Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements

Abstract

In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements.

Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

“…24 Table 2.2. Current and past satellites of the most commonly used bands in sea ice monitoring and their utility and advantage (Dierking and Busche 2006;Dierking 2013;Johannessen et al 2016;Singha et al 2018 Table 3.2. Pearson's correlation coefficients (r) between rms surface roughness, and backscatter coefficients and polarimetric ratios during winter.…”

“…0.75-1.2 1.7-2.5 2.5-4 4-8 8-15 15-30 60-120 Table 2.2. Current and past satellites of the most commonly used bands in sea ice monitoring and their utility and advantage (Dierking and Busche 2006;Dierking 2013;Johannessen et al 2016;Singha et al 2018).…”

Towards Operational Sea Ice Type Retrieval Using L-Band Synthetic Aperture Radar

“…The Artificial Neural Network (ANN) has been used for sea ice classification for X-, C-and L-band SAR images and the setup of the network is explained in [5,6]. The ANN has already shown to produce high accuracy results for sea ice classification when a self-trained-validated classification is performed [5] and a comparison with spatially and temporally overlapping high resolution airborne laster scanner measurements confirmed high accuracy sea ice classification [6]. The incidence angle dependency for the different sea ice types as noted by several previous studies, e.g.…”

Sea-ice information and forecast needs for industry maritime stakeholders

“…Detection within pack ice is difficult, due to the surrounding radar that returns from ice edges and ridges. More recently, the capability of some SAR sensors to provide fully polarimetric data has allowed improved classification of icebergs within sea-ice, and the reliability of this would be enhanced by a multi-frequency approach, particularly with the addition of lower frequency, L-band, SAR information (Johansson et al, 2018;Singha et al, 2018). This differs from the standard single polarization, Constant False Alarm Rate (CFAR) approach where a pixel comparison is made with the characteristics of the surrounding background (Buus-Hinkler et al, 2014).…”