The study focuses on extending the InSAR analysis of seasonal ground deformations to boreal forest regions, where monitoring ground deformation is crucial for understanding active layer dynamics in various ecological and geological conditions. During the summer season, the interferometric coherence is low for estimating displacement using InSAR. Therefore, displacement estimation is conducted during freezing seasons when coherence is relatively higher. To estimate the seasonal ground deformation during the winter season, the SBAS InSAR processing method was applied. However, to address the remaining spatial gaps resulting from decorrelation, dual-polarimetry SAR interferometry technique is employed to compensate for the spatial gaps. Additionally, to assess the magnitude of ground deformation during the freezing season, a piecewise linear model based on the Stefan solution was proposed to examine the temporal deformation pattern associated with the progression of the freezing front in the soil, allowing for the interpretation of hydrological characteristics in active layer.

Remote sensing, particularly Synthetic Aperture Radar (SAR) systems, has lots of operational advantages in continuous change monitoring with its all-weather, day and night imaging capability. In particular, the fully polarimetric SAR systems can offer a more efficient and reliable means of change monitoring by providing additional observations about the Earth's surface. Recently, previous change detection techniques using SAR data focus on detecting changed areas from non-changed areas. However, the utilization of microwave scattering information can offer complementary information on various scatterers and can be a potential capability in discriminating change types without in-situ information. Therefore, this study aims to propose a new multi-class change detection method based on the fusion of polarimetric features. The multi-class change detection results show that the proposed method can discriminate the various land cover type changes in terms of radar scattering mechanism.

As climate change accelerates in recent decades, boreal permafrost areas have been undergone various changes. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, it is difficult to acquire optical data for monitoring the long-term land cover changes in the boreal permafrost areas due to the Polar Night and weather conditions. In this study, a new classification method was proposed to classify spatiotemporally sparse and irregularly acquired Landsat data in central Yakutia. The proposed method showed spatially consistent results in the overlap regions of neighboring Landsat data. The classification results of time series multiple Landsat images were used for understanding the land cover changes during the study period of about 20 years. The result of analyzing the long-term land cover changes revealed the spatial and temporal patterns for each Landsat path/row.

Permafrost, accounting for a quarter of the northern hemisphere, is widely distributed in Central Yakutia. Since permafrost is very sensitive to the changing climate, it shows significant changes due to the repetition of freezing and thawing resulting in unstable landforms such as thermokarst lake, pingo, patterned ground, and other thermokarst landscapes. It is necessary to understand its dynamics and the response of permafrost to the cryogenic process. To obtain dense in-situ observations is challenging under harsh conditions, remote sensing is a useful technique that can be used to observe and analyze dynamics spatially and temporally from a local to global scale. In our study, we represent the effectiveness of using optical satellite images and Synthetic Aperture Radar (SAR) images to monitor dynamic changes over permafrost region focusing on changes of thermokarst lake area.

With the development of high-resolution spaceborne synthetic aperture radar (SAR) systems, it has been possible to use SAR data for identifying man-made structures in urban areas. In this letter, a new approach to extract urban areas in the SAR image is presented by using the morphological characteristics of the radar scattering mechanism. In order to detect the shape and location of a building, the radar shadow index and the double-bounce index are proposed. In addition, an automatic urban area extraction method is proposed using the fuzzy-based fusion of the morphological indices. The detection performance is evaluated by two X-band SAR data acquired from different systems. Despite the differences in observation and environmental conditions in the two data, the proposed method can provide consistent detection performance with the overall accuracy of about 88% and the Kappa coefficient of about 68%.

In this study, a new urban change detection method to effectively detect building damages from Kompsat-5 SAR images having relatively long temporal baseline and different observational conditions is presented. The proposed urban change detection method includes the extraction of building areas using morphological operations and the detection of damage-related changes based on morphological indices. Experimental results indicate that the use of morphological building indices associated with characteristic scattering mechanisms in urban areas can reduce false-alarms in natural changes and improve detectability in building-damaged areas.

