Satellite-borne Synthetic Aperture Radar Research Articles

Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE

In this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq Al-Hadid, United Arab Emirates (UAE) was investigated. Satellite-borne synthetic aperture radar (SAR) is proposed as the main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. Results from SAR feature extraction and geospatial analyses showed detection of the areas on the site that were already under excavation and predicted new, hitherto unexplored archaeological areas. The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. The validated results can provide guidance for future on-site archaeological work. The pilot process developed in this work can therefore be applied to similar arid environments for the detection of archaeological features and guidance of on-site investigations.

A domain specific knowledge extraction transformer method for multisource satellite-borne SAR images ship detection

Multisource satellite-borne synthetic aperture radar (SAR) images have different probability distributions. Traditional supervised learning, consequently, cannot achieve good test performance on one novel satellite-borne SAR dataset while training a good model on another existing satellite-borne SAR dataset. In this article, a domain adaptation (DA) Transformer object detection method is proposed to solve the unlabeled multisource satellite-borne SAR image object detection problem. Unlike existing DA methods based on convolutional neural network (CNN) that focus more on multi-level local feature extraction, we choose to use Vision Transformer (ViT) Faster region CNN (FRCNN) as the baseline network to cope with the extraction of global features of SAR images. Then, two classification tokens are used to learn the mapping of different domains and fully extract domain-specific knowledge, generating two different feature spaces that rely on the original label and pseudo-label to train the source and target domains feature spaces, respectively. Besides, the pseudo-label of target domain is also refined and reconstructed by feature clustering in order to improve the accuracy of target domain knowledge. Finally, the original detection head of FRCNN is employed to detect the target domain SAR image objects. Extensive experiments on image datasets from multisource satellite-borne SAR such as Gaofen-3, TerraSAR-X, Sentinel-1, and RadarSAT-2 show that compared to the other state of the art (SOTA) methods, the proposed method can achieve the greatest object detection accuracy. Especially, taking the recently proposed Transformer-based method as an example, our method has more than 5% improvement in accuracy and more than 16% reduction in training time.

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

This paper introduces a method for estimating a series of sea state parameters from satellite-borne synthetic aperture radar (SAR). The method was realized in a near real time (NRT) application which allows for the processing of data from different satellites and modes. The algorithm estimates the total significant wave height Hs, dominant and secondary swell and windsea wave heights, first, and second moment wave periods, the mean wave period and the period of wind sea. The algorithm was applied to the Sentinel-1 (S1) C-band Interferometric Wide Swath Mode (IW), Extra Wide (EW) and Wave Mode (WV) Level-1 (L1) products and also extended to X-band TerraSAR-X (TS-X) StripMap (SM) products. The scenes are processed in raster and result in continuous sea state fields, with the exception of S1 WV, where averaged values for sea state parameters for along-orbit imagettes of 20 km × 20 km are presented.The developed empirical algorithm consists of two parts: a first CWAVE_EX (extended CWAVE) part, based on a linear regression approach, and a subsequent machine learning part using the support vector machine (SVM) technique. A series of new data preparation steps (i.e. filtering, denoising) and new features estimated from SAR images are also introduced. The algorithm was tuned and validated using two independent global wave model hindcasts, WaveWatch-3 and MFWAM as well as National Data Buoy Center (NDBC) measurements. The achieved root mean squared errors (RMSE) for CWAVE_EX for the total Hs are 0.60 m for low-resolution modes S1 IW (10 m pixel) and EW (40 m pixel) and 0.35 m for S1 WV and TS-X SM (pixel spacing ca.1–4 m) in comparison to model predictions. The RMSEs of the retrieved wave periods are in the range of 0.45–0.90 s for all of the satellites and models considered. Similarly, the dominant and secondary swell, and wind sea wave height RMSEs are in the range of 0.35–0.80 m. The SVM postprocessing improves the accuracy of the initial results of CWAVE_EX for Hs and reaches an RMSE of 0.25 m for S1 WV. Comparisons to 64 NDBC buoys, collocated at distances shorter than 50 km to S1 WV imagettes worldwide, result in an RMSE of 0.41 m. All results and the methods presented are novel in terms of the accuracy achieved, combining the classical approach with machine learning techniques, and performing an automatic NRT processing of multiparametric sea state fields from L1 data with automatic switching for different satellites and modes.The complete archive of S1 WV L1 Single Look Complex products from December 2014 until February 2021 was processed to create a sea state parameter database and validated using model hindcast and buoy measurements. The derived parameters are available to the public within the scope of the European Space Agency's Climate Change Initiative.

