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Scattering identification ambiguity has significant effects in the polarimetric SAR (Synthetic Aperture Radar) decomposition and can be reduced by discriminating the permanent and time-varying features using PolInSAR (Polarimetric SAR Interferometry) coherence. PolInSAR based polarimetric decomposition using cosine-squared distribution (PCSD decomposition) is an algorithm that demonstrates the incorporation of PolInSAR coherence in the decomposition model by assuming the distribution of forest structure and vegetation as cosine-squared. This study presents a multifrequency analysis of PCSD decomposition to evaluate its potential to rightly represent the scatterer by reducing the identification ambiguity. The false-color composites (FCC) of PCSD decomposed images are presented to examine the results. A comparison study is also conducted between the PCSD decomposition and the Yamaguchi-4 component (Y4C) decomposition to demonstrate the improvement in the representation of the ground targets. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m × 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates. [ABSTRACT FROM AUTHOR]