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Fault activity property is one of the main reasons caused by the motion of crustal fault, and the research of fault activity characteristics has become an important direction in the study of earthquake prediction, which is an important source of new theory and new method in earthquake prediction science. The disastrous May 12, 2008, Mw7.9 Wenchuan earthquake in the Longmenshan fault zone (LFZ) took the local population as well as scientists by surprise. To analyze the temporal and spatial deformation characteristics of the Mao county–Wenchuan fault, the Beichuan-Yingxiu fault, and Jiangyou-Guan County in the central south segment of the LFZ after the Wenchuan earthquake, the SBAS-InSAR method was adopted to derive surface deformation rate with 20 Envisat SAR images acquired between August 6, 2007, and July 26, 2010. Our analysis shows that the overall movement speed of LFZ increased significantly, reaching about − 40 mm/year, which shows a dextral compression strike-slip. From west to east, the velocity changes of each section are different, and the movement of the front-range fault is dominant in the middle and south sections of Longmenshan, which is close to the epicenter. The reason may be related to the fact that the middle and south section of Longmenshan is the epicenter of the earthquake. The southern and mid-southern sections of the LFZ change from west to east, and the direction of profile movement increases gradually. In the middle and north segment of the fault zone between the two, the variation characteristics are not obvious. To a certain extent, it indicates that the fault is characterized by dextral strike-slip compression in the southern segment and the mid-southern segment. The difference in the profile movement direction in the LFZ may be related to the stress release of the southern segment of the LFZ after the earthquake while the movement of the northern segment of the LFZ was blocked. The research results will reveal the mechanism of earthquake pregnancy and earthquake generation of LFZ, enrich the knowledge of the impact on the aftershock distribution of the Wenchuan earthquake, and promote the development of earthquake prediction research. [ABSTRACT FROM AUTHOR]

Sea ice drift data at high spatial resolution and surface wind model output are used to explore atmosphere-sea ice interactions in the Western Ross Sea including the three main polynyas areas; McMurdo Sound polynya (MSP), Terra Nova Bay polynya (TNBP), and the Ross Sea polynya (RSP). This study quantifies the relationship between the winds and sea ice drift and observes the average and annual anomalies across the region. Sea ice drift velocities are based on high-resolution (150 m) Advanced Synthetic Aperture Radar (ASAR) images from Envisat for winters between 2002 and 2012. Sea ice motion vectors were first correlated with the corresponding Antarctic Mesoscale Prediction System (AMPS) surface wind velocities, and the sensitivity of the spatial correlations and residuals were examined. Four drift parameters were selected (mean drift, the correlation between drift and wind, drift to wind scaling factor, and the directional drift constancy) to perform an unsupervised k-means classification to automatically distinguish six zones of distinctive sea ice characteristics solely based on ice drift and wind information. Results indicate a heterogeneous pattern of sea ice movement at a rate ranging from 0.41 to 2.24% of the wind speed in different areas. We also find that the directional constancy of sea ice drift is closely related to the wind fields. Sea ice drift and wind velocities display the highest correlation in free-drift areas (R = 0.70), followed by deformational drift zones (R = 0.54), and more random drift areas (R = 0.28). The classification illustrates the significance of localized wind-driven sea ice drift in this coastal area resulting in zones of convergence, shear, and free drift. The results also indicate that the most persistent patterns of sea ice motion are near the RSP and TNBP areas, both being driven by strong localized winds. Our findings identify that large-scale sea ice motion is predominantly wind-driven over much of the study area while ocean currents play only a minor role. [ABSTRACT FROM AUTHOR]

Abstract The classification of Synthetic Aperture Radar (SAR) images by knowledge-based algorithms with elevation and backscatter thresholds were used in several studies to detect the Wet Snow Radar Zone (WSZ) in the Antarctic Peninsula. To identify it more accurately based on its seasonal variations, this study proposed the additional use of a threshold in synthetic images, created by rationing summer and winter sigma linear images. In our algorithm we used the following thresholds to detect the WSZ in Envisat ASAR imageries, using the Radarsat Antarctic Map Digital Elevation Model as ancillary data: i) -25 dB < s0 < -14 dB; ii) slinear summer / slinear winter < 0.4; iii) elevation H < 1,200 m for northern tip and H < 800 m for southern tip of the Antarctic Peninsula. The classified images were post-processed by a focal majority 5 x 5 filter and superimposed by an image of rock outcrops derived from the Antarctic Digital Database. The ratio image threshold allowed discriminating the WSZ from the Dry Snow Radar Zone and radar shadows, as well as transitional areas between this glacier zone and the Frozen Percolation Radar Zone, which would be classified incorrectly if we used only elevation and backscatter thresholds.

Permafrost in Qinghai–Tibet Plateau (QTP) has been suffering from global warming in recent years, characterized by the deepening of the permafrost active layer. Seasonal changes in permafrost are usually reflected as ground surface deformation, which can be monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR) technology. Owing to the extreme environment in the QTP, there are few ground-based deformational observation data available, and records of permafrost monitoring by MT-InSAR with ground validation are limited. Here we present a study of surface deformation monitoring for permafrost with MT-InSAR technology validated by a large number of in-situ observations compared with the previous published results. In this study, a small baseline subset (SBAS) method was used with ENVISAT ASAR data in WuDaoLiang, QTP, to acquire the surface deformation and to analyze the corresponding characteristics. The results were first validated with 24 GNSS leveling observation points along the Qinghai–Tibet Railway, including numeric validation (e.g., statistics and KS test) between the InSAR derived deformation and the time-interpolated GNSS leveling values, and the variation trend of the two deformation sequences during a permafrost deformation period, at each observation point. Considering both the differences in magnitudes and trends, the deformation at 22 out of 24 points detected by InSAR corresponded well to the GNSS observation series over one year, which indicates the reliability of MT-InSAR for permafrost monitoring. After validation, the amplitudes and linear velocity of the InSAR deformation in this region were calculated and analyzed, together with selected points in different types of terrain. Generally, in the deformation map, most pixels show a trend of periodic and seasonal displacement, uplift in winter and subsidence in summer, with amplitudes of 3–10 mm in most regions. The deformation in mountain areas is less than that of flat lands in amplitude, and shows more randomness in periodic characteristics. Meanwhile, some points with obvious settlement have been detected, probably corresponding with permafrost degradation.

