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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]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

Target characterization is an essential aspect of polarimetric decomposition. This technique is capable of categorizing polarimetric signatures for different types of targets based on the scattering mechanisms they follow, enabling straightforward physical interpretation of the targets. The geometric anomalies associated with human‐made targets escalate the degree of randomness in the scattering process, which causes scattering ambiguity for such targets. The second‐order model descriptors do not relate to the actual physical structure and yield predominant volume scattering power. Such urban targets are decomposed as natural targets leading to irrelevant decomposition results. The methods developed to curb the problem are unable to maintain the consistency in the decomposition modeling as they underestimate volume scattering powers for natural landcover. A hybrid decomposition model is proposed herein to solve the problem of predominant volume scattering observed from urban targets by preserving volume scattering powers for natural targets. The model uses eigenvalue‐based decomposition parameters and polarimetric interferometric synthetic aperture radar (PolInSAR) coherence to decompose ambiguous targets. The proposed model has been tested on NISAR UAVSAR PolInSAR data acquired over the Greenville region, MS, USA. The proposed model has increased the double‐bounce scattering from the urban targets and enhanced the volume scattering from natural landcover as well. By comparing the results with existing decomposition models, it is observed that the proposed model gives a more robust representation of the landcover than the compared decomposition models. Plain Language Summary: The SAR sensor illuminates the surface with polarized microwaves and receives the interacted backscattered wave. The backscattered wave is transformed into information, which is represented by a scattering matrix. Due to different geophysical properties of the targets, there is a change in the backscattered wave. Polarimetric SAR decomposition is a technique that can relate the change in backscatter to the physical structure of the target, enabling simple and robust classification of the landcover. The techniques decompose or disintegrate the scattering matrix into scattering mechanisms, and each scattering mechanism represents a broad category of different landcover. Owning to the random nature of real‐world observations, the backscatter for two different categories of landcover can be the same, such as a building and a tree. Therefore, for such cases, the polarimetric decomposition yields inaccurate classification results, and from the polarimetric point of view, this problem is referred to as overestimation of volume scattering. The reason for this is the assumptions and the design of the model. Various modifications have been proposed to solve the problem of overestimation. We find that direct modification may yield inconsistent decomposition results, which can be critical for different utilities of polarimetric decompositions. We propose a new model that associates SAR interferometry with polarimetric decompositions to solve the problem of predominant volume scattering. The new model has been tested on L‐band simulated NISAR UAVSAR data acquired over the Greenville region, MS, USA. Key Points: PolInSAR coherence to characterize targets as permanent or dynamic featuresEntropy describes the randomness of the scattering processModel‐based decompositions have been proposed for the scattering‐based characterization of manmade and natural features [ABSTRACT FROM AUTHOR]