Dual-polarization Synthetic Aperture Radar Research Articles

Large-Area Land Use and Land Cover Classification With Quad, Compact, and Dual Polarization SAR Data by PALSAR-2

In this paper, we demonstrated the possibility of performing land use and land cover (LULC) classification over a wide area by an L-band polarimetric synthetic aperture radar (SAR). In previous studies, there has been scant LULC classification by polarimetric SAR data over a wide area. We used satellite-based SAR data with an area of ca. 320 000 km2 obtained by the Phased Array type L-band SAR (PALSAR)-2 phase array. We performed the LULC classification using full polarimetry (FP), compact polarimetry (CP), and dual polarimetry (DP) data by PALSAR-2 and compared their classification accuracy. Our results show FP to be the most accurate. The CP and the DP have the advantages of large-scale coverage and compact data volume but is slightly less accurate than the FP. To further improve accuracy of the classification process, texture analysis, observation date information, and feature elimination are effective. We determined the classification accuracy for seven classes to be 73.4% (the kappa coefficient is 0.668). We found the rice paddy, forest, grass, and urban areas to be sufficiently accurate (84.5%) for practical application. We compared the obtained classification map with an existing LULC map to grasp the LULC changes induced by a recent disaster and successfully detected the damage areas of the disaster. These results indicate the possibility of large-scale LULC monitoring by an L-band polarimetric SAR, which can acquire images rapidly without being affected by weather.

Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.

In-season biomass estimation of oilseed rape (Brassica napus L.) using fully polarimetric SAR imagery

Accurate estimation of crop biophysical and biochemical parameters during crop growing seasons is essential for improving site-specific management and yield estimation. The potential ability of fully polarimetric synthetic aperture radar (SAR) data in estimating above-ground biomass of oilseed rape was investigated in this study. The temporal profile of different scattering intensity and polarimetric features during the entire growing season was identified with ground measurements. A polarimetric feature, relying on the polarimetric decomposition method, was put forward to estimate the biomass of oilseed rape. Validation results revealed great potential with a determination coefficient (R2) of 0.85, root mean squared error (RMSE) of 41.6 g/m2, and relative error (RE) of 28.5% for dry biomass, and an R2 of 0.76, RMSE of 527.4 g/m2 and RE of 28.6% for fresh biomass. Moreover, the use of full polarization SAR data was compared with single and dual polarization SAR data. The results suggest that when full polarization SAR data is available, a simpler model, higher saturation point and better accuracy can be achieved in biomass estimation of oilseed rape, which highlights the importance and value of polarimetry information in quantitative crop monitoring. This study provides guidelines for in-season monitoring of crop growth parameters with SAR data, which further improves crop monitoring capability in adverse weather conditions.

Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007.

Synthetic aperture radar (SAR) has been widely used to detect oil-spill areas through the backscattering intensity difference between oil and background pixels. However, since the signal is similar to that produced by other phenomena, positive identification can be challenging. In this study we developed an algorithm to effectively analyze large-scale oil spill areas in SAR images by focusing on optimizing the input layer to artificial neural network (ANN) through removal the factor of lowering the accuracy. An ANN algorithm was used to generate probability maps of oil spills. Highly accurate pixel-based data processing was conducted through false or un-detection element reduction by normalizing the image or applying a non-local (NL) means filter and median filter to the input neurons for ANN. In addition, the standard deviation of co-polarized phase difference (CPD) was used to reduce false detection from the look-alike with weak damping effect. The algorithm was validated using TerraSAR-X images of an oil spill caused by stranded oil tanker Volganefti-139 in the Kerch Strait in 2007. According to the validation results of the receiver operating characteristic (ROC) curve, the oil spill was detected with an accuracy of about 95.19% and un-detection or false detection by look-alike and speckle noise was greatly reduced.