Over-exploitation of groundwater has caused severe land subsidence in Beijing during the past two decades. Since the middle route of South-to-North Water Diversion Project (SNWDP), the biggest water diversion project in China, started to deliver water to Beijing in December 2014, the groundwater shortage has been greatly alleviated. This study aims to analyze the impact of SNWDP on the spatiotemporal evolution of land subsidence in Beijing. Change in surface displacement in Beijing after SNWDP was retrieved and the spatiotemporal patterns of the change were analyzed based on long time-series Envisat Advanced Synthetic Aperture Radar (ASAR) (2004–2010), Radarsat-2 (2011–2014), and Sentinel-1 (2015–2017) satellite datasets using Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) techniques. Land subsidence unevenness index (LSUI) was proposed to represent the spatial unevenness of surface displacement. PS-Time approach was then adapted to examine the time series evolution of LSUI. The results showed that the InSAR measurements agree well with leveling measurements with R2 over 0.96. Although the maximum annual displacement rate reached −159.7 mm/year by 2017, over 57% of the area within 25 mm/year contour line showed decreasing or unchanged displacement rate after the south-north water delivered to Beijing. The settlement rate in Chaoyang-Dongbalizhuang (CD) subsidence center has decreased for 26 mm/year from 2011–2014 to 2015–2017. Only around 15% of the area experienced continued accelerating settlement rate through the three time periods, which was mainly located in the area with the compressible layer thickness over 190 m, while the magnitude of velocity increment considerably decreased after SNWDP. Land subsidence unevenness, represented by LSUI, developed more slowly after SNWDP than that during 2011–2014. However, LSUI at the edge of settlement funnel has kept developing and reached 1.7‰ in 2017. Decreasing groundwater level decline after SNWDP and the positive relationship (R2 > 0.74) between land subsidence and groundwater level clearly showed impacts of SNWDP on the alleviating land subsidence. Other reasons include geological background, increasing precipitation, and strict water management policies implemented during these years.

In this study, digital elevation models (DEMs) derived from AlosPalsar data (Japanese Space Agency—JAXA), Sentinel-1A data, and Envisat ASAR data (European Space Agency—ESA) were compared by using a global navigation satellite system (GNSS). In addition, AW3D30, SRTM, and ASTER GDEM (open-access DEMs) data were also included in the accuracy evaluation. The DEM accuracies were investigated in three different terrain types, namely a plain area, mountainous area and agricultural area, and compared at elevation values on a pixel-based. The accuracy obtained from the ALOS PALSAR satellite data was found to be more reliable for all three terrain types. The standard deviation and root mean square values were calculated and compared to each other. The results of the accuracy assessments showed that the best result for the plain area was obtained with the Sentinel-1A and SRTM data, for the mountainous area was obtained with the SRTM data and for agricultural area was obtained with the ALOS PALSAR and SRTM data. [ABSTRACT FROM AUTHOR]

In recent decades, agricultural plains have been subject to significant subsidence in different parts of the world including Iran. Tehran plain is one of these parts exposed to land subsidence. In this paper, as the most effective method to monitor the subsidence, synthetic aperture radar interferometry (InSAR) makes use of two radar images to measure the ground surface deformation between two acquisitions. For this purpose, two different radar datasets including 16 ENVISAT ASAR images spanning from 2003 to 2008 and 12 Sentinel-1A images spanning from 2016 to 2017 were applied in InSAR small baseline subset (SBAS) time series analysis. Two distinct subsidence time series achieved from the two datasets were integrated using an appropriate model which was fitted to all data obtained from two time series results from 2003 to 2017 and verified running leveling measurements. The average value of the root mean square errors (RMSEs) calculated between the model and the achieved time series at all pixels is 0.02 m which is an illustration of the accuracy of the proposed model to join the two deformation time series results. Furthermore, the integration results were additionally used to discover the storage properties of the aquifer system by stress–strain relationship and the effect of aquitard thickness on subsidence of Tehran plain by subsidence-aquitard thickness (fine-grained interbed) relationship, respectively. The stress–strain relationship shows a low correlation between subsidence and groundwater exploitation where a direct relationship between subsidence and aquitard thickness is clearly obvious. [ABSTRACT FROM AUTHOR]

In the context of global warming,the calving events of Antarctic ice shelves are becoming more and more frequent.The icebergs calved from ice shelves are an active component of Antarctic ice sheet-ice shelf-ocean system,while the drifting characteristics and temporal-spatial distribution of icebergs are very important in Antarctic ocean currents,ecology and hydrology.To better understand the underlying relationship between Antarctic icebergs and global climate change,it is essential to investigate the icebergs’ drifting characteristics,mass loss due to calving and melting,icebergs distribution,and the interaction between icebergs and the ocean.Based on the object-oriented multiscale image segmentation algorithm of Easy Interpretation software,a total of 32 267 Antarctic icebergs in coastal waters with area larger than 0.06 km2 were extracted from 63 ENVISAT ASAR images acquired in August 2006.The spatial distribution characteristics of icebergs were also analyzed,and it is found that small-sized icebergs play an important role in freshwater flux into the Southern Ocean. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to propose a newly developed ocean Significant Wave Height (SWH) retrieval method from Envisat Advanced Synthetic Aperture Radar (ASAR) imagery. A series of wave mode imagery from January, April and May of 2011 are collocated with ERA-Interim reanalysis SWH data. Based on the matched datasets, a simplified empirical relationship between 22 types of SAR imagery parameters and SWH products is developed with the Genetic Algorithms Partial Least-Squares (GA-PLS) model. Two major features of the backscattering coefficient S10 and the frequency parameter S10 are chosen as the optimal training feature subset of SWH retrieval by using cross validation. In addition, we also present a comparison of the retrieval results of the simplified empirical relationship with the collocated ERA-Interim data. The results show that the assessment index of the correlation coefficient, the bias, the root-mean-square error of cross validation (RMSECV) and the scattering index (SI) are 0.78, 0.07 m, 0.76 m and 0.5, respectively. In addition, the comparison of the retrieved SWH data between our simplifying model and the Jason-2 radar altimeter data is proposed in our study. Moreover, we also make a comparison of the retrieval of SWH data between our developed model and the wellknown CWAVE_ENV model. The results show that satisfying retrieval results are acquired in the low-moderate sea state, but major bias appears in the high sea state, especially for SWH>5 m. [ABSTRACT FROM AUTHOR]