In this study, different single-pol and quad-pol parameters to highlight building damages from the time-series SAR data were reviewed and the effects of the polarimetric diversity and temporal resolution in detection of damaged urban areas were investigated. Experimental results showed the necessity of baseline time-series data constructed before the earthquake to detect damaged areas using single-pol SAR data, and the shorter the time gap between the baseline time-series data and the onset of earthquake, the more effective to detect the building damages. On the other hand, the use of quad-pol damage indicators associated with characteristic scattering mechanisms in urban areas can reduce false-alarms in natural changes and improve detectability in building-damaged areas despite of relatively longer temporal baseline.

In this study, capability of the L-band PALSAR-2 data in detecting structural failures and landslides was investigated. In particular, the usability of polarimetric change detection for automatic identification of damages was evaluated. Experimental results indicate a high potential of utilizing fully polarimetric measurements for detecting damaged area. The dominant scatter type indicator can provide useful information on identifying collapsed building. In addition, the co-polarization coherence and the volume scattering power indicating signal depolarization properties can be useful parameters for detecting landslide affected areas.

Ecological patterns and process in the active layer overlying permafrost are expected to undergo significant changes. This study aims to evaluate the capability of L-band fully polarimetric SAR data for monitoring spatio-temporal variability of permafrost ecosystems and underlying soil conditions. ALOS PALSAR polarimetric mode data acquired in freezing and melting seasons over Eastern Siberia are used in this study. Experimental results show that the fully polarimetric scatter type indicators have a great potential for mapping geo-cryological features in the permafrost active layer.

This study aims to understand the effects of spatial resolution on the surface backscattering characteristics of polarimetric radar. Surface scattering models based on approximate methods are formulated by the roughness second-order statistics to obtain a closed-form expression for the radar scattering response. Most studies have been carried out based on the roughness parameters of the infinite surface. In this letter, we propose the roughness autocorrelation function of truncated surfaces for a more realistic description of the roughness parameters of high-resolution radar. The use of roughness parameters for a truncated surface in the scattering model is pertinent to explain the dependence of the backscattering coefficient on the spatial resolution. Simulation results indicate that the traditional computation of the surface backscattering based on the autocovariance function of an infinite surface leads to an underestimation of the backscattering signature of the high-resolution radar.

In this paper, we address the theoretical aspects of coherence region modeling and its inversion methods for polarimetric interferometric synthetic aperture radar (PolInSAR). Instead of only considering the geometrical shape of the coherence region, we focus on directly modeling the whitened interferometric cross-correlation matrix of the PolInSAR data. In particular, we consider three classes of generic scattering models that respectively contain single, double, and triple phase centers within one resolution cell. We then demonstrate, for each class, that the modeled whitened interferometric cross-correlation matrix is normal. Based on this property, we propose, for each case, efficient algorithms to obtain the best normal matrix approximation solution for model inversion from the observed whitened interferometric cross-correlation matrix. In addition, we show how a simple and effective criterion can be designed from the derived solutions for model validation. Finally, we verify the solutions with both simulated and real data. The results prove the proposed method to be a very promising tool for PolInSAR applications.

In this paper, a general scheme for complete model-based decomposition of the polarimetric synthetic aperture radar (POLSAR) coherency matrix data is presented. We show that the POLSAR coherency matrix can be completely decomposed into three components contributed by volume scattering and two single scatterers (characterized by rank-1 matrices). Under this scheme, solving for the volume scattering power amounts to a generalized eigendecomposition problem, and the nonnegative power constraint uniquely determines the minimum eigenvalue as the volume scattering power. Furthermore, in order to discriminate the remaining components, we propose two approaches. One is based on eigendecomposition, and the other is based on model fitting, both of which are shown to properly resolve the surface and double-bounce scattering ambiguity. As a result, this paper in particular contributes to two pending needs for model-based POLSAR decomposition. First, it overcomes negative power problems, i.e., all the decomposed powers are strictly guaranteed to be nonnegative; and second, the three-component decomposition exactly accounts for every element of the observed coherency matrix, leading to a complete utilization of the fully polarimetric information.

