Advanced Synthetic Aperture Radar Data Research Articles

Estimation of Sea Ice Production in the North Water Polynya Based on Ice Arch Duration in Winter During 2006–2019

AbstractThe North Water Polynya (NOW) is the largest recurrent Arctic coastal polynya. The formation of the NOW is critically dependent on the development of an ice arch that defines its northern boundary. In this study, high‐resolution ENVISAT Advanced Synthetic Aperture Radar data, Sentinel‐1A data, and Moderate Resolution Imaging Spectroradiometer data were employed to identify the spatio‐temporal characteristics of the ice arch during 2006–2019. Polynya pixels were identified based on the thin ice thickness (TIT), using a threshold of TIT <0.2 m, from which the polynya extent, heat flux, and ice production (IP) were estimated. The results show the different locations of the ice arch in different years, with a mean duration of 132 ± 69 days. The average annual polynya extent over the 14 years is ∼38.8 ± 8 × 103 km2, and we found that it is more closely correlated with wind speed during the winter and air temperature during early spring. The average heat flux drops from about 248 W/m2 in the winter months to about 34 W/m2 in May. The average accumulated IP varies significantly every year, with an average of 144 ± 103 km3, and peak values in March in most years. No apparent interannual trends are shown for the polynya area, heat flux, and IP during 2006–2019. The results also show that IP calculated based on the ice arch data is approximately 25% lower than that obtained by assuming a fixed time, location, and duration for the polynya.

Estimation of Thin-Ice Thickness and Discrimination of Ice Type From AMSR-E Passive Microwave Data

Detection of thin-ice thickness with microwave radiometers, such as the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), is very effective for the estimation of sea-ice production, which causes dense water driving ocean thermohaline circulation. In previous thin-ice thickness algorithms, ice thickness is estimated by utilizing a negative correlation between ice thickness and polarization ratio (PR) of AMSR-E. However, in these thin-ice algorithms, the relationship has large dispersion. We consider that the problem is caused by not taking account of ice type. We classified thin-ice regions around Antarctica into two ice types: 1) active frazil, comprising frazil and open water and 2) thin solid ice, areas of the relatively uniform thin ice, using Moderate Resolution Imaging Spectroradiometer and Advanced Synthetic Aperture Radar data. For each ice type, we examined the relationship between the AMSR-E PR of 36 GHz and ice thickness, showing that the active frazil type has a much smaller thickness than the thin solid ice type for the same PR. The two ice types can be discriminated by a simple linear discriminant method in the plane of the PR and gradient ratio of AMSR-E, with the misclassification of 3%. From these results, we propose a new thin-ice algorithm. The two ice types are classified by the linear discriminant method, and then empirical equations are used to obtain the ice thickness for each ice type. This algorithm significantly improves the accuracy of the thin-ice thickness.

Ground Deformations around the Toktogul Reservoir, Kyrgyzstan, from Envisat ASAR and Sentinel-1 Data—A Case Study about the Impact of Atmospheric Corrections on InSAR Time Series

We present ground deformations in response to water level variations at the Toktogul Reservoir, located in Kyrgyzstan, Central Asia. Ground deformations were measured by Envisat Advanced Synthetic Aperture Radar (ASAR) and Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) imagery covering the time periods 2004–2009 and 2014–2016, respectively. The net reservoir water level, as measured by satellite radar altimetry, decreased approximately 60 m (∼13.5 km3) from 2004–2009, whereas, for 2014–2016, the net water level increased by approximately 51 m (∼11.2 km3). The individual Small BAseline Subset (SBAS) interferograms were heavily influenced by atmospheric effects that needed to be minimized prior to the time-series analysis. We tested several approaches including corrections based on global numerical weather model data, such as the European Centre for Medium-RangeWeather Forecasts (ECMWF) operational forecast data, the ERA-5 reanalysis, and the ERA-Interim reanalysis, as well as phase-based methods, such as calculating a simple linear dependency on the elevation or the more sophisticated power-law approach. Our findings suggest that, for the high-mountain Toktogul area, the power-law correction performs the best. Envisat descending time series for the period of water recession reveal mean line-of-sight (LOS) uplift rates of 7.8 mm/yr on the northern shore of the Toktogul Reservoir close to the Toktogul city area. For the same area, Sentinel-1 ascending and descending time series consistently show a subsidence behaviour due to the replenishing of the water reservoir, which includes intra-annual LOS variations on the order of 30mm. A decomposition of the LOS deformation rates of both Sentinel-1 orbits revealed mean vertical subsidence rates of 25 mm/yr for the common time period of March 2015–November 2016, which is in very good agreement with the results derived from elastic modelling based on the TEA12 Earth model.

