Synthetic Aperture Radar Analysis Research Articles

Instability of energy spectrum disturbance for ship turbulent wakes: SAR imaging simulation and analysis

We present a ship turbulent wake synthetic aperture radar (SAR) simulator (STW-SARSim) producing two-dimensional true-scale turbulent wakes in SAR images that extend for kilometers. In this paper, we innovatively overcome the sea surface time-varying problem in solving the spectral energy balance equation by transforming the unsteady problem into a steady problem and considering the dark trailing centerline region of the turbulent wake in SAR images as a texture modulation effect of the Bragg wave for sea surface scattering. We also present SAR images simulated in three different bands: (i) Sentinel-1A C-band, (ii) TerraSAR X-band, and (iii) ALOS PALSAR L-band to validate the reliability of our proposed method and analyze the effects of wind speed, wind direction, and dissipation rates on the turbulent wake simulation. This paper provides new insights into the simulation of turbulent wakes in SAR images of large scenes inspired by recent research on detection algorithms. It sets the stage for future dataset generation for detection.

MODELING LAND DEFORMATIONS IN MOUNTAINS BY COMBINING TIME-SERIES L-BAND SAR IMAGES AND SPATIOTEMPORAL STATISTICAL MODELS

Abstract. Predicting land deformations using remote-sensing technology is extremely challenging because the simulation models that are involved have many parameters, including geological features, that must be calibrated against areas of interest, and no universal model is available. Instead, this paper proposes a method for modeling land deformations with time-series L-band synthetic aperture radar (SAR) images and spatiotemporal statistical models for prediction. First, time-series SAR analysis generates temporal land deformation. Then, water-related products are obtained from the Today’ Earth system of the Japan Aerospace Exploration Agency. The three-day consecutive maximum values are calculated and then interpolated spatially. Finally, a descriptive or dynamic spatiotemporal statistical model is applied. The low-rank Gaussian process model, a descriptive model, is found to be highly accurate for explaining land deformations, even for actual slid areas, as long as the areas of analysis are limited, e.g., 150 m × 50 m. It is concluded that the proposed method is effective for predicting land deformations spatially and temporally and so can be used to identify possible landslide locations.

An improved volume power approach to estimate LAI from optimized dual-polarized SAR decomposition

ABSTRACT Synthetic Aperture Radar (SAR) imagery has proven to be a valuable tool for monitoring the Earth’s surface, particularly vegetation. However, many studies using SAR have focused solely on backscattering sensitivity without considering the structural and physical properties of the vegetation being analysed. This study proposes a new approach for vegetation monitoring using the Volume Power (VP) analysis technique. The proposed method aims to improve the accuracy of VP derived from the Freeman-Durden (FD) decomposition technique for dual polarimetric SAR data. To increase the sensitivity of vegetation scattering in SAR analysis, this study modified the VP using Depolarized Volume Power (DVP) and Anisotropic Volume Power (AVP). The first modification, DVP, was achieved by incorporating the degree of polarization () in the analysis. The second modification, AVP, is achieved by considering the anisotropic scattering properties of vegetation. The modified VP is then used to estimate Leaf Area Index (LAI) using an empirical relationship between LAI and the modified VPs. The accuracy of the LAI estimation is evaluated using ground truth measurements. The results demonstrate that the proposed method provides more accurate LAI estimates than traditional methods. The approach also shows improved sensitivity to vegetation scattering compared to the original VP from the FD, indicating the effectiveness of the degree of polarization and anisotropic scattering in reducing the impact of unwanted scattering mechanisms from the surface. The accuracy (R 2) between in-situ LAI and the LAI retrieved from AVP and DVP was 0.85 and 0.82, whereas for FDVP, it was 0.74. These findings highlight the potential of the proposed approach for improving the accuracy of VP estimation in dual-polarimetric SAR data and enabling more accurate and efficient estimation of LAI.

