## 5 Conclusions  
Results from this analysis indicate that the quad-polarized Kennaugh Matrix elements K0, K1, K3, K4 and K7 were highly correlated with corresponding elements of the dual-polarized Kennaugh Matrices; therefore, to a certain extent, dual-polarized data provide a useful substitute for the full quad-polarized data. The Kennaugh Matrix offers a unified framework to store, process and analyze PolSAR data in the same manner, and the Kennaugh elements offer comparable information from dual- or quad-polarized data. Thus, there is nearly no difference between the two acquisitions modes when using Kennaugh elements. Among the investigated Model-based Decompositions and the Eigen-decompositions the features of the Two Component Decompositions models of [14] (based on HH/VV dual-polarized data) were highly correlated with the corresponding elements of the Yamaguchi Decomposition (based on quad-polarized data). Independent of the wavelength and polarization mode, the Eigen-decomposition features Entropy and the Alpha scattering angles were highly correlated and of less value for class separation. Therefore, this approach does not seem suitable for this low depolarizing as well as low entropy environment. The X-, C- and L-Band data exhibit distinct scattering characteristics for the different land cover classes. The PolSAR data of all wavelengths are sensitive to the land cover classes: open water (OL), sand (BS) and mixed sediment (BM); L-Band data were most sensitive to the BW; X- and C-Band features were most sensitive to the inundated low-lying tundra (WI) and wetland WT, and herb dominated tundra (HT) and shrub dominated tundra (ST). The use of shorter wavelengths (X- and C-Band) is beneficial for characterizing wetland vegetation. The L-Band data exhibited the differences of the bare ground classes BS, BM and BW best. Thus, in accordance to previous studies L-Band data are favorable for InSAR applications in this region, due to the observed distinct surface scattering and the low volume scattering contribution. In contrast, C- and X-Band data are favorable for the characterization of the tundra land cover due to the observed sensitivity of the cross- and co-polarized information to tundra vegetation. Nevertheless, the assessment of the class separability pointed out that PolSAR data of any wavelength--also of L-Band--were valuable for class separation and PolSAR information is beneficial for class discrimination. The results showed that quad-polarized data of ALOS-2 and R-2 offered the best separation of the land cover classes, followed by the HH/VV-, HH/HV- or VV/VH-polarized data of ALOS-2, R-2 and TSX. Further, full PolSAR information is better suited for class separation than less diverse polarimetric feature spaces, like all dual-polarimetric measurements (HH/VV, HH/HV or VV/VH). The Kennaugh Matrices offered the highest class separability among the investigated decompositions, and among the ten elements of the quad-polarized C- and L-Band Kennaugh Matrix the elements K0, K1, K2, K3 and K4 were found to be most valuable for class discrimination. This also indicates that the phase-relation between HH and VV (K3, K4) provides crucial information for separating the investigated tundra land cover classes, since it contains the distinction of surface from diplane scattering. Further, the intensity-based information of the elements K0, K1 and K2, which explain the diagonal elements of the Coherency Matrix, are favorable for class discrimination. In light of the results presented in this manuscript, future work should focus on investigating the combined use of short- and long-wave PolSAR data, e.g., of C-/X-Band and L-Band. It is anticipated that such multi-frequency data will provide complementary information useful for accurate classification and the description of land surface parameters, as well as biophysical parameters of the tundra vegetation.