Abstract Due to highly compressible soil and a large amount of human activity, the costal deltas are more prone to ground subsidence. Many major costal deltas in the world are facing subsidence and are consequently more susceptible to flooding, salinization and seawater infusion or even permanent submergence. Therefore, ground subsidence has been a significant problem in coastal delta areas worldwide. The Yellow River Delta (YRD) is the second largest river delta in China. On the one hand, the YRD contains a large area of wetlands rich in biodiversity, and on the other hand, industrial activities and urbanization are extensive due to abundant underground resources such as oil, gas and brine. Excessive land use has caused different degrees of ground subsidence in this area. However, a detailed and comprehensive description of the ground subsidence pattern over the YRD has not been provided. Also, widespread non-urban area in Yellow River Delta region, such as wetlands, farmland and coastal tidal areas, hinders the application of persistent scatterer interferometry method (PSI) for comprehensive subsidence measurement over the whole area. In this paper, we developed a multitemporal InSAR method to map ground subsidence over the YRD area by exploiting both persistent scatterers (PS) and distributed scatterers (DS). This method is characterized by employing the coherence-weighted phase-linking algorithm for fast and reliable optimal phase reconstruction of each DS point and a two-tier network of PS and DS for the robust analysis of land subsidence. To extract the detailed and comprehensive ground subsidence over the whole YRD, we apply our method to 30 ENVISAT ASAR images (2007–2010) and 49 Sentinel-1A (S-1A) images (2015–2018) and obtain measurements of the ground subsidence during these two periods. Forty-one Sentinel-1B (S-1B) images (2016–2018) are also exploited for cross-sensor consistency validation with the result derived from the S-1A dataset. Our method shows a great advantage over the PSI method, providing much higher measuring point (MP) density in mapping land subsidence over the YRD, including 15-fold higher density for the ASAR dataset, 5.1-fold for the S-1A dataset and 5.3-fold for the S-1B dataset, which enables a very detailed description of local ground deformation patterns. Cross-track consistency in the derived measurements from the S-1A and S-1B datasets shows a standard deviation of 9.6 mm/yr for the vertical subsidence rate. A quantitative validation of the derived subsidence results compared with leveling measurements suggests an accuracy of 4.58 mm/yr for the standard deviation term. By comparing the ground deformation over the YRD during the periods of 2015–2018 and 2007–2010, we find that the subsidence in this region shows an overall intensification trend and many new and severe subsidence depressions appear along the coastline, with a maximum vertical subsidence rate of 432 mm/yr. Subsequently, the overextraction of underground brine for salt production is identified as the primary factor causing the ground subsidence near the YRD coastal area. [ABSTRACT FROM AUTHOR]

In the presented research ERS1-2 and Envisat ASAR archive data were used for the periods 1993 – 2000 and 2003 – 2010. The radar images were acquired over Upper Silesia in southern Poland. DinSAR (Differential InSAR) and SBAS (Small Baseline Subset) methods were applied for the detection of the most subsided areas. The DinSAR images were layer stacked for an image using 26 interferometry pairs of ERS1-2 SAR and 16 pairs from Envisat ASAR images in an ascending-descending orbit combination. The stacking of these images showed the most subsided parts of these cities even under low coherent areas, but the results are less precise. In the Upper Silesian Coal Basin, intensive underground coal exploitation has resulted in several surface deformations under Bytom (~8-17 km2), Piekary Śląskie (~9-15 km2), Ruda Śląska (~32-42 km2) and Katowice (~20-23 km2) with 25-40 cm of subsidence (in general) in the studied time periods. The SBAS technique has also shown that coal mining caused subsidence in the cities of Bytom, Katowice, and Piekary Śląskie of 5-7 cm/yr. The presented SBAS method did not work for low coherent areas, e.g. dense forested areas. DInSAR data also pointed to several decreasingly less active mining areas, which relate to the mine closures in Bytom and Ruda Śląska, which is also verified by the time series analysis.

The Ross Sea region, including three main polynya areas in McMurdo Sound, Terra Nova Bay, and in front of the Ross Ice Shelf, has experienced a significant increase in sea ice extent in the first four decades of satellite observations. Here, we use Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) to estimate 894 high-resolution sea ice motion fields of the Western Ross Sea in order to explore ice-atmosphere interactions based on sequential high-resolution Advanced Synthetic Aperture Radar (ASAR) images from the Envisat satellite acquired between 2002–2012. Validation of output motion vectors with manually drawn vectors for 24 image pairs show Pearson correlation coefficients of 0.92 ± 0.09 with a mean deviation in direction of −3.17 ± 6.48 degrees. The high-resolution vectors were also validated against the Environment and Climate Change Canada sea ice motion tracking algorithm, resulting in correlation coefficients of 0.84 ± 0.20 and the mean deviation in the direction of −0.04 ± 17.39 degrees. A total of 480 one-day separated velocity vector fields have been compared to an available NSIDC low-resolution sea ice motion vector product, showing much lower correlations and high directional differences. The high-resolution product is able to better identify short-term and spatial variations, whereas the low-resolution product underestimates the actual sea ice velocities by 47% in this important near-coastal region. The large-scale pattern of sea ice drift over the full time period is similar in both products. Improved image coverage is still desired to capture drift variations shorter than 24 h.

