ERS-1 Synthetic Aperture Radar Images Research Articles

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Synthetic Aperture Radar (SAR) data have proven to be a very useful source of information for the calibration of flood inundation models. Previous studies have focused on assigning uncertainties to SAR images in order to improve flood forecast systems (e.g. Giustarini et al. (2015) and Stephens et al. (2012)). This paper investigates whether the timing of a SAR acquisition of a flood has an important impact on the calibration of a flood inundation model. As no suitable time series of SAR data exists, we generate a sequence of consistent SAR images through the use of a synthetic framework. This framework uses two available ERS-2 SAR images of the study area, one taken during the flood event of interest, the second taken during a dry reference period. The obtained synthetic observations at different points in time during the flood event are used to calibrate the flood inundation model. The results of this study indicate that the uncertainty of the roughness parameters is lower when the model is calibrated with an image taken before rather than during or after the flood peak. The results also show that the error on the modelled extent is much lower when the model is calibrated with a pre-flood peak image than when calibrated with a near-flood peak or a post-flood peak image. It is concluded that the timing of the SAR image acquisition of the flood has a clear impact on the model calibration and consequently on the precision of the predicted flood extent.

Deriving Changjiang coastal zone wind from C-band SAR and its application to salinity simulation

Wind plays an important role in hydrodynamic processes such as the expansion of Changjiang (Yangtze) River Diluted Water (CDW), and shelf circulation in the Changjiang estuary. Thus, it is essential to include wind in the numerical simulation of these phenomena. Synthetic aperture radar (SAR) with high resolution and wide spatial coverage is valuable for measuring spatially inhomogeneous ocean surface wind fields. We have collected 87 ERS-2 SAR images with wind-induced streaks that cover the Changjiang coastal area, to verify and improve the validity of wind direction retrieval using the 2D fast Fourier transform method. We then used these wind directions as inputs to derive SAR wind speeds using the C-band model. To demonstrate the applicability of the algorithms, we validated the SAR-retrieved wind fields using QuikSCAT measurements and the atmospheric Weather Research Forecasting model. In general, we found good agreement between the datasets, indicating the reliability and applicability of SAR-retrieved algorithms under different atmospheric conditions. We investigated the main error sources of this process, and conducted sensitivity analyses to estimate the wind speed errors caused by the effect of speckle, uncertainties in wind direction, and inaccuracies in the normalized radar cross section. Finally, we used the SAR-retrieved wind fields to simulate the salinity distribution off the Changjiang estuary. The findings of this study will be valuable for wind resource assessment and the development of future numerical ocean models based on SAR images.

SAR monitoring of coastline change in Shanghai, China

In this paper, we extract the waterline from synthetic aperture radar (SAR) images to illustrate coastline change in Shanghai, China. Nine ERS-2 and ENVISAT SAR images acquired between 1998 and 2006 were selected and a new land-water classification scheme was implemented to obtain coastline. The results show that the seaward coastline change between 1998 and 2000 is faster in the eastern part and slower in the western part in the study area. Later, between 2000 and 2005, coastline changes faster in both eastern and western sides. Coastline remained virtually unchanged between 2005 and 2006.

Satellite Oil Spill Detection Using Artificial Neural Networks

Oil spills represent a major threat to ocean ecosystems and their health. Illicit pollution requires continuous monitoring and satellite remote sensing technology represents an attractive option for operational oil spill detection. Previous studies have shown that active microwave satellite sensors, particularly Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish oil spills from `look-alikes'. This paper describes the development of a new approach to SAR oil spill detection employing two different Artificial Neural Networks (ANN), used in sequence. The first ANN segments a SAR image to identify pixels belonging to candidate oil spill features. A set of statistical feature parameters are then extracted and used to drive a second ANN which classifies objects into oil spills or look-alikes. The proposed algorithm was trained using 97 ERS-2 SAR and ENVSAT ASAR images of individual verified oil spills or/and look-alikes. The algorithm was validated using a large dataset comprising full-swath images and correctly identified 91.6% of reported oil spills and 98.3% of look-alike phenomena. The segmentation stage of the new technique outperformed the established edge detection and adaptive thresholding approaches. An analysis of feature descriptors highlighted the importance of image gradient information in the classification stage.