One of the main limitations in measuring ground deformation using synthetic aperture radar interferometry (InSAR) is atmospheric phase delay effects. In volcanic regions, the atmospheric phase delay effects can cause serious problems in detecting volcanic unrest because atmospheric thickness is inversely related with the elevation of a volcanic mountain. It is commonly known that the atmospheric phase screen (APS) can be decomposed spatially into stratified and turbulent components. In this paper, the stratified and turbulent atmospheric conditions of a volcanic area were simulated using weather research and forecasting (WRF) model, and the simulated atmospheric conditions were compared with in situ radiosonde data. The comparison results proved that the stratified APS from the WRF model could reflect the reasonable patterns of seasonal changes and vertical profiles with dependable quality. We also found that the stratified APS was significantly correlated with time and sometimes severely contaminated the quality of volcanic deformation estimation. These results indicate that the temporal high-pass (HP) filtering, which has been usually applied in time series InSAR analysis for extracting and removing APS, cannot work properly in volcanic area. Thus, we propose a new method that employs the stratified APS obtained from the WRF model by correlating with topography and the (residual) turbulent APS that can be effectively eliminated by temporal HP filtering in persistent scatterer interferometry (PSI). We applied the proposed method (atmosphere-corrected PSI) to Advanced Land Observing Satellite Phased Array type L-band SAR data that cover Shinmoedake volcano, Japan, and found that the estimated surface deformation and APS agreed well with those measured from GPS and Moderate-Resolution Imaging Spectroradiometer data, respectively.

This study discusses the capability assessment of fully polarimetric L-band spaceborne synthetic aperture radar (SAR) for detection of seasonal snow covered areas. In this paper, ALOS PALSAR time-series data sets obtained in quad-pol modes have been investigated to evaluate the polarimetric signal scattered from a snow-covered mountainous ecosystem in Niigata, Japan. Results show that changes in the scattering mechanism across the various snow states can be identified from polarimetric parameters. In particular, different polarimetric parameters offer complementary information on the snow properties. Based on the characteristic seasonal changes of polarimetric parameters, a new method to map snow-covered areas is proposed in this study using an information fusion approach. Snow extent can be identified successfully by combining polarimetric indices with an overall accuracy of 74.4% as compared with in situ measurements and 77.0% as compared with optical images.

This paper examines the capability assessment of fully polarimetric L-band data for the snow and nonsnow-area classifications. The data sets used are the fully polarimetric Advanced Land Observation Satellite–Phased Array-Type L-Band Synthetic Aperture Radar data, optical Advanced Land Observing Satellite (ALOS)-advanced visible and near-infrared radiometer-2 data close to the radar acquisition, and environmental satellite–advanced synthetic aperture radar data. Several parameters are used to discriminate the snow-covered areas from nonsnow-covered areas in the Indian Himalayan region, including backscattering coefficients, the ratio of cross/copolarized backscattering power and polarization fraction (PF) value. Supervised classification schemes are employed using polarimetric decomposition methods based on the complex Wishart classifier. The accuracy of the classification was found to be 97.95% for the Wishart-supervised classification. Among various parameters and methods, it was found that the alternative newly proposed PF scheme, based on the implementation of fully polarimetric synthetic aperture radar data, yielded the best classification result in the absence of the training samples. The PF value has been effective for discrimination of the snow-covered areas from nonsnow-covered areas, debris-covered glacier, and vegetation. The results of this investigation show that L-band fully polarimetric SAR data provide considerable improvement but may not possess the optimal capability to discriminate snow from other inherent natural and man-made scatterers in heavy snow-laden mountainous scenarios, which may require fully polarimetric S-band or C-band PolSAR measurements.

In this study, the usability of the co-polarization correlation coefficients of polarimetric SAR data for characterization of terrain types in the natural and urban areas has been investigated. The co-polarization coherence signature plot and the co-polarization phase correlation plot, which display the correlation coefficients as a function of the polarization state, are introduced with the unitary basis transformation of the polarimetric covariance matrix. Experimental results indicate that the co-polarization coherence signatures can be an effective way to characterize terrain scatter types.

