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In this article, an algorithm for snow depth (SD) and snow water equivalent (SWE) retrieval is proposed based on a polarimetric synthetic aperture radar (SAR) decomposition model and field measured snow data. The field campaigns were conducted at the Dhundi observatory (in the Indian Himalaya) in January 2016 and 2018. The field-measured data are used here to build a linear regression between wetness (w) and the imaginary part of the snow permittivity (ε''), and the validation of retrieved SD and SWE. The snow density (ρs) and w are calculated with a generalized volume parameter derived using a theoretical model and SAR data (coherency matrix). These snow parameters and the field-based regression relating w and ε'' are eventually used for the SD and SWE retrieval. Three TerraSAR-X scenes of the quad-polarization X-band data acquired in January 2016 are used to study the effect of the snow conditions on the accuracy of the proposed algorithm. The mean absolute error (MAE), root-mean-square error (RMSE), and index of agreement (IOA) for SD are 4.84 cm, 5.12 cm, and 0.71, respectively. On the other hand, for SWE, it is 1.42 cm, 1.53 cm, and 0.71, respectively. [ABSTRACT FROM AUTHOR]

The $2\times 2$ relative scattering matrix $[S]$ is characterized by five elements (three amplitudes and two relative phases). A more suitable representation of the data in terms of power expression for target identification exists in the $3\times 3$ Pauli-based polarimetric coherency matrix $[T]$. The aim of this work is to expand the physical interpretation of $[T]$ elements in terms of a physical scattering mechanism, which has not yet been achieved completely. Compound scattering matrix nature is considered to interpret the elements of $[T]$. Compound scattering phenomenon, and thereby generated matrices, are verified with measured compound scattering matrices in an anechoic chamber by comparing measured and theoretical polarization signature responses. The behavior of measured polarization signature(s) is consistent with the theoretical/hypothetical polarization signature(s). It is also observed that the combination of two or more dipole-type targets formed the scattering generators and helped to reveal the unknown scattering mechanisms in the coherency matrix. Furthermore, the existence of compound scattering phenomenon is also justified with airborne and spaceborne fully polarimetric synthetic aperture radar (POLSAR) data by implementing recent physical model-based scattering power decomposition methods. [ABSTRACT FROM AUTHOR]

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 $T_{23}$ ($\text {Re}\{{T}_{23}\}$) and develop a new scattering power decomposition model, called as the seven-component scattering decomposition (7SD). Previously developed scattering power models have eliminated $\text {Re}\{T_{23}\}$ , 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 $\text {Re}\{T_{23}\}$ 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. [ABSTRACT FROM AUTHOR]

Low-frequency synthetic aperture radar (SAR) sensors are prone to ionospheric irregularity structures that affect the amplitude and/or phase of the radar signal. Azimuthal striping caused by amplitude scintillation in SAR data is an initial observation of such effects. In the absence of any physical model and/or technique to mitigate scintillation stripes, we have applied a 2-D fast Fourier transform (FFT) approach for improved interpretation and target identification in fully polarimetric SAR (POLSAR) model-based decomposition scattering powers. Few scenes of fully polarimetric Advanced Land Observation Satellite-Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) and ALOS-2/PALSAR-2 under different ionospheric conditions are acquired and used in this study. As a reference, data sets over the same areas are also acquired on different dates with negligible ionospheric activity (ionospheric quiet day). All the data sets are corrected for the Faraday rotation angle ($\Omega$), which is estimated using the Bickel–Bates approach. Three correction strategies of scintillation-affected model-based scattering power decomposition images are presented. The strategies (zero masking, thresholding, and averaging-post-thresholding) are implemented on 2-D FFT of scattering power images. An intercomparison of corrected decomposition results from the correction strategies demonstrates the capability of 2-D-FFT-based correction strategies to improve the dominant scattering component by 3%–5% for homogeneous terrains. The correction method based on averaging-post-thresholding gives the best results that are further tested by performing a supervised classification. The overall accuracy and kappa coefficient (${OA}, \hat {k}$) of the averaging-post-thresholding technique (72.87%, 0.59) is comparable to those of the reference data (78.74%, 0.69). [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a novel algorithm for deriving snowpack density over the snow-covered region of the Himalayas. The analysis utilizes fully polarimetric TerraSAR-X synthetic aperture radar data sets, field observations, and other ancillary information for the retrieval of snowpack density. The algorithm involves the development of a new generalized hybrid decomposition model. The generalized volume scattering parameter from the decomposition model is inverted for snow density estimation. A few field data measurements’ campaigns were carried out, within near-real time of satellite passing over the area, to collect various parameters such as temperature, water content, and the density of the snowpack at varying depths. These field observations are further used for validation of the results obtained from the inversion algorithm. It is also found that the model-estimated snowpack density is highly congruent with the field-measured snowpack density. The mean absolute error of snowpack density, root-mean-square error, and index of agreement are found to be 9.9 kg/ \textm^3 , 10 kg/ \textm^3 , and 0.96, respectively, which are well within the acceptable range. [ABSTRACT FROM PUBLISHER]