A C-Band Geophysical Model Function for Determining Coastal Wind Speed Using Synthetic Aperture Radar

A new geophysical model function (GMF), called C_SARMOD2, has been developed to relate high-resolution C-band normalized radar cross section (NRCS), acquired in VV polarization over the ocean, to the 10-m height wind speed. A total of 3078 RADARSAT-2 and Sentinel-1A VV-polarized synthetic aperture radar (SAR) images, acquired under different wind speed conditions, were collocated with in situ buoy measurements. The paired dataset was used to derive transfer functions and coefficients of C_SARMOD2, and then to validate the wind speed retrievals. The comparison between SAR-retrieved wind speeds and buoy measurements show almost no bias and a root mean square error of 1.84 m/s. Two representative quad- and dual-polarization SAR images, acquired from coastal regions, are used as case studies to examine C_SARMOD2 performances. The case study and statistical validation results suggest that the proposed C_SARMOD2 has the potential to measure coastal wind speeds at subkilometer resolutions. Although derived from low resolution NRCS measurements, this study also confirms the great robustness of CMOD5.N and recent CMOD7 when applied to SAR data. In addition, it shows that with the new generation of SAR satellite-borne sensors, it is no longer mandatory to rely on scatterometers in order to build a GMF, which will be used for SAR applications. Such an approach is particularly important in view of the upcoming RADARSAT Constellation Mission with new polarization configurations. Moreover, it also opens new perspectives on the derivation of GMFs in HH-polarization. However, these results also suggest that for coastal areas, the increase of the resolution to define the GMF is less important than adding other geophysical parameters to improve wind retrieval performance. This advocates for the necessity of revisiting the methodologies for ocean surface wind speed measurements in coastal areas.

Dual-Polarized Backscatter Features of Surface Currents in the Open Ocean during Typhoon Lan (2017)

Ocean surface current measurements from satellites have historically been limited. We propose a new approach to detect ocean surface currents as observed by dual-polarized (VV and VH) spaceborne synthetic aperture radar (SAR). This approach is based on the assumptions that the VH-polarized SAR signal is only generated by the effects of ocean winds creating surface waves, whereas the VV-polarization data are due to the effects of both ocean winds and surface currents. Therefore, the surface currents features may be extracted after retrieving the ocean winds from VH-polarized backscatter and inputting signal due to the wind to the VV-polarized backscatter data. To investigate the performance of this approach under extreme wind conditions, we consider a scene of C-band RADARSAT-2 dual-polarized ScanSAR images over Typhoon Lan (2017) in the open ocean, and we verify our results with current estimates from altimeter data. The ocean current features extracted from the backscatter data that were collected from the SAR images are shown to correspond to an area of strong currents and an oceanic front observed by altimeters. We suggest that the proposed method has the potential capacity to provide information about ocean surface currents from high-resolution dual-polarized ScanSAR images.

Building Damage Assessment in the 2015 Gorkha, Nepal, Earthquake Using Only Post-Event Dual Polarization Synthetic Aperture Radar Imagery

This paper takes the 2015 Nepal earthquake as a case study to explore the use of post-event dual polarimetric synthetic aperture radar images for earthquake damage assessment. The radar scattering characteristics of damaged and undamaged urban areas were compared by using polarimetric features derived from PALSAR-2 and Sentinel-1 images, and the results showed that distinguishing between damaged and undamaged urban areas with a single polarimetric feature is challenging. A split-based image analysis, feature selection, and supervised classification were employed on a PALSAR-2 image. The texture features derived from the intensity of cross-polarization show higher correlations with the damage class. Additionally, feature selection revealed a positive influence on the overall performance. Employing 70% of the data for training and 30% data for testing, the support vector machine classifier achieved an accuracy of 80.5% compared with the reference data generated from the damage map that was provided by the United Nations Operational Satellite Applications Programme.

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

ABSTRACTSentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world.

Corrections to "Change Detection in Full and Dual Polarization, Single- and Multi-Frequency SAR Data" [Aug 15 4041-4048

When the covariance matrix formulation is used for multi-look polarimetric synthetic aperture radar (SAR) data, the complex Wishart distribution applies. Based on this distribution a test statistic for equality of two complex variance-covariance matrices and an associated asymptotic probability of obtaining a smaller value of the test statistic are given. In a case study airborne EMISAR C- and L-band SAR images from the spring of 1998 covering agricultural fields and wooded areas near Foulum, Denmark, are used in single- and bi-frequency, bi-temporal change detection with full and dual polarimetry data.