We analyzed the evolution processes of two tabular icebergs, C28A and C28B, originating from iceberg C28, using continuous multisource radar remote sensing data. The monitoring began during the calving of iceberg C28 from the Mertz Ice Tongue in February 2010 and ended in April 2012. The evolution of the iceberg area was determined from ENVISAT ASAR images, and the freeboard change was derived from CryoSat-2 profiles. Two patterns of iceberg area decreases are observed during iceberg drifting. In austral summer (from October to next March), iceberg area shows a gradual decrease and it is interpreted to represent edge wasting, sidewall melting and wave erosion, the gradual decrease of the area of C28A occurred at rates between 8.1 km 2 /month and 16.5 km 2 /month. In austral winter (from April to September), iceberg area has several discrete decreases (sudden drop) while it remained constant for most of the time, and it is interpreted to be large rift calving primarily caused by ocean swell and basal melting. Calving area of the two studied icebergs is observed to be greater in the winter. The freeboard decreased at different rates at different locations on the same iceberg (between −0.02 ± 0.22 and −0.82 ± 0.28 m/month for C28A and C28B), suggesting a laterally inhomogeneous melting of icebergs. The average rate of change of the C28B freeboard during its 22-month grounding period was −0.03 ± 0.01 m/month, which is considerably smaller than the −0.76 ± 0.23 m/month observed when it was freely drifting in the ocean. The cold-water regime surrounding the iceberg can help the iceberg maintain a stable state. This study reveals the potential use of radar remote sensing when monitoring Antarctic icebergs. [ABSTRACT FROM AUTHOR]

There are only a few examples in technical literature about the use of Synthetic Aperture Radar (SAR) dataset to extract human settlement extents. This paper shows how it is possible to extract a global urban layer from multiyear ENVISAT Advanced SAR (ASAR) backscatter measurements, overcoming some of the drawbacks of a preliminary release. Additionally, this research shows that the same technique can be applied with minor changes to SAR data from the new Sentinel-1A platform. The current ASAR urban layer shows improvements with respect to the preliminary version in the test sites of the 2011 Round Robin (RR) on urban mapping promoted by the European Space Agency (ESA). For Sentinel-1, results are similarly validated on the test sites of the 2015 edition of the RR on urban mapping. Results prove that UEXT can be used for SAR-based urban extent extraction using multiple sensors. [ABSTRACT FROM AUTHOR]

Large-scale reclamation projects during the past decades have been recognized as one of the driving factors behind land subsidence in coastal areas. However, the pattern of temporal evolution in reclamation settlements has rarely been analyzed. In this work, we study the spatio-temporal evolution pattern of Linggang New City (LNC) in Shanghai, China, using space-borne synthetic aperture radar interferometry (InSAR) methods. Three data stacks including 11 X-band TerraSAR-X, 20 L-band ALOS PALSAR, and 35 C-band ENVISAT ASAR images were used to retrieve time series deformation from 2007 to 2010 in the LNC. An InSAR analysis from the three data stacks displays strong agreement in mean deformation rates, with coefficients of determination of about 0.9 and standard deviations for inter-stack differences of less than 4 mm/y. Meanwhile, validations with leveling data indicate that all the three data stacks achieved millimeter-level accuracies. The spatial distribution and temporal evolution of deformation in the LNC as indicated by these InSAR analysis results relates to historical reclamation activities, geological features, and soil mechanisms. This research shows that ground deformation in the LNC after reclamation projects experienced three distinct phases: primary consolidation, a slight rebound, and plateau periods. [ABSTRACT FROM AUTHOR]

Nowadays, spaceborne Synthetic Aperture Radar (SAR) has become a powerful tool for providing significant wave height (SWH). Traditionally, validation of SAR derived SWH has been carried out against buoy measurements or model outputs, which only yield an inter-comparison, but not an “absolute” validation. In this study, the triple collocation error model has been introduced in the validation of Envisat ASAR derived SWH products. SWH retrievals from ASAR wave mode using ESA’s algorithm are validated against in situ buoy data, and wave model hindcast results from WaveWatch III wave model, covering a period of six years. From the triple collocation validation analysis, the impacts of the collocation distance and water depth on the error of ASAR SWH are discussed. It is found that the error of Envisat ASAR SWH product is linear to the collocation distance, and decrease with the decreasing collocation distance. Using the linear regression fit method, the absolute error of Envisat ASAR SWH was obtained with zero collocation distance. The absolute Envisat ASAR wave height error of 0.49 m is presented in deep and open ocean from this triple collocation validation work, in contrast to a larger error of 0.56 m in coastal and shallow waters. One of the reasons for the larger Envisat ASAR SWH errors in the coastal waters may be the inaccurate Modulation Transfer Function (MTF) adopted in the Envisat ASAR wave retrieval algorithm.

A surface soil moisture (SSM) product at a 1-km spatial resolution derived from the Envisat Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode data was evaluated over the entire African continent using coarse spatial resolution SSM acquisitions from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Noah land surface model from the Global Land Data Assimilation System (GLDAS-NOAH). The evaluation was performed in terms of relative soil moisture values (%), as well as anomalies from the seasonal cycle. Considering the high radiometric noise of the ASAR GM data, the SSM product exhibits a good ability (Pearson correlation coefficient (R) = ~0.6 for relative soil moisture values and root mean square difference (RMSD) = 11% when averaged to 5-km resolution) to monitor temporal soil moisture variability in regions with low to medium density vegetation and yearly rainfall >250 mm. The findings agree with previous evaluation studies performed over Australia and further strengthen the understanding of the quality of the ASAR GM SSM product and its potential for data assimilation. Problems identified in the ASAR GM algorithm over arid regions were explained by azimuthal effects. Diverse backscatter behavior over different soil types was identified. The insights gained about the quality of the data were used to establish a reliable masking of the existing ASAR GM SSM product and the identification of areas where further research is needed for the future Sentinel-1-derived SSM products.

