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Glacier comprises distinct features (snow, ice, and debris cover) and their identification and classification using satellite imagery is still a challenging task. Classification of different glacier features (zones) using remote sensing data is useful for numerous environmental and societal applications. The purpose of this study is to develop the fully polarimetric SAR (PolSAR) deep neural networks classification approach for the extraction of different features of the alpine glaciers. The developed approach was tested and classification results were compared with the support vector machines-based classification over the part of two glaciers: Siachen glacier and Bara Shigri glacier. The overall accuracy (OA) of GF-DNN classification is relatively high (91.17% for Siachen and 89% for Bara Shigri) with a good kappa coefficient (0.88 for Siachen and 0.85 for Bara Shigri) as compared to SVM for both the selected glaciers. An improvement of more than 10% is achieved in the OA of GF-DNN classification as compared to SVM for both the glaciers. The obtained classified results and accuracy demonstrates the potential of deep neural networks-based glacier features classification approach for glaciated terrain features. [ABSTRACT FROM AUTHOR]

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

In this study, mass balance for Chorabari glacier of the Central Himalaya, India has been estimated. This glacier has been considered for the case study as it feeds the Mandakini River and was one of the reasons for flash floods in Rudraprayag district, Uttarakhand in 2013. The observations are based on glacier area/length change and rate of melting in the multidecade (1976-2016) using Landsat data. The study estimates an overall decrease in area at 0.8% per year. Elevation change has been studied using geodetic method utilizing shuttle radar topography mission and TanDEM-X datasets, which have shown a decrease in elevation in 16 years (2000-16). From these remotely observed parameters, a negative mass balance for the decade 2000-11 indicates glacier retreat. This study highlights the applicability of optical and radar remote sensing for the Himalayan glaciers, for better disaster management and understanding glacier dynamics in response to climate change. [ABSTRACT FROM AUTHOR]

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]

Sri Lanka being an agrarian country, the role of water is important for agricultural production. In Sri Lanka, various tank cascade systems, earthen dams and distribution canals have been accepted as few of the most complex ancient traditional water systems of the world. Rainfall, surface water, groundwater and runoff are linked with each other, they have close interactions to land cover classes such as forests and agriculture. The monitoring of vegetation conditions can show subsurface manifestations of groundwater. In this study, an effort to understand the role of traditional water reservoirs and groundwater recharge was made using remote sensing techniques. We have analyzed various vegetation indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI-2), Soil-Adjusted Vegetation Index (SAVI), tasselled cap transformation analysis (TCA brightness, greenness and wetness) and their relations with the existence of soil, vegetation and water. Result shows that EVI, SAVI, and TCA-based Greenness Index indicates good relationship with the vegetation conditions as compared to other indices. Therefore, these indices could play a crucial role in depicting the interaction between soil, vegetation, and water. However, multi-temporal observations can provide significant results about these interactions more accurately. [ABSTRACT FROM AUTHOR]

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 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]

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping. [ABSTRACT FROM PUBLISHER]

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-polarized HV component 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 the HV component 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. [ABSTRACT FROM PUBLISHER]

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Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite- (ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of C-band synthetic aperture radar (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 horizontal-horizontal (hh) and vertical-vertical (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 vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm3. [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]