Detecting Urban Floods with Small and Large Scale Analysis of ALOS-2/PALSAR-2 Data

When a large-scale flood disaster occurs, it is important to identify the flood areas in a short time in order to effectively support the affected areas afterwards. Synthetic Aperture Radar (SAR) is promising for flood detection. A number of change detection methods have been proposed to detect flooded areas with pre- and post-event SAR data. However, it remains difficult to detect flooded areas in built-up areas due to the complicated scattering of microwaves. To solve this issue, in this paper we propose the idea of analyzing the local changes in pre- and post-event SAR data as well as the larger-scale changes, which may improve accuracy for detecting floods in built-up areas. Therefore, we aimed at evaluating the effectiveness of multi-scale SAR analysis for flood detection in built-up areas using ALOS-2/PALSAR-2 data. First, several features were determined by calculating standard deviation images, difference images, and correlation coefficient images with several sizes of kernels. Then, segmentation on both small and large scales was applied to the correlation coefficient image and calculated explanatory variables with the features at each segment. Finally, machine learning models were tested for their flood detection performance in built-up areas by comparing a small-scale approach and multi-scale approach. Ten-fold cross-validation was used to validate the model, showing that highest accuracy was offered by the AdaBoost model, which improved the F1 Score from 0.89 in the small-scale analysis to 0.98 in the multi-scale analysis. The main contribution of this manuscript is that, from our results, it can be inferred that multi-scale analysis shows better performance in the quantitative detection of floods in built-up areas.

A Study of Hypersonic Vehicle-Borne SAR Imaging Under Plasma Sheath

Hypersonic vehicle-borne synthetic aperture radar (SAR) imaging has promising application prospects for remote sensing owing to the characteristics of fast response and flexible trajectory. However, a newly observed phenomenon that occurs during hypersonic flight, called the plasma sheath, seriously affects hypersonic vehicle-borne SAR imaging. Therefore, in this study, the effect of plasma sheath on hypersonic vehicle-borne SAR imaging is investigated systematically. First, a hypersonic vehicle-borne SAR signal model under plasma sheath is developed. Then, the phase shift and amplitude attenuation which are critical in the SAR signal model under plasma sheath are deduced. Moreover, the analytical formulas for the linear and quadratic phase errors are derived. Based on the developed signal model, point target response and an SAR image of an area under plasma sheath show that linear phase error will cause image shift, and quadratic phase error will lead to pulse broadening. In addition, the amplitude attenuation will make the point target response submerged in noise, and signal amplitude nonlinear distortion will result in an asymmetric distortion phenomenon. To further explore the effect of plasma sheath on hypersonic vehicle-borne SAR imaging, quantitative evaluation and analysis of SAR imaging degradation are conducted under different electron densities, carrier frequencies, and bandwidths. The evaluation results in this study will benefit the application of hypersonic vehicle-borne SAR imaging under plasma sheath, and the analytical formulas of linear and quadratic phase error provide the possibility to compensate for SAR imaging degradation under plasma sheath in the future.

Significant Wave Height Retrieval Using XGBoost from Polarimetric Gaofen-3 SAR and Feature Importance Analysis

Empirical algorithms have become the mainstream of significant wave height (SWH) retrieval from synthetic aperture radar (SAR). But the plentiful features from multi-polarizations make the selection of input for the empirical model a problem. Therefore, the XGBoost models are developed and evaluated for SWH retrieval from polarimetric Gaofen-3 wave mode imagettes using the SAR features of different polarization combinations, and then the importance of each feature on the models is further discussed. The results show that the reliability of SWH retrieval models is independently confirmed based on the collocations of the SAR-buoy and SAR-altimeter. Moreover, the combined-polarization models achieve better performance than single-polarizations. In addition, the importance of different features to the different polarization models for SWH inversion is not the same. For example, the normalized radar cross section (NRCS), cutoff wavelength (λc), and incident angle (θ) have more decisive contributions to the models than other features, while peak wavelength (λp) and the peak direction (φ) have almost no contribution. Besides, NRCS of cross-polarization has a more substantial effect, and the λc of hybrid polarization has a stronger one than other polarization models.

Unusually thick freshwater ice and its impacts on aquatic resources in the National Petroleum Reserve in Alaska (NPR-A) during the winter of 2020–2021

Despite a long-term thinning trend in freshwater ice in northern Alaska, cold low-snow cover winters can still emerge to grow thick ice. In 2021, we observed abnormally thick ice by winter's end on lakes and rivers throughout the Fish Creek Watershed in the National Petroleum Reserve in Alaska. This recent and anomalous winter presented an opportunity to assess how such conditions, more typical of many decades previous, affected aquatic habitat and winter water supply. Observed maximum ice thickness in 2021 of 1.9 m closely matched low-snow ice growth simulations, whereas previous records averaged 1.5 m and more closely matched high-snow ice growth simulations. The resulting extent of bedfast lake ice from late winter synthetic aperture radar (SAR) analysis in 2021 was the highest on record since 1992. This SAR analysis suggests a 33% reduction in liquid water below ice by lake surface area compared with the recent thin-ice winter of 2018 (1.2 m). Together, these results help place the cold, low-snow winter of 2020–2021 in context of the long-term trend toward warmer, snowier winters that appear to becoming more common in Arctic Alaska.

Multi-polarization radar backscatter signatures of internal waves at L-band

ABSTRACT In this study, the scattering mechanisms associated to internal waves (IWs) are investigated at L-band. IWs represent key geophysical factors for sea-air heat exchange and play a paramount role in the biological primary production and in the understanding of the evolution of climate ecosystem. In addition, a better understanding of IWs microwave scattering mechanisms can improve the modeling capability and, therefore, can boost the development on advanced synthetic aperture radar (SAR)-based added-value products to mitigate the risk for offshore drilling operations and aquaculture activities associated to IWs. The analysis of L-band multi-polarization SAR scattering of IWs under the influence of surface current straining is performed using a meaningful full-polarimetric Advanced Land Observing Satellite Phased Array type L-band 1 SAR data set collected over IWs observed under different imaging and wind conditions. Time and space co-located ancillary information is also available. Experimental results demonstrate that the non-polarized scattering mechanisms constitute a significant contribution to the total IW backscattering, especially in the case of surface current gradients owing to IWs (about 48–57%). It is also found that the non-polarized scattering contribution associated to IW concentrates along the wave crests, i.e. it is at least 60% larger than the one observed along the wave troughs. In addition, considering the IW traveling directions relative to that of the wind, the non-polarized scattering contribution associated to IWs is more remarkable at upwind direction while it is less significant at down/crosswind directions. The non-polarized scattering mechanisms also calls for a modulation induced by IWs which is much more significant,i.e. at least three times, that the one that characterizes the polarized scattering mechanism.

EXPLORING CLOUD-BASED PLATFORMS FOR RAPID INSAR TIME SERIES ANALYSIS

Abstract. The idea of near real-time deformation analysis using Synthetic Aperture Radar (SAR) data as a response to natural and anthropogenic disasters has been an interesting topic in the last years. A major limiting factor for this purpose has been the non-availability of both spatially and temporally homogeneous SAR datasets. This has now been resolved thanks to the SAR data provided by the Sentinel-1A/B missions, freely available at a global scale via the Copernicus program of the European Space Agency (ESA). Efficient InSAR analysis in the era of Sentinel demands working with cloud-based platforms to tackle problems posed by large volumes of data. In this study, we explore a variety of existing cloud-based platforms for Multi-Temporal Interferometric SAR (MTI) analysis and discuss their opportunities and limitations.

SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

The availability of high spatial resolution synthetic aperture radar (SAR) sensors with a wide range of acquisition modes has increased greatly over the past decade and contributed significantly to the study of wetland ecosystems. However, the relative influence of acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) in wetland analysis remains poorly explored. This article investigated the relative influence of X-/C-band frequency, dual-/quad-polarization and single-/multi-acquisition features on discrimination of vegetation types in 632 ha Ramsar-protected temperate riverine marshes (Mont-Saint-Michel Bay, France). Three SAR datasets (i.e. quad-pol/C-band, dual-pol/C-band and dual-pol/X-band) were generated from five pairs of TerraSAR-X and RADARSAT-2 images. First, a set of 25 SAR features, including backscattering coefficients and polarimetric parameters, was extracted from the SAR datasets. Second, correlation between each pair of images was calculated using the polarimetric parameter Shannon entropy to select the most similar pairs in the time series. Third, the importance of each SAR feature and modeling accuracy were calculated using a conditional random forest model for each of the three datasets. Finally, analysis of variance was performed to assess the impact of band frequency, polarization mode and number of acquisition dates on the classification of vegetation types. The results highlighted that although the time-shift of each pair of TerraSAR-X and RADARSAT-2 images was short (3–11 days), only three pairs were sufficiently similar, highlighting the high variability in wetland ecosystems. The polarimetric parameter Shannon entropy was the most discriminating feature, regardless of the frequency or polarization. Most variance in the model accuracy was explained by the number of acquisition dates (68%), followed by the frequency (23%), while polarization explained little. This article will help select the most suitable SAR sensor acquisition modes for wetland conservation.

Early estimation of ground displacements and building damage after seismic events using SAR and LiDAR data: The case of the Amatrice earthquake in central Italy, on 24th August 2016

The increasing accessibility of Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) data, grants the opportunity to experiment new methods to support disaster risk management. However, while SAR analyses are becoming extremely popular, thanks, in particular, to the availability of open source satellite images such as those from the Copernicus project, LiDAR analyses are still less common because of the scarce availability of this type of data over significant time frequencies.In this paper we propose an innovative procedure based on the use of SAR and LiDAR data to rapidly assess seismic damage in the early post-emergency phases. The methodology was applied to the case study of the town of Amatrice (Central Italy), which was hit by a strong earthquake swarm that started in August 2016. Specifically, SAR data is used for a large-scale analysis of terrain displacements following the seismic event, while LiDAR reliefs are used to carry out a change detection and to identify the level of damage at a building-scale in the urban settlement of Amatrice. Results will show how the proposed approach can be extremely effective both in the non-emergency phases to monitor seismic-affected areas and support emergency planning, as well as during the immediate post-earthquake phases to assess the damage it has caused and to support first aid dispositions.

SENTINEL-1 BASED FLOOD MAPPING USING INTERFEROMETRIC COHERENCE AND INTENSITY CHANGE DETECTION APPROACH

Abstract. This study presents a semi-automatic algorithm for mapping floods. Both Optical and Synthetic Aperture Radar (SAR) data are used to observe the flood that hit the Cukurova region of Adana (Turkey) in 2019. The performance of the interferometric coherence in complementing intensity component of SAR data is investigated for mapping the floods occurred in agricultural and urban environments. There was no ground truth data available from the flooded area, thus classification result of optical satellite image is used as a seed for the region growing algorithm that defines the classes according to a threshold value. The advantage of using both intensity and coherence change detection is verified with the results. The results have been evaluated through very high-resolution SPOT-6 optical image which acquired simultaneously with Sentinel-1B SAR image. The comparison with the SPOT-6 data results shows that the proposed approach can map flooded areas with acceptable accuracy using the SAR data from Sentinel-1 satellite mission. Highly affected agricultural areas along with the river line could be mapped both by optical and SAR analysis. Comparison of results from VV and VH polarization provided that cross-polarization VH has a very little effect on flood mapping. The proposed algorithm successfully distinguishes the classes among the affected region, especially in urban areas.

Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring.

In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km2 of buildings and 65 km of pavement are at an immediate risk of land subsidence.

Landslide detection in mountainous forest areas using polarimetry and interferometric coherence

The cloud-free, wide-swath, day-and-night observation capability of synthetic aperture radar (SAR) has an important role in rapid landslide monitoring to reduce economic and human losses. Although interferometric SAR (InSAR) analysis is widely used to monitor landslides, it is difficult to use that for rapid landslide detection in mountainous forest areas because of significant decorrelation. We combined polarimetric SAR (PolSAR), InSAR, and digital elevation model (DEM) analysis to detect landslides induced by the July 2017 Heavy Rain in Northern Kyushu and by the 2018 Hokkaido Eastern Iburi Earthquake. This study uses fully polarimetric L-band SAR data from the ALOS-2 PALSAR-2 satellite. The simple thresholding of polarimetric parameters (alpha angle and Pauli components) was found to be effective. The study also found that supervised classification using PolSAR, InSAR, and DEM parameters provided high accuracy, although this method should be used carefully because its accuracy depends on the geological characteristics of the training data. Regarding polarimetric configurations, at least dual-polarimetry (e.g., HH and HV) is required for landslide detection, and quad-polarimetry is recommended. These results demonstrate the feasibility of rapid landslide detection using L-band SAR images.

Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo)

The European Space Agency’s (ESA) “SAR for REDD” project aims to support complementing optical remote sensing capacities in Africa with synthetic aperture radar (SAR) for Reducing Emissions from Deforestation and Forest Degradation (REDD). The aim of this study is to assess and compare Sentinel-1 C-band, ALOS-2 PALSAR-2 L-band and combined C/L-band SAR-based land cover mapping over a large tropical area in the Democratic Republic of Congo (DRC). The overall approach is to benefit from multi-temporal observations acquired from 2015 to 2017 to extract statistical parameters and seasonality of backscatters to improve forest land cover (FLC) classification. We investigate whether and to what extent the denser time series of C- band SAR can compensate for the L-band’s deeper vegetation penetration depth and known better FLC mapping performance. The supervised classification differentiates into forest, inundated forest, woody savannah, dry and wet grassland, and river swamps. Several feature combinations of statistical parameters from both, single and multi-frequency observations in a multivariate maximum-likelihood classification are compared. The FLC maps are reclassified into forest, savannah, and grassland (FSG) and validated with a systematic sampling grid of manual interpretations of very-high-resolution optical satellite data. Using the temporal variability of the dual-polarized backscatters, in the form of either wet/dry seasonal averages or using the statistical variance, in addition to the average backscatter, increased the classification accuracies by 4–5 percent points and 1–2 percent points for C- and L-band, respectively. For the FSG validation overall accuracies of 84.4%, 89.1%, and 90.0% were achieved for single frequency C- and L-band, and C/L-band combined, respectively. The resulting forest/non-forest (FNF) maps with accuracies of 90.3%, 92.2%, and 93.3%, respectively, are then compared to the Landsat-based Global Forest Change program’s and JAXA’s ALOS-1/2 based global FNF maps.

Detection of Flood Areas Using Sentinel-1 Satellite Imagery and Evaluation of Its Applicability

The flood detection technique based on synthetic aperture radar (SAR) imagery was assessed. This technique was applied to a flood event in Busan in 2017. Sentinel-1 imagery was acquired, and then different multitemporal change detection methods were applied for SAR data analysis. Observed data were collected from newspapers, social media, and the Korean Fire Protection Association (KFPA). Specifically, flood and non-flood data were collected and used for analysis. As a result of receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) value was 0.67, which indicates a generally efficient technique, but it showed lower performance than the random model according to the threshold value. Four of 15 flood sites and a threshold value of 0.02 were selected to compare the flood area between the observed data and the flood maps derived from SAR analysis. As a result, all four flood maps included the observed flood areas, with some overestimation of flood area. A criterion for selecting the optimal threshold was suggested using ROC curve analysis, and the flood map results from this threshold were validated, but follow-up study is required to assess the accuracy numerically. Keywords: Synthetic Aperture Radar (SAR), Sentinel-1, ROC, Flood Analysis

A-DInSAR Performance for Updating Landslide Inventory in Mountain Areas: An Example from Lombardy Region (Italy)

This work focuses on the capabilities and limitations of the Advanced Satellite SAR (Synthetic Aperture Radar) Interferometry (A-DInSAR) in wooded and mountainous regions, with the aim to get insights on the performances for studying slow-moving landslides. The considered critical issues are related to the SAR acquisition geometries (angle of incidence of the satellite line of sight, ascending and descending geometries) and to the physical and morphological features of the slopes (land use, aspect and slope angles), which influence the measuring points coverage. 26 areas in Lombardy Region (Italy), affected by known slope instability phenomena, have been analyzed through A-DInSAR technique, using COSMO-SkyMed images. The results allowed to outline general considerations about the effectiveness of A-DInSAR analysis of a single dataset (descending or ascending dataset), selected accordingly to the aspect of the slopes. Moreover, we aimed to quantitatively describe the capability to update the state of activity of several previously mapped landslides using satellite SAR Interferometry results. Although in a wooded and mountainous region, where the chances of retrieving radar targets for satellite SAR analysis are generally low, the A-DInSAR results have allowed to detect landslides’ reactivations or new landslides and to update the inventory for about 70% of the investigated areas.

Building a SAR-Enabled Data Cube Capability in Australia Using SAR Analysis Ready Data

A research alliance between the Commonwealth Scientific and Industrial Research Organization and Geoscience Australia was established in relation to Digital Earth Australia, to develop a Synthetic Aperture Radar (SAR)-enabled Data Cube capability for Australia. This project has been developing SAR analysis ready data (ARD) products, including normalized radar backscatter (gamma nought, γ0), eigenvector-based dual-polarization decomposition and interferometric coherence, all generated from the European Space Agency (ESA) Sentinel-1 interferometric wide swath mode data available on the Copernicus Australasia Regional Data Hub. These are produced using the open source ESA SNAP toolbox. The processing workflows are described, along with a comparison of the γ0 backscatter and interferometric coherence ARD produced using SNAP and the proprietary software GAMMA. This comparison also evaluates the effects on γ0 backscatter due to variations related to: Near- and far-range look angles; SNAP’s default Shuttle Radar Topography Mission (SRTM) DEM and a refined Australia-wide DEM; as well as terrain. The agreement between SNAP and GAMMA is generally good, but also presents some systematic geometric and radiometric differences. The difference between SNAP’s default SRTM DEM and the refined DEM showed a small geometric shift along the radar view direction. The systematic geometric and radiometric issues detected can however be expected to have negligible effects on analysis, provided products from the two processors and two DEMs are used separately and not mixed within the same analysis. The results lead to the conclusion that the SNAP toolbox is suitable for producing the Sentinel-1 ARD products.

Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations.

Glacier facies of Vestfonna (Svalbard) based on SAR images and GPR measurements

The warming climate of the Arctic affects the mass budget of glaciers, and changes in the distribution of glacier facies are indicative of their response to climate change. The glacial mass budget over large land ice masses can be estimated by remote sensing techniques, but selecting an efficient remote sensing method for recognizing and mapping glacier facies in the Arctic remains a challenge. In this study, we compared several methods of distinguishing the facies of the Vestfonna ice cap, Svalbard, based upon Synthetic Aperture Radar (SAR) images and terrestrial high frequency Ground Penetrating Radar (GPR) measurements. Glacier zones as determined using the backscattering coefficient (sigma0) of SAR images were compared against GPR data, and an alternative application of Internal Reflection Energy (IRE) calculated from terrestrial GPR data was also used for differentiating the extent of glacier facies. The IRE coefficient was found to offer a suitable method for distinguishing glacier zones and for validating SAR analysis. Furthermore, results of analysis of fully polarimetric Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) and European Remote Sensing Synthetic Aperture Radar (ERS-2 SAR) images were compared with the IRE coefficient classification. Especially promising method is H-α segmentation, where the glacier zone boundaries corresponded very well with both GPR visual interpretation and IRE classification results. The IRE coefficient's simplicity of calculation makes it a good alternative to the subjective GPR visual interpretation method, where results strongly depend on the operator's level of experience. We therefore recommend for GPR profiles to be used for additional validation of SAR image analysis in studies of glacier facies on the High Arctic ice masses.