ERS-1 Synthetic Aperture Radar Research Articles

Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation

We present a neural network-based method to detect anomalies in time-dependent surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. The presented methodology is based on a supervised classification approach using combinations of line of sight multitemporal, multi-geometry interferometric synthetic aperture radar (InSAR) time series of displacements. We demonstrate this method with a set of 170 million time series of surface deformation generated for the entire Italian territory and derived from ERS, ENVISAT, and COSMO-SkyMed Synthetic Aperture Radar satellite constellations. We create a training dataset that has been compared with independently validated data and current state-of-the-art classification techniques. Compared to state-of-the-art algorithms, the presented framework provides increased detection accuracy, precision, recall, and reduced processing times for critical infrastructure and landslide monitoring. This study highlights how the proposed approach can accelerate the anomalous points identification step by up to 147 times compared to analytical and other artificial intelligence methods and can be theoretically extended to other geodetic measurements such as GPS, leveling data, or extensometers. Our results indicate that the proposed approach would make the anomaly identification post-processing times negligible when compared to the InSAR time-series processing.

The pattern of surface deformations in the Dąbrowa Basin (Poland) based on PSInSAR analysis

Slow, long-term ground deformations in the Dąbrowa Basin (southern Poland) were identified based on ERS-1 , ERS-2 and ENVISAT Synthetic Aperture Radar (SAR) images that were processed by means of Permanent Scatterer SAR Interferometry (PSInSAR). The Dąbrowa Basin is a region where two major factors can affect surface stability: intensive coal exploitation and neotectonic processes. In this study, in order to clarify the origin of surface deformations, the authors propose applying a newly developed algorithm of spatio-temporal PSInSAR data analysis. This analysis revealed that subsidence is a characteristic feature of the Dąbrowa Basin. A significant correlation exists between slow, long-term ground deformations and the location of the main tectonic structure of this region. The proposed spatiotemporal analysis of the PSInSAR data additionally showed some degree of correlation between mining activity and the studied deformations. This interconnection is a significant achievement of this study since the deformation values determined by means of PSInSAR were identified in earlier works solely on the basis of Dąbrowa Basin neotectonics. Normal 0 21 false false false PL X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Standardowy; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;}

Monitoring of a Sea-Dike in Northern Germany by Means of ERS-1, ENVISAT/ASAR, and Sentinel-1 SAR Interferometry

Flood events are one of the most severe natural hazards threatening billions of people living in coastal areas. Synthetic aperture radar (SAR) interferometry can contribute significantly to civil security by providing a highly accurate tool for spatially explicit monitoring of flood protection structures. This article analyzes multitemporal SAR interferometry techniques like persistent scatterer interferometry and small baseline subset (SBAS) for C -band radar data in order to show the potential for a precise dike monitoring. These techniques not only achieve accuracies of few millimeters, but they also enable to reconstruct the deformation process in time. Thus, critical deformation processes can be identified timely and precautions can be taken to prevent dike bursts during flood events. In this article, sea dikes located at the North Sea coast in Germany are analyzed in order to show the potential but also the difficulties of this approach, using 34 ERS-1/2, 55 Envisat ASAR, and 44 Sentinel-1 scenes. All results point out that long-term deformation processes can be measured quite well. Due to decorrelation effects, the results on vegetated dikes are ambiguous. Applying SBAS to Sentinel-1 data is very promising to serve as a tool to precisely detect and understand deformation processes of dike bodies on large spatial scales.

Approaches for land cover monitoring over Europe based on backscatter and coherence properties of Envisat and ERS SAR data

The purpose of this study is the exploitation of Synthetic Aperture Radar (SAR) satellite data for land cover monitoring. The properties of the recorded radar data heavily depend on the type of land cover, season and weather conditions. Therefore, it is possible to utilize the variability of these factors, in order to develop various techniques and methodologies that can be used for classifying land surfaces. In this context, this paper proposes approaches, which include the application of mathematical expressions and application of thresholds on multi-temporal data, for recognizing and classifying various types of land cover, on the basis of ERS and Envisat C-band SAR backscatter and coherence properties. These can be useful for any kind of contemporary SAR data, such as those of the current two Sentinel-1 satellites. Although this study focuses on four main land cover types (urban, mountainous, agricultural-low vegetation and forested areas) over specific areas in Europe, the same principles can be extended worldwide, leading to useful insights for designing future SAR satellite missions or for establishing guidelines for in-depth studies of specific land cover types.