Comparative flood area analysis of C-band VH, VV, and L-band HH polarizations SAR data

ABSTRACTThe role of synthetic aperture radar (SAR)-image-based flood area mapping is proved beyond the doubts. It is also well known that different wavelength, polarization SAR reacts in varying ways over the same land-use/land-cover region. In line to this, this article mainly brings out the significance of comparing and analysing different wavelength, polarization SAR data of the same inundated region against the land-use classes of the study area. The C-band ENVISAT advanced synthetic aperture radar data of vertically transmitted horizontally received (VH), vertically transmitted vertically received (VV) polarizations data, and L-band ALOS-1 PALSAR data of horizontally transmitted horizontally received (HH) polarization data has been obtained as both these satellites captured the same flood event of Andhra Pradesh state of India. Initially, the SAR images are classified with the help of digital elevation model of the disaster region which supports in mapping the fully submerged, partially submerged and non-flooded pixels of disaster region. The fully submerged regions includes the natural waterbodies, adjacent flood plain regions which are completely submerged, as well as not accessible, whereas the partially submerged regions are spatially discontinuous and scattered regions which are inundated due to recent disaster but accessible. In this study, much emphasis has been given in comparing and analysing the fully submerged, partially submerged, and non-flooded regions of classified SAR images against each land use of the disaster region by which the response of individual land-use units of the disaster region at different wavelength, polarization has been brought out. From this comparative assessment, it has been observed that the areal extent of fully submerged regions is considerably more in L-band HH image than in the C-band polarization images. It is also been noticed that C-band VH polarization image is able to map and quantify considerable part of the land-use classes as partially submerged regions than the L-band HH polarization image. In addition to this, the proposed technique is able to rectify in classifying mangrove regions as non-flooded regions due to the land-use/land-cover-based approach.

Study on spatial variation of land subsidence over Minagish–Umm Gudair oil fields of Kuwait using synthetic aperture radar interferometry technique

Land subsidence can be a major problem where there are large-scale underground activities such as oil extraction. This paper addresses the spatial variability of land subsidence over Minagish and Umm Gudair oil fields of Kuwait. Synthetic aperture radar interferometry (InSAR) with multiple reference scenes using a persistent scatterer InSAR toolchain was employed in this study. Twenty-nine scenes of advanced synthetic aperture radar data (for the period January 2005 to August 2009) were used to make 20 pairs of interferograms (with high coherence and low noise) of stable point-like reflectors. The output of this study is the land subsidence maps of Minagish and Umm Gudair oil fields with a spatial resolution of 40 m. The results indicate that there is land subsidence of 29.9 mm/year in the southern part of the oil field (Umm Gudair). This is the first detailed assessment of land subsidence in the Minagish–Umm Gudair oil fields; therefore, no ground-truth data are available to compare the subsidence results. The results were consistent, indicating their validity.