Incidence Angle Dependence of First-Year Sea Ice Backscattering Coefficient in Sentinel-1 SAR Imagery Over the Kara Sea

We have studied the incidence angle ( $\theta _{0}$ ) dependence of the sea ice backscattering coefficient ( $\sigma$ °) for Sentinel-1 (S-1) extra wide (EW) mode dual-polarization (HH/HV) synthetic aperture radar (SAR) imagery acquired over the Kara Sea under winter and summer melting conditions. The determination of the $\sigma$ ° versus $\theta _{0}$ dependence was based on SAR image pairs acquired on ascending and descending orbits over the same sea ice area with a short time difference. The SAR noise floor was subtracted from the HV images. From the image pairs 1.1 by 1.1 km windows representing level first-year ice (LFYI) and deformed first-year ice (DFYI) were manually selected, and a linear regression was fit between the resulting $\sigma$ ° and $\theta _{0}$ differences of the windows to estimate the slope ${b}_{1}$ (dB/1°) between $\sigma$ ° and $\theta _{0}$ . For example, under winter condition ${b}_{1}$ for DFYI at HH- and HV-polarizations was found to be −0.24 and −0.16 dB/1°, respectively, and ${b}_{1}$ for LFYI at HH-polarization was −0.25 dB/1°. It was not possible to determine a reliable ${b}_{1}$ for LFYI at HV due to a contamination effect of the S-1 noise floor. The ${b}_{1}$ values at HH compared well with previous studies. They can be used to compensate the $\sigma$ ° incidence angle variation in the S-1 EW SAR images with good accuracy. The HH ${b}_{1}$ values are applicable to other S-1 imaging modes and other C-band SAR sensors like RADARSAT-2. Unfortunately, the HV ${b}_{1}$ values are specific to the S-1 EW mode due to the noise floor problem.

X/Ka-Band Dual-Polarized Digital Beamforming Synthetic Aperture Radar

This paper presents a digital beamforming (DBF) synthetic aperture radar (SAR) for future spaceborne earth observation systems. The objective of the DBF-SAR system is to achieve a low cost, lightweight, low-power consumption, and dual-band (X/Ka) dual-polarized module for the next-generation spaceborne SAR system in Europe. The architectures and modules of the proposed DBF-SAR system are designed according to a realistic mission scenario, which is compatible with the future small/microsatellites platforms. This system fills an important gap in the conception of the future DBF-SAR, facilitating a high level of integration and complexity reduction. The proposed system is considered not only the first demonstrator of a receive-only spaceborne DBF system, but also the first X/Ka-band dual-polarized SAR system with shared aperture. This paper presents a description of the proposed instrument hardware and first experimental validations. The concept and design of the DBF multistatic SAR system are discussed and presented first, followed by the design of subsystems such as DBF networks, microwave integrated circuit, and antennas. Simulated and measured results of the subsystems are presented, demonstrating that the proposed SAR instrument architecture is well-suited for the future SAR applications.

Performance Comparison Between Reflection Symmetry Metric and Product of Multilook Amplitudes for Ship Detection in Dual-Polarization SAR Images

The reflection symmetry metric (RSM) and product of multilook amplitudes (PMA) detectors, which were proposed recently, have been demonstrated to be promising methods for processing dual-polarimetric synthetic aperture radar (SAR) data for ship detection. The improvements in ship detection performance by using the RSM, compared to that using the PMA, are investigated in this paper. As the ship-sea contrast (or the signal-clutter-ratio, SCR) is a central index to assess the performance of a detection method, the SCRs in the RSM and PMA are first defined and compared. Next, a theoretical explanation for why the RSM outperforms the PMA in detection performance is provided. The detection performance is then characterized by calculating the receiver operating characteristic (ROC) curves. The preliminary experimental results performed on measured RADARSAT-2, ALOS-PALSAR, and NASA/JPL AIRSAR images verify the accuracy of the theoretical analysis.

