A random forest regression strategy was used to identify an appropriate combination of variables for aboveground biomass prediction by integrating SAR backscatter data from S-band NovaSAR and forest stand height from LiDAR. The results show that combining backscattering of HH and HV polarizations from S-band with LiDAR-interpolated stand height produced favorable results with a relative root mean square error (rmse) of 25.17%. This study demonstrates the effectiveness of combining different characteristics from different sensors to improve the precision of aboveground biomass estimation.

In modern embedded systems, the demand for increased digital input/output (I/O) capabilities often exceeds the native pin count of micro controllers. One of the common solutions is the usage of shift registers to expand the I/O capabilities. It is common to use 2–4 shift register chips for expanding the GPIO pins, and references are available; in this paper, we provide a reference for technical support for using shift registers in large numbers. We present a practical approach to expanding I/O pins while handling a large number of shift registers in both directions by using Parallel In Serial Out (PISO) and Serial In Parallel Out (SIPO) shift registers, the SN74HC595 and SN74HC165, respectively. This paper dwells on the implementation by including circuit design, software integration, and challenges faced while cascading many shift registers and providing solutions.

This paper aims to establish physical interpretation of nine-component scattering power decomposition using all coherency matrix elements/parameters for fully polarimetric SAR data analysis. It has been known that complete scattering mechanisms can be characterized by using all nine parameters of coherency matrix. We try to decompose the coherency matrix data in a physical scattering manner, as previously reported in a geometrical way by Huynen. New physical scattering models of real and imaginary part ofT12are introduced, which represent dipole scattering power and quarter wave plate scattering power, respectively. These models are added to the existing scattering models for ideal case of surface scattering, double-bounce scattering, and volume scattering. A quantitative analysis of the oriented urban patch of Mumbai in the L-band dataset result reveals a 9.7% decrease in volume scattering, which avoids misinterpretation between vegetation and the oriented urban area, and the 22.5% and 13.5% contributions come from dipole and quarter wave scattering powers, respectively. It is confirmed that proposed method produces better results and interpretations when compared to those by the existing decomposition methods.

This research article presents a comprehensive investigation of leaf area index (LAI) estimation using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Optical L2A datasets for the wheat crop. The water cloud model (WCM) and PROSAIL radiative transfer models (RTMs) are used to estimate LAI from SAR and optical data, respectively. To model the surface backscattering in WCM, the integral equation model (IEM) at VV and VH polarizations is used with the Gaussian correlation function. The results demonstrate that LAI derived from SAR at VH polarization ($R^{2}=0.72, \ \text {RMSE}= 0.60~\text {m}^{2}\text {m}^{-2}$) exhibits superior accuracy compared with optical LAI ($R^{2}=0.70,\ \text {RMSE}=0.82~\text {m}^{2}\text {m}^{-2}$). A fusion approach incorporating deep learning, principal component analysis (PCA), and nonlinear regression techniques is applied to fuse the SAR and optical datasets to further enhance LAI estimation accuracy. The accuracy of these estimations is tested against the ground-truth LAI taken at different locations. Among the fusion methods tested, deep learning emerges as the most effective and accurate approach ($R^{2}=0.91,\ \text {RMSE}= 0.38~ \text {m}^{2}\text {m}^{-2}$). This study provides valuable insights into the estimation of LAI using multisource remote sensing data and highlights the potential of deep learning for improved accuracy in fusion applications.

The main objective of this study is to implement and evaluate five different models namely Support Vector Machine using radial basis function (SVM - RBF), Support Vector Machine using polynomial (SVM - P), Support Vector Machine using sigmoid (SVM - S), Minimum Distance (MD), and Parallelepiped (PP) classifier for glacier surface facies mapping of Samudra Tapu glacier using data from ALOS-1 PALSAR satellite. Since the quantitative and qualitative assessment of monitoring glaciers is one of the most efficient means to observe the glaciated terrain, there is a prerequisite to examining the accuracy of different algorithms for glaciated features classification to identify the best classifier for the selected study region. Our evaluation showed that with optimal tuning parameters, the SVM-RBF classifier with penalty parameter 100 yielded the highest overall accuracy (OA) of 80%, performing better compared to other methods.

Icebergs have been a major concern to the environmentalists, researchers and maritime workers since decades. Especially with the temperatures rising globally the rate of calving of icebergs has increased and thus increasing their probability of them drifting into the major ship lanes posing various threats to people all across the world. Being an open hazard to the ocean, monitoring the iceberg behaviour is critical to ensure the safety of maritime activities. Synthetic Aperture Radar (SAR) images prove to be of major help in studying these icebergs since they strongly influence the SAR backscattering. However due to similarities in scattering behaviour of icebergs and background clutter because of their irregular shapes and sizes, it becomes challenging to accurately classify/identify them. Although the current state of the art techniques like decompositions, model-based scattering power decomposition and eigenvalue/eigenvector decomposition are quite helpful but they come with their own set of limitations. Therefore, the objective of this paper is to explore the application of Deep learning on PolSAR data with Six-component scattering matric power decomposition for efficient identification and classification of the icebergs.