Spatial–temporal evolution patterns of land subsidence with different situation of space utilization

Long-term over-exploitation of underground water, static and dynamic load is increasing year by year, which influenced the occurrence and development of regional land subsidence in Beijing, China. We used Envisat advanced synthetic aperture radar data acquired from 2003 to 2009 and PSI (persistent scatterers for SAR interferometry) and small baseline technology to estimate regional land subsidence information in Beijing, China. In different situation of space utilization, we chose five typical settlement areas according to classified information of land-use, multi-spectral remote sensing images and geological data. We analyzed the time-series evolution characteristics of uneven subsidence by GIS spatial analysis. The comparative analysis results suggest that for five typical settlement areas, the complex situations of space utilization affect the trend of uneven subsidence, the simpler space utilization situation (relatively fewer transport lines, construction), the smaller settlement differences and the smaller trend of the uneven subsidence.

Deformation monitoring of slow-moving landslide with L- and C-band SAR interferometry

SAR interferometry (InSAR) is an effective tool for wide-area earth surface deformation detection and mapping. In this letter, Persistent Scatterers InSAR method was employed to derive the deformation histories of Huangtupo and Zhaoshuling landslides located in Badong County of the Three Gorges area. One Environmental Satellite C-band Advanced Synthetic Aperture Radar (ASAR) data stack acquired from descending orbit and two Advanced Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar (PALSAR) data stacks collected from two adjacent ascending orbits were used. Different deformation trends were observed from the descending and ascending data stacks. Disagreements were found on Huangtupo and Zhaoshuling landslides between measurements of ASAR data and PALSAR data, which might be primarily attributed to different viewing geometries. Meanwhile, due to smaller decorrelation effects for longer wavelength, L-band PALSAR data can be used to detect more point-like targets than C-band ASAR data, which makes it more suitable for monitoring landslides in vegetated areas such as the Three Gorges. Preliminary temporal correlation analyses between deformation measurements and water level fluctuation as well as rainfall were performed to explore the driving mechanism for landslide motion within the Three Gorges Reservoir area.

Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor

With more than half of the world population now living in cities and 1.4 billion more people expected to move into cities by 2030, urban areas pose significant challenges on local, regional and global environment. Timely and accurate information on spatial distributions and temporal changes of urban areas are therefore needed to support sustainable development and environmental change research. The objective of this research is to evaluate spaceborne SAR data for improved global urban mapping using a robust processing chain, the KTH-Pavia Urban Extractor. The proposed processing chain includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., SAR data preprocessing, enhancement, and post-processing. ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-VV data at 30m resolution were selected over 10 global cities and a rural area from six continents to demonstrate the robustness of the improved method. The results show that the KTH-Pavia Urban Extractor is effective in extracting urban areas and small towns from ENVISAT ASAR data and built-up areas can be mapped at 30m resolution with very good accuracy using only one or two SAR images. These findings indicate that operational global urban mapping is possible with spaceborne SAR data, especially with the launch of Sentinel-1 that provides SAR data with global coverage, operational reliability and quick data delivery.

Spatial correlation between land subsidence and urbanization in Beijing, China

The large-scale construction of buildings, extensive road and rail networks, and increased traffic flow associated with urbanization has the potential to cause land subsidence. Land subsidence caused by urbanization is an increasingly significant problem in Beijing, China; therefore, it is important to investigate the relationship between urbanization and land subsidence. Landsat TM images covering the Beijing plain were used to acquire spatial changes information of built-up areas by calculating an index-based built-up index (IBI). We used ENVISAT Advanced Synthetic Aperture Radar data acquired from 2003 to 2009 and persistent scatterers for SAR interferometry (PSI) technology to estimate land subsidence. Geographic information systems spatial analysis method was used to identify the relationship between the settlement rate and the IBI value for three different sampling units. The result showed that it was a positive correlation between construction density and land subsidence; for land subsidence, the effect from the combination of high-density building clusters and extensive transportation networks was more significant than the presence of buildings alone. However, there may be a delay between the completion of building construction and the development of land subsidence.