Underwater topography detection of Taiwan Shoal with SAR images

Under suitable conditions of tidal current and wind, underwater topography can be detected by synthetic aperture radar (SAR) indirectly. Underwater topography SAR imaging includes three physical processes: radar ocean surface backscattering, the modulation of sea surface short wave spectrum by the variations in sea surface currents, and the modulation of sea surface currents by the underwater topography. The first process is described usually by Bragg scattering theory because the incident angle of SAR is always between 20°–70°. The second process is described by the action balance equation. The third process is described by an ocean hydrodynamic model. Based on the SAR imaging mechanism for underwater topography, an underwater topography SAR detection model and a simplified method for its calculation are introduced. In the detection model, a two-dimensional hydrodynamic model — the shallow water model is used to describe the motion of tidal current. Due to the difficulty of determining the expression of SAR backscattering cross section in which some terms can not be determined, the backscattering cross section of SAR image used in the underwater topography SAR detection is pro-processed by the simulated SAR image of the coarse-grid water depth to simplify the calculation. Taiwan Shoal, located at the southwest outlet of Taiwan Strait, is selected as an evaluation area for this technique due to the occurrence of hundreds of sand waves. The underwater topography of Taiwan Shoal was detected by two scenes of ERS-2 SAR images which were acquired on 9 January 2000 and 6 June 2004. The detection results are compared with in situ measured water depths for three profiles. The average absolute and relative errors of the best detection result are 2.23 m and 7.5 %, respectively. These show that the detection model and the simplified method introduced in the paper is feasible.

A Level Set Filter for Speckle Reduction in SAR Images

Despite much effort and significant progress in recent years, speckle removal for Synthetic Aperture Radar (SAR) image still is a challenging problem in image processing. Unlike the traditional noise filters, which are mainly based on local neighborhood statistical average or frequencies transform, in this paper, we propose a speckle reduction method based on the theory of level set, one form of curvature flow propagation. Firstly, based on partial differential equation, the Lee filter can be cast as a formulation of anisotropic diffusion function; furthermore, we continued to deduce it into a level set formulation. Level set flow into the method allows the front interface to propagate naturally with topological changes, where the speed is proportional to the curvature of the intensity contours in an image. Hence, small speckle will disappear quickly, while large scale interfaces will be slow to evolve. Secondly, for preserving finer detailed structures in images when smoothing the speckle, the evolution is switched between minimum or maximum curvature speed depending on the scale of speckle. The proposed method has been illustrated by experiments on simulation image and ERS-2 SAR images under different circumstances. Its advantages over the traditional speckle reduction filter approaches have also been demonstrated.

Calibration of uncertain flood inundation models using remotely sensed water levels

Summary A traditional method of validating the performance of a flood model when remotely sensed data of the flood extent are available is to compare the predicted flood extent to that observed. The performance measure employed often uses areal pattern-matching to assess the degree to which the two extents overlap. Recently, remote sensing of flood extents using synthetic aperture radar (SAR) and airborne scanning laser altimetry (LiDAR) has made more straightforward the synoptic measurement of water surface elevations along flood boundaries (waterlines), and this has emphasised the possibility of using alternative performance measures based on height. This paper considers the advantages that can accrue from using a performance measure based on waterline elevations rather than one based on areal patterns of wet and dry pixels. The two measures were compared for their ability to estimate flood inundation uncertainty maps from a set of LISFLOOD-FP model runs carried out to span the acceptable model parameter range. A 1-in-5-year flood on the Thames in 1992, observed in an ERS-1 SAR image, was used as a test event. Waterlines were delineated in fused SAR and LiDAR data using an active contour model (snake). The performance measure based on height differences of corresponding points along the observed and modelled waterlines was found to be significantly more sensitive to the channel friction parameter than the measure based on areal patterns of flood extent. A result of this was that there was less uncertainty in the final flood hazard map. The height-based measure was found to be more sensitive when increased heighting accuracy was achieved by requiring that observed waterline heights varied slowly along the reach. The technique was shown to allow the decomposition of the reach into sections, with different effective channel friction parameters used in different sections. However, an evaluation of the modelled inundation uncertainty using the calibration event showed significant differences between the uncertainty map and the observed flood extent, especially for the height-based measure. This was probably due to the conceptually simple flood inundation model and the coarse application resolution employed in this case. The increased sensitivity of the height-based measure may lead to an increased onus being placed on the model developer in the production of a valid model.