This paper investigates polarimetric scattering characteristics for accurately detecting stricken man-made objects. In order to understand the polarimetric scattering features generated from various damaged man-made buildings, we carry out the Finite-Difference Time-Domain (FDTD) polarimetric scattering analysis for deformed and/or collapsed man-made target models. The dependency of the polarimetric scattering feature on the variation of squint angle is in detail investigated. It is confirmed from the estimation based on various polarimetric analyses that the double-bounce scattering, the co-pol correlation coefficient in LR circular porlarization basis, and its normalized one are useful markers for stricken man-made objects detection/identification.

In this letter, an advanced version of the hybrid Freeman/eigenvalue decomposition technique for land parameter extraction is presented with an illustrative example of application. The motivation arises from decomposition problems in obtaining a meaningful volume scattering estimation, so that the technique can be used for both oriented objects and vegetation/forest areas. The idea is to improve the accuracy of the required parameter extraction. Two strategies are adopted to increase the applicability of a hybrid Freeman/eigenvalue decomposition technique: One is the unitary transformation of the coherency matrix; the other is to use an extended volume scattering model. The extension of the volume scattering model plays an essential role for the hybrid Freeman/eigenvalue decomposition technique. Since the volume scattering power is evaluated by assuming that the$HV$component is caused by vegetation only in the existing technique, an extended volume scattering power approach is utilized. It is shown that vegetation areas and oriented objects such as urban building areas are well discriminated by the proposed technique as compared to the existing techniques.

This paper reflects the polarimetric synthetic aperture radar (POLSAR) data utilization for near-real-time earthquake and/or tsunami damage assessment in urban areas. In order to show the potential of the fully polarimetric high-resolution polarimetric SAR (POLSAR) image data sets, a four-component scattering power decomposition scheme has been developed and applied to monitor near-real-time earthquake and tsunami disaster damages. The test site for natural disaster damages has been selected: parts of the coastal area affected by the March 11, 2011, 9.0 magnitude earthquake that struck off Japan's northeastern coast and triggered a super-tsunami. The color-coded images of the scattering power decomposition scheme are a simple and straightforward tool to interpret the changes over the earthquake/tsunami affected urban areas and man-made infrastructures. This method also holds other types of natural (typhoon or tornado) and man-made disaster assessment applications. It is found that the double-bounce scattering power is the most promising of the input parameters to detect automated disaster affected urban areas at pixel level. It is also observed that the very-high-resolution POLSAR images are required for superior urban area monitoring over the oriented urban blocks with respect to the illumination of radar.

The statistical behavior of the sea clutter in synthetic aperture radar (SAR) images is characterized by both the marginal distribution and the spatial correlation. However, simultaneous modeling of the joint information remains a difficult job because of the non-Gaussian clutter nature. In this paper, a semiparametric approach is proposed for addressing this problem. First, we investigate the applicability of the nonparametric kernel density estimator (KDE) for estimating the marginal distribution of the SAR clutter and show that the KDE is most applicable in the log-intensity domain. Second, we propose to estimate the underlying spatial correlation structure with a copula approach and show that the Gaussian copula is a sufficiently accurate model. Consequently, the KDE, together with the Gaussian copula, offers a full characterization of the joint probability distribution, based on which a quadratic detector of null distribution governed by the well-known chi-squared law can be conveniently designed for constant false alarm rate detection. In the experiment, results with both simulated and real SAR data demonstrate that, compared with the single-point detector using only the marginal distribution, the proposed method, which incorporates spatial correlation, significantly improves the detection performance with regard to either the receiver-operating-characteristic curve or detected target pixels. The tradeoff, however, lies in a loss of false alarm rate control resulting from increased uncertainty in estimating higher dimensional distributions.