3-D movement mapping of the alpine glacier in Qinghai-Tibetan Plateau by integrating D-InSAR, MAI and Offset-Tracking: Case study of the Dongkemadi Glacier

Three-dimensional (3-D) movements of the Dongkemadi Glacier in the Qinghai-Tibetan Plateau over 2007–2010 are fully determined by using L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ascending and C-band Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) descending acquisitions. In order to yield an optimal 3-D solution, a variance component estimation (VCE) algorithm is applied to weigh the D-InSAR and MAI measurements derived from PALSAR data and the Offset-Tracking measurements from ASAR data under the scheme of weighted least squares adjustment. By exploiting the InSAR measurements themselves to determine the weights iteratively, the presented approach results in an accuracy of centimeter to decimeter per year for all the three velocity vectors. The horizontal component shows that the four main tributary streams in the Dongkemadi Glacier are all flowing from the central area of the glacier to its surroundings, along the steepest slope descent direction and with a rate up to about 5m/yr. The glacier thickening or thinning is resolved from the vertical component by subtracting the down-slope movement. A number of interesting accumulation and ablation areas are also detected, with vertical variations of 1–2m/yr, as a result of the horizontal glacier movement or the existence of concave terrain. This can be used as a good indication of the ice dynamics and the location of the subglacial water in the alpine glacier.

Monitoring blooms and surface accumulation of cyanobacteria in the Curonian Lagoon by combining MERIS and ASAR data

This study shows how different remote sensing techniques can be used to distinguish between surface accumulations (scum) and dense blooms of cyanobacteria in the Curonian Lagoon, the largest lagoon in Europe. Cyanobacteria blooms are a major concern in this region due to water quality issues interfering with the conservation of the whole ecosystem. Chlorophyll-a (Chl-a) concentrations can be extremely high (up to about 500mg/m3 in some cases) during cyanobacteria blooms, which are often associated with a surface accumulation of algae. The Medium Resolution Imaging Spectrometer (MERIS) was used to acquire 52 images covering the summers of 2004 to 2011. These images were analyzed to map Chl-a concentrations and the presence of scum using two different band ratio algorithms applied to atmospherically-corrected data. The results identify wind speed as the main driving factor in the surface accumulation of algae, as well as in the spatial distribution of Chl-a. The utility of microwave images was also assessed, as since any cloud cover obviously hampers the use of optical data. Advanced Synthetic Aperture Radar (ASAR) images were collected synchronously with the MERIS data and the normalized radar cross-section (NRCS) signal was corrected for the contribution of wind for the purposes of correlating the results with the MERIS-derived Chl-a concentrations. In general, there was a stepwise decrease in the NRCS for high values of Chl-a (>50mg/m3) with wind speeds in the range of 2 to 6m/s. Under these conditions, our results demonstrate that optical and microwave signals can be used in combination to improve our understanding of cyanobacteria blooming.

Assimilating ASAR data for estimating soil moisture profile using an ensemble Kalman filter

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar (ASAR) observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter (EnKF), the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important.

Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping

The objectives of this research are to develop robust methods for segmentation of multitemporal synthetic aperture radar (SAR) and optical data and to investigate the fusion of multitemporal ENVISAT advanced synthetic aperture radar (ASAR) and Chinese HJ-1B multispectral data for detailed urban land-cover mapping. Eight-date multiangle ENVISAT ASAR images and one-date HJ-1B charge-coupled device image acquired over Beijing in 2009 are selected for this research. The edge-aware region growing and merging (EARGM) algorithm is developed for segmentation of SAR and optical data. Edge detection using a Sobel filter is applied on SAR and optical data individually, and a majority voting approach is used to integrate all edge images. The edges are then used in a segmentation process to ensure that segments do not grow over edges. The segmentation is influenced by minimum and maximum segment sizes as well as the two homogeneity criteria, namely, a measure of color and a measure of texture. The classification is performed using support vector machines. The results show that our EARGM algorithm produces better segmentation than eCognition, particularly for built-up classes and linear features. The best classification result (80%) is achieved using the fusion of eight-date ENVISAT ASAR and HJ-1B data. This represents 5%, 11%, and 14% improvements over eCognition, HJ-1B, and ASAR classifications, respectively. The second best classification is achieved using fusion of four-date ENVISAT ASAR and HJ-1B data (78%). The result indicates that fewer multitemporal SAR images can achieve similar classification accuracy if multitemporal multiangle dual-look-direction SAR data are carefully selected.

