RADARSAT-2 Synthetic Aperture Radar Research Articles

Statistical properties of soil moisture images derived from Radarsat-1 SAR data

In this article, we report on the assessment of the spatial variability of soil moisture using synthetic aperture radar (SAR) data. The imagery was acquired during five different periods over the Roseau River watershed in southern Manitoba, Canada. For validation purposes, ground measurements were carried out at 62 locations simultaneous with the satellite data acquisitions. The first step in this analysis was to assess the performance of the Integral Equation Model (IEM) in simulating backscatter coefficients for selected bare soils. In order to reduce the surface roughness effect on radar backscatter, a semi-empirical calibration technique was implemented. This calibrated model was then implemented in a simplex inversion routine in order to estimate and map soil moisture. Derived spatial patterns of near-surface moisture content were then examined using scale analyses. It has been confirmed that the variance of radar-based soil moisture images follows power law decay versus the observation scale. Also, more explicit analysis of the same soil moisture maps shows a ln–ln linear spatial scale with statistical moments. Concave shape dependency of the corresponding slopes with the moment order was observed during all radar acquisition periods. The latter indicates the presence of multifractal effects.

Synthesis of Remote Sensing Label Fields Using a Tree-Structured Hierarchical Model

The systematic evaluation of synthetic aperture radar (SAR) data analysis tools, such as segmentation and classification algorithms for geographic information systems, is difficult given the unavailability of ground-truth data in most cases. Therefore, testing is typically limited to small sets of pseudoground-truth data collected manually by trained experts, or primitive synthetic sets composed of simple geometries. To address this issue, we investigate the potential of employing an alternative approach, which involves the synthesis of SAR data and corresponding label fields from real SAR data for use as a reliable evaluation testbed. Given the scale-dependent nonstationary nature of SAR data, a new modeling approach that combines a resolution-oriented hierarchical method with a region-oriented binary tree structure is introduced to synthesize such complex data in a realistic manner. Experimental results using operational RADARSAT SAR sea-ice data and SIR-C/X-SAR land-mass data show that the proposed hierarchical approach can better model complex nonstationary scale structures than local MRF approaches and existing nonparametric methods, thus making it well suited for synthesizing SAR data and the corresponding label fields for potential use in the systematic evaluation of SAR data analysis tools.

Radar Backscattering of Intertidal Mudflats Observed by Radarsat-1 SAR Images and Ground-Based Scatterometer Experiments

This paper presents the variation of the radar backscattering of intertidal mudflats in the west coast of Korea observed by Radarsat-1 synthetic aperture radar (SAR) images and investigates the related factors by an indoor experiment and a field experiment using a ground-based C-band scatterometer. The 15 Radarsat-1 SAR images of the intertidal mudflat near Jebu Island used in this study were all taken at around 6:30 p.m., which is an ideal local time for evaluating the daytime evaporation effects during the 12.4-h tidal cycle. An exposure time map and an evaporation time map of mudflats at the time of each SAR acquisition are calculated based on the tidal records, a digital elevation map generated by the waterline extraction method, and the normalized daily evaporation index. The radar backscattering of the upper intertidal mudflat did not show a monotonic change with evaporation time but a complex pattern. An indoor experiment using a C-band scatterometer on drying mud revealed an M-shaped change (increase-decrease-increase-decrease) of radar backscattering due to various factors that affect dielectric constant and roughness such as the drainage of surface water (increase), evaporation (decrease), mud cracking (increase), and further evaporation (decrease). The variation of backscattering observed from SAR images agreed well with the initial three stages of the indoor experiment (increase-decrease-increase) but did not show the final stage of decrease from continuous evaporation. A field experiment on natural intertidal mudflats showed not only the pattern related to the initial drainage and evaporation but also speckles from biological activity.