효과적인 인공위성 SAR 기만 재밍을 위한 요구 조건 분석

In this study, we analyzed the deceptive jamming performance of a synthetic aperture radar (SAR). Four cases were considered: (1) time synchronization between the SAR and the jammer, (2) the number of fast Fourier transform (FFT) points that the jammer can use, (3) the minimum signal processing time of the jammer, and (4) the minimum feasible time interval in the jammer. To analyze the effectiveness of each case, the satellite-borne SAR and jammer simulation environment were used. From the results, it was confirmed that the four cases of jammer performance can affect the false SAR image quality, leading to the deterioration of its deceptive jamming performance. Therefore, we suggest that an analysis of the ability of the jammer is necessary for implementing more sophisticated SAR deceptive jamming signal processing.

Spatio-temporal distribution of sea-ice thickness using a machine learning approach with Google Earth Engine and Sentinel-1 GRD data

Knowledge of ice thickness and its distribution is of great interest to plan ship and offshore operations in ice areas. It is also a major direct factor in climate change. However, it is currently the most inaccurate sea-ice parameter according to a large majority of the scientific community. Satellite-borne synthetic aperture radar (SAR) data are highly valuable for monitoring daily ice-covered oceans. In this study, we develop a new technique based on a machine learning Random Forest (RF) regression approach using the combination of the in-situ thickness measurements and the backscattering information from Sentinel-1 to retrieve the level first-year ice (FYI) thickness. By applying the technique over the Sentinel-1 ground range detected (GRD) data set available in Google Earth Engine (GEE) over the Beaufort Sea spanning a time period from Apr 2015 to Sep 2018, a thickness of up to 1.5 m with a root mean square error (RMSE) of 22 cm is retrieved.

Use of multifrequency (C‐band and L‐band) SAR data to monitor peat subsidence based on time‐series SBAS InSAR technique

AbstractPeatlands in tropical regions like Indonesia are undergoing irreversible subsidence due to changes in land use (e.g., deforestation) and land management practices (e.g., drainage alteration), resulting in massive amounts of soil carbon loss. Several satellite‐borne synthetic aperture radar (SAR) sensors are operating concurrently at different frequencies, providing potentially useful data for monitoring surface motion over tropical peatlands. This study focused on the capability of C‐band (SENTINEL‐1) and L‐band (PALSAR‐2) SAR data to monitor the surface changes in the tropical peatlands area of Bengkalis Island, Indonesia, by applying time‐series interferometric SAR (InSAR) with the small baseline subset (SBAS) technique. The average vertical velocity measured by SENTINEL‐1 and PALSAR‐2 for the period of 2018–2019 was −1.41 and −2.65 cm yr−1, respectively. We also explored the potential of groundwater level (GWL) data converted to vertical displacement for validating SBAS InSAR. PALSAR‐2 performed the best, exhibiting lower RMSE values for each land use compared to SENTINEL‐1, with an overall RMSE of 1.383 and 1.988 cm yr−1, respectively. Also, the subsidence rates of SENTINEL‐1 were underestimated, showing a significantly lower mean subsidence difference (0.96 cm yr−1) than the reference. Our GWL‐based subsidence data offered an alternative validation method for InSAR‐based subsidence estimation. Therefore, the integration of time‐series InSAR and GWL data can provide crucial information for monitoring the degradation of tropical peatlands.

Single-chip multi-processing architecture for spaceborne SAR imaging and intelligent processing

The satellite-borne SAR image intelligent processing system needs to process on-orbit real-time imaging and various tasks of applications, for which reason designing a dedicated high-efficient single-chip multi-processor is of prioritized necessity that can simultaneously satisfy requirements of real-time and low power consumption. Aiming at on-chip data organization and memory access structure, two typical models of SAR(synthetic aperture radar) imaging CSA (chirp scaling) and neural network VGG-11 are analyzed, and then a collaborative computing model for the intelligent processing on remote sensing is extracted. A strip Tile data processing scheme and a dedicated multi-processing architecture is not only proposed, but a data organization and a caching strategy of Tile space synchronization splicing is also presented. In addition, the designed data caching structure among the processing units greatly reduces off-chip access memory bandwidth while supporting parallel pipeline execution of multi-task model. The chip adopts 28 nm CMOS technology featuring with merely 1.83 W of the overall power consumption, whose throughput and energy efficiency reaches 9.89TOPS and 5.4 TOPS/W, respectively. The present architecture can improve real-time performance of the on-orbit remote sensing intelligent processing platform while reducing the complexity of system designing, which is highly adaptive to differentiated expansions according to different models of algorithm.