A Fast Level Set Method for Synthetic Aperture Radar Ocean Image Segmentation

Segmentation of high noise imagery like Synthetic Aperture Radar (SAR) images is still one of the most challenging tasks in image processing. While level set, a novel approach based on the analysis of the motion of an interface, can be used to address this challenge, the cell-based iterations may make the process of image segmentation remarkably slow, especially for large-size images. For this reason fast level set algorithms such as narrow band and fast marching have been attempted. Built upon these, this paper presents an improved fast level set method for SAR ocean image segmentation. This competent method is dependent on both the intensity driven speed and curvature flow that result in a stable and smooth boundary. Notably, it is optimized to track moving interfaces for keeping up with the point-wise boundary propagation using a single list and a method of fast up-wind scheme iteration. The list facilitates efficient insertion and deletion of pixels on the propagation front. Meanwhile, the local up-wind scheme is used to update the motion of the curvature front instead of solving partial differential equations. Experiments have been carried out on extraction of surface slick features from ERS-2 SAR images to substantiate the efficacy of the proposed fast level set method.

Sea Ice Deformation State From Synthetic Aperture Radar Imagery—Part II: Effects of Spatial Resolution and Noise Level

C- and L-band airborne synthetic aperture radar (SAR) imagery acquired at like- and cross-polarizations over sea ice under winter conditions is examined with the objective to study the discrimination between level ice and ice deformation features. High-resolution low-noise data were analyzed in the first paper. In this second paper, the main topics are the effects of spatial resolution and signal-to-noise ratio. Airborne high-resolution SAR scenes are used to generate a sequence of images with increasingly coarser spatial resolution from 5 to 25 m, keeping the number of looks constant. The signal-to-noise ratio is varied between typical noise levels for airborne imagery and satellite data. Areal fraction of deformed ice and average deformation distance are determined for each image product. At L-band, the retrieved values of the areal fraction get larger as the image resolution is degraded. The areal fraction at C-band remains constant. The retrieved average distance between deformation features increases both at C- and L-bands as the image resolution gets coarser. The influence of noise becomes noticeable if its level is equal or larger than the average intensity backscattered from the level ice. The retrieval of deformation parameters using simulated images that resemble ERS-2 SAR, Envisat ASAR, and ALOS PALSAR data products is discussed. Basic differences between real and simulated ERS-2 SAR images are analyzed.

Study on Ocean Wind Vector Retrieval from ERS‐2 SAR Image

AbstractSAR (Synthetic Aperture Radar) wind retrieval is a hot research topic in current Remote Sensing field. In this paper the principle of space borne SAR ocean wind retrieval and three related retrieval algorithms are introduced firstly. Then, the flowchart of the retrieval procedures is provided. As an example, ERS‐2 SAR image covering Hong Kong region acquired on May 7, 2002 is used for wind vector retrieval. The classic SAR Wind Direction Algorithm ‐ spectral method is improved, and applied for the retrieval. The wind direction thus estimated is however with 180º ambiguity. Buoy data collected by the Hong Kong Observatory are then used to resolve the uncertainty. Finally, the GMF (Geophysical Model Function)‐CMOD4 is adopted to estimate the wind speed at the height of 10 m above sea level. Compared with the data recorded by Hong Kong Observatory, the error in the wind direction and average wind speed are very small. The results show that accurate ADC (Analog to Digital Converter) compensation and image calibration, together with improved SWDA and CMOD4 algorithms, could offer high quality wind vector.