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

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

Unsupervised change detection of flood affected areas in SAR images using Rayleigh‐based Bayesian thresholding

In the Bayesian context, the rationale for using a particular probability density function (pdf) for a synthetic aperture radar (SAR) image improves the performance of unsupervised change detection (CD). So, this work is concentrated on promoting the consequence of selecting an empirical pdf and its parameter estimation technique. Rayleigh pdf is used to model approximation and detail sub-bands of a dual tree complex wavelet transform (DTCWT) of the log ratio difference image. The chosen pdf has a potential impact on impulsive SAR images. The scale parameter of Rayleigh pdf is estimated using first-order moments by exploiting Mellin transform. This iteration-free parameter estimation technique, made the method faster. In each sub-band at each level of DTCWT decomposition, the binary threshold is estimated to represent the change signal at different scales. The performance in the final change map is improved by combining intra-scale along with inter-scale sub-band coefficients. The proposed method is tested on three flooding SAR image data sets acquired from European Space Agency environmental satellite, European remote-sensing satellite (ERS)-2 and ERS-1 SAR instruments. The experimental results show that the proposed method achieves good CD performance in terms of false alarm rate and overall detection accuracy in less computing time.

Subsidence related to groundwater pumping for breweries in Merchtem area (Belgium), highlighted by Persistent Scaterrer Interferometry

ERS, ENVISAT and TerraSAR-X Synthetic Aperture Radar scenes covering the time span 1992–2014 were processed using a Persistent Scatterer technique to study the ground movements in Merchtem (25km NW of Brussels, Belgium). The processed datasets, covering three consecutive time intervals, reveal that the investigated area is affected by a global subsidence trend related to the extraction of groundwater in the deeper Cambro-Silurian aquifer. Through time the subsidence pattern is reduced and replaced by an uplift related to the rising water table attested by piezometers located in this aquifer. The subsidence is finally reduced to a zone where currently three breweries are very active and pump groundwater in the Ledo-Paniselian aquifer and in the Cambro-Silurian for process water for the production.

Multi-Stack Persistent Scatterer Interferometry Analysis in Wider Athens, Greece

The wider Athens metropolitan area serves as an interesting setting for conducting geodetic studies. On the one hand, it has a complex regional geotectonic characteristic with several active and blind faults, one of which gave the deadly M w 5.9 Athens earthquake on September 1999. On the other hand, the Greek capital is heavily urbanized, and construction activities have been taking place in the last few decades to address the city’s needs for advanced infrastructures. This work focuses on estimating ground velocities for the wider Athens area in a period spanning two decades, with an extended spatial coverage, increased spatial sampling of the measurements and at high precision. The aim is to deliver to the community a reference geodetic database containing consistent and robust velocity estimates to support further studies for modeling and multi-hazard assessment. The analysis employs advanced persistent scatterer interferometry methods, covering Athens with both ascending and descending ERS-1, ERS-2 and Envisat Synthetic Aperture Radar data, forming six independent interferometric stacks. A methodology is developed and applied to exploit track diversity for decomposing the actual surface velocity field to its vertical and horizontal components and coping with the post-processing of the multi-track big data. Results of the time series analysis reveal that a large area containing the Kifisia municipality experienced non-linear motion; while it had been subsiding in the period 1992–1995 (−12 mm/year), the same area has been uplifting since 2005 (+4 mm/year). This behavior is speculated to have its origin on the regional water extraction activities, which when halted, led to a physical restoration phase of the municipality. In addition, a zoom in the area inflicted by the 1999 earthquake shows that there were zones of counter-force horizontal movement prior to the event. Further analysis is suggested to investigate the source and tectonic implications of this observation.

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.