Future PolSAR missions are expected to collect vast quantities of data, which can significantly add to the storage cost of various geospatial cloud driven applications. Data compression techniques like those prescribed by the JPEG2000 (JP2) standard might help counteract this cost. However, it is important to measure the impact on target application performance due to these techniques. In this paper, the impact of JP2 and JPEG compression on classification performance of PolSAR data is studied and it has been found that compression has no significant impact on Deep Neural Network (DNN) classification performance.

Land-cover classification is one of the core applications in the field of remote sensing. It is a valuable resource for city planners to achieve sustainable development. Many metropolitan cities are experiencing disorganized growth with a high intensity of urban sprawl due to the economic pull and better standards of living offered in metropolitan cities when compared to the surrounding rural areas. If this pattern of growth continues, it will lead to unsustainable development. This leads to an increase in pressure on urban infrastructure and the ecosystem. The traditional methods which are used for urban mapping are time consuming. Instead, Microwave Remote Sensing can be used to acquire geographical data which can be used to develop a decision support system to help urban settlement planners. This paper suggests the use of Semantic Segmentation to extract the various land cover features from Polarimetric Synthetic Aperture Radar (PolSAR) images using the Fully Convolutional Network (FCN) based modified UNet architecture, that will help in the analysis of land-cover in areas prone to urban sprawl by utilizing the elements of coherency matrix.

Glaciers are an important indicator of climatic fluctuations. Deviations in the glacier zones (wet snow facies or ice facies) have been seen as good indicators of climatic variations. However, identification/extraction of glacier zones is still a challenging task. The purpose of this study is to develop a method using six-component scattering model based decomposition (6SD) in combination with maximum likelihood classifier (MLC) for recognizing scattering variations in different glacier zones from fully polarimetric synthetic aperture radar. Proposed method is implemented on Advanced Land Observing Satellite/Phased-array L-band Synthetic Aperture Radar (ALOS/PALSAR) data over Samudra Tapu glacier, Himachal Pradesh, India.

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.

The glaciers of High Mountain Asia form one of the largest assemblies of ice-mass outside the Polar regions. The meltwater originating from these glaciers feed into innumerable river streams acting as an important perennial source of water. Within this region, we study one basin, Tarim which consists of glaciers that show surging activity. Of the 200 glaciers in the selected region of study, six glaciers show strong surge at the terminus. This has been explained with the high velocity observed for these glaciers. However, there are glaciers which are in the transient phase of their surge-cycle which tend to behave differently. Such events when compared to previously reported activities suggest that glaciers have surging cycles that are not the same throughout.

This study analyses the potential of integrating spaceborne radar and LiDAR remote sensing products for above ground biomass estimation. Vegetation height product derived from ICESat-2 was interpolated into a 2-D surface using ordinary kriging. Interpolated forest height was validated with in situ data which resulted in determination coefficient of 0.6221 and RMSE of 3.17 m. Interpolated forest surface height and terrain corrected backscatter from L-band ALOS-2/PALSAR-2 data was used to develop linear regression models for above ground biomass estimation. A combination of HV backscatter and ICESat-2 interpolated height-based regression model performed better with a relative RMSE of 30.67%.

Glaciers are the most important component in the cryosphere and are a reliable indicator of climate change. Thus, observation of glacier dynamics helps understand glacier health and monitor climate change. Glacier velocity, thickness and mass balance are the few important parameters to consider regarding glacier dynamics. The main aim of this study is to estimate these three dynamic parameters of a glacier and deriving mass balance form the velocity. DEM differencing method is the most familiar recent remote sensing technique to estimate mass balance. But for the first time, we are using velocity to derive the mass balance and radar interferometry technique used to estimate the velocity. The potential of Differential Interferometric Synthetic Aperture Radar (DInSAR), a SAR technique, to measure surface movement with millimeter level accuracy is utilized to observe the glacier movement. Laminar flow law used here to derive the thickness from velocity. In this study, we used 15 years' time difference data (1999 and 2015 datasets) to check the health of the largest glacier in Himachal Pradesh, Bara Shigri glacier through dynamic parameters. Bara Shigri glacier velocity and thickness both were decreased in the 2015 year compared with 1999. We used these two thickness maps (1999 and 2015) to estimate ice thickness change and we mainly observed more thinning in the ablation region but in accumulation region, ice thickness was increased from 1999 to 2015 year. Thickness change (2015-1999) used to calculate the mass balance of Bara Shigri glacier, observed this as negative and the yearly thinning rate is 1.18 m. This further used to estimate the specific mass balance using density and area of the glacier.