Sirjan located in Southwest of Kerman City, Iran, is a city with average annual rainfall of 132mm. Over-exploitation of groundwater used for irrigation of pistachios gardens has caused serious land subsidence in Sirjan Plain. In this research we have used the Interferometric Synthetic Aperture Radar technique to estimate the subsidence rate in Sirjan plain. 18 Interferograms extracted from 12 ENVISAT ASAR images spanning between April 2004 and September 2010, have been studied. The SBAS algorithm was then applied in order to estimate the mean subsidence velocity map. Maximum subsidence rate is estimated as 28cm/yr. Furthermore, groundwater level fluctuations in the study area has been investigated in the piezometer wells located in the study area. Comparing between the results obtained from the interferometry and groundwater level fluctuation maps, shows a strong correlation between head decline in groundwater and land subsidence. [ABSTRACT FROM AUTHOR]

The annual flood cycle of the Sudd wetland in South Sudan plays an important role in the Nile River Basin water balance. The wetland, however, is extensive and sparsely instrumented, which has inhibited credible understanding of regional flooding across space and time. Here we explore the potential to apply low resolution C-band ENVISAT Advanced SAR imagery for remote estimation of Sudd flooded area. Over a five year study period (2007–2011) the time-averaged flooded Sudd area was found to be 18,033 km 2 with an average annual high of 29,702 km 2 in late September and a low of 10,128 km 2 in early May. Annual peak flood area ranges considerably from 19,259 km 2 in 2009 to 36,649 km 2 in 2007, but we found no systematic trend over the five year study period. Derived flood frequency maps identify areas of open water and permanent flooding (12% of total area), seasonal flooding (29%), and intermittent flooding (48%). To evaluate the certainty of our results, we consider their consistency with (1) prior studies, (2) evapotranspiration estimates from the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, (3) watershed storage anomaly estimates from GRACE, (4) supervised classification of open water area using Landsat, and (5) a rough measure of water availability (antecedent precipitation). The analyses show reasonable temporal and spatial consistency with available lines of evidence. We conclude that low resolution C-band SAR imagery shows promise for study of Sudd wetland flood dynamics. [ABSTRACT FROM AUTHOR]

Since Lagrangian model coefficients vary with different conditions, it is necessary to calibrate the model to obtain optimal coefficient combination for special oil spill accident. This paper focuses on proposing a new method to calibrate Lagrangian model with time series of Envisat ASAR images. Oil slicks extracted from time series images form a detected trajectory of special oil slick. Lagrangian model is calibrated by minimizing the difference between simulated trajectory and detected trajectory. mean center position distance difference (MCPD) and rotation difference (RD) of Oil slicks' or particles' standard deviational ellipses (SDEs) are calculated as two evaluations. The two parameters are taken to evaluate the performance of Lagrangian transport model with different coefficient combinations. This method is applied to Penglai 19-3 oil spill accident. The simulation result with calibrated model agrees well with related satellite observations. It is suggested the new method is effective to calibrate Lagrangian model. [ABSTRACT FROM AUTHOR]

Peatlands are one of the most important types of wetlands and significant to the balance of global change and ecosystems. On the basis of Held investigation and back-scatter coefficient comparison of different land cover types in different polarization radar images， ENVISAT ASAR , Landsat TM，and DEM data were taken as basic data for a classitication method combining object-oriented and decision tree approaches that were to extract peatlands and adjacent land cover types in a typical peatland zone located in West Xiao-Xing? an Mountains. The overall classification accuracy was 93. 54% with a Kappa coefficient of 0. 92, which indicates that the proposed method used is effective for peatland extraction. Compared to previous work, there is some improvement in classification accuracy. [ABSTRACT FROM AUTHOR]

An object-based method for automatic iceberg detection from Advanced Synthetic Aperture Radar (ASAR) images has been developed and applied in the Amundsen Sea, Antarctica. The automatic identification is based on brightness and spatial parameters of the ASAR images at five scale levels, and was verified with manual classification in four areas chosen to represent varying environmental conditions. The presented algorithm works comparatively well with images of the ocean in freezing temperatures and strong wind conditions, common in the Amundsen Sea. The detection rate was 96.2% which corresponds to 93.2% of the icebergs area, for all seasons. The algorithm generated 3.8% errors in the form of ‘misses’ and 7.0% of ‘false alarms’, mainly caused by the presence of ice floes. The method was applied on 432 radar images acquired in 2011 under different meteorological, oceanographic and sea ice conditions. As an output a map showing the probability of finding icebergs has been created. It shows that high probability coincides with depth contours and indicates a westward drift of the bergs throughout the whole region. [ABSTRACT FROM AUTHOR]

A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Québec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.3×106 km2 were mapped with a 0.01° pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1° and 0.5° was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5° was consistently within a magnitude of 20–30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available information.