Detecting modern desert to urban transitions from space in the surroundings of the Giza World Heritage site and Greater Cairo

Abstract During the last decades, Greater Cairo, Egypt, is increasing in population and in built-up extension. Some of the new buildings are informal, constructed in absence of government planning processes, and threaten the Heritage Cultural Site of the Giza Pyramids. In addition, the fertile land of the Nile floodplain is being urbanized despite the government's building prohibition since the 1990s. Therefore, constant monitoring of construction activity is crucial in the rapidly changing environment of this area. Here, we present a data fusion approach that overcomes the limitations of single medium resolution sensor approaches, and also identifies areas in transition from desert to urban. We use multi-temporal multi-sensor supervised land use classification and include a new land use class for detecting undefined disturbances. Synthetic aperture radar (SAR) data is combined with multi-spectral data for creating the land use land cover (LULC) maps using artificial neural networks (ANN). Specifically, ERS SAR data is combined with Landsat 5TM for 1998 and Envisat ASAR IMS with Landsat 7 ETM+ for 2004 and 2010. With this data fusion approach, it is measured an increase of 73% of Greater Cairo built-up extent from 1998 to 2010. Finally, we show the relationship between the aforementioned disturbances and the new built-up areas, detecting 26% of the total new built-up areas constructed from 1998 to 2010 where undefined disturbances were identified in previous land use maps.

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

An On-Demand Web Tool for the Unsupervised Retrieval of Earth’s Surface Deformation from SAR Data: The P-SBAS Service within the ESA G-POD Environment

This paper presents a web tool for the unsupervised retrieval of Earth’s surface deformation from Synthetic Aperture Radar (SAR) satellite data. The system is based on the implementation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, within the Grid Processing on Demand (G-POD) environment that is a part of the ESA’s Geohazards Exploitation Platform (GEP). The developed on-demand web tool, which is specifically addressed to scientists that are non-expert in DInSAR data processing, permits to set up an efficient on-line P-SBAS processing service to produce surface deformation mean velocity maps and time series in an unsupervised manner. Such results are obtained by exploiting the available huge ERS and ENVISAT SAR data archives; moreover, the implementation of the Sentinel-1 P-SBAS processing chain is in a rather advanced status and first results are already available. Thanks to the adopted strategy to co-locate both DInSAR algorithms and computational resources close to the SAR data archives, as well as the provided capability to easily generate the DInSAR results, the presented web tool may contribute to drastically expand the user community exploiting the DInSAR products and methodologies.

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.

A Prototype Software Package to Retrieve Soil Moisture From Sentinel-1 Data by Using a Bayesian Multitemporal Algorithm

The Sentinel-1 mission will offer the opportunity to obtain C-band radar data characterized by short revisit time, thus allowing for the generation of frequent soil moisture maps. This work presents a prototype software implementing a multitemporal approach to the problem of soil moisture retrieval using Synthetic Aperture Radar (SAR) data. The approach exploits the short revisit time of Sentinel-1 data by assuming the availability of a time series of SAR images that is integrated within a retrieval algorithm based on the Bayesian maximum a posteriori probability statistical criterion. The paper focuses on the combination of on-line and off-line processing that has been designed in order to decrease the time necessary to produce a soil moisture map, which may be a critical aspect of multitemporal approaches. It describes also the optimization of the algorithm carried out to find the set of algorithm parameters that allow obtaining the best tradeoff between accuracy of the estimates and computational efficiency. A set of simulations of C-band SAR data, produced by applying a well-established radar-backscattering model, is used to perform the optimization. The designed system is tested on a series of ERS-1 SAR data acquired on February-April 1994 in Central Italy with a revisit time of three days. The results indicate that the temporal trend of estimated soil moisture is consistent with the succession of rain events occurred throughout the period of ERS-1 acquisitions over the observed geographic area.

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.

Detecting a moorland wildfire scar in the Peak District, UK, using synthetic aperture radar from ERS-2 and Envisat ASAR