Monitoring a boreal wildfire using multi-temporal Radarsat-1 intensity and coherence images

Twenty-five C-band Radarsat-1 synthetic aperture radar (SAR) images acquired from the summer of 2002 to the summer of 2005 are used to map a 2003 boreal wildfire (B346) in the Yukon Flats National Wildlife Refuge, Alaska under conditions of near-persistent cloud cover. Our analysis is primarily based on the 15 SAR scenes acquired during arctic growing seasons. The Radarsat-1 intensity data are used to map the onset and progression of the fire, and interferometric coherence images are used to qualify burn severity and monitor post-fire recovery. We base our analysis of the fire on three test sites, two from within the fire and one unburned site. The B346 fire increased backscattered intensity values for the two burn study sites by approximately 5–6 dB and substantially reduced coherence from background levels of approximately 0.8 in unburned background forested areas to approximately 0.2 in the burned area. Using ancillary vegetation information from the National Land Cover Database (NLCD) and information on burn severity from Normalized Burn Ratio (NBR) data, we conclude that burn site 2 was more severely burned than burn site 1 and that C-band interferometric coherence data are useful for mapping landscape changes due to fire. Differences in burn severity and topography are determined to be the likely reasons for the observed differences in post-fire intensity and coherence trends between burn sites.

Polarization Phase Difference Analysis for Selection of Persistent Scatterers in SAR Interferometry

In this letter, we propose a technique for selecting persistent scatterers (PSs) based on their polarization phase difference (PPD). We analyze a normalized PPD between HH and VV channels averaged over a temporal set of images and select pixels that demonstrate predominantly even or odd bounce scattering properties. We compare selected scatterers to PSs selected by applying an amplitude dispersion threshold as suggested by a standard PS interferometry (PSI) approach and show that both methods are complementary. However, the proposed approach can be potentially used on a small set of synthetic aperture radar (SAR) images, which can be beneficial in the early stage of data acquisition. We apply the proposed technique to produce a deformation map for the San Francisco region from six quad-pol RADARSAT-2 SAR images acquired during 2008-2009. The coverage and the precision of the produced deformation map are higher than if it was calculated with the standard PSI technique applied to the same data set.

Rice Crop Monitoring in South China With RADARSAT-2 Quad-Polarization SAR Data

This letter presents preliminary results of an attempt to monitor rice crop growth using RADARSAT-2 quad polarization synthetic aperture radar (SAR) data. Three RADARSAT-2 quad-polarization SAR images are collected from transplanting to rice crop harvesting. Ground truth data, such as rice height and biomass, are measured during RADARSAT-2 data acquisition in Hainan Province, South China. The correlation between backscattering coefficient and rice growth parameters is analyzed, and then, a rice field mapping method with quad polarization SAR image is developed. Experiments show that an HV or VH image backscattering coefficient exhibits the best correlation with rice age after transplantation. Furthermore, the HV or VH image is also more suitable for retrieving rice growth parameters, such as rice height and dry biomass, for FQ4 RADARSAT-2 SAR data. The ratio image of HH/VV possesses high separability required to distinguish rice crop from banana, forest, and river. Results indicate that RADARSAT-2 quad polarization SAR data presented enormous potential for monitoring rice crop growth.

Comparison between mahalanobis classification and neural network for oil spill detection using RADARSAT-1 SAR data