Forecasting and detection of avalanche activities in alpine regions are critical for planning safe traverse routes and landing sites for snow operations and environmental change. The recreational activities (skiing and snowmobile) and public commutation during the winter season lead to fatality spatially when avalanche forecasting is not accurate. Visually, the RGB composite image formed using pre- and post-avalanche data can give primary proxy on the avalanche site. However, in the complex terrain (like Himalaya), RGB composite cannot be used as primary a proxy of avalanche activities. In this paper, we are proposing snow characterization and the avalanche detection algorithm using X-band full polarization synthetic aperture radar (SAR) data. The snowpack parameters are considered in the characterization of snow and avalanche detection. The algorithm also utilizes the SAR power decomposition parameters (Model-based, and mathematical decomposition). Furthermore, statistical indices (Canberra distance and Euclidean distance) are used as a change detection parameter.

The 2 × 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 × 3 Paulibased 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.

Stock volume is an important forest inventory parameter. In case of agro-forests and plantation forests, stock volume estimates are important as they provide reliable indicator of the productivity of these species. In this study stock volume loss due to harvest of polar plantations between 2017 and 2018 are estimated using ALOS-2/PALSAR-2 backscatter data. Using simple linear regression models the AGB of the plantations before and after harvest are estimated. These are converted to stock volume loss per hectare. From field inventory, the actual stock volume during harvest are measured. These are validated against the estimations using two models - M1 and M2. Model M1, utilizes only HV-pol backscatter data and provides a lower accuracy with r2= 0.46. Model M2 utilizes HH- and HV-pol backscatter and provides stock volume loss estimation with r2= 0.51.

This research study demonstrates the potential of simple two stage classification algorithm based on polarimetric descriptor alpha angle to categorize forests into different forest dynamic states. The forest dynamics states such as maturity, disturbance and regeneration/afforestation are discussed in this study. Three group of species -Eucalyptus, Teak and Mixed plantation were selected, and sample plots representing different forest dynamic states were analyzed. The trend analysis formed the basis for two stage classification, which categorized the forest into different forest dynamic states. An accuracy assessment of the algorithm was performed using ground truth data, yielded an overall accuracy of 79.15% and kappa coefficient of 0.74. Alpha angle (α) time series shows high potential to characterize the forests, and stratify it qualitatively into different carbon dynamic fragments.

Glacier is an important component in the Cryosphere and glacier velocity is one of the glacier dynamic parameter, mainly depends on climate and temperature changes. Glacier velocity gives information about glacier health and can also derive the thickness from it. Remote sensing techniques are very helpful to monitor these glacier dynamics. Glacier velocity can be estimated using either Differential SAR Interferometry (DInSAR) or offset tacking technique. DInSAR is the radar interferometry technique to measure surface movement with an accuracy of millimeter range by differencing two Interferograms. We estimated the velocity of six benchmark glaciers in Chandra basin using DInSAR technique. Out of 6 benchmark glaciers, Bara Shigri and Samudra Tapu are moving with high velocity rate and remaining 4 small benchmark glaciers are moving with an average velocity of 1-2 cm/day.

We have analyzed one rapidly expanding glacial lake and one stagnant glacial lake located in the central Himalaya to understand the impact of local topography on the expansion and evolution of glacial lakes using remote sensing data. The slope, aspect, incoming solar radiation and compactness ratio of glaciers associated with the glacial lakes have been studied and analyzed. Glacier topography play important role in the expansion of glacial lakes as observed from the study..

Over the last decade, many snowpack parameter retrieval algorithms utilizing C- and X-band Synthetic Aperture Radar (SAR) data have been proposed. These algorithms are capable of retrieving snow density and snow wetness accurately, but not Snow Water Equivalent (SWE). In this paper, a novel approach of SWE estimation is proposed based on TerraSAR-X full-polarimetric data. A unique relationship between the extinction coefficient of the snowpack and snow wetness is built and verified with field data. This relationship enables the retrieval of Snow Depth (SD) and SWE using existing snow density algorithm and volume scattering power. The validation of SD and SWE is carried out with near-real-time ground truth measurements. The accuracy assessment for SD showed Mean Absolute (MAE) and Root Mean Squared (RMSE) Errors of 15 cm and 16 cm, respectively. On the other hand, SWE retrievals resulted in MAE and RMSE of 22.7 mm and 30 mm, respectively.

Scattering power decomposition has been a hot topic for two decades for the analysis of fully polarimetric SAR data. It has advantages such that 1) easy to interpret the decomposition image, 2) RGB color-coding represents scattering mechanism directly, 3) easy to implement, 4) fast computation time, 5) decomposition powers can be used for further analyses, classification, etc. This paper reviews the development of scattering power decomposition from the original 3-component to recent 6-component schemes, and present a new 7-component decomposition using San Francisco image acquired with ALOS-2 quad. pol. data.

