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 thusdecrease the separability of some ice. In summary, the melting season represents the most challenging time of the year forreliable ice-water classification from SAR data. We propose here a new approach to overcome these problems by using amixture statistical distribution based conditional random fields (MSTA-CRF) model. To obtain reliable ice-waterclassification 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 statisticaldistributions (Gamma, Weibull, Alpha-Stable, etc.) to model the backscatters of ice and water and overcome the effects ofspeckle noise and wind-roughened water. Dual-polarization Extended Wide (EW) mode Sentinel-1A/1B SAR data with40 m spatial resolution is available several times per day within the Fram Strait region. Observations from June toSeptember 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 andautomatic classification of ice-water are discussed. In the experiment part, we demonstrate that the MSTA-CRF can providea good performance with about 90 % accuracy for ice-water classification, which is better than most of other state-of-theart 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 Juneto September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.

Highlights

  • Synthetic Aperture Radar (SAR) is widely used in sea ice monitoring of sea ice concentration, area, leads, ice floe, ice edge, ice classification and deformation (Dierking, 2010)

  • We propose a mixture of statistical distributions based on fully connected Conditional random fields (CRF) for ice-water classification using Sentinel-1 dual polarized SAR data with the following goals: 1. Roughened ice-free water caused by winds and ocean currents make it difficult to distinguish water from ice in the co-polarization (HH or VV) channel of SAR data

  • The SPAN takes the advantage of the two channels and is used in all following steps. (II) Prior to classification we model the SAR data by combining a Mixture of three STAtistical (MSTA) distributions (Log-Normal, Rayleigh, and Alpha-Stable) with conditional random field (CRF) theory (MSTA-CRF)

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Summary

Introduction

Synthetic Aperture Radar (SAR) is widely used in sea ice monitoring of sea ice concentration, area, leads, ice floe, ice edge, ice classification and deformation (Dierking, 2010). Newer SAR sensors introduced a cross polarization HV (horizontal transmit 15 and vertical receive polarization), or the opposite VH, channel where the backscattering coefficients are relatively independent of the water surface roughness conditions caused by high wind speed (Dierking, 2013). This can be utilized to improve water detection as it provides good contrast between wind-roughened water and ice during freeze-up periods. The co/cross polarization (i.e. HH/HV or VV/VH; Komarov et al, 2021) is useful in sea ice classification since it provides a better contrast between ice and water, and decreases the instability of backscatter value in open water areas caused by high speed wind.

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