Oil spill or leakage into waterways and ocean spreads very rapidly due to the action of wind and currents. The study of the behavior and movement of these oil spills in sea had become imperative in describing a suitable management plan for mitigating the adverse impacts arising from such accidents. But the inherent difficulty of discriminating between oil spills and look-alikes is a main challenge with Synthetic Aperture Radar (SAR) satellite data and this is a drawback, which makes it difficult to develop a fully automated algorithm for detection of oil spill. As such, an automatic algorithm with a reliable confidence estimator of oil spill would be highly desirable. The main objective of this work is to develop comparative automatic detection procedures for oil spill pixels in multimode (Standard beam S2, Wide beam W1 and fine beam F1) RADARSAT-1 SAR satellite data that were acquired in the Malacca Straits using two algorithms namely, post supervised classification, and neural network (NN) for oil spill detection. The results show that NN is the best indicator for oil spill detection as it can discriminate oil spill from its surrounding such as look-alikes, sea surface and land. The receiver operator characteristic (ROC) is used to determine the accuracy of oil spill detection from RADARSAT-1 SAR data. The results show that oil spills, lookalikes, and sea surface roughness are perfectly discriminated with an area difference of 20% for oil spill, 35% look–alikes, 15% land and 30% for the sea roughness. The NN shows higher performance in automatic detection of oil spill in RADARSAT-1 SAR data as compared to Mahalanobis classification with standard deviation of 0.12. It can therefore be concluded that NN algorithm is an appropriate algorithm for oil spill automatic detection and W1 beam mode is appropriate for oil spill and look-alikes discrimination and detection. Key words: Oil Spill, RADARSAT-1 SAR data, Mahalanobis classification, neural network (NN).

Comparison of Ocean-Surface Winds Retrieved From QuikSCAT Scatterometer and Radarsat-1 SAR in Offshore Waters of the U.S. West Coast

In this letter, we generate a temporal/spatial matchup data set between QuikSCAT scatterometer and RADARSAT-1 synthetic aperture radar (SAR) wind products in offshore waters along the U.S. West Coast. Analysis of the resulting three-year database shows that, in general, the wind products from both sensors have characteristics similar to those reported in the literature. Then, we perform an error analysis in the space domain and find that there is significant discrepancy between the two wind products as the matchup points move closer to the coast. The root-mean-square error (rmse) and standard deviation (STD) between the two data sets increases markedly for points matched within about 100 km of the coastline. Beyond 100 km, the rmse, STD, and systematic bias become small and stable. In addition, an empirical relationship between QuikSCAT and SAR winds in coastal region is proposed. Thus, the bias and errors should be taken into account if the standard operational QuikSCAT wind products are used for forcing models in the coastal ocean.

Ocean Surface Wind Speed of Hurricane Helene Observed by SAR

Abstract The hurricanes can be detected by many remote sensors, but synthetic aperture radar (SAR) can yield high-resolution (sub-kilometer) and low-level wind information that cannot be seen below the cloud by other sensors. In this paper, an assessment of SAR capability of monitoring high-resolution hurricane was conducted. A case study was carried out to retrieve ocean surface wind field from C-band RADARSAT-1 SAR image which captured the structure of hurricane Helene over the Atlantic Ocean on 20 September, 2006. With wind direction from the outputs of U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) model, C-band geophysical model functions (GMFs) which describe the normalized radar cross section (NRCS) dependence on the wind speed and the geometry of radar observations (i.e., incidence angle and azimuth angle with respect to wind direction) such as CMOD5 and newly developed CIWRAP models have been tested to extract wind speed from SAR data. The SAR retrieved ocean surface winds were compared to the aircraft wind speed observations from stepped frequency microwave radiometer (SFMR). The results show the capability of hurricane wind monitoring by SAR.

Simulation of sea surface current velocity from Synthetic Aperture Radar (SAR) data

This study is done to solve the Doppler spectra equation by involving two -dimensional Fourier transform (2DFFT) to obtain a linear formula of sea radial component. The problem of influence of bandwidth on imaging moving ocean surface is analytically solved. Based on the maximum peak of Doppler spectra, the horizontal surface current is computed. The Doppler spectra model has been performed in the RADARSAT-1 SAR data. This study concludes that Doppler spectra model is an appropriate method to compute the sea surface current in the RADARSAT-1 SAR data.