Ocean surface wind speed retrieval from C-band quad-polarized SAR measurements at optimal spatial resolution

ABSTRACT This work investigates the effect of the resampling spatial resolution (RSR) on the retrieval of ocean surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images. For this purpose, 358 RADARSAT-2(RS-2) fine-beam quad-polarized SAR images and in-situ National Data Buoy Centre (NDBC) and China State Oceanic Administration (SOA) buoy measurements from selected geographic locations are used in this paper. In order to obtain better accuracy SAR-retrieved wind speeds, CMOD5.N function, Z2011 polarization ratio (PR) model and C2PO model constitute the best models for VV-, HH- and cross-polarized SAR images, respectively. The resulting statistical analysis suggests that better accuracy wind speeds can be retrieved at somewhat coarser resolutions. The optimal RSR for the VV-, HH-, VH- and HV-polarized channels are 850, 1050,1350 and 1450 m, respectively, with RMSEs of 1.74, 2.03, 1.61 and 1.83 m/s.

A New SAR Imaging Method for Sparse Field Traget

Satellite borne Synthetic Aperture Radar (SAR) has become an indispensable means of earth observation because of its ability of all-weather, all-weather and global observation. However, at present, the space borne SAR still has the characteristics of large volume and high cost. In recent years, the vigorous development of micro satellite field has also promoted the research of small, intelligent and distributed cooperative imaging field of space borne SAR. The traditional spaceborne SAR imaging method is to process the echo of the received LFM signal in the two-dimensional pulse compression domain. Due to the targets in the ocean are usually sparsed, it is an effective method to detect the region of interest and targets by analyzing the echo and transform domain. In this paper, the method of azimuth pulse compression is used to realize the fast perception and imaging of sparse field targets. Through comparative analysis, it can be seen that the method has a greater improvement in resource and time consumption performance compared with the traditional method.

Multi-objective Heuristic Design Approach for SAR Mission for Monitoring Local Target Area

The Korea first synthetic aperture radar (SAR) satellite with a 1-m resolution—Korea Multi-purpose Satellite-5 (KOMPSAT-5)—was successfully launched in Korea in 2013, and its successors will be launched continuously to monitor specific target areas. The major requirements of the orbit design for KOMPSAT-5 are that the mean average revisit time (ART) over the Korean peninsula is no longer than 24 h and that the repeat ground track orbit is guaranteed. For this type of problem, an iterative and tedious process may be used to derive the appropriate mission orbit to satisfy the mission requirements. During the design process, sophisticated coverage analysis software should be employed to evaluate the mean ART over a specific local target area. Moccia et al. (Acta Astronaut 47(11):819–829, 2000) presented a feasibility study of a new space-based observation technique using a bistatic SAR and performed a numerical simulation to estimate the measurement accuracy for the bright point target position and velocity. Kim and Chang (Aerosp Sci Technol 40:17–32, 2015) developed an optimal scheduling method employing a genetic algorithm (GA) to reduce the system response time for an SAR satellite constellation. Wei and Chunsheng (Adv Sp Res 50:272–281, 2012) proposed a new design method for a distributed satellite-borne SAR system with an error-propagation model and used a monostatic design to determine the key parameters of a single SAR satellite. Apart from the SAR mission, the orbit design for Earth-observation missions over a specific area or target has been studied by others. Abdelkhlik et al. (J Guid Control Dyn 29(5):1231–1235, 2006) utilized the RGT concept to design natural orbits for visiting a target area without the use of propulsion systems. Kim et al. (J Spacecr Rocket 46(3):725–728, 2009) proposed a new strategy employing a GA to search the current mission orbit for a temporary target orbit to achieve a temporary reconnaissance mission over a particular target site using a low-Earth-orbit satellite during a specific period. We propose an effective approach for the design of the SAR mission for a local target area. Here, the ART and average transmitted power are key parameters for the mission requirements and the bus system, respectively. To our knowledge, no previous studies have considered this kind of problem. To satisfy the mission requirements for the ART over a local target area and simultaneously consider the average transmitted power, multi-objective heuristic algorithms including GEs, particle swarm optimization, and differential evolution are used, and their performances are compared. The computational approach of the design strategy proposed by the authors is based on the optimization algorithms in Matlab® and the powerful coverage analysis tool STK®. Therefore, the proposed strategy is adaptable for various types of SAR mission designs having complex requirements regarding both the orbit and the bus system, particularly for monitoring a specific local target area.