The recent observed thinning of the glacier ice shelves in the Amundsen Sea (Antarctica) has been attributed to warm deep currents, possibly induced by along-coast winds in the vicinity of the glacial ice sheet. Here, high resolution maps of wind fields derived from Synthetic Aperture Radar (SAR) data have been studied and correlated with subsurface measurements of the deep water velocities in the Amundsen Sea area. Focus is on periods with low ice coverage in 2010 and 2011. In 2010, which had comparatively low ice coverage, the results indicate a more rapid response to wind forcing in the deep currents than in 2011. The SAR wind speed maps have better spatial resolution than available reanalysis data, and higher maximum correlation was obtained with SAR data than with reanalysis data despite the lower temporal resolution. The maximum correlation was R = 0.71, in a direction that is consistent with wind-driven Ekman theory. This is significantly larger than in previous studies. The larger correlation could be due to the better spatial resolution or the restriction to months with minimum ice coverage. The results indicate that SAR is a useful complement to infer the subsurface variability of the ocean circulation in remote areas in polar oceans.

Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR), and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) radar signals at horizontally emitted and received polarization (HH) for pasture monitoring at the paddock scale in order to guide farmers for better management. The pasture site is selected, in Otway, Victoria, Australia. The biomass, water content of grass, and soil moisture over this site were analyzed with these three bands of SAR images, through linear relationship between SAR backscattering coefficient, and vegetation indices Normalized Differential Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI)), together with soil moisture index (MI). NDVI, NDWI, and MI are considered as proxy of pasture biomass, plant water content, and soil moisture, respectively, and computed from optical images and climate data. SAR backscattering coefficient and vegetation indices are computed within a grass zone, defined by classification with MODIS data. The grass condition and grazing activities for specific paddocks are detectable, based on SAR backscatter, with all three wavelengths datasets. Both temporal and spatial analysis results show that the X-band SAR has the highest correlation to the vegetation indices. However, its accuracy can be affected by wet weather due to its sensitivity to the water on leaves. The C-band HH backscattering coefficient showed moderate reliability to evaluate biomass and water content of grass, with limited influence from rainfall in the dry season. The L-band SAR is the less accurate one for grass biomass measurement due to stronger penetration.

An automatic classification approach is performed to classify major crop types cultivated in Karacabey Plain, Bursa, through multi-temporal Kompsat-2 and Envisat ASAR data. First, the single-date pancromatic and multispectral Kompsat-2 images are fused with an appropriate image fusion method and 1m colour Kompsat-2 images are generated. Next, different parameter combinations are applied on the fused images in spatial and colour space to find out the optimum segmentation results. The optimum segments are then evaluated using multiple evaluation criteria. Two different classification approaches, pixel-based and segment-based, are tested in this study. First, Image classification are performed on the multispectral Kompsat-2 images. Then the Kompsat-2 images (4m) are classified with Envisat ASAR data. In this way contribution of the Envisat ASAR images to the classification accuracy are tested. Next, distance maps are produced for each thematic map to combine the information of multi-temporal images.The produced thematic maps are evaluated based on pixel-based and segment-based manner using confusion matrices. Results indicate that Envisat ASAR data improve the accuracy of thematic maps. The highest accuracies are obtained for the combined thematic maps of June-August and June-July-August (%88.71 overall accuracy and 0.86 kappa) computed for the segment-based approach.

The applicability of optical and synthetic aperture radar (SAR) data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) MRV (measuring, reporting and verification) services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment. The overall accuracy for forest and non-forest classification varied between 95% for the RapidEye classification and 74% for the Envisat ASAR classification. For more detailed land cover classification, the accuracies varied between 89% and 70%, respectively. A combination of Landsat TM and ALOS PALSAR data sets provided only 1% improvement in the overall accuracy. The biases were small in most classifications, varying from practically zero for the Landsat TM based classification to a 7% overestimation of forest area in the Envisat ASAR classification. Considering the pros and cons of the data types, we recommend optical data of 10 m spatial resolution as the primary data source for REDD MRV purposes. The results with L-band SAR data were nearly as accurate as the optical data but considering the present maturity of the imaging systems and image analysis methods, the L-band SAR is recommended as a secondary data source. The C-band SAR clearly has poorer potential than the L-band but it is applicable in stratification for a statistical sampling when other image types are unavailable.

The floods are an annual phenomenon on the Pacific Coast of Ecuador and can become devastating during El Niño years, especially in the Guayas watershed (32,300 km2), the largest drainage basin of the South American western side of the Andes. As limited information on flood extent in this basin is available, this study presents a monitoring of the spatio-temporal dynamics of floods in the Guayas Basin, between 2005 and 2008, using a change detection method applied to ENVISAT ASAR Global Monitoring SAR images acquired at a spatial resolution of 1 km. The method is composed of three steps. First, a supervised classification was performed to identify pixels of open water present in the Guayas Basin. Then, the separability of their radar signature from signatures of other classes was determined during the four dry seasons from 2005 to 2008. In the end, standardized anomalies of backscattering coefficient were computed during the four wet seasons of the study period to detect changes between dry and wet seasons. Different thresholds were tested to identify the flooded areas in the watershed using external information from the Dartmouth Flood Observatory. A value of -2.30 ± 0.05 was found suitable to estimate the number of inundated pixels and limit the number of false detection (below 10%). Using this threshold, monthly maps of inundation were estimated during the wet season (December to May) from 2004 to 2008. The most frequently inundated areas were found to be located along the Babahoyo River, a tributary in the east of the basin. Large interannual variability in the flood extent is observed at the flood peak (from 50 to 580 km2), consistent with the rainfall in the Guayas watershed during the study period. [ABSTRACT FROM AUTHOR]