Atmospheric Fronts along the East Coast of Taiwan Studied by ERS Synthetic Aperture Radar Images

Abstract The existence of quasi-stationary alongshore atmospheric fronts typically located 30–70 km off the east coast of Taiwan is demonstrated by analyzing synthetic aperture radar (SAR) images of the sea surface acquired by the European Remote Sensing Satellites ERS-1 and ERS-2. For the data interpretation, cloud images from the Japanese Geostationary Meteorological Satellite GMS-4 and the American Terra satellite, rain-rate maps from ground-based weather radars, sea surface wind data from the scatterometer on board the Quick Scatterometer (QuikSCAT) satellite, and meteorological data from weather maps and radiosonde ascents have also been used. It is shown that these atmospheric fronts are generated by the collisions of the two airflows from opposing directions: one is associated with a weak easterly synoptic-scale wind blowing against the high coastal mountain range at the east coast of Taiwan and the other with a local offshore wind. At the convergence zone where both airflows collide, air is forced to move upward, which often gives rise to the formation of coast-parallel cloud bands. There are two hypotheses about the origin of the offshore wind. The first one is that it is a thermally driven land breeze/katabatic wind, and the second one is that it is wind resulting from recirculated airflow from the synoptic-scale onshore wind. Air blocked by the mountain range at low Froude numbers is recirculated and flows at low levels back offshore. Arguments in favor of and against the two hypotheses are presented. It is argued that both the recirculation of airflow and land breeze/katabatic wind contribute to the formation of the offshore atmospheric front but that land breeze/katabatic wind is probably the main cause.

Sea ice monitoring by L-band SAR: an assessment based on literature and comparisons of JERS-1 and ERS-1 imagery

Spaceborne single-polarization C-band synthetic aperture radar (SAR) imagery is widely used to gather information about the state of the sea ice cover in the polar regions. C-band is regarded as a reasonable choice for all-season monitoring capabilities. For specific mapping tasks, however, other frequency bands can be more suitable. In the first part of this paper, the summary of a literature study dealing with the utilization of L-band SAR imagery for sea ice monitoring is presented. Investigations reveal that if deformation features such as ice ridges, rubble fields, and brash ice are to be mapped, L-band radar is superior in a number of cases. The second part of this paper addresses the comparison of JERS-1 and ERS-1 SAR images that were acquired over sea ice east of Svalbard and along the east coast of Greenland. The effects of the different frequencies, polarizations, and incidence angles of the two SAR systems are discussed. It is demonstrated that the images of both sensors complement one another in the analysis of ice conditions, resulting in a more detailed view of the sea ice cover state.

Urban monitoring using multi-temporal SAR and multi-spectral data

In some key operational domains, the joint use of synthetic aperture radar (SAR) and multi-spectral sensors has shown to be a powerful tool for Earth observation. In this paper, we analyze the potentialities of combining interferometric SAR and multi-spectral data for urban area characterization and monitoring. This study is carried out following a standard multi-source processing chain. First, a pre-processing stage is performed taking into account the underlying physics, geometry, and statistical models for the data from each sensor. Second, two different methodologies, one for supervised and another for unsupervised approaches, are followed to obtain features that optimize the urban related information. Finally, classification of ‘Urban/Non-Urban’ areas is performed using standard algorithms. Multi-temporal data acquisition was carried out in the areas of Rome and Naples (Italy) in 1995 and 1999. The data set includes images from Landsat TM and 35-day interferometric pairs of ERS2 SAR images. We analyze the dependence of the classification accuracy on the selected input features. The good results obtained using selected features improve the overall classification accuracy, thus confirming the validity of our proposal.

An improvement of ship wake detection based on the radon transform

The purpose of this article is to present a new process for ship wake detection in synthetic aperture radar (SAR) images. Most of detection algorithms uses the Radon transform. In fact, due to its properties, this approach is particularly suited for such a detection. The main originality of our work is the wedding between the Radon transform and a filtering method used here to interpolate the image in a rotating reference system, introduced by the Radon transform. This filtering method is the stochastic matched filtering technique, which allows to maximize the signal to noise ratio after process. Because of the amount of calculus this method implies, we used a new formulation based on the signal expansion into discrete cosine transform. Experimental results on SIR-C/X-SAR and ERS SAR images are presented and compared to those obtained using some classical approaches.