Estimation of sea state from Sentinel-1 Synthetic aperture radar imagery for maritime situation awareness

ABSTRACTA new empirical algorithm CWAVE_S1-IW for estimation of significant wave height Hs including swell and wind sea from C-band satellite-borne Synthetic Aperture Radar (SAR) data has been developed for Sentinel-1 (S1) Interferometric Wide Swatrh Mode (IW) imagery. The algorithm was implemented into the Sea Sate Processor (SSP) for fully automatic processing for near real time (NRT) services and allow the estimation of wave fields of thousands of kilometres in the flight direction and 250 km swath from S1 IW scenes consisting of a sequence of individual images.The priority of CWAVE_S1-IW development was an automatic, fast and robust raster processing independent of wave patterns, applicable even when only clutter is visible in the SAR images. The algorithm is based on the spectral analysis of subscenes in wavenumber space. The empirical function allows direct Hs estimation from image spectra without first converting them into wave spectra and uses integrated image spectra parameters as well as estimated local wind information. A texture analysis based on Grey Level Co-occurrence Matrices (GLCM) is also applied. In this way, also the parameters of short waves can be estimated, which are not visible in S1 IW images and are only represented by clutter.The algorithm was tuned worldwide using in-situ collocated measurements of 92 buoys with more than 2500 acquisitions. The validated SSP allows automatic processing of worldwide S1 IW images in VV or HH polarization, including Atlantic storms, cyclones, and huge storms in the Gulf of Alaska with a root-mean-square (RMSE) error of 80 cm for Hs. For the closed seas like the North Sea, Baltic Seas and Black Sea the accuracy is higher with an RMSE = 55 cm. The algorithm is integrated into a demonstration service, used for further validation at the DLR ground station in Neustrelitz. The NRT processing has been tested by supporting a research ship cruise in the Antarctic Sea.

Urban Tomographic Imaging Using Polarimetric SAR Data

In this paper, we investigate the potential of polarimetric Synthetic Aperture Radar (SAR) tomography (Pol-TomoSAR) in urban applications. TomoSAR exploits the amplitude and phase of the received data and offers the possibility to resolve multiple scatters lying in the same range–azimuth resolution cell. In urban environments, this issue is very important since layover causes multiple coherent scatterers to be mapped in the same range–azimuth image pixel. To achieve reliable and accurate results, TomoSAR requires a large number of multi-baseline acquisitions which, for satellite-borne SAR systems, are collected with long time intervals. Then, accurate tomographic reconstructions would require multiple scatterers to remain stable between all the acquisitions. In this paper, an extension of a generalized likelihood ratio test (GLRT)-based tomographic approach, denoted as Fast-Sup-GLRT, to the polarimetric data case is introduced, with the purpose of investigating if, in urban applications, the use of polarimetric channels allows for reduction of the number of baselines required to achieve a given scatterer’s detection performance. The results presented show that the use of dual polarization data allows the proposed detector to work in an equivalent or better way than use of a double number of independent single polarization channels.

Adaptive land classification and new class generation by unsupervised double-stage learning in Poincare sphere space for polarimetric synthetic aperture radars

Polarimetric satellite-borne synthetic aperture radar (PolSAR) is expected to provide land usage information globally and precisely. In this paper, we propose a unsupervised double-stage learning land state classification system using a self-organizing map (SOM) that utilizes ensemble variation vectors. We find that the Poincare sphere parameters representing the polarization state of scattered wave have specific features of the land state, in particular, in their ensemble variation rather than spatial variation. Experiments demonstrate that the proposed PolSAR double-stage SOM system generate new classes appropriately, resulting in successful fine land classification and/or appropriate new class generation.