Effect of the Vegetation Fire on Backscattering: An Investigation Based on Sentinel-1 Observations

This paper aims at investigating the potential of Sentinel-1 C-band synthetic aperture radar (SAR) observations for detecting fire scars in vegetated areas at regional scale. A comprehensive analysis of the backscattering coefficients is carried out. The experimental analysis is conducted by analyzing the scenario of the Sardinia Island, which is one of the Italian regions most affected by fire events over the summer season. The detection capability of fire scars in such an environment is demonstrated by exploiting information extracted from dual-polarized SAR data. Our results reveal a significant decrease of the VH response over the fire-disturbed forests, thus, highlighting the effectiveness of such cross-polarized observations. In order to prove the validity of the proposed approach for the detection of fire scars in the vegetation layer, the results of the conducted experiments have been suitably compared with burned areas identified by using an existing fuzzy-based algorithm, which has been applied to multispectral Landsat-8 operational land imager data. This investigation opens the way to systematic methods for monitoring fire scars in heterogeneous environments, and in particular in fire-prone Mediterranean ecosystems.

A Hurricane Wind Speed Retrieval Model for C-Band RADARSAT-2 Cross-Polarization ScanSAR Images

A hybrid backscattering model is built to provide a consistent description for C-band VH- and VV-polarized normalized radar cross sections (NRCSs). Ocean surface coand cross-polarized NRCS are both treated as a sum of Bragg and non-Bragg scattering components. To better understand the synthetic aperture radar (SAR) observed NRCS signals under high-wind conditions, five C-band RADARSAT-2 dual-polarization SAR hurricane images and the collocated wind vectors measured by the airborne stepped-frequency microwave radiometer (SFMR) are collected. Based on the match-up data, we add a non-Bragg term in the composite Bragg theory to explain the discrepancy between the measurements in the cross-polarization channel and the existing theory results. The non-Bragg scattering to Bragg scattering ratio (Br) is found to be a constant. We build the hybrid backscattering model with Br and establish a relationship between the cross-polarization NRCS and the radar incidence angle under different wind conditions. The NRCS dependence on incidence angle is simulated by the hybrid backscattering model. Finally, a C-band Cross-Polarization Coupled-Parameters Ocean (C-3PO) model is developed to retrieve hurricane winds using VH-polarized ScanSAR by including the radar incidence angle. The collocated SAR and SFMR data sets are separated into two parts: data set-A, for hybrid backscattering model derivation and C-3PO model coefficients tuning, and data set-B, for hurricane wind validation. C-3PO model validation results show that the model is suitable for ocean surface wind mapping from RADARSAT-2 cross-polarization ScanSAR images. The retrieval has a rootmean-square error less than 3 m/s for wind speed up to 40 m/s.

Ship Detection in Dual-Channel ATI-SAR Based on the Notch Filter

Synthetic aperture radar (SAR) in along-track interferometry (ATI) mode has been extensively applied in velocity measurements of ocean currents and ship detection. A notch filter was recently proposed and was demonstrated to be a promising tool for ship detection exploiting quad- or dual-polarization SAR information. In this paper, the investigation of the notch filter is extended to the dual-channel ATI-SAR mechanism for ship detection on the ocean surface. First, a theoretical proof that the interferometric magnitude performs better in ship detection than single-channel amplitude/intensity is given based on the signal-clutter-ratio improvement, which validates the advantages of interferometric SAR against conventional single-channel SAR for the purpose of ship observations on the ocean surface. Second, based on the proof, the version of the notch filter that is suitable for the ATI-SAR, that is, the interferometric notch filter (INF), is proposed. We then analyze the statistical models of the INF distance, which can facilitate the adaptive realization of INF for ship detection in ATI-SAR. Finally, the experimental results for dual-channel ATI-SAR data measured by NASA/JPL L-band AIRSAR verify the accuracy of the theoretical analysis and effectiveness of the INF.