A novel methodology is proposed in this paper for the estimation of snow surface dielectric constant from polarimetric SAR (PolSAR) data. The dominant scattering-type magnitude proposed by Touzi et al. is used to characterize scattering mechanism over the snowpack. Two methods have been used to obtain the optimized degree polarization of a partially polarized wave: 1) the Touzi optimum degree of polarization given by Touzi  et al. in 1992. The maximum (pmax) and the minimum (pmin) degree of polarizations are obtained along with the optimum transmitted polarizations (\chi t^{\rm{opt}},\psi t^{\rm{opt}}). 2) The adaptive generalized unitary transformation-based optimum degree of polarization mE^{\rm{opt}} proposed by Bhattacharya  et al. in 2015. This optimum degree of polarization is obtained either by a real or a complex unitary transformation of the 3 \times 3 coherency matrix. These two degrees of polarizations are used and compared in this study as a criterion to select the maximum number of pixels with surface dominant scattering. These pixels were then used to invert the snow surface dielectric constant. It has been observed that the mE^{\rm{opt}} have increased the number of pixels for inversion by \approx \text{9--10}\% compared to the original data. On the other hand, it was observed that the Touzi maximum degree of polarization pmax has increased the number of pixels for inversion by \approx 2\% compared to that of mE^{\rm{opt}}. The proposed methodology is applied to Radarsat-2 PolSAR C-band datasets over the Indian Himalayan region. It is observed that the correlation coefficient between the measured and the estimated snow surface dielectric constant is 0.95 at 95% confidence interval with a root mean square error of 0.20. [ABSTRACT FROM PUBLISHER]

An adaptive general four-component scattering power decomposition method (AG4U) is proposed in this letter. The degree of polarization $m$ is used as a criterion for the adaptive nature of the proposed decomposition. In this method, one among the two complex special unitary transformation matrices is chosen to transform a real unitary rotated coherency matrix based on the largest value of $m$. This transformed matrix is then utilized for the existing Yamaguchi et al. four-component decomposition scheme with an extended volume scattering model. The proposed decomposition is applied to Radarsat-2 full-polarimetic C-band data over San Francisco and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) full-polarimetric L-band data over the Hayward Fault in California. The scattering powers estimated from the decomposition techniques of Yamaguchi et al. (Y4O), Singh et al. (G4U) , and AG4U are compared. AG4U shows appreciable improvements in the scattering powers, particularly in urban areas oriented about the radar line of sight compared with the Y4O and G4U decompositions. It also shows reduced percentage of pixels with negative powers considerably compared with the Y4O decomposition. [ABSTRACT FROM PUBLISHER]

In this paper, a general scheme for complete model-based decomposition of the polarimetric synthetic aperture radar (POLSAR) coherency matrix data is presented. We show that the POLSAR coherency matrix can be completely decomposed into three components contributed by volume scattering and two single scatterers (characterized by rank-1 matrices). Under this scheme, solving for the volume scattering power amounts to a generalized eigendecomposition problem, and the nonnegative power constraint uniquely determines the minimum eigenvalue as the volume scattering power. Furthermore, in order to discriminate the remaining components, we propose two approaches. One is based on eigendecomposition, and the other is based on model fitting, both of which are shown to properly resolve the surface and double-bounce scattering ambiguity. As a result, this paper in particular contributes to two pending needs for model-based POLSAR decomposition. First, it overcomes negative power problems, i.e., all the decomposed powers are strictly guaranteed to be nonnegative; and second, the three-component decomposition exactly accounts for every element of the observed coherency matrix, leading to a complete utilization of the fully polarimetric information. [ABSTRACT FROM PUBLISHER]

This study discusses the capability assessment of fully polarimetric L-band spaceborne synthetic aperture radar (SAR) for detection of seasonal snow covered areas. In this paper, ALOS PALSAR time-series data sets obtained in quad-pol modes have been investigated to evaluate the polarimetric signal scattered from a snow-covered mountainous ecosystem in Niigata, Japan. Results show that changes in the scattering mechanism across the various snow states can be identified from polarimetric parameters. In particular, different polarimetric parameters offer complementary information on the snow properties. Based on the characteristic seasonal changes of polarimetric parameters, a new method to map snow-covered areas is proposed in this study using an information fusion approach. Snow extent can be identified successfully by combining polarimetric indices with an overall accuracy of 74.4% as compared with in situ measurements and 77.0% as compared with optical images. [ABSTRACT FROM AUTHOR]

This paper presents a new general four-component scattering power decomposition method by implementing a set of unitary transformations for the polarimetric coherency matrix. There exist nine real independent observation parameters in the 3 \times 3 coherency matrix with respect to the second-order statistics of polarimetric information. The proposed method accounts for all observation parameters in the new scheme. It is known that the existing four-component decomposition method reduces the number of observation parameters from nine to eight by rotation of the coherency matrix and that it accounts for six parameters out of eight, leaving two parameters (i.e., the real and imaginary parts of T13 component) unaccounted for. By additional special unitary transformation to this rotated coherency matrix, it became possible to reduce the number of independent parameters from eight to seven. After the unitary transformation, the new four-component decomposition is carried out that accounts for all parameters in the coherency matrix, including the remaining T13 component. Therefore, the proposed method makes use of full utilization of polarimetric information in the decomposition. The decomposition also employs an extended volume scattering model, which discriminates volume scattering between dipole and dihedral scattering structures caused by the cross-polarized HV component. It is found that the new method enhances the double-bounce scattering contributions over the urban areas compared with those of the existing four-component decomposition, resulting from the full utilization of polarimetric information, which requires highly improved acquisitions of the cross-polarized HV component above the noise floor. [ABSTRACT FROM PUBLISHER]