Geomorphological mapping of ice-free areas using polarimetric RADARSAT-2 data on Fildes Peninsula and Ardley Island, Antarctica

Satellite-borne Synthetic Aperture Radar (SAR) has been used for characterizing and mapping in two relevant ice-free areas in the South Shetland Islands. The objective has been to identify and characterize land surface covers that mainly include periglacial and glacial landforms, using fully polarimetric SAR C band RADARSAT-2 data, on Fildes Peninsula that forms part of King George Island, and Ardley Island. Polarimetric parameters obtained from the SAR data, a selection of field based training and validation sites and a supervised classification approach, using the support vector machine were chosen to determine the spatial distribution of the different landforms. Eight periglacial and glacial landforms were characterized according to their scattering mechanisms using a set of 48 polarimetric parameters. The mapping of the most representative surface covers included colluvial deposits, stone fields and pavements, patterned ground, glacial till and rock outcrops, lakes and glacier ice. The overall accuracy of the results was estimated at 81%, a significant value when mapping areas that are within isolated regions where access is limited. Periglacial surface covers such as stone fields and pavements occupy 25% and patterned ground over 20% of the ice-free areas. These are results that form the basis for an extensive monitoring of the ice-free areas throughout the northern Antarctic Peninsula region.

Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar

Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH–VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to be more useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.

Meteo-marine parameters for highly variable environment in coastal regions from satellite radar images

The German Bight of the North Sea is the area with highly variable sea state conditions, intensive ship traffic and with a high density of offshore installations, e.g. wind farms in use and under construction. Ship navigation and the docking on offshore constructions is impeded by significant wave heights HS>1.3m. For these reasons, improvements are required in recognition and forecasting of sea state HS in the range 0–3m. Thus, this necessitates the development of new methods to determine the distribution of meteo-marine parameters from remote sensing data with an accuracy of decimetres for HS. The operationalization of these methods then allows the robust automatic processing in near real time (NRT) to support forecast agencies by providing validations for model results.A new empirical algorithm XWAVE_C (C=coastal) for estimation of significant wave height from X-band satellite-borne Synthetic Aperture Radar (SAR) data has been developed, adopted for coastal applications using TerraSAR-X (TS-X) and Tandem-X (TD-X) satellites in the German Bight and implemented into the Sea Sate Processor (SSP) for fully automatic processing for NRT services.The algorithm is based on the spectral analysis of subscenes and the model function uses integrated image spectra parameters as well as local wind information from the analyzed subscene. The algorithm is able to recognize and remove the influence of non-sea state produced signals in the Wadden Sea areas such as dry sandbars as well as nonlinear SAR image distortions produced by e.g. short wind waves and breaking waves. Also parameters of very short waves, which are not visible in SAR images and produce only unsystematic clutter, can be accurately estimated. The SSP includes XWAVE_C, a pre-filtering procedure for removing artefacts such as ships, seamarks, buoys, offshore constructions and slicks, and an additional procedure performing a check of results based on the statistics of the whole scene.The SSP allows an automatic processing of TS-X images with an error RMSE=25cm and Scatter Index SI=20% for total significant wave height HS from sequences of TS-X StripMap images with a coverage of ∼30km×300km across the German Bight. The SSP was tuned spatially with model data of the German Weather Service’s (DWD) CWAM (Coastal WAve Model) with 900m horizontal resolution and tuned in situ with 6 buoys located in DWD model domain in the German Bight. The collected, processed and analyzed data base for the German Bight consists of more than 60 TS-X StripMap scenes/overflights with more than 200 images since 2013 with sea state acquired in the domain HS=0–7m with a mean value of 1.25m over all available scenes at buoy locations.The paper addresses the development and implementation of XWAVE_C, and presents the possibilities of SSP delivering sea state fields by reproducing local HS variations connected with local wind gusts, variable bathymetry and moving wind fronts under different weather conditions.

Ship Detection with Spectral Analysis of Synthetic Aperture Radar: A Comparison of New and Well-Known Algorithms

The surveillance of maritime areas with remote sensing is vital for security reasons, as well as for the protection of the environment. Satellite-borne synthetic aperture radar (SAR) offers large-scale surveillance, which is not reliant on solar illumination and is rather independent of weather conditions. The main feature of vessels in SAR images is a higher backscattering compared to the sea background. This peculiarity has led to the development of several ship detectors focused on identifying anomalies in the intensity of SAR images. More recently, different approaches relying on the information kept in the spectrum of a single-look complex (SLC) SAR image were proposed. This paper is focused on two main issues. Firstly, two recently developed sub-look detectors are applied for the first time to ship detection. Secondly, new and well-known ship detection algorithms are compared in order to understand which has the best performance under certain circumstances and if the sub-look analysis improves ship detection. The comparison is done on real SAR data exploiting diversity in frequency and polarization. Specifically, the employed data consist of six RADARSAT-2 fine quad-polacquisitions over the North Sea, five TerraSAR-X HH/VV dual-polarimetric data-takes, also over the North Sea, and one ALOS-PALSAR quad-polarimetric dataset over Tokyo Bay. Simultaneously to the SAR images, validation data were collected, which include the automatic identification system (AIS) position of ships and wind speeds. The results of the analysis show that the performance of the different sub-look algorithms considered here is strongly dependent on polarization, frequency and resolution. Interestingly, these sub-look detectors are able to outperform the classical SAR intensity detector when the sea state is particularly high, leading to a strong clutter contribution. It was also observed that there are situations where the performance improvement thanks to the sub-look analysis is not so noticeable.