This paper presents a new general four-component scattering power decomposition method by implementing a set of unitary transformations for the polarimetric coherency matrix. There exist nine real independent observation parameters in the 3$\times$3 coherency matrix with respect to the second-order statistics of polarimetric information. The proposed method accounts for all observation parameters in the new scheme. It is known that the existing four-component decomposition method reduces the number of observation parameters from nine to eight by rotation of the coherency matrix and that it accounts for six parameters out of eight, leaving two parameters (i.e., the real and imaginary parts of$T_{13}$component) unaccounted for. By additional special unitary transformation to this rotated coherency matrix, it became possible to reduce the number of independent parameters from eight to seven. After the unitary transformation, the new four-component decomposition is carried out that accounts for all parameters in the coherency matrix, including the remaining$T_{13}$component. Therefore, the proposed method makes use of full utilization of polarimetric information in the decomposition. The decomposition also employs an extended volume scattering model, which discriminates volume scattering between dipole and dihedral scattering structures caused by the cross-polarized$HV$component. It is found that the new method enhances the double-bounce scattering contributions over the urban areas compared with those of the existing four-component decomposition, resulting from the full utilization of polarimetric information, which requires highly improved acquisitions of the cross-polarized$HV$component above the noise floor.

In this study, changes of the L-band PALSAR polarimetric signal scattered from earthquake/tsunami damaged areas has been investigated. Since it is not always possible to get data sets having sufficiently good spatial/temporal resolutions to detect large area of damages precisely, it is of specific interest to use of wave polarization information appropriate to assess disaster-induced changes of scattering phenomena. Experimental results indicate that different polarimetric parameters and similarity measures provide complimentary information on damages. Consequently, it is important to utilize polarimetric features maximizing target changes and minimizing the false-alarm and the missed-alarm rates in developing damage assessment algorithms.

The moisture content of terrain or forest, which is highly related to the permittivity, is a key parameter to understand global water cycle. This study is focused on retrieving permittivity from radar backscattering measurements. The basic concept of the proposal is to utilize Brewster's angle effect of double-bounce backscattering. If the Brewster's angle is known, it is possible to estimate the permittivity of medium directly. In order to demonstrate the Brewster's angle effects on the backscattering of forested area, the authors have carried out a simple laboratory experiment and the results are shown in this paper.

There exist 9 independent polarization parameters in the coherency or covariance matrix as the second order statistics. Various decomposition methods have been presented based on the physical scattering model using these parameters. However, none of them accounts for all polarimetric information, typically leaving T13element un-accounted in the coherency matrix. This paper presents a complete four-component scattering power decomposition method using all polarimetric information. Using double unitary transformation of measured coherency matrix, it is possible to eliminate T23element, which results in a reduction of polarization parameters from 9 to 7. Then by unitary transformation of expansion matrices, it becomes possible to account for T13term, which has never been accounted for in the physical model-based decomposition. By the double unitary transformations to minimize the T33component, all polarimetric parameters are accounted. This methodology also reduces the negative power problem significantly. This method is a further extension of the existing four-component decomposition. The four scattering powers (surface, double bounce, volume, helix) are assigned to blue, red, green, and yellow to compose full-color image. An example image of San Francisco area acquired with ALOS-PALSAR is shown to validate the decomposed result.

In the three- or four-component decompositions, polarimetric scattering properties and corresponding physical scattering models play essential roles for power decomposition. This letter proposes an improved four-component scattering power decomposition method that employs a suitable volume scattering model for single- or double-bounce scattering in the polarimetric synthetic aperture radar image analysis. The cross-polarizedHVcomponent is created by both single-bounce object (such as vegetation) and double-bounce structures (such as oriented building blocks). It has been difficult to discriminate these two objects (vegetation against oriented buildings) in the decomposed images since theHVcomponent is assigned to the volume scattering due to vegetation only. We propose to extend the volume scattering model suited for two physical scattering models. It is shown that a vegetation area and an oriented urban building area are well discriminated compared to those resulting from the implementation of the existing four-component scattering power decomposition.