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.

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 (Ω), 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 (O A, k̂) of the averaging-post-thresholding technique (72.87%, 0.59) is comparable to those of the reference data (78.74%, 0.69).

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 [TI 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.

To assess the health of a glacier, mass balance budget is the most acceptable method. Accurate Digital Elevation Models (DEMs) play an important role in mass balance estimation. High resolution TanDEM-X and SRTM data have been utilized for this study over Chorabari, a Central Himalayan glacier. The glacier elevation change from 2000-2016 is calculated as 5.10 ±1.23 m and specific mass balance is -0.73 ± 0.05 m w eq. a-1. Ground observations corroborate these results and thus highlight the efficacy of SAR dataset for studying Himalayan glaciers.

Spaced-based synthetic aperture radar (SAR) operating at low frequencies and capable of earth observation are affected by the layer of ionosphere. The interaction of the signal with the irregularity layer degrades and distorts the SAR image quality. The accurate assumption of size of the irregularities, defined by the axial ratio, in determining the effects of scintillation on the signal is critical. In the present work, the authors have compared the impact of different axial ratios on a pair of SAR images over the Indian subcontinent to estimate scintillation and ionospheric parameters. The parameters are then compared with the available ground-based measurements to determine the precise size of irregularity structure. The amplitude scintillation index, S4, estimated from the Global Positioning Satellite (GPS) receivers for two Indian stations of Tirunelveli and Rajkot are used in this study. The assumption of axial ratio value is 60: 1 to estimate the parameters was found to correctly fit into the model.

In this study, a methodology to retrieve Snow Water Equivalent (SWE) information for shallow snow coverage is proposed, something where traditional methods fail, as they require more snow depth to function properly. The proposed algorithm uses the thermal resistance of snowpack to retrieve the SWE by linearly relating the thermal resistance to the backscattering ratios of winter and autumn (snow-free) images acquired with C-band SAR (Sentinel-lA). The SWE (absolute value) is then estimated from the thermal resistance. The results are verified using field data collected at Dhundi observatory maintained by the Snow and Avalanche Study Establishment (SASE), located in the Himalaya (Himachal Pradesh, India; 32°21'N and 77°07'). One winter and two autumn images were used to check the feasibility of the algorithm. The RMSE of SWE estimated using the retrieval algorithm is 3 cm for Pair-l (30 Jan 2016 and 02 Oct 2015), and 2.8 cm for Pair-2 (30 Jan 2016 and 20 Oct 2016).

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.

TomoSAR provides 3D information of complex targets such as forests. Space-borne SAR data utility for TomoSAR analysis is limited due to temporal decorrelation. A novel technique is introduced which utilizes multiple TanDEM-X data sets to generate accurate tomograms. The technique is demonstrated using multiple space-borne SAR acquisitions over Indian tropical forest. Cross-validation with PolInSAR estimation forest height and field height shows high accuracy of generated tomograms. Fully polarimetric space-borne X-band SAR data shows surprising capability to extract 3D scattering information of forested regions.

This paper proposes a model-based and 6-component scattering power decomposition method, which accounts for all 9 independent polarimetric parameters in the coherency matrix formulation. Both the real and imaginary parts of T13 component are accounted for using +/-45 degree oriented dipoles and newly introduced compound scattering matrices. The compound scattering matrix can be realized by combination of oriented dipoles. By using 6-components and rotation of coherency matrix around the radar line of sight, all coherency elements are accounted for the decomposition. The volume scattering power is reduced compared to those of the existing decomposition methods. The proposed decomposition becomes suitable, especially for vegetation area analysis in more detail.

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/m3, 10 kg/m3, and 0.96, respectively, which are well within the acceptable range.

Polarimetric SAR Interferometry is extensively used for forest biophysical parameter estimation. The variability of PolInSAR height for acquisitions in different seasons is discussed. Deciduous trees such as teak shed leaves during dry season, which for Indian tropical forests can start as early as January. X-band TerraSAR-X/TanDEM-X PolInSAR data sets are acquired over teak and non-teak plantations before and during leaf-fall. The accuracy assessment between PolInSAR height and field based measurements is carried out and results are presented. It was observed that the accuracy of height estimation increases for acquisition during leaf-fall season, with the RMSE decreasing from 4.1 m to 3.2 m for December and February acquisitions respectively. However species wise accuracy analysis shows some interesting results which are presented in the paper.

