Fully polarimetric model-based decompositions are developed by accounting for the physical scattering model and experimental polarimetric SAR data acquisition processes. These decompositions offer the promising straightforward interpretation and highly improved inversion models for visualizing images of scattering scenarios optimally. However, the attempts in existing decompositions to implement the split real and imaginary components of the $T_{13}$ element of the coherency matrix have been hampered by the absence of physical models to fit the coherency matrix. In this paper, two additional physical scattering submodels are derived. The real and imaginary parts of $T_{13}$ are accounted for by implementing two newly developed physical scattering models. (One is for oriented dipole scattering and the other is for oriented quarter-wave reflection.) Furthermore, this paper is extended by implementing these physical models into a six-component scattering power model-based decomposition. To this date, the developed novel decompositions account for the maximum elements of the coherency matrix in a physical manner compared to the existing model-based decompositions. The proposed novel decomposition is tested on L-band and X-band fully polarimetric SAR data sets of the Advanced Land Observing Satellite-2/Phased Array L-band Synthetic Aperture Radar-2 and the X-band TerraSAR-X, respectively. This new decomposition produces additional two scattering submatrix components. Such scattering components are prevalent in vegetation and urban areas and even dominant over highly oriented urban scenarios. The new method enhances the truly existing double-bounce scattering contributions and reduces the overrated volume scattering from double-bounce scatterers. By comparing the results, it is found that the proposed decomposition considerably enhances the SAR image quality and its more correct visualizing presentation compared to existing decompositions. It is also found to be more robust over the oriented urban areas than the existing decompositions, resulting from the utilization of both the real and imaginary components of $T_{13}$ polarimetric information in a physical scattering manner.

Applications of fully polarimetric synthetic aperture radar (POLSAR) have increased in the past few decades. The potential of model-based decompositions is coupled with polarimetric information extraction from the POLSAR data for target identification and classification. The coherency matrix [ T ] with nine independent parameters, and associated with some physical scattering models, serves as input to these decompositions. This paper attempts to assign one such physical scattering model to the real part of T₂₃ (Re{T₂₃}) and develop a new scattering power decomposition model, called as the seven-component scattering decomposition (7SD). Previously developed scattering power models have eliminated Re{T₂₃}, assuming the orientation angle compensation condition, to reduce the number of independent [T] parameters. The proposed 7SD model has been tested on fully polarimetric SAR data sets acquired by the spaceborne Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) and airborne F-SAR, and the results are compared with the existing scattering power decompositions. The physical scattering model for Re{T₂₃} is derived from a particular configuration of dipoles (referred to as ``mixed dipole'' configuration), which gives rise to compound scattering. The mixed-dipole scattering occurs in urban areas that are highly oriented to the radar illumination direction as well as in vegetation areas. 7SD also delivers an additional mixed dipole scattering power compared to the previous six-component scattering model. The mixed-dipole scattering model reduces the contribution of volume scattering power in double-bounce predominant areas (such as oriented urban blocks), thereby imparting improved understanding of the polarimetric information contained in the coherency matrix.

This paper aims to demonstrate the potential of space-borne datasets for forest height estimation over Indian tropical forests. Fully polarimetric space-borne SAR interferometry (PolInSAR) data is acquired at the L-, C-, and X-band frequencies. The X-band data are acquired in two modes—single pass and repeat pass. The datasets are compensated for decorrelation due to SNR and varying spatial baselines. The remaining major decorrelation is the volumetric decorrelation, which is modeled using random volume over ground model to estimate the PolInSAR height for all the three frequencies. The modified Three-stage inversion algorithm is utilized for forest stand height estimation. The effect of forest biomass on height inversion accuracy is assessed. Furthermore, a first-order estimate of the absolute temporal decorrelation is demonstrated for the repeat-pass space-borne PolInSAR datasets. Extensive field validation campaigns are carried out in the tropical forest ranges. The forest height is inverted using data at all the three frequencies and validated with field measured values. The zero-temporal baseline bistatic TerraSAR-X/TanDEM-X and the L-band ALOS-2/PALSAR-2 result in a good height inversion with ${\bm{r}^2}$ and RMSE of 0.77 and 1.86 m (X-band) and 0.75 and 1.94 m (L-band), respectively. Furthermore, a comparison of estimated height of ALOS-2/PALSAR-2, TerraSAR-X, and RadarSAT-2 has been done with respect to the estimated height of TerraSAR-X/TanDEM-X. It is observed that RMSE of height inversion with respect to TerraSAR-X/TanDEM-X height is found to be 5.4 m, 7.6 m, and 12.8 m for ALOS-2/PALSAR-2, RadarSAT-2, and TerraSAR-X, respectively.

This paper deals with the retrieval of snow depth (SD) and snow water equivalent (SWE) using dual-polarization (HH-VV) synthetic aperture radar (SAR) data. The effect of different snowpack conditions on the SD and SWE inversion accuracy was demonstrated by using three TerraSAR-X acquisitions. The algorithm is based on the relationship between the SD, the co-polar phase difference (CPD), and particle anisotropy. The Dhundi observatory in the Indian Himalaya was selected as a validation test site where a field campaign was conducted for ground truth measurements in January 2016. Using the field measured values of the snow parameters, the particle anisotropy has been optimized and provided as an input to the SD retrieval algorithm. A spatially variable snow density ( ${\rho }_s$ ) was used for the estimation of the SWE, and a temporal resolution of 90 m was achieved in the inversion process. When the retrieval accuracy was tested for different snowpack conditions, it was found that the proposed algorithm shows good accuracy for recrystallized dry snowpack without distinct layering and low wetness (w). The statistical indices, namely, the root mean square error (RMSE), the mean absolute difference (MAD), and percentage error (PE), were used for the accuracy assessment. The algorithm was able to retrieve SD with an average MAE and RMSE of 6.83 cm and 7.88 cm, respectively. The average MAE and RMSE values for SWE were 17.32 mm and 21.41 mm, respectively. The best case PE in the SD and the SWE retrieval were 8.22 cm and 18.85 mm, respectively.

Himalayan glaciers have long been the focus of glaciologists across the world while trying to understand the contrasting patterns of elevation and mass changes. However, with limited number of ground observations, a comprehensive assessment of mass balance on a regional scale still remains elusive. Using the synoptic coverage of remote sensing data, we estimate a detailed spatial variation of glacier ice thickness change in the Central Himalaya of Uttarakhand using geodetic method, on a catchment scale. High resolution TerraSAR-X/TanDEM-X (12 m) and SRTM (30 m) digital elevation models (DEMs) have been utilized. The mean elevation change in the catchments is found to be −9.56 ± 0.2 m (mean annual elevation change rate is −0.68 ± 0.01 m a⁻¹). To highlight the water potential of this region, the total ice mass loss has been estimated to be 16.0 ± 1.2 Gigatonne (Gt) from 2000–2014 from eight identified catchments namely Yamunotri, Bhagirathi, Mandakini, Alaknanda, Dhauliganga, Pindar, Goriganga and Kali/Sarda. The estimated mass balance has been validated using reported observations on five selective glaciers and the coefficient of determination is 0.93. This spatial variation of ice thickness estimated in the eight catchments is critical, as the melt-water from these glaciers contribute to the upper Ganga basin.