# Satellite SAR Data-based Sea Ice Classification: An Overview
## 5 Discussion  
### Ice Classification and SAR Technical Abilities  
Due to the large extent of sea ice in polar and sub-Arctic regions as well as the difficulties with in situ observations in those regions, remote sensing is the only practical way to estimate the ice cover conditions at the space and time scales required. The qualitative improvement of the environmental monitoring on a regional and global basis using SAR can be reached with advances in satellite capabilities, as well as in processing algorithm development. The technical ability to obtain multi-polarization (full- and double-polarization) SAR images improves the classification and segmentation of sea ice compared to single polarization images. Calm water (at low wind speed) has a low reflected signal resulting in a dark tone in SAR image, almost the same as a level FYI. Detection of OW affected by strong wind on a background of sea ice is simplified using SAR images with two polarizations (HH and HV) due to the significant difference in the backscatter signal from water and ice. The quantitative measurements by [PERSON] et al. [43] substantiate that co-polarized channels provide valuable information to an ice analyst. The co- and cross-polarized ratio is known to provide an excellent measure to distinguish OW and ice types [10; 44; 45; 46]. [PERSON] et al. [44] proposed a polarimetric decomposition technique to classify sea ice of the Okhotsk Sea based on multi-polarization (HH and VV) and dual-frequency (L- and X-band) SAR-data. The VV/HH backscattering ratio provides better sea ice classification capability. The algorithm for retrieving ice thickness based on the backscattering ratio was improved by the empirical Integral-Equation Method (IEM) surface scattering model [47]. The algorithm performs better than previous retrievals, showing good agreement with in situ measurements of thickness data using L-band Pi-SAR (the multipolarized airborne SAR with X- and L-bands) with the root mean square error (MSE) of 7 cm (compared to C-band ASAR data with MSE of 13.6 cm). [PERSON] et al. [47] also concluded that L-band SAR was more useful for thickness retrieval than the C-band. More advanced dual-polarized SAR data show the possibility of a decision tree classifier for sea ice types and OW discrimination using estimated statistical thresholds for winter. Open water is unambiguously discriminated (except thin sea ice) from smooth FYI, rough FYI, and MYI using a co-polarized ratio threshold with >99% accuracy [48]. The advantages of using the RS-2 double-polarized SAR image data for automatic unsupervised segmentation of sea ice are discussed in [49]. The assessment of the full polarimetric SAR images (HH, HV, VH, and VV) is presented in [50]. The full polarimetric mode of SAR imagery contains complete information about ice cover properties and can improve the sea ice classification accuracy [10; 51; 52; 53]. An approach to analyzing the fully polarimetric images was introduced by [PERSON] and [PERSON] [54], who decomposed the received signal of fully polarimetric data into the contributions of independent scattering mechanisms (Freeman-Durden decomposition). [PERSON] et al. [55; 56] applied the proposed technique in combination with the Wishart distribution to the multifrequency and multipolarized SIR-C (the shuttle Spaceborne Imaging Radar) data and the airborne SAR imagery. The decomposition method provides a consistent physical description of the scattering processes showing the potential for the identification of ice types using polarimetric SAR data. Applied to dual-frequency data, the method can utilize the different information content of both frequencies and allows thin ice, FYI and MYI to be distinguished. The potential of supervised K-means and maximum likelihood (ML) classifications of various SAR polarimetric data to three pre-identified sea ice types and wind-roughened OW is explored in [57].