The properties of ionosphere govern the way in which radar signals traverse, predominantly at the L-band and lower frequencies. The interaction of ions with the traversing radar signal cause rotation of polarization vector, commonly called as Faraday rotation (FR). For a well calibrated monostatic radar, the backscatter measurement reciprocity is disrupted due to FR and other system parameters, like system noise. With the knowledge of full polarimetric ALOS-2/PALSAR-2 data available, FR estimation and compensation is described in this study over Mumbai, India. T4coherency matrix based estimated Faraday rotation angle is applied to correct PALSAR data. The work is extended to calculate total electron content (TEC) and compare the Y4R and G4U scattering power decompositions. The mean two way FR values of 8.47° was observed with the mean TEC over Mumbai calculated to be 7.069 TECU. Scattering powers results show a significant increase in the double bounce powers over the region.

We analyzed the surface elevation change and geodetic mass change of Gangotri glacier, over the period 2011 and 2013, utilizing high horizontal and vertical resolution topographic data, acquired by bistatic radar interferometry of the TanDEM-X/TerraSAR-X satellite formation. Short term investigation of surface elevation change of glaciers at reasonably good accuracy is possible by using multi temporal TanDEM-X data. The surface elevation change further can be converted into glacier volume change and mass change. The mass change of glaciers is direct response of climate change and hence can be taken as a proxy to study climate change. The study area includes the Gangotri group of glaciers, located in the Central Himalaya, India.

Extraction of polarimetric information from SAR data is one of the most important issues in SAR applications. Since fully polarimetric SAR data and model-based scattering power decomposition are now available, its real utilization becomes important topic. In this presentation, some data sets from PiSAR-L2, ALOS, ALOS-2 systems are shown using the existing model-based scattering power decompositions. The observation areas are chosen so that each scattering mechanism correspond specific scattering power utilization, i.e., landslide area for the surface scattering, urban expansion for the double bounce scattering, deforestation for the volume scattering. The final color-coded images provide us with direct way to understand the scattering scenario in the real world.

This paper presents model-based scattering power decomposition images acquired with Advanced Land Observing Satellite 2 (ALOS-2) operating at the L-band frequency. Using the advantages of penetrating and fully polarimetric capabilities in the L-band, real applications on the environmental monitoring such as forest change, flooding, volcano investigation, etc, became feasible. Some evident images are shown in this paper using the existing model-based scattering power decomposition.

An adaptive general four-component scattering power decomposition method (AG4U) is proposed in this letter. The degree of polarization mis 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.

Agriculture in both industrialized and developing countries is a unique sector, characterized by complex issues and problems, ranging from macro (economic) policy levels all the way to the micro (smallholder) farming household and field plot levels. Agriculture, being predominantly a (small-scale) family and/or communal enterprise differs in fundamental ways from administrative services and industrial sectors in terms of relative unpredictability, uncertainty and variability in geo-physical (soil and weather) conditions on which the primary production processes rely. Also, there is a huge diversity in production strategies and objectives among farming households as well as household individuals. Agriculture in Africa is mainly seasonal and faces high levels of risks, which are in-turn compounded by poor infrastructure and isolated rural communities [1]. Fluctuating market and trade conditions, as well as political instability further add to farmer uncertainty. Agriculture therefore, faces rather unique problems with respect to research and development including the planning, implementation and evaluation processes that are involved as well as the assessments of impacts at various levels [2].

In this paper, a new snow wetness estimation methodology is proposed for dual-coherent polarimetric Synthetic Aperture Radar (SAR) data. Surface and volume are the dominant scattering components in the wet-snow conditions. These components, with a limit of penetration depth of high frequency SAR, have been taken into account to estimate the snow-pack wetness. In this new methodology, snow surface wetness has been estimated using the IEM scattering model and snow volume wetness has been estimated under the Rayleigh scattering assumption. The estimated snow wetness is validated using the in-situ field measurements, which were collected synchronous with the satellite pass. In this study we have used dual-coherent TerraSAR-X data acquired over Solang, on 23 January 2009, Himachal Pradesh, India. Typically the snow wetness ranges from 0% to 15% by volume. On comparison with ground measurements, the proposed method shows that the mean absolute error in snow wetness inferred from the SAR imagery was 1.63% by volume.

Many North Indian perennial rivers originate from Himalayas. Glacier melt and seasonal snow melt are the major contributing factors for these rivers runoff at higher and middle altitudes. As these rivers flow through high altitudes and steep slopes, they have potential sites for hydropower generation. In the current study a snowmelt runoff simulation is carried out by using SNOWMOD and SRM models with the help of remote sensing and GIS techniques for the Bhagirathi river basin. These models require snow cover, maximum and minimum temperature, snow fall, rainfall and discharge data. For this purpose Bhagirathi river basin has been selected up to Bhojwasa. Total area of the basin is divided into 7 elevation zones, each of 500 m height using SRTM DEM and snow cover area for each elevation zone is derived from MODIS10A2 snow cover products during the snow melt season over the two years. The snow melt model parameters lapse rate and critical temperatures are estimated using observatories located in the basin. The study reveals that glacier and snow melt form the major contribution to the discharge of the Bhagirathi river, measured at Bhojwasa. The overall efficiency of the SRM and SNOWMOD are compared.