Information on soil surface state is valuable for many applications such as climate studies and monitoring of permafrost regions. C-band scatterometer data indicate good potential to deliver information on surface freeze/thaw. Variation in state or amount of water contained in the soil causes significant alteration of dielectric properties of the soil which is markedly observable in scatterometer backscattered signal. A threshold-analysis method is developed to derive a set of parameters to be used in evaluating the normalized backscatter measurements through decision trees and anomaly detection modules for determination of freeze/thaw conditions. The model parameters are extracted from two years (2007-2008) backscatter data from ASCAT scatterometer onboard Metop satellite collocated with ECMWF ReAnalysis (ERA-Interim) soil temperature. Backscatter measurements are flagged as indicator of frozen/unfrozen surface, and snowmelt or existing water on the surface. The output product, so-called surface state flag (SSF), compares well with two modeled soil temperature data sets as well as the air temperature measurements from synoptic meteorological stations across the northern hemisphere. The SSF time series are also validated with soil temperature data available at four in situ observation sites in Siberian and Alaska regions showing the overall accuracy of about 80% to 90%.

This study aims to understand the effects of the topographic slope variation on the vegetation backscattering characteristics of polarimetric imaging radar. Most of the previous studies in modeling microwave backscattering signatures of vegetated area have been carried out over relatively flat areas. In order to model vegetation scattering mechanisms of mountainous forests, this paper presents an improvement of the radiative transfer model that accounts for the tilted scattering surface beneath a forest canopy. An L-band polarimetric synthetic aperture radar (SAR) data set acquired by the NASA/Jet Propulsion Laboratory Airborne SAR system was used to test and to assess the vegetation scattering model for a sloping forest area. Experimental result shows the proposed model to be adequate for evaluating slope-induced changes in microwave scattering mechanisms.

Soil moisture is an import parameter for high latitude research focusing on carbon exchange and permafrost issues. This paper reviews requirements, constrains and possibilities of satellite derived soil moisture data for high latitude applications. Major points are freezing and thawing, and landscape heterogeneity. Special focus is on data derived from ENVISAT ASAR. The different ScanSAR modes (wide swath and global monitoring mode) can be used to address various issues. Sensitivity over tundra at this wavelength is similar to semi-arid regions in mediterranean and subtropic climates.

This study aims to evaluate the effects of topographic slope variation on the vegetation backscattering characteristics of polarimetric imaging radar. The experimental analysis of those parameters is carried out on the basis of the simulation of target matrix from the microwave vegetation scattering model. The relationship between polarimetric parameters and vegetation scattering mechanisms can be significantly affected by the presence of topography. Therefore, changes in vegetation scattering mechanisms, particularly in the vegetation-ground interaction terms, should be carefully taken into account in the analysis of SAR signal from mountainous areas.

This investigation presents an improved methodology for decomposing the fully polarimetric SAR data based on the 4-Component Scattering Power Decomposition (4-CSPD) scheme. Using a phase rotation of 3×3 coherency matrix and the minimization of phase rotated matrix element T33, 4-CSPD model has been applied on fully polarimetric SAR images. This new decomposition methodology shows accurate decomposition of fully polarimetric SAR data over oriented urban area and mountainous area as compared the original 4-CSPD model.

This paper presents the comparison between the potential of SAR data in compact polarimetry (CP) and full polarimetry mode for snow detection. Eigenvalues based analysis and developed procedure have been used for this inter-comparison. It has been found over the part of rugged Siachen glaciated terrain, Indian Himalaya that dual polarization and compact polarization mode show-30% and-15% less capability than FP mode respectively.

Due to the high temporal sampling rate of ASAR Global Monitoring mode, it has an application potential for analyzing seasonal changes in permafrost environment. The objective of the study is to develop a robust method for monitoring freeze/thaw cycles beyond threshold approaches. In order to use ASAR GM time-series for analyzing freeze/thaw states, a least square fitting of piecewise step function is introduced in this paper. An experiment result for Siberian permafrost area near Yakutsk illustrates that it can be a promising approach in monitoring permafrost ecosystems.

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of spaceborne sensor dual-polarization C-band data of ENVISAT- ASAR can enhance the accuracy in measurement of snow physical parameters as compared to single polarization data measurement. This study shows the capability of C-band SAR data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both HH and VV polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's, semi empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR HH and HH polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 24.38 kg/m3.

Since December 2008 the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has been disseminating global 25 km ASCAT surface soil moisture data in near real-time (within 135 minutes after sensing) over its broadcast system EUMETCast. The ASCAT surface soil moisture product is thus the first truly operational satellite soil moisture product that may be used for Numerical