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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]

Recently, with the extensive availability of fully polarimetric synthetic aperture radar (SAR) data, methods that are simple and efficient, and involve lesser computation and data processing, are needed to be explored for snow cover mapping. This paper analyzes different polarimetric parameters such as entropy, anisotropy, and the mean scattering angle for the identification of snow cover area. We present a novel index for mapping snow cover based on the assessment of entropy (H) and anisotropy (A) using fully polarimetric SAR data and refer to it as the radar snow fraction (RSF). The RSF is proposed as an extension of the H(1‐A) metric by applying a sigmoidal function to this metric. The experiments to evaluate the applicability of the proposed RSF are carried out using fully polarimetric SAR data of L‐band ALOS‐2/PALSAR‐2 and C‐band RADARSAT‐2 data sets corresponding to different geographical locations in the Indian Himalayas. The developed snow cover maps from the proposed method were validated with respect to reference snow cover maps derived by thresholding the Normalized Differenced Snow Index developed from multispectral data (e.g., Landsat‐8 imagery). These maps were also statistically compared with those obtained from the conventional radar snow index, which is based on the polarization fraction. We determined a mean overall accuracy of 0.8 between the developed snow cover maps and the reference maps for the different data sets used for experiments. The results showed that, in general, the RSF outperformed the other polarimetric parameters for snow cover detection. [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]

This paper presents the comparison between the potential of synthetic aperture radar (SAR) data in compact polarimetry (CP) versus the fully polarimetric POLSAR mode (FP) for glacier areas. The eigenvalue-based constructed parameters, decompositions and supervised classification schemes were used for this inter-comparison. The comparison is focused on compact polarimetric techniques versus full polarimetric techniques based on both of qualitative and quantitative analysis of the glaciated parameters extraction. Overall performance of CP mode is lower than FP mode for glaciated terrain parameter extraction. Furthermore, it was found over the part of rugged Gangotri glaciated terrain, Indian Himalaya that the reconstruction pseudo polarimetry data is erroneous and highly biased because of data sets do not satisfy the pseudo polarimetric reconstruction conditions in the glaciated terrain. [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]

The environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) offers the opportunity for monitoring snow parameters with dual polarization and multi-incidence angle. Snow wetness is an important index for indicating snow avalanche, snowmelt runoff modelling, water supply for irrigation and hydropower stations, weather forecasts and understanding climate change. We used a first-order scattering model that includes both volume and air/snow surface scattering based on a developed inversion model to estimate snow dielectric constant, which can be further related for estimating snow wetness. Comparison with field measurement showed that the correlation coefficient for snow permittivity estimated from ASAR data was observed to be 0.8 at 95% confidence interval and model bias was observed as 2.42% by volume at 95% confidence interval. The comparison of ASAR-derived snow permittivity with ground measurements shows the average absolute error 2.5%. The snow wetness range varies from 0 to 15% by volume. [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]