Application of the synthetic aperture radar for monitoring activity and environmental parameters during oil-platform installation

The high risk of oil and gas exploration in the sea must expect a high-quality level of safety insurance to prevent environmental damage. This safety insurance can be supported and provided though satellite monitoring. In the period from 23 June to 5 July 2007, Sakhalin Energy Investment Company Ltd. completed the installation of the Piltun-Astokhskoye-B production platform topsides on the gravity base structure in the Piltun-Astokhskoye Field on the shelf to the northeast of Sakhalin Island. Operational monitoring of this event was based on acquisition of Envisat and Radarsat-1 synthetic aperture radar (SAR) images and was aimed at monitoring ship activity, oil spills and environmental parameters (wind, waves and sea ice). By combining and providing the quasi-real-time information derived from SAR images, an accurate understanding of activity during the tow-out, positioning and installation, as well as environmental conditions, can be obtained in a cost-effective manner. This paper discusses the approach used and presents the results of this monitoring.

Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data

The aim of this study was to estimate soil moisture from RADARSAT-2 Synthetic Aperture Radar (SAR) images acquired over agricultural fields. The adopted approach is based on the combination of semi-empirical backscattering models, four RADARSAT-2 images and coincident ground measurements (soil moisture, soil surface roughness and vegetation characteristics) obtained near Saskatoon, Saskatchewan, Canada during the summer of 2008. The depolarization ratio (χ v ), the co-polarized correlation coefficient ( ρ vvhh ) and the ratio of the absolute value of cross polarization to crop height (Λ vh ) derived from RADARSAT-2 data were analyzed with respect to changes in soil surface roughness, crop height, soil moisture and vegetation water content. This sensitivity analysis allowed us to develop empirical relationships for soil surface roughness, crop height and crop water content estimation regardless of crop type. The latter were then used to correct the semi-empirical Water–Cloud model for soil surface roughness and vegetation effects in order to retrieve soil moisture data. The soil moisture retrieved algorithm is evaluated over mature crop fields (wheat, pea, lentil, and canola) using ground measurements. Results show average relative errors of 19%, 10%, 25.5% and 32% respectively for the retrieval of crop height, soil surface roughness, crop water content and soil moisture.

The oil spill from a shipwreck in Kerch Strait: radar monitoring and numerical modelling

An oil-spill emergency occurred in Kerch Strait (between the Black Sea and the Sea of Azov) on 11 November 2007 after the sinking of the tanker Volgoneft-139 in a severe storm. The results of monitoring with Radarsat-1, TerraSAR-X and Envisat synthetic aperture radar (SAR)-equipped satellites with results from the numerical SPILLMOD model are compared. Analysis of the spatial distribution of oil pollution visible on the SAR images shows that the majority of oil was the result of this accident. SAR images were used to locate and calculate areas of regions with the greatest contamination by fuel oil. Comparison of these oil areas with results from the oil-spill prediction model generally shows good agreement.

Interpretation of Radarsat-1 SAR Imagery of Riyadh for Mapping Applications

A synthetic aperture radar (SAR) image obtained by the Canadian Radarsat-1 system and covering the City of Riyadh is evaluated for thematic accuracy levels required for planimetric mapping applications. After a brief account on the elements and factors affecting radar image interpretation, and the various parameters involved in these processes, a systematic approach was followed in interpreting the various elements that are likely to be shown on planimetric maps. Simple interpretational instruments were used to measure linear features and areas on the radar image. These were then compared with their map equivalents. The results showed that 80% of major roads can be interpreted with conviction; while only 30% of dry streams (wadi's) were discernible. Built-up areas could be interpreted to 85% accuracy and hills and raised ground to 70%. On the other hand, only 10-20% of vegetational areas were correctly identified. Combining these findings with the geometric testes carried out by the present author and other investigators, it seems that reasonable scale range obtainable from Radarsat-1 SAR image type, as tested in this experiment, will be 1/00000 to 1/500000. This is commensurate with the requirements of some basic thematic mapping applications such as forestry, hydrology, geography etc.

