Since the inception of the RADARSAT program, Canada has been continuously providing C-band Synthetic Aperture Radar (SAR) data with the launch of RADARSAT-1 in 1995 and its successor RADARSAT-2 in 2007 [
1]. The RADARSAT Constellation Mission (RCM), to be launched in late 2018, is the evolution of the RADARSAT program with the objective of ensuring data continuity, improved operational use of SAR data, and enhanced system reliability [
2]. The mission, with its three identical C-band SAR satellites, will provide daily complete coverage of Canada’s territory and marine regions, including the entire Arctic region [
3]. The short revisit time of the mission (four days) affords a range of applications that are based on the regular collection of data and creation of composite images that highlight changes over time, such as those induced by climate change, land use evolution, coastal modifications, urban subsidence, and even human impacts on local environments [
2,
3]. The compact polarimetric (CP) SAR configuration will be included in the RCM, enabling the use of CP SAR data in wide swath imagery. RCM satellites will transmit a right-circular polarization and receive two mutually coherent orthogonal (horizontal and vertical) linear polarizations (RH and RV), providing compact polarimetry as an imaging polarization option [
2,
3,
4,
5].
Operational sea ice monitoring and classification usually relies on SAR data from single- or dual-polarized beam modes, such as the ScanSAR mode of RADARSAT-2 [
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17]. However, imagery from such modes provides partial information about the radar target, which could affect the accuracy of sea ice classification. The complete information about the radar target contained in the Full Polarimetric (FP) SAR imagery can improve the sea ice classification accuracy [
18,
19,
20,
21,
22,
23]. However, the small image swath in the case of FP SAR data limits the operational sea ice classification to a local scale. Thus, the recently proposed CP SAR configuration for Earth observation could be a compromised choice for operational sea ice observation using SAR imagery [
4,
5,
24]. The main advantage of such SAR systems is that they provide increased radar target information in comparison to standard dual-pol SAR systems, while covering much greater swath widths compared to FP SAR systems [
24]. Such SAR architecture is already included in the current Indian Radar Imaging Satellite (RISAT-1) and the Japanese Advanced Land Observing Satellite (ALOS-2) carrying the Phased Array type L-band Synthetic Aperture Radar (PALSAR-2). The potential of CP SAR imagery for sea ice characterization is still an active research area. The first evaluation of CP SAR imagery in the ability to discriminate sea ice types and open water was reported in [
24]. This evaluation was conducted visually using CP SAR data derived from FP airborne SAR images acquired over the Canadian Arctic. In [
25], RISAT-1 CP SAR data was evaluated for its capability in seasonal sea ice observation over Northeastern Greenland during the melt season. A mutual information analysis was applied for the extraction of optimum CP parameters for sea ice classification. Extracted CP parameters were ingested into a trained Artificial Neural Network (ANN) classifier for sea ice classification. Results of this study showed that the first element of the Stokes vector and the Shannon entropy are useful CP parameters for sea ice classification. These results confirmed earlier findings reported in [
4] using simulated CP SAR data in dry ice winter conditions. In [
25], a comparison of the CP results with those obtained by spatially and temporally near-coincident RADARSAT-2 FP SAR data indicated promising results of the CP SAR configuration for sea ice observation. In another study [
26], a set of CP parameters extracted from RISAT-1 CP SAR data were compared with CP parameters extracted from simulated CP SAR data obtained using near collocated RADARSAT-2 FP SAR imagery over seasonal sea ice during the melt season. This study was the first to investigate the possible effect of the non-perfectly circular RISAT-1 signal on the discrimination between sea ice types. Results of this study showed that the second element of the Stokes vector, the right co-polarized ratio and the α
s angle are three CP parameters that can be affected by a possible non-circularity of the transmitted radar signal from a CP SAR system. On the other hand, [
26] found that the first element of the Stokes vector and the
(right-circular transmit and linear horizontal receive) and
(right-circular transmit and receive) backscattering coefficients are the best CP parameters for the separability between various sea ice types. These findings are also consistent with those reported earlier in [
4] for simulated CP data, but for dry ice winter conditions. Contrary to the aforementioned studies, which are directly assessing the CP data, [
27] attempted to reconstruct pseudo polarimetric covariance matrices from CP SAR data and evaluated them for sea ice classification. Herein, new reconstruction techniques for sea ice covered SAR scenes were proposed [
27]. In another study [
28], the reconstruction of FP information from CP SAR data was attempted considering two CP SAR configurations; the Circular Transmit and Linear Receive (CTLR) configuration and the π/4 (linear polarization oriented 45° with respect to the horizontal) configuration. The CTLR configuration was found to be suitable for the reconstruction of entropy, alpha angle, and co-polarizations, while the π/4 configuration was found to be suitable for the reconstruction of co-polarized correlation, first and second eigenvalues of the polarimetric coherency matrix, and cross-polarizations. Also, the CTLR configuration was recommended for sea ice classification. In [
29], it was found that the sea ice classification results using the m-χ decomposition technique are comparable to those obtained by the Pauli decomposition.
The RCM is planned to have ten SAR imaging modes and the compact polarization option will be available at all RCM imaging modes except for the FP SAR mode. Only two studies have investigated the potential of CP option from the different planned RCM SAR modes for sea ice monitoring and classification. The first study was conducted by [
4], where simulated CP SAR data from three RCM SAR modes with different spatial resolutions (medium and low) and nominal noise floors had been evaluated for the classification of first year ice (FYI; seasonal sea ice thicker than 30 cm [
6]), multiyear ice (MYI; sea ice that has survived at least one melting season [
6]), and open water in dry ice winter conditions. Results showed promising performance of the tested RCM SAR modes in the discrimination of sea ice types and open water. The expected performance of the three tested RCM SAR modes in [
4] was further validated in a second study in [
5] using a large set of simulated RCM CP SAR data with different sea ice types, during all sea ice seasons. In [
5], the effect of the radar incidence angle in the discrimination between sea ice types was also examined.
The innovative aspect of this study lies in: (1) the first evaluation of the compact polarization of the RCM High Resolution (HR) SAR mode for sea ice monitoring and (2) the performance comparison of the RCM HR mode with other RCM modes of lower spatial resolutions and noise floors. The RCM HR mode will have a nominal spatial resolution of 5 m and a nominal swath of 30 km. Although the swath of the examined mode is small, evaluation of its potential for sea ice monitoring is necessary due to its expected high Noise Equivalent Sigma Zero (NESZ) (−19 dB), compared to RCM SAR modes of medium and low spatial resolutions (NESZ from −22 dB to −25 dB). Hence, simulated CP SAR data from the RCM HR SAR mode were derived and twenty three CP parameters were extracted and evaluated for the discrimination between FYI and MYI. Histograms of FYI and MYI were created and visually interpreted and the separation between the two ice types was quantitatively estimated using the Kolmogorov-Smirnov (K-S) distance [
30]. Also, the correlation between the CP parameters was analyzed by estimating the Spearman correlation coefficient values between all possible CP parameter combinations to detect possible information redundancy. Results of the RCM HR mode were compared with the results of three RCM modes we studied in [
4]. These modes are the RCM Low Noise (LN; 100 m spatial resolution and −25 dB NESZ), Low Resolution (LR; 100 m spatial resolution and −22 dB NESZ), and Medium Resolution 50 m (MR50; 50 m spatial resolution and −22 dB NESZ) SAR modes. Finally, sea ice classification using a trained Random Forest (RF) classifier was performed on identified effective CP parameters of the RCM HR mode and results were evaluated and compared with classification results using FP SAR data.