26-39962
- Sea Ice–Water Classification of RADARSAT-2 Imagery Based on Residual Neural Networks (ResNet) with Regional Pooling M. Jiang et al. 10.3390/rs14133025
- Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized SAR Sea Ice Imagery X. Chen et al. 10.1109/TGRS.2022.3233871