Image fusion for land cover change detection

Image fusion is an effective approach for enriching multi-source remotely sensed information. In order to compensate the insufficiency of single-source remote sensing data during the change detection process, and to combine the complementary features from different sensors, this article presents the results of different temporal synthetic aperture radar (SAR) and optical image fusion algorithms for land cover change detection. First, pixel-level image fusion is performed, and its applicability for change detection is assessed by a quantitative analysis method. Second, change detection at the decision-level is put forward, which comprises object-oriented image information extraction from high-resolution optical image, multi-texture feature and support vector machines (SVM)-based information extraction from single band and single polarisation SAR image, and hard- and soft-decision based change detection. Change detection uncertainty is also evaluated at the scale of pixel using the extended probability vector and probability entropy model. The imagery used in this image fusion research was SPOT5 and RADARSAT-1 SAR data.

Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach

The objective of this research is to evaluate Quickbird multi-spectral (MS) data, multi-temporal RADARSAT Fine-Beam C-HH synthetic aperture radar (SAR) data and fusion of Quickbird MS and RADARSAT SAR for urban land-use/land-cover mapping. One scene of Quickbird multi-spectral imagery was acquired on 18 July 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August 2002. Quickbird MS images and RADARSAT SAR data were classified using an object-based and rule-based approach. The results demonstrated that the object-based and knowledge-based approach was effective in extracting urban land-cover classes. For identifying 16 land-cover classes, object-based and rule-based classification of Quickbird MS data yielded an overall classification accuracy of 87.9% (kappa: 0.868). For identifying 11 land-cover classes, object-based and rule-based classification of RADARSAT SAR data yielded an overall accuracy: 86.6% (kappa: 0.852 ). Decision level fusion of Quickbird classification and RADARSAT SAR classification was able to take advantage of the best classifications of both optical and SAR data, thus significantly improving the classification accuracies of several land-cover classes (25% for pasture, 19% for soybeans, 17% for rapeseeds) even though the overall classification accuracy of 16 land-cover classes increased only slightly to 89.5% (kappa: 0.885).

Simulation of the CryoSat-2 satellite radar altimeter sea ice thickness retrieval uncertainty

Although it is well known that radar waves penetrate into snow and sea ice, the exact mechanisms for radar altimeter scattering and its link to the depth of the effective scattering surface from sea ice are not well known. Previously proposed mechanisms linked the snow-ice interface, i.e., the dominating scattering horizon, directly with the depth of the effective scattering surface. However, simulations using a multilayer radar scattering model show that the effective scattering surface is affected by snow-cover and ice properties. With the coming CryoSat-2 (planned launch in 2010) satellite radar altimeter, it is proposed that sea ice thickness can be derived during winter by measuring its freeboard. In this study we evaluate the radar altimeter sea ice thickness retrieval uncertainty in terms of floe buoyancy, radar penetration, and ice type distribution using both a scattering model and Archimedes’ principle. The effect of the snow cover on the floe buoyancy and radar penetration and on the ice cover spatial and temporal variability is assessed from field campaign measurements in the Arctic resulting in ice thickness uncertainties of about 0.3 m for the snow depth variability and 0.3 m for the snow density variability. In addition to these well-known uncertainties, we use high-resolution RADARSAT synthetic aperture radar (SAR) data to simulate errors due to the variability of the effective scattering surface as a result of the subfootprint spatial backscatter and elevation distribution, sometimes called preferential sampling. In particular, in areas where ridges represent a significant part of the ice volume (e.g., the Lincoln Sea), the average simulated altimeter thickness estimate of 2.68 m is lower than the real average footprint thickness of 3.85 m, making preferential sampling the single most important error source. This means that the errors are large and yet manageable if the relevant quantities are known a priori. Radar altimeter ice thickness retrieval uncertainties are discussed.