Estimation of near surface soil moisture in a sloping terrain of a Himalayan watershed using ENVISAT ASAR multi-incidence angle alternate polarisation data

Soil moisture is one of the important input variables in hydrological and water erosion models. The extraction of information on near surface soil moisture from synthetic aperture radar (SAR) is well established mostly for flat terrain and using low incidence angle single polarisation data. The ENVISAT advanced SAR (ASAR) data available in multiple incidence angles and alternate polarisation modes were investigated in this study for soil moisture estimation in sloping terrain. The test site was Sitla Rao watershed in the Lesser Himalayas of northern India. Empirical models were developed to estimate near surface soil moisture in bare agricultural fields using alternate polarisation ASAR data. Both soil moisture and surface roughness field measurements were performed during the satellite passes. Backscatter from medium incidence angle (IS-4) and vertical-vertical (VV) polarisation signal is correlated better with volumetric soil moisture content compared to other incidence angles. The model parameters were further improved, and soil moisture estimation was refined by combining medium incidence angle (IS4) vertical-horizontal polarisation response as another variable along with VV polarisation response. The effect of slope on the radar backscatter was minimized by incorporating local incidence angles derived from an ASTER DEM. Copyright © 2012 John Wiley & Sons, Ltd.

Evaluation of the potential of ASAR data to estimate impervious surface area

Estimation of impervious surface area (ISA) from satellite images is crucial towards many environmental issues. However, many difficulties remain to be addressed, especially in cloudy areas. In this article, the potential of Advanced Synthetic Aperture Radar (ASAR) data is evaluated for mapping ISA. First, the spectral mixture analysis is employed to estimate ISA from Enhanced Thematic Mapper Plus (ETM+) image. Second, the ISA is extracted from ASAR data according to the characteristics of synthetic aperture radar. Third, a decision fusion is used to fuse the results from ETM+ and ASAR images. Accuracy assessment shows that by just using ASAR image, the accuracy (overall accuracy: 0.6815; kappa coefficient: 0.3557) is lower than that using only ETM+ image (overall accuracy: 0.7450; kappa coefficient: 0.4806). However, the fused result (overall accuracy: 0.7612; kappa coefficient: 0.5159) gives a better accuracy than using single data source, indicating that ASAR data are able to provide complementary information on ISA, since microwave works on all-weather conditions.

Evidence for flash floods over deserts from loss of coherence in InSAR imagery

Flash floods in complex terrain play an important role for sediment transport in arid regions and thus potentially for dust production, but observations of these phenomena are scarce over most of the world's deserts. Here, methods from radar interferometry, applied to 8 years (2003–2010) of ENVISAT Advanced Synthetic Aperture Radar data over the northwestern Sahara, are used to demonstrate the potential of these data to identify significant flash floods. Loss of coherence between two consecutive images is indicative of changes in surface characteristics. Tropical Rainfall Measuring Mission daily precipitation estimates, together with the digital elevation model from the Shuttle Radar Topography Mission, are analyzed to assess the likelihood of these changes to be related to flash floods. Four interferometric pairs representing periods with different rainfall amounts are examined with regard to changes in surface characteristics caused by precipitation. To do this, ratios of coherences are calculated, highlighting the changes in soil texture through loss of coherence in particular for desert valleys. Many pixels within wadis show large coherence during dry periods and a significant loss during wet periods, while others show low coherence irrespective of rainfall, possibly due to Aeolian processes. In the long term, findings from this study will be used to investigate the relation between flash floods and interannual variability of local dust emission fluxes.

