###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Sensor** & **Task and Method** & **Year and Reference** \\ \hline Alaska, USA & ERS & Bathymetric mapping of shallow water & 1996 \& 2000 [1; 2] \\ Mackenzie Delta & & via time series Analysis & 1996 \& 2000 [1; 2] \\ Region, Canada & ERS & Delineation of delta ecozones via InSAR—Coherence & 2001 [3] \\ Nova Scotia, Canada & R-1 & Mapping of geomorphological units & 2001 [4] \\ Quebec, Canada & TSX & Monitoring of permafrost dynamics via InSAR & 2011 [5] \\ Herschel Island, Canada & R-2 & Monitoring of surface movements via InSAR & 2009 \& 2011 [16; 17] \\ Mackenzie Delta & ALOS & Classification of tundra land cover & 2011 \& 2014 [6; 7] \\ Region, Canada & R-2 & and shoreline types via PolSAR & 2011 \& 2014 [6; 7] \\ Lena Delta, Russia & TSX & Characterization of melt onset and & 2012 [8] \\  & R-2 & geomorphological units via PolSAR & 2012 [9] \\ Alaska, USA & TSX & Characterization of post-drainage succession & 2012 [10] \\ Sodankyla, Finland & R-2 & Identification of soil freezing and thawing states & 2014 [10] \\ Richards Island, Canada & TSX & Classification of tundra land cover via PolSAR & 2014 [11] \\ Baffin Island, Canada & R-2 & Monitoring of surface movements via InSAR & 2014 [18] \\ Northern Canada & R-2 & Modeling of phytomass via PolSAR & 2014 [12] \\ Dease Strait, Nunavut, Canada & R-2 & Classification of shoreline types via PolSAR & 2015 [13] \\ Barroro, Alaska & ALOS & Active-layer thickness estimation via InSAR & 2015 [19] \\ Mackenzie Delta & TSX & Characterization of tundra land cover via PolSAR & 2016 [14] \\ Region, Canada & R-2 & Active-layer change and & 2016 [20] \\ Northern Alaska, USA & ALOS & subsidence monitoring via InSAR & 2016 [20] \\ Northern & & Qinghai-Tibetan & ALOS & Active-layer change via InSAR & 2017 [21] \\ Qinghai-Tibetan & & & \\ Plateau & & & \\ Yamal Peninsula, Russia & TSX & Active-layer thickness estimation & 2017 [15] \\  & & & \\ \end{tabular}
\end{table} 
Table 1: Select studies that employed Synthetic Aperture Radar (SAR), polarimetric SAR (PolSAR) and InSAR (SAR interferometry) data and methods for the characterization of tundra (permafrost) landscapes and their dynamics; ERS: European Remote Sensing Satellite; R-1: Radarsat-1; TSX: TerraSAR-X; R-2: Radarsat-2; ALOS: Advanced Land Observing Satellite. and the dual HH/VV-polarization mode of TerraSAR-X. For these different datasets, several polarimetric decomposition approaches were applied, including: the Eigen-decomposition (with the features Entropy, Anisotropy and Alpha Scattering Angle) [25; 26], the Yamaguchi Decomposition [27], the Freeman-Durden Decomposition [28], and the Touzi Decomposition [29]. Two decomposition models for HH/VV-polarized data were further proposed by [30] and recently by [14]. In light of this previous research, and our preliminary investigations [6; 7; 11; 13; 14], we analyze X-, C- and L-Band PolSAR data in order to characterize scattering properties of select tundra land cover classes for a test site in the Arctic. In addition, this study incorporates quad-polarized data of ALOS-2, a novel compilation of in situ data for the test site, and a complete utilization of the Kennaugh Matrix approach, recently presented by [31]. The Kennaugh Matrix approach offers a unified framework for processing polarimetric information of different polarization modes (quad-, dual- and compact-polarized data). It can be used to represent targets both incoherently and coherently, and can be converted into all of the well-established decomposition models, for all wavelengths [25]. Information on the Kennaugh Matrix framework is provided in the subsequent Section 2.2.2 Polarimetric SAR Data and Decompositions of this manuscript and in Appendix A. The objectives of this research are therefore: First, process and analyze decomposition features of quad- and dual-polarized data of different sensors at three different frequencies.