Identification of Bed Forms Through ERS SAR Images in San Matías Gulf, Argentina

The European Space Agency (ESA) funded two projects in 1991 and 1994 concerning the observation of the Argentine coastal environment with synthetic aperture radar (SAR). Studies were carried out for the periods 1992–1994 and 1994–1997. Several ERS-1/2 satellite SAR images were acquired over the San Matías Gulf in the Patagonian coast during these two periods. SAR is a side-looking imaging radar that operates from either a satellite or an aircraft. The instrument emits a series microwave pulses toward the earth in a direction perpendicular to the flight path. Imagery is constructed from the strength and time delay of the returned signals, which depend primarily on the roughness and dielectric properties of the surface under observation and the distance from the radar. Ocean surface roughness wave-like patterns, imaged as a series of bright and dark linear features by SAR, are persistently observed over the San Matías Gulf mouth region. A total of thirty-three (33) ERS-1/2 SAR images (100 km × 100 km) from 15 different orbits from 1992 to 2000 were analyzed. This series of observations has allowed for a detailed examination of the location, persistence, and the conditions involved in the imaging of the observed wave-like patterns. Very strong tidal currents of the order of 2 m/s characterize the gulf region. The characteristics of recurrent surface patterns in the SAR images indicate that they are caused by the interaction of the tidal currents with bed forms in the bottom topography of the gulf. The location of these bed forms is poorly documented in the available bathymetric charts of the region. The SAR images show the significant potential that satellite radar observations have as a tool for detecting unmapped coastal ocean bottom features, particularly, where bathymetric mapping activities can be extremely difficult, dangerous, or costly.

Automatic detection of change direction of multi-temporal ERS-2 SAR images using two-threshold EM and MRF algorithms

An algorithm of two-threshold expectation maximum (EM) and Markov random field (MRF) is developed to detect the change in direction in urban surfaces from multitemporal synthetic aperture radar (SAR) images. The difference in the SAR images demonstrates a variation in backscattering caused by the surface change over all image pixels. Two thresholds are derived by the EM iterative process and are applied to change the direction of three classes: enhanced scattering, reduced scattering and unchanged regimes. Initializing from the EM result, the iterated conditional modes (ICM) algorithm of the MRF is then applied to analyse the detection of contexture change in the urban area. As an example, two images of ERS-2 SAR in 1996 and 2002 over Shanghai City are studied.

Clean seas: a North Sea test site

The Clean Seas project focused on the role that existing Earth observing satellites might play in monitoring marine pollution. Results are presented here from August 1997, for the North Sea test site, using sea surface temperature (SST), colour and synthetic aperture radar (SAR) images in conjunction with a hydrodynamic model. There was good correlation between data sources, e.g. between SST and ERS-2 SAR images. Both datasets showed the development of fine plume structures close to the Rhine outflow, apparently associated with the outflow, and possibly caused by tidal pulsing of the Rhine Plume. The model reproduced general temperature and sediment distributions well, but fine structures were not reproduced. Model sediment distribution patterns were verified using ‘chlorophyll concentration’ data from colour sensors, representative of sediment concentration in turbid water. In conjunction with the visible channels of the Advanced Very High Resolution Radiometer and Along-Track Scanning Radiometer, they give an uncalibrated measure of the sediment load. The model gives a more complete picture of the temporal dispersion of the Rhine Plume over time than is evident from the remotely sensed data alone.

MAPPING OIL SPILLS IN THE CASPIAN SEA USING ERS-1/ERS-2 SAR IMAGE QUICK-LOOKS AND GIS

This application study highlights the potential of the synthetic aperture radar (SAR) imagery for regional oil pollution mapping. The paper presents an approach and methodology for oil spill mapping in the Caspian Sea based on analysis of the ERS-1/ERS-2 SAR image quick-looks; this image seta has been collected in May 1996. By using expert evaluation all oil spill candidates on the SAR images have been detected, analyzed and finally introduced into geographical information system (GIS). GIS consisting of layers (databases) including coastal line, oil production infrastructure, hydrography, currents, bathymetry, allowed, first, to create oil spill distribution map and, second, to define oil spill accumulations and outline risk areas. Detected oil slicks were mostly located in the southern part of the Caspian Sea and were related to the oil exploitation and production areas, oil rigs, terminals, river runoff and bottom seepages. Results indicate that periodically refreshing oil spill distribution maps are a value source of information on oil spill distribution, statistics and sources. Moreover, GIS-based oil spill maps together with ground truth data is considered to be an important element in establishing of oil spill monitoring and risk management system