A New Region-Based Active Contour Model with Skewness Wavelet Energy for Segmentation of SAR Images

A new method of segmentation for Synthetic Aperture Radar (SAR) images using the skewness wavelet energy has been presented. The skewness is the third order cumulant which measures the local texture along the region-based active contour. Nonlinearity in intensity inhomogeneities often occur in SAR images due to the speckle noise. In this paper we propose a region-based active contour model that is able to use the intensity information in local regions and to cope with the speckle noise and nonlinear intensity inhomogeneity of SAR images. We use a wavelet coefficients energy distribution to analyze the SAR image texture in each sub-band. A fitting energy called skewness wavelet energy is defined in terms of a contour and a functional so that, the regions and their interfaces will be modeled by level set functions. A functional relationship has been calculated on these level sets in terms of the third order cumulant, from which an energy minimization is derived. Minimizing the calculated functions derives the optimal segmentation based on the texture definitions. The results of the implemented algorithm on the test images from the Radarsat SAR images of agricultural and urban regions show a desirable performance of the proposed method.

Evaluation of the Dubois, Oh, and IEM radar backscatter models over agricultural fields using C-band RADARSAT-2 SAR image data

Backscatter from synthetic aperture radar (SAR) is correlated with surface characteristics such as soil dielectric properties, surface roughness, and vegetation cover. The purpose of this study is to evaluate the capability of surface radar backscatter models to estimate soil moisture over agricultural fields from fully polarimetric RADARSAT-2 C-band SAR responses. For validation purposes, ground measurements over 44 and 42 sampling sites in eastern Ontario and southern Manitoba, respectively, were carried out in the spring of 2008 simultaneous with satellite data acquisitions. A comparison was made between the backscatter coefficient results derived from three scattering models (the semi-empirical models of Dubois and Oh and the theoretical integral equation model (IEM)) and the SAR image backscatter. Discrepancies between measured radar backscatter coefficients and those predicted by the models have been investigated. Overall, the results show that semi-empirical approaches tend to overestimate the radar response, but correction factors of about 3.5 and 2.0 dB, respectively, were found sufficient to correct the Dubois HH and VV backscatter coefficients. The Oh model backscatter estimations were less variable than those derived from the Dubois model; however, a correction factor of about 5.0 dB was necessary in this case. Data simulated by the IEM showed significant fluctuations. This result was somewhat expected, since previous studies have shown that correlation length measurements are very sensitive to profile length, and a relatively short profile length was used in this study. Correlation agreements were significantly improved when using the IEM semi-empirical calibration technique. These results indicate that further work is needed to assess the requirement for correction factors, such that these models can be operationally implemented to estimate soil moisture in support of agricultural monitoring.

Sea Surface Manifestation of Along-Tidal-Channel Underwater Ridges Imaged by SAR

A group of submerged ocean bottom sand ridges in the Bohai Sea, China, are shown in RADARSAT-1 and ENVISAT synthetic aperture radar (SAR) images. The sand ridges appear as fingerlike quasi-linear features in the SAR images. Examining the detailed local bathymetry chart, we find that these features coincide with the satellite images. The heights of the sand ridges are less than 10 m, and the water depth is between 10 and 30 m. The spacing of the sand ridges is about 10 km, and the length of the sand ridges is about 20 km. The same sand ridges are also visible on a Moderate Resolution Imaging Spectroradiometer (MODIS) true-color image. The semidiurnal and diurnal tidal currents in this area are almost parallel to the major axis of these sand ridges. These observations cannot be explained using the existing 1-D SAR imaging model, which is not applicable to sand ridges parallel to the tidal current. In this paper, we consider the shallow-water current bathymetry in a 2-D space. An analytical ocean model was applied to demonstrate the temporal variations of the current divergence and convergence that are induced by the along-sand-ridge-direction current and ridge interaction. A radar simulation model is used to simulate the variation of normalized radar cross section (NRCS) induced by the ocean surface current. The simulated NRCS variation is similar to that extracted from the calibrated SAR image. Simulation results also show that the NRCS variation becomes negligible when the ocean current is set to about half of the maximum tidal current.