This paper presents very high resolution (30 cm) and fully polarimetric images acquired with a new airborne Polarimetric Interferometric Synthetic Aperture Radar 2 (Pi-SAR2) system operating at the X-band. Pi-SAR2 is a successive version of the former X-band Pi-SAR aiming at exploring the possibility of fully polarimetric SAR. In collaborative works among NICT and research institutions in Japan, data acquisition flights over Niigata have been carried out on August 25 and November 17, 2013. Some imaging results with interesting phenomena are shown using polarimetric scattering power decomposition.

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.

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.

This paper examines the capability assessment of fully polarimetric L-band data for the snow and nonsnow-area classifications. The data sets used are the fully polarimetric Advanced Land Observation Satellite–Phased Array-Type L-Band Synthetic Aperture Radar data, optical Advanced Land Observing Satellite (ALOS)-advanced visible and near-infrared radiometer-2 data close to the radar acquisition, and environmental satellite–advanced synthetic aperture radar data. Several parameters are used to discriminate the snow-covered areas from nonsnow-covered areas in the Indian Himalayan region, including backscattering coefficients, the ratio of cross/copolarized backscattering power and polarization fraction (PF) value. Supervised classification schemes are employed using polarimetric decomposition methods based on the complex Wishart classifier. The accuracy of the classification was found to be 97.95% for the Wishart-supervised classification. Among various parameters and methods, it was found that the alternative newly proposed PF scheme, based on the implementation of fully polarimetric synthetic aperture radar data, yielded the best classification result in the absence of the training samples. The PF value has been effective for discrimination of the snow-covered areas from nonsnow-covered areas, debris-covered glacier, and vegetation. The results of this investigation show that L-band fully polarimetric SAR data provide considerable improvement but may not possess the optimal capability to discriminate snow from other inherent natural and man-made scatterers in heavy snow-laden mountainous scenarios, which may require fully polarimetric S-band or C-band PolSAR measurements.

Snow wetness is a very important parameter for forecasting snow avalanche and for snow melt run off modeling in cragged areas specifically for Himalayan regions of India. In this paper, a new snow wetness estimation approach is used for fully polarimetric Synthetic Aperture Radar (SAR) data. In this new methodology, Freeman surface scattering and Cloude volume scattering components are introduced which account for all independent relative polarimetric phase parameters of the coherency matrix. Snow particle has been considered to be of spheroidal shape in volume scattering model. The estimated snow wetness is validated using the field data, which was collected, synchronized with the satellite pass. The results were also compared with the Shi and Dozier [1] inversion model based snow wetness estimation.

In this letter, an advanced version of the hybrid Freeman/eigenvalue decomposition technique for land parameter extraction is presented with an illustrative example of application. The motivation arises from decomposition problems in obtaining a meaningful volume scattering estimation, so that the technique can be used for both oriented objects and vegetation/forest areas. The idea is to improve the accuracy of the required parameter extraction. Two strategies are adopted to increase the applicability of a hybrid Freeman/eigenvalue decomposition technique: One is the unitary transformation of the coherency matrix; the other is to use an extended volume scattering model. The extension of the volume scattering model plays an essential role for the hybrid Freeman/eigenvalue decomposition technique. Since the volume scattering power is evaluated by assuming that the$HV$component is caused by vegetation only in the existing technique, an extended volume scattering power approach is utilized. It is shown that vegetation areas and oriented objects such as urban building areas are well discriminated by the proposed technique as compared to the existing techniques.

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.

The statistical behavior of the sea clutter in synthetic aperture radar (SAR) images is characterized by both the marginal distribution and the spatial correlation. However, simultaneous modeling of the joint information remains a difficult job because of the non-Gaussian clutter nature. In this paper, a semiparametric approach is proposed for addressing this problem. First, we investigate the applicability of the nonparametric kernel density estimator (KDE) for estimating the marginal distribution of the SAR clutter and show that the KDE is most applicable in the log-intensity domain. Second, we propose to estimate the underlying spatial correlation structure with a copula approach and show that the Gaussian copula is a sufficiently accurate model. Consequently, the KDE, together with the Gaussian copula, offers a full characterization of the joint probability distribution, based on which a quadratic detector of null distribution governed by the well-known chi-squared law can be conveniently designed for constant false alarm rate detection. In the experiment, results with both simulated and real SAR data demonstrate that, compared with the single-point detector using only the marginal distribution, the proposed method, which incorporates spatial correlation, significantly improves the detection performance with regard to either the receiver-operating-characteristic curve or detected target pixels. The tradeoff, however, lies in a loss of false alarm rate control resulting from increased uncertainty in estimating higher dimensional distributions.