The mapping of water bodies at global scale has been undertaken primarily using multi-spectral optical Earth Observation data. Limitations of optical data associated with non-uniform and temporally variable spectral signatures suggested investigating alternative approaches towards a more consistent and reliable detection of water bodies. Multi-year (2005–2012) observations of SAR backscattered intensities at moderate resolution from the Envisat Advanced Synthetic Aperture Radar (ASAR) instrument were used in this study to generate an indicator of open permanent water bodies (SAR-WBI) for the year 2010 time frame and for all land surfaces excluding Antarctica and the Greenland ice sheet. A first map of potential water bodies with a spatial resolution of 150 m was obtained with a global detection algorithm based on a set of thresholds applied to multi-temporal metrics of the SAR backscatter (temporal variability, TV, and minimum backscatter, MB). Local refinements were then used to reduce systematic commission and omission errors (4.6% of the total area mapped) due to the similarity of TV and MB over open water bodies and other land surface types primarily in cold and arid environments. The refinement rules are here explained by means of a detailed signature analysis of the SAR backscatter in such environments. The accuracy of the SAR-WBI was 80% when compared against 2078 manually interpreted footprints with a size of 150 × 150 m 2 . Omission errors were primarily observed along coast- and shorelines whereas commission errors were associated with (i) ephemeral water bodies, (ii) seasonally inundated areas, and (iii) an incorrect choice of the local refinement. [ABSTRACT FROM AUTHOR]

This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10°N) from hyper-temporal observations of Envisat Advanced Synthetic Aperture Radar (ASAR) backscattered intensity using the BIOMASAR algorithm. Approximately 70,000 ASAR images at a pixel size of 0.01° were used to estimate GSV representative for the year 2010. The spatial distribution of the GSV across four ecological zones (polar, boreal, temperate, subtropical) was well captured by the ASAR-based estimates. The uncertainty of the retrieved GSV was smallest in boreal and temperate forest (< 30% for approximately 80% of the forest area) and largest in subtropical forest. ASAR-derived GSV averages at the level of administrative units were mostly in agreement with inventory-derived estimates. Underestimation occurred in regions of very high GSV (> 300 m 3 /ha) and fragmented forest landscapes. For the major forested countries within the study region, the relative RMSE between ASAR-derived GSV averages at provincial level and corresponding values from National Forest Inventory was between 12% and 45% (average: 29%). [ABSTRACT FROM AUTHOR]

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 30 m 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 30 m 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. [ABSTRACT FROM AUTHOR]

A common precursor to ice shelf disintegration, most notably that of Larsen B Ice Shelf, is unusually intense or prolonged surface melt and the presence of surface standing water. However, there has been little research into detailed patterns of melt on ice shelves or the nature of summer melt ponds. We investigated surface melt on Larsen C Ice Shelf at high resolution using Envisat advanced synthetic aperture radar (ASAR) data and explored melt ponds in a range of satellite images. The improved spatial resolution of SAR over alternative approaches revealed anomalously long melt duration in western inlets. Meteorological modelling explained this pattern by föhn winds which were common in this region. Melt ponds are difficult to detect using optical imagery because cloud-free conditions are rare in this region and ponds quickly freeze over, but can be monitored using SAR in all weather conditions. Melt ponds up to tens of kilometres in length were common in Cabinet Inlet, where melt duration was most prolonged. The pattern of melt explains the previously observed distribution of ice shelf densification, which in parts had reached levels that preceded the collapse of Larsen B Ice Shelf, suggesting a potential role for föhn winds in promoting unstable conditions on ice shelves. [ABSTRACT FROM AUTHOR]

The objective of this research is to classify major crop types cultivated in Karacabey Plain of north western Turkey using multitemporal Kompsat-2 and Envisat ASAR data with an object-based methodology. First a pansharpening algorithm is applied to each panchromatic and multispectral Kompsat-2 data to produce colour images having 1m spatial resolution. Next, Mean-Shift image segmentation procedure is applied to the pansharpened Kompsat-2 data with multiple parameter combinations. Multiple goodness measures are utilized to evaluate the object-based results. The optimum objects are then employed in object-based classifications of the single-date images. Next, single-date multispectral (MS) Kompsat-2 images and Kompsat-2 images along with the Envisat ASAR data are classified with the Support Vector Machines (SVMs) method. The training samples are provided automatically by the selected objects based on spatial statistical properties. Next, probability maps are generated for each image in pixel-based manner during the image classification operations. The maximum probabilities are then assigned to the pixels as class labels and the combined thematic maps (June-July, June-August, June-July-August) are generated in pixel-based and object-based manners. The produced thematic maps are evaluated through the confusion matrices and compared also with the results of parcel-based classifications using original agricultural parcels. Results indicate that the combined thematic maps of June-August and June-July- August provide the highest overall accuracy and kappa value approximately 92% and 0.90, respectively. [ABSTRACT FROM AUTHOR]

A surface soil moisture (SSM) product at a 1-km spatial resolution derived from the Envisat Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode data was evaluated over the entire African continent using coarse spatial resolution SSM acquisitions from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Noah land surface model from the Global Land Data Assimilation System (GLDAS-NOAH). The evaluation was performed in terms of relative soil moisture values (%), as well as anomalies from the seasonal cycle. Considering the high radiometric noise of the ASAR GM data, the SSM product exhibits a good ability (Pearson correlation coefficient (R) = ~0.6 for relative soil moisture values and root mean square difference (RMSD) = 11% when averaged to 5-km resolution) to monitor temporal soil moisture variability in regions with low to medium density vegetation and yearly rainfall >250 mm. The findings agree with previous evaluation studies performed over Australia and further strengthen the understanding of the quality of the ASAR GM SSM product and its potential for data assimilation. Problems identified in the ASAR GM algorithm over arid regions were explained by azimuthal effects. Diverse backscatter behavior over different soil types was identified. The insights gained about the quality of the data were used to establish a reliable masking of the existing ASAR GM SSM product and the identification of areas where further research is needed for the future Sentinel-1-derived SSM products. [ABSTRACT FROM AUTHOR]

