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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]

Studies pertaining to small scale structures producing scintillations using synthetic aperture radars (SARs) have predominantly been conducted at low-latitude regions. The high-latitude region (auroral belt and polar caps) is highly dynamic and varies in response to stimuli from solar winds and the magnetosphere in complex ways. In this paper, the authors have shown the capability of SAR for scintillation observation in the auroral region. An attempt has been made to fit an irregularity anisotropy model to SAR measurements for characterizing the ionospheric irregularities in the auroral regions. The dependency of anisotropy irregularity model on parameters, such as irregularity structure (axial ratio), their orientation with respect to magnetic field lines, and the ionospheric plasma drift, is closely studied using Advanced Land Observing Satellite (ALOS)-2 datasets acquired over Alaska. Geomagnetic indices and total electron content data are consistent with the occurrence of the scintillation event under study. Drift velocity measurements from high-frequency radars in the super dual array radar network (SuperDARN) showed that the anisotropy is independent of the magnitude and the azimuth angle of the plasma drift. The typical range of orientation angle suitable for the high latitude regions probed by ALOS-2 is demonstrated to be between 120°–135°. This paper explores the idea of inferring irregularity anisotropy by comparing the amplitude scintillation (S4) index measured in SAR data pairs using two well-established techniques. The image contrast technique heavily relies on the accurate modeling of anisotropy, whereas the radar cross-sectional enhancement method is independent of it. This feature has been exploited in the $S_{4}$ comparison to finally fit the choice of irregularity axial ratio and conclude that the sheet-like structures best describe the ionospheric irregularity structure in the region under observation. [ABSTRACT FROM AUTHOR]

TerraSAR-X/TanDEM-X form the first space-borne interferometer providing full-PolInSAR acquisitions with a near-zero temporal gap. This paper explores the potential of X-band SAR data for forest height estimation over tropical Indian forests. Unique TanDEM-X data sets with varied spatial baselines are acquired over a tropical, deciduous Indian forest. Multiple dual polarimetric and fully polarimetric acquisitions are analysed and the best performance is achieved using fully polarimetric data acquired over both high- and low-biomass forests ( r 2 = 0.86 and r 2 = 0.80 respectively). It is shown for the first time that, for deciduous Indian tropical forests, seasonal changes such as leaf-fall affect the PolInSAR based height inversion performance. For species exhibiting leaf-fall, acquisition during leaf-on season provides better height inversion accuracy ( r 2 = 0.75) than for leaf-fall season ( r 2 = 0.65). Results show consistent PolInSAR height inversion performance for both high- and low-biomass forests in both natural and managed forest tracts. Spatial baseline is a critical factor for selection of suitable acquisitions for PolInSAR based height estimation. The effect of spatial baseline on height inversion performance is also discussed. [ABSTRACT FROM AUTHOR]

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]

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 ( ρ 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. [ABSTRACT FROM AUTHOR]

Managed forests, dynamic with activities of harvest and afforestation, have become an area of imminent research opportunity to understand the effects of these disturbances and regrowth dynamics. As low frequency polarized radar signals have the ability to penetrate forest canopies, the fully polarized Synthetic Aperture Radar (SAR) data gives an ideal representation of conditions in the forest. However, this valuable information has not been optimally utilized in understanding the forest disturbances and regrowth dynamics. Several forest change monitoring studies have been performed using single or dual polarization SAR data, whereas limited studies utilized full polarimetric information. In this study, L-band ALOS/PALSAR and ALOS-2/PALSAR-2 fully polarimetric data is used to study and understand the behavioural dynamics of homogeneous and heterogeneous forest compartments having single and multiple species composition, respectively. Radar scattering pattern of different species, and its variation with respect to phenology, disturbances, harvesting and plantation regrowth are analysed to understand the trend in scattering components with respect to above said forest changes. Based on the scattering properties and their analyses, a two stage rule based classifier has been developed to categorize the forest based on its disturbance and regrowth status into disturbed, re-growth, stable/no change and non forest classes. The classifier performed with, considerably, high accuracy having an overall accuracy of 85.39% and a kappa coefficient of 0.8. The classifier developed in this work presents the potential of fully polarimetric data in understanding the level and extent of disturbances and regrowth dynamics of a forest. [ABSTRACT FROM AUTHOR]

Large-scale forest clear-cut identification is one of the major application of remote sensing techniques. ALOS-2/PALSAR-2 is the latest SAR satellite providing multi-polarized L-band SAR data. With increasing deforestation, it is important to assess the potential of SAR data for identifying clear-cuts in forest regions. In this research work, multi-temporal ALOS-2/PALSAR-2 SAR data and supplementary Landsat-8 optical data sets are acquired over Indian tropical forest, and SAR parameters are analysed over a progressively clear-cut Teak plantation. Sensitivity of the SAR parameters to progressive clear-cuts is estimated and found that the cross-polarized backscatter σ H V 0 and entropy parameter H are most sensitive to both partial and complete clear-cut in forest compartments. An entropy thresholding based classification is carried out to identify clear-cut regions with a good accuracy. The study highlights the utility of SAR parameters to monitor forest clear-cuts for better forest management. [ABSTRACT FROM AUTHOR]