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$3 coherency matrix with respect to the second-order statistics of polarimetric information. The proposed method accounts for all observation parameters in the new scheme. It is known that the existing four-component decomposition method reduces the number of observation parameters from nine to eight by rotation of the coherency matrix and that it accounts for six parameters out of eight, leaving two parameters (i.e., the real and imaginary parts of$T_{13}$component) unaccounted for. By additional special unitary transformation to this rotated coherency matrix, it became possible to reduce the number of independent parameters from eight to seven. After the unitary transformation, the new four-component decomposition is carried out that accounts for all parameters in the coherency matrix, including the remaining$T_{13}$component. Therefore, the proposed method makes use of full utilization of polarimetric information in the decomposition. The decomposition also employs an extended volume scattering model, which discriminates volume scattering between dipole and dihedral scattering structures caused by the cross-polarized$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.

In this study, changes of the L-band PALSAR polarimetric signal scattered from earthquake/tsunami damaged areas has been investigated. Since it is not always possible to get data sets having sufficiently good spatial/temporal resolutions to detect large area of damages precisely, it is of specific interest to use of wave polarization information appropriate to assess disaster-induced changes of scattering phenomena. Experimental results indicate that different polarimetric parameters and similarity measures provide complimentary information on damages. Consequently, it is important to utilize polarimetric features maximizing target changes and minimizing the false-alarm and the missed-alarm rates in developing damage assessment algorithms.

There exist 9 independent polarization parameters in the coherency or covariance matrix as the second order statistics. Various decomposition methods have been presented based on the physical scattering model using these parameters. However, none of them accounts for all polarimetric information, typically leaving T13element un-accounted in the coherency matrix. This paper presents a complete four-component scattering power decomposition method using all polarimetric information. Using double unitary transformation of measured coherency matrix, it is possible to eliminate T23element, which results in a reduction of polarization parameters from 9 to 7. Then by unitary transformation of expansion matrices, it becomes possible to account for T13term, which has never been accounted for in the physical model-based decomposition. By the double unitary transformations to minimize the T33component, all polarimetric parameters are accounted. This methodology also reduces the negative power problem significantly. This method is a further extension of the existing four-component decomposition. The four scattering powers (surface, double bounce, volume, helix) are assigned to blue, red, green, and yellow to compose full-color image. An example image of San Francisco area acquired with ALOS-PALSAR is shown to validate the decomposed result.

In the three- or four-component decompositions, polarimetric scattering properties and corresponding physical scattering models play essential roles for power decomposition. This letter proposes an improved four-component scattering power decomposition method that employs a suitable volume scattering model for single- or double-bounce scattering in the polarimetric synthetic aperture radar image analysis. The cross-polarizedHVcomponent is created by both single-bounce object (such as vegetation) and double-bounce structures (such as oriented building blocks). It has been difficult to discriminate these two objects (vegetation against oriented buildings) in the decomposed images since theHVcomponent is assigned to the volume scattering due to vegetation only. We propose to extend the volume scattering model suited for two physical scattering models. It is shown that a vegetation area and an oriented urban building area are well discriminated compared to those resulting from the implementation of the existing four-component scattering power decomposition.

This investigation presents an improved methodology for decomposing the fully polarimetric SAR data based on the 4-Component Scattering Power Decomposition (4-CSPD) scheme. Using a phase rotation of 3×3 coherency matrix and the minimization of phase rotated matrix element T33, 4-CSPD model has been applied on fully polarimetric SAR images. This new decomposition methodology shows accurate decomposition of fully polarimetric SAR data over oriented urban area and mountainous area as compared the original 4-CSPD model.

This paper presents the comparison between the potential of SAR data in compact polarimetry (CP) and full polarimetry mode for snow detection. Eigenvalues based analysis and developed procedure have been used for this inter-comparison. It has been found over the part of rugged Siachen glaciated terrain, Indian Himalaya that dual polarization and compact polarization mode show-30% and-15% less capability than FP mode respectively.

This paper is concerning the estimation of snow wetness from multi-polarization SAR data. In this paper, microwave interaction with snow covered terrain and different scattering mechanism from snowpack and their backscattering model for developing inversion algorithm with wet snow conditions are described in order to estimate snow wetness. SAR data processing and field measurement of snow parameters are also discussed. In this study, snow wetness has been measured with a dielectric moisture meter with synchronous satellite passes over the part of snow covered Indian Himalayan region (e.g. Dhundi observatory in Himachal Pradesh, India).

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of spaceborne sensor dual-polarization C-band data of ENVISAT- ASAR can enhance the accuracy in measurement of snow physical parameters as compared to single polarization data measurement. This study shows the capability of C-band SAR data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both HH and VV polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's, semi empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR HH and HH polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 24.38 kg/m3.