Chronic, Anthropogenic Hydrocarbon Discharges in the Gulf of Mexico

Satellite-borne Synthetic Aperture Radar (SAR) was used to obtain more precise estimates of the magnitude of the chronic hydrocarbon discharges described in qualitative pollution reports associated with the production and transportation network of the U.S. coast of the Gulf of Mexico. The National Response Center (NRCen) oil pollution reports were collected and filtered for the period of 2001 to 2012 to determine which of the reports coincided with archived SAR images. Some of the images covered multiple reports and some of the oil discharges described in one report could be observed in more than one image. In all, 177 reports could be investigated from 137 SAR images collected on or near the corresponding report dates. Further analysis found that oil slicks observed in 66 of these SAR images could be attributed to 67 of the reported incidents. Objective measurements indicated that the area of these transient oil slicks visible in SAR images was, on average, significantly larger than what was reported to the NRCen. The only recurring point source for oil slicks was the former site of the Taylor offshore platform. Here chronic, oil slicks were observed that were consistently much larger than other anthropogenic discharges. The SAR images of floating oil discharged from the Taylor site were verified by visual inspection from a boat and aerial photography. For some of the oil slicks discharged from the Taylor site, the accuracy of SAR images for detecting oil slick areas was validated by comparing SAR results to Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. These results show that surveillance by SAR would improve accuracy for estimates of chronic anthropogenic oil pollution, particularly where continuous discharges are on-going.

Ocean Feature Analysis Using Automated Detection and Classification of Sea-Surface Temperature Front Signatures inRADARSAT-2Images

The Royal Canadian Navy produces a semiweekly map of major water mass boundaries in the Western North Atlantic using temperature measurements from several data sources, including satellite sea surface temperature (SST) images from the Advanced Very High Resolution Radiometer (AVHRR). Temporal–spatial detail that can be provided by AVHRR of the location of important SST boundaries such as the Gulf Stream North Wall is limited due to pervasive cloud cover. The ability of satellite-borne synthetic aperture radar (SAR) to image SST front signatures unrestrained by cloud cover makes it a potentially significant additional data source. The Spaceborne Ocean Intelligence Network project has developed an automated procedure to detect candidate SST front signatures in RADARSAT-2 SAR images of the ocean surface and classify them with greater than 80% accuracy.

Comparison of Geophysical Model Functions for SAR Wind Speed Retrieval in Japanese Coastal Waters

This work discusses the accuracies of geophysical model functions (GMFs) for retrieval of sea surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images in Japanese coastal waters characterized by short fetches and variable atmospheric stability conditions. In situ observations from two validation sites, Hiratsuka and Shirahama, are used for comparison of the retrieved sea surface wind speeds using CMOD (C-band model)4, CMOD_IFR2, CMOD5 and CMOD5.N. Of all the geophysical model functions (GMFs), the latest C-band GMF, CMOD5.N, has the smallest bias and root mean square error at both sites. All of the GMFs exhibit a negative bias in the retrieved wind speed. In order to understand the reason for this bias, all SAR-retrieved wind speeds are separated into two categories: onshore wind (blowing from sea to land) and offshore wind (blowing from land to sea). Only offshore winds were found to exhibit the large negative bias, and short fetches from the coastline may be a possible reason for this. Moreover, it is clarified that in both the unstable and stable conditions, CMOD5.N has atmospheric stability effectiveness, and can keep the same accuracy with CMOD5 in the neutral condition. In short, at the moment, CMOD5.N is thought to be the most promising GMF for the SAR wind speed retrieval with the atmospheric stability correction in Japanese coastal waters, although there is ample room for future improvement for the effect from short fetch.