Multi-temporal synthetic aperture radar (SAR) metrics are assessed to map open water bodies. High temporal variability and low minimum value in a time series of Envisat Advanced SAR (ASAR) Wide Swath Mode (WSM) backscatter measurements characterize open water bodies with respect to other land cover types. Confusion occurs in the case of steep terrain (slope angle > 10°), less than 10 backscatter observations and for mixed pixels with a water fraction. The behavior of the two SAR multi-temporal metrics is consistent at six study areas in Europe and Central Siberia. A simple thresholding algorithm applied to the multi-temporal SAR metrics to map open water bodies performs with overall accuracies above 90% in the case of pure pixels of water or land. The accuracy decreases when mixed pixels are accounted for in the reference dataset and for increasing land fraction in the reference samples. An overall accuracy of approximately 80% was obtained for a 50% threshold of the water fraction. Omissions of water areas occur mostly along shorelines. Specific conditions of the land surface can distort the minimum, causing commission in the water class. The use of a low order rank or percentile instead of the lowest backscatter value can reduce such commission error. [ABSTRACT FROM PUBLISHER]

Growing stock volume is an important biophysical parameter describing the state and dynamics of the Boreal zone. Validation of growing stock volume (GSV) maps based on satellite remote sensing is challenging due to the lack of consistent ground reference data. The monitoring and assessment of the remote Russian forest resources of Siberia can only be done by integrating remote sensing techniques and interdisciplinary collaboration. In this paper, we assess the information content of GSV estimates in Central Siberian forests obtained at 25 m from ALOS-PALSAR and 1 km from ENVISAT-ASAR backscatter data. The estimates have been cross-compared with respect to forest inventory data showing 34% relative RMSE for the ASAR-based GSV retrievals and 39.4% for the PALSAR-based estimates of GSV. Fragmentation analyses using a MODIS-based land cover dataset revealed an increase of retrieval error with increasing fragmentation of the landscape. Cross-comparisons of multiple SAR-based GSV estimates helped to detect inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands. [ABSTRACT FROM AUTHOR]

Mt. Baekdu, located in the northeastern Korean peninsula close to the Korea-China border, has been documented to have had a few destructive eruptions in history. Even though it has been dormant since the major eruption in 1702 A.D, the seismic activity in Mt. Baekdu has increased over the last few years. Together with the small-scale but constant earthquakes, other precursors, such as increasing geothermal activity in hot springs and emission of gaseous components, have also been identified. Since the reactivation of the volcano is suspected, we applied the Pifferential Inter Ferometric Synthetic Aperture Radar (D-InSAR) processing over Mt. Baekdu to detect possible topographic deformation caused by magma activities. To address the harsh environmental factors, such as water vapor, heavy vegetation canopy and steep slope around summit, for D-InSAR processing, the SBAS approach was applied to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data together with ALOS PALSAR two pass D-InSAR analysis for the comparison and a clear deformation signal over the monitoring area was detected. In spite of the absence of direct measurements, the deformation in Small Baseline Subsets (SBAS) interpretation can be taken as the evidence for possible volcanic activity and employed as part of a warning system. Based on the findings revealed in this paper, it is strongly proposed that an intensive observation network covering the extent of Mt. Baekdu, together with international collaboration, is urgently required. [ABSTRACT FROM AUTHOR]

In this paper, an operational algorithm is proposed for the mapping of surface moisture over the northern and central parts of Tunisia, in North Africa. A change detection approach is applied, using 160 multiincidence Envisat ASAR Wide Swath images acquired in the horizontal polarization over a 7 year period. Parameterization of this algorithm is considered for three classes of vegetation cover density (NDVI < 0.25, 0.25 < NDVI < 0.5, and NDVI > 0.5), retrieved from SPOT-VGT decadal images. A relative soil moisture index, ranging between 0 (for the driest surfaces) and 1 (for saturated soils), is proposed for each date, with a resolution of 1 km. The retrieved soil moistures are validated by means of ground measurements based on continuous thetaprobe measurements, as well as low-resolution (25 km) ERS and ASCAT soil moisture products from the Vienna University of Technology (TU Wien). A qualitative relationship between spatiotemporal variations of moisture and precipitation is also discussed. [ABSTRACT FROM AUTHOR]

Satellite Synthetic Aperture Radar ( SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm- and tidal-related flooding of spatially extensive coastal marshes within the north-central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite ( ALOS) Phased Array type L-Band SAR ( PALSAR) (L-band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite ( ENVISAT) Advanced SAR ( ASAR) (C-band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006-2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR- and ASAR-based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference-scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR-based inundation accuracies averaged 84% ( n = 160), while ASAR-based mapping accuracies averaged 62% ( n = 245). [ABSTRACT FROM AUTHOR]

This paper examines the relationship of C-band radar backscatter from the Advanced Synthetic Aperture Radar on board the ENVISAT satellite with the local angle of incidence, whose influence on the received signal is significant, particularly in the modes of sensor operation that use the full swath of the orbit track. Linear regression is carried out for each pixel throughout a large time series of radar data over the whole of the state of Queensland, Australia, and at Great Salt Lake, Utah, USA. In the first case, the resultant coefficients are analysed for correlation against various parameters, with regolith showing the highest correlation. Class separability analysis shows the potential to use the resultant coefficients as a supplement to absolute threshold values in order to distinguish between classes of vegetation and/or geology, where cloud cover may preclude the use of optical data. It is observed that the separability between water and land is greatly higher using the slope coefficient B than using backscatter σ 0 , which may be of great benefit in the remote sensing of water where cloud cover is present (from which radar is largely independent). This is especially the case when considering the observed overlapping of backscatter values from water, with values from aeolian sand and lacustrine and alluvial sediments, rendering the use of backscatter alone problematic. In order to test the potential use of B to map water extents, the study over the Great Salt Lake compares the classification accuracy of B against that of σ 0 . It is found that the σ 0 classification misrepresents desert, salt flat and dry lake basin areas, where the B classification differentiates these regions accurately. The resultant classification achieves a kappa statistic around 0.9, which shows very high conformance. An accurate and novel method to classify water is therefore demonstrated, which awaits the launch of anticipated improved synthetic aperture radar instruments on satellite missions in the coming few years. [ABSTRACT FROM AUTHOR]