Building Damage Assessment Using Multisensor Dual-Polarized Synthetic Aperture Radar Data for the 2016 M 6.2 Amatrice Earthquake, Italy

On 24 August 2016, the M 6.2 Amatrice earthquake struck central Italy, well-known as a seismically active region, causing considerable damage to buildings in the town of Amatrice and the surrounding area. Damage from this earthquake was assessed quantitatively by means of multitemporal synthetic aperture radar (SAR) coherence and SAR intensity methods using dual-polarized SAR data obtained from the Sentinel-1 (VV, VH) and ALOS-2 (HH, HV) satellites. We developed linear discriminant functions based on three items: (1) the differential coherence values; (2) the differential backscattering intensity values of pre- and post-event images; and (3) a binary damage map of the optical pre- and post-event imagery. The accuracy of the proposed model was 84% for the Sentinel-1 data and 76% for the ALOS-2 data. The damage proxy maps deduced from the linear discriminant functions can be useful in the parcel-by-parcel assessment of building damage and development of spatial models for the allocation of urban search and rescue operations.

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Abstract. Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

Polarimetric–Anisotropic Decomposition and Anisotropic Entropies of High-Resolution SAR Images

In the booming era of high-resolution synthetic aperture radar (SAR) technology, SAR advanced information retrieval is critical for effective utilization of huge-volume SAR data. One important aspect of high-resolution SAR interpretation is to explore the anisotropic and dispersive information embedded among subaperture and subband SAR images. This paper formulates the polarimetric subaperture analysis as a singular-value decomposition problem, where polarimetric and anisotropic features can be simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigenanalysis-based polarimetric target decomposition, whereas the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic patterns, where two anisotropic entropy parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these proposed anisotropic entropies can effectively identify specific types of scatterers depending on their geometric scale, curvature, and form of spatial distribution. The proposed anisotropic entropies could be applied to single- and dual-polarization high-resolution SAR data as well.

Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang

Classification is an important process in interpretation of synthetic aperture radar (SAR) imagery. As an advanced instrument for remote sensing, the polarimetric SAR has been applied widely in many fields. The main aim of this paper is to explore the ability of the full-polarization SAR data in classification. The study area is a part of Dunhuang, Gansu Province, China. An L-band full-polarization image of Dunhuang which includes quad-polarization modes was acquired by the ALOS-PALSAR (Advanced Land Observing Satellite-the Phased Array type L-band Synthetic Aperture Radar). Firstly, new characteristic information was extracted by the difference operation, ratio operation, and principal component transform based on the full-polarization (HH, HV or VH, VV) modes SAR data. Then the single-, dual-, full-polarization SAR data and new SAR characteristic information were used to analyze quantitatively the classification accuracy based on the Support Vector Machines (SVM). The results show that classification overall accuracy of single-polarization SAR data is poor, and the highest is 56.53% of VV polarization. The classification overall accuracy of dual-polarization SAR is much better than single-polarization, the highest is 74.77% of HV & VV polarization data. The classification overall accuracy of full-polarization SAR is 80.21%, adding the difference characteristic information, ratio characteristic information and the first principal component (PC1) respectively, the overall accuracy increased by 3.09%, 3.38%, 4.14% respectively. When the full-polarization SAR data in combination with the all characteristic information, the classification overall accuracy reached to 91.01%. The full-polarization SAR data in combination with the band math characteristic information or the PC1 can greatly improve classification accuracy.

Automatic Detection of the Ice Edge in SAR Imagery Using Curvelet Transform and Active Contour

A novel method based on the curvelet transform and active contour method to automatically detect the ice edge in Synthetic Aperture Radar (SAR) imagery is proposed. The method utilizes the location of high curvelet coefficients to determine regions in the image likely to contain the ice edge. Using an ice edge from passive microwave sea ice concentration for initialization, these regions are then joined using the active contour method to obtain the final ice edge. The method is evaluated on four dual polarization SAR scenes of the Labrador sea. Through comparison of the ice edge with that from image analysis charts, it is demonstrated that the proposed method can detect the ice edge effectively in SAR images. This is particularly relevant when the marginal ice zone is diffuse or the ice is thin, and using the definition of ice edge from the passive microwave ice concentration would underestimate the ice edge location. It is expected that the method may be useful for operations in marginal ice zones, such as offshore drilling, where a high resolution estimate of the ice edge location is required. It could also be useful as a first guess for an ice analyst, or for the assimilation of SAR data.