In this study, the capability assessment of fully polarimetric L-band ALOS PALSAR data has been carried out for snow discrimination from other targets. Eigenvaluve based polarization fraction value has been determined for assessing the capability of PALSAR data for snow discrimination. Radar snow index has been developed using polarization fraction and normalized third eigenvalue of coherency matrix. It has been found that radar snow index is more robust and simple to implement that supervised classification.

Remote sensing of radar polarimety has great potential to determine the extent and properties of snow cover. Availability of spaceborne sensor dual polarimetric C-band data of ENVISAT-ASAR can enhance the accuracy in measurement of snow physical parameters as compared to single fixed polarization data measurement. This study shows that the capability of C-band SAR data for estimating dry snow density over snow coverer rugged terrain in Himalayan region. The study area lies in Beas, Chandra and Bhaga catchments of Himachal state (India). For this investigation, the main assumptions are that the snow is dry and at C-band, total backscattering coefficient comes from snowpack and snow ground interface. An algorithm for estimating snow density has been developed based on snow volume scattering and snow-ground scattering components. Snow density estimation algorithm requires HH and VV polarization combination data. The radar backscattering coefficients of both HH and VV polarization and incidence angle are given as input to the developed algorithm. Finally, the algorithm gives the snow dielectric constant which can further be related to snow density using Looyenga's semi empirical formula. Comparison was done between algorithm estimated snow density and field value of snow density in the study region. The mean absolute error between estimated and measured snow density was 21.3 kg/m3.

An endeavor is made for the study of movement of Himalayan glaciers using Spaceborne InSAR technique, which is based on preserving the coherence between two acquisitions of the same scene. Gangotri, Siachen, Bara Shigri and Patsio are major glaciers in the Himalayan region, which are showing retreat, and their respective tributary glaciers completely disconnected from main body of glaciers. ERS-1/2 observations show high correlation on glacier area and hence movement of Siachen and Gangotri glacier are measured. Information about dynamism of glaciated terrain can be retrieved by counting differential interferograms. Displacement of Gangotri glacier in the radar look direction has been observed as 8.4 cm represented by 3 fringes. Siachen glacier exhibits a displacement of 22 cm represented by 8 fringes. ERS-1/2 tandem data over all these glaciers show highest correlation over glacier areas but ENVISAT ASAR data shows coherence loss over glacier area due to decorrelation. Coherence loss is usual phenomena in glaciated terrain as repeativity of sensor is high (35 days for ENVISAT). Among all these, Siachen glacier shows highest coherence and then Gangotri, respectively. A tandem pair of ERS-1&2 acquired on April 1 and 2, 1996 in descending pass over Siachen shows high coherence than the ascending pair acquired on May 2 and 3, 1996. It is due to change in climate between two acquisitions at glacier locations. A systematic monitoring of dynamism of Himalayan glaciers can be done using Permanent Scatterer Interferometric SAR (PSInSAR) if we place a corner reflector on the glaciers.

This study discusses the capability of full polarimetric L-band ALOS PALSAR data for snow classification. In this study, Polarimetric decomposition and the complex Wishart classifier are applied on ALOS-PALSAR data. Optical (ALOS-AVNIR) data within six days difference was used for visual interpretation of snow and non-snow classes. Application of Entropy-Anistropy-Alpha-Wishart classifier for training samples gives better classification results. To reduce speckle effects and to improve classification results, the refined Lee filter was applied on the covariance matrix several times, each time increasing the size of the moving window. Over all classification accuracy were observed 75.61%, 91.46%, 94.91%, 96.19%, 97.16% and 98.37% using different window sizes 1times1 (without filtered image), 3times3, 5times5, 7times7, 9times9 and 11times11 respectively for refined lee speckle filter. It is observed that classification accuracy increases as size of the filter window increases for speckle reduction. Polarization signatures of various features have also been generated using polarization synthesis techniques and signatures are represented in 3-D plot.

Snow cover area is a very important parameter for snowmelt runoff modeling and forecasting. Snow cover information is also useful for managing transportation and avalanche forecasting. Our study area includes Gangotri glacier region, Siachen glacier region and Beaskund glacier region in the North-West Himalayas of India. Our previous studies discuss the capability of several algorithms for optical sensor as well as SAR to map the snow cover area in Himalayan regions. In recent years, the SAR interferometry has provided number of attractive applications in landuse/landcover mapping. This study discusses the capability of both backscattering ratio techniques and InSAR coherence measurement techniques for snow cover mapping in Himalayan region with repeat passes data of ERSfrac12 and ENVISAT-ASAR. By analyzing the several pairs of ENVISAT repeat passes ASAR images for the study area, we find that the coherence measurement from bare soil, bare rock and vegetation are high and snow covered area and glacier area have very low coherence except in one day difference image.