This paper reflects the polarimetric synthetic aperture radar (POLSAR) data utilization for near-real-time earthquake and/or tsunami damage assessment in urban areas. In order to show the potential of the fully polarimetric high-resolution polarimetric SAR (POLSAR) image data sets, a four-component scattering power decomposition scheme has been developed and applied to monitor near-real-time earthquake and tsunami disaster damages. The test site for natural disaster damages has been selected: parts of the coastal area affected by the March 11, 2011, 9.0 magnitude earthquake that struck off Japan's northeastern coast and triggered a super-tsunami. The color-coded images of the scattering power decomposition scheme are a simple and straightforward tool to interpret the changes over the earthquake/tsunami affected urban areas and man-made infrastructures. This method also holds other types of natural (typhoon or tornado) and man-made disaster assessment applications. It is found that the double-bounce scattering power is the most promising of the input parameters to detect automated disaster affected urban areas at pixel level. It is also observed that the very-high-resolution POLSAR images are required for superior urban area monitoring over the oriented urban blocks with respect to the illumination of radar. [ABSTRACT FROM PUBLISHER]

Synthetic Aperture Radar (SAR) backscatter is the coherent combination of the scattering from multiple individual scatterers within the radar resolution cell, which results into a 2-D radar image. Tomographic SAR (TomoSAR) takes advantage of multiple SAR acquisitions to provide 3-D vertical structure of the imaged target. Over forests, parameters such as canopy structure, canopy density, leaf area and phenology contribute to different SAR scattering mechanisms along the vertical dimension of the trees. Tomography has been demonstrated in various earlier works for forest bio-physical parameter estimation using airborne data. In this research work, multi-polarimetric space-borne TanDEM-X tomograms are examined for the first time over a multi-species Indian tropical forest. Multiple TanDEM-X acquisitions are focused using Capon beamforming at five polarimetric channels — HH, HV, VV, HH − VV and HH + VV. Four distinct forest species compartments are selected representing different canopy structure and density. The tomograms obtained in different polarizations for these species are analyzed in detail to understand the scattering patterns across different forest species. Field surveys carried out in several forest locations provide in situ observations of forest height and vertical structure. It was observed that canopy gaps and leaf density play a crucial role for X-band SAR signal penetration through ground. For dense canopy species the backscatter contributions are spread through the canopy, while in case of sparse canopy species, the ground scattering is dominant. Vertical profiles obtained at surveyed plot locations are plotted in all polarizations, and provide a good agreement with field observations. Further, the obtained TomoSAR backscatter layers have a good correlation with field measured above-ground biomass (AGB). The AGB is modeled from TomoSAR with the HH-pol TomoSAR backscatter layer at 27 m canopy height leading to an AGB estimation with correlation r = 0.76 and RMSE of 50.4 t/ha. • First space-borne multi-polarimetric tomograms over Indian tropical forests • Analysis of effect of species on X-band TanDEM-X tomograms presented • SAR scattering along height of tropical forest visualized for different species • Potential of single-baseline space-borne SAR systems explored [ABSTRACT FROM AUTHOR]

The seasonal snow cover contributes significantly to the water resource in the Indian Himalayas, and snow density is one of the vital parameters in the determination of the hydrological potential of snow. The application of conventional methods for snow density retrieval using fully polarimetric SAR data is constrained by the properties of snow primarily due to the melt and frost cycles, as compared to fresh dry snow. The surface component of backscatter is significant in case of melt and frost. In the conventional decomposition based methods, the surface component is not considered for the inversion of permittivity, as proposed in this study. In this paper, we also propose a methodology for the estimation of snow density using bi-temporal fully polarimetric C-band RADARSAT-2 synthetic aperture radar (SAR) data. We utilize the relation between the differential modified Mueller matrix components, the attenuation constants, and the Fresnel transmission coefficients. The permittivity of snow is derived using the inversion of the Fresnel transmission coefficients which is used to determine the snow density using a state of the art empirical relation. The snow density estimates from the proposed method are compared with other methods based on coherency matrix decomposition and evaluated against in-situ measurements collected during a field campaign carried out in Dhundi in the state of Himachal Pradesh in India. The snow density estimates using the proposed method are observed to correlate with the in-situ measurements and were also found to be better than the decomposition based methods. • C-band microwave signal sensitivity to the mountainous snowpack • New method for snow density using bi-temporal RADARSAT-2 data • Inversion of snow density with partial information from Quad-PolSAR data • Comparison of existing PolSAR decomposition based snow density retrieval methods • Inversion of snow density without a prior knowledge of the study region [ABSTRACT FROM AUTHOR]