Is there a single surge mechanism? Contrasts in dynamics between glacier surges in Svalbard and other regions

During the 1990s, Monacobreen, a 40‐km‐long tidewater glacier in Svalbard, underwent a major surge. We mapped the surge dynamics using ERS synthetic aperture radar images, differential dual‐azimuth interferometry and intensity correlation tracking. A series of 11 three‐dimensional (3‐D) velocity maps covering the period 1991–1997 show a months‐long initiation and years‐long termination to the surge, with no indication of a surge front travelling downglacier. During the surge, the front of the glacier advanced ∼2 km, the velocity and derived strain rate increased by more than an order of magnitude, and maximum ice flow rates measured during 1994 were ∼5 m d−1. The spatial pattern of both velocity and strain rate was remarkably consistent and must therefore be controlled by spatially fixed processes operating at the glacier bed. We combine these results with those published in the literature to construct a typical Svalbard glacier surge cycle and compare this to surge dynamics of glaciers from other cluster regions, especially those of Variegated Glacier in Alaska. The strong contrast in dynamics suggests that there exist at least two distinct surge mechanisms.

The Rhine Outflow Plume Studied by the Analysis of Synthetic Aperture Radar Data and Numerical Simulations

The dynamics of the Rhine outflow plume in the proximity of the river mouth is investigated by using remote sensing data and numerical simulations. The remote sensing data consist of 41 synthetic aperture radar (SAR) images acquired by the First and Second European Remote Sensing satellites ERS-1 and ERS-2 over the outflow region of the river Rhine. Most of them show sea surface signatures of oceanic phenomena, for example, surface current and wind variations, ship wakes, and oil slicks. In particular, in 36 of these images pronounced frontal features are visible as narrow zones of mainly enhanced, sometimes enhanced/reduced radar backscatter that can be associated with the Rhine surface front. Within the area enclosed by the frontal line, large zones characterized by a lower radar backscatter than in the outer area are often visible. The analysis of the ERS SAR images suggests that the form and the location of the frontal features are mainly linked to the semidiurnal tidal phase in the outflow region, although their variability suggests also that they weakly depend on river discharge, residual currents, and neap-spring tidal cycle. In order to test this observational hypothesis, the results obtained from the analysis of the ERS SAR images are compared with the results obtained from the numerical simulation of the hydrodynamics of the Rhine outflow region carried out using a two-layer, frontal model, which is based on the nonlinear, hydrostatic shallow-water equations on an f plane. The model is forced by prescribing tidal and residual currents and river discharge at the open boundaries. Several simulations are performed by varying the values of these forcing parameters. The numerical results corroborate the observational conjecture: It is found that the form and the location of the simulated interface outcropping lines in the proximity of the river mouth are mainly determined by the semidiurnal tidal phase in the outflow region and that river discharge, residual currents, and neap-spring tidal cycle contribute only secondarily to their determination. Inserting the simulated surface velocity field into a simple radar-imaging model that relates the modulation of the backscattered radar power to the surface velocity convergence in radar look direction, narrow, elongated bands of enhanced radar backscatter emerge near the model frontal line while patches of low radar backscatter appear within the simulated Rhine plume area. The consistency of the model results with the results obtained from the analysis of the SAR images enables one to infer a mean spatial and temporal evolution of the Rhine outflow plume over a semidiurnal tidal cycle from the analysis of spaceborne SAR images acquired during different tidal cycles over the Rhine outflow area and suggests the possibility of using numerical modeling, in conjunction with the analysis of spaceborne measurements, for monitoring the oceanic variability in the Rhine outflow area.