Wildfires occur annually in UK moorland environments, especially in drought years. They can be severely damaging to the ecosystem when they burn deep into the peat, killing ground-nesting birds and releasing CO2 into the atmosphere. Synthetic aperture radar (SAR) was evaluated for detecting the 18 April 2003 Bleaklow wildfire scar (7.4 km2). SAR’s ability to penetrate cloud is advantageous in this inherently overcast area. SAR can provide fire scar boundary information which is otherwise labour intensive to collect in the field using a global positioning system (GPS). This article evaluates the potential of SAR intensity and InSAR coherence to detect a large peat moorland wildfire scar in the Peak District of northern England. A time-series of pre-fire and post-fire ERS-2 and advanced synthetic aperture radar (ASAR) Single Look Complex (SLC) data were pre-processed using SARScape 4.2 to produce georeferenced greyscale images. SAR intensity and InSAR coherence values were analysed against Coordinate Information on the Environment (CORINE) land‐cover classes and precipitation data. SAR intensity detected burnt peat well after a precipitation event and for previous fire events within the CORINE peat bog class. For the 18 April 2003 fire event, intensity increased to 0.84 dB post-fire inside the fire scar for the peat bog class. InSAR coherence peaked post-fire for moors and heathland and natural grassland classes inside the fire scar, but peat bog exposed from previous fires was less responsive. Overall, SAR was found to be effective for detecting the Bleaklow moorland wildfire scar and monitoring wildfire scar persistence in a degraded peat landscape up to 71 days later. Heavy precipitation amplified the SAR fire scar signal, with precipitation after wildfires being typical in UK moorlands. Further work is required to disentangle the effects of fire size, topography, and less generalized land‐cover classes on SAR intensity and InSAR coherence for detecting fire scars in degraded peat moorlands.

Retrieving parameters of bare soil surface roughness and soil water content under arid environment from ERS-1, -2 SAR data

Evaluation of the environmental and agricultural potential of arid lands is often limited by the lack of information on soil surface roughness and water content. The current study proposes an efficient method to retrieve these parameters of bare soil from single-channel ERS-1, -2 synthetic aperture radar (SAR) data. New equations were derived by combining the model for vertically co-polarized mode backscattering coefficient the model for the real part of dielectric constant and the empirical equation interrelating parameters of roughness. These equations allowed for calculation of the root mean square (RMS) height h of small surface roughness (h ≤ 1 cm) for naturally sandy, flat areas of the Negev desert (Israel) during dry periods when is extremely low and generally known. As soil roughness was found to be sufficiently constant under the arid environment, this study showed that calculated h could be reliably used to retrieve during the wet period. Statistical analysis of the relative errors of retrieved h and showed their high independence on the absolute values. Retrieved values of h and obtained from ERS-2 SAR data showed acceptable correlation with the direct ground measurements. Therefore, the effectiveness of the proposed methodology for h and retrieval was proved.

Mesoscale surface current fields in the Baltic Sea derived from multi-sensor satellite data

We demonstrate the use of multi-sensor satellite images for the computation of mesoscale surface currents in the Northern and Southern Baltic Proper by enhancing and combining image-processing techniques. The sequential satellite images were acquired by the Thematic Mapper (TM), the ERS-2 Synthetic Aperture Radar (SAR), the Wide-Field Scanner (WiFS) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) during extensive cyanobacterial blooms in July 1997 and in July/August 1999. We also used a pair of Advanced SAR (ASAR) images from May 2005 showing imprints of singular oil spills in the Southern Baltic Proper. Different marine surface films and accumulated algae at the water surface were taken as tracers for the local motion of the sea surface. Data from sensors working at different electromagnetic bands (e.g. TM and SAR) were used to apply high-speed feature-matching (cross-correlation) techniques for motion detection. The sufficiently short time lags between the multiple image acquisitions (from less than 1 h to approximately 1 day) and the high spatial coverage allowed for the calculation of optical flow (i.e. surface motion) fields, which include small-scale turbulent structures that are not resolved by operational numerical models. Our computed surface currents range from 4 to 35 cm s−1 and are generally larger than those provided by the numerical models for the same dates and areas. We attribute this difference to local wind forcing, causing higher drift velocities at the very sea surface, which is seen from space, but which is not resolved by the numerical models.

THE FEASIBILITY OF APPLYING SAR IMAGERY TO THE ESTIMATION OF WAVE DEPTH AND AMPLITUDE

A model consisting of the KdV Equation, Action Balance Equation and Bragg Scattering Model was established based on synthetic aperture radar (SAR) imaging mechanism for internal waves to retrieve internal wave depth and amplitude. Methods for internal wave depth and amplitude calculation were derived from this model. Imagery from ERS-1 SAR was used as case studies in seas around China. Comparison with in situ measurements shows that the methods are reasonable.