Flood Mapping Using ASAR and Landsat Data in Poyang Lake

Poyang Lake is the largest freshwater lake in China which located in the Jiangxi province. Flood happened around the lake almost each summer. Remote sensing technology is an effective way to monitoring flood at large scale. This study employs images of Advanced Synthetic Aperture Radar (ASAR) and Landsat Enhance Thematic Mapping Plus (ETM+) to map the flooding area in Poyang Lake. Two ASAR scenes are adopted in this study: one was acquired in flooding period on 1st July 2010, and another was acquired on 9th September 2010 in dry season. Water body is extracted using threshold method, and flood inundation area is detected using the two images. Texture analysis of ASAR data and ETM+ indices are synchronously used in classification. Texture layers of ASAR data are computed, and Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) are also calculated using ETM+ data acquired in the dry season. NDVI and MNDWI derived from ETM+ data are sensitive to soil moisture and vegetation cover., therefore, these two classes are more separable from dry and non-flooded objects than they are in a “ASAR only” classification. The indices derived from ETM+ are used as additional layers for water body classification. Supervised classification is implemented on both ASAR dataset and ASAR+TM data using Maximum Likelihood classifier. Results suggest the classification of ASAR and ETM+ combined data is more homogeneous because the indices reduced speckle-noise, and features can be clearly separated.

Crop classification using multi-configuration SAR data in the North China Plain

Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late jointing and flowering periods achieved sufficient classification accuracy, and adding data to the early jointing period had little effect on improving classification accuracy. In addition, texture features of SAR data were also useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring and can become a suitable complementary data source to optical data.

Comparison and analysis of Envisat ASAR ocean wave spectra with buoy data in the northern Pacific Ocean

The validation and assessment of Envisat advanced synthetic aperture radar (ASAR) ocean wave spectra products are important to their application in ocean wave numerical predictions. Six-year ASAR wave spectra data are compared with one-dimensional (1D) wave spectra of 55 co-located moored buoy observations in the northern Pacific Ocean. The ASAR wave spectra data are firstly quality control filtered and spatio-temporal matched with buoy data. The comparisons are then performed in terms of 1D wave spectra, significant wave height (SWH) and mean wave period (MWP) in different spatio-temporal offsets respectively. SWH comparison results show the evident dependence of SWH biases on wind speed and the ASAR SWH saturation effect. The ASAR wave spectra tend to underestimate SWH at high wind speeds and overestimate SWH at low wind speeds. MWP comparison results show that MWP has a systematic bias and therefore it should be bias-modified before used. The comparisons of 1D wave spectra show that both wave spectra agree better at low frequencies than at high frequencies, which indicates the ASAR data cannot resolve the high frequency waves.

Coherence-based land cover classification in forested areas of Chattisgarh, Central India, using environmental satellite—advanced synthetic aperture radar data

In the present work, the potential of synthetic aperture radar (SAR) interferometric coherence in land cover classification is studied over forested areas of Bilaspur, Chattisgarh, India using Environmental Satellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data. Single look complex (SLC) interferometric pair ASAR data of 24th September 2006 (SLC-1) and 29th October 2006 (SLC-2) covering the study area were acquired and processed to generate backscatter and interferometric coherence images. A false colored composite of coherence, backscatter difference, and mean backscatter was generated and subjected to maximum likelihood classification to delineate major land cover classes of the study area viz., water, barren, agriculture, moist deciduous forest, and sal mixed forests. Accuracy assessment of the classified map is carried out using kappa statistics. Results of the study suggested potential use of ENVISAT-ASAR C-band data in land cover classification of the study area with an overall classification accuracy of 82.5%, average producer's accuracy of 83.69%, and average user's accuracy of 81%. The present study gives a unique scope of SAR data application in land cover classification over the tropical deciduous forest systems of India, which is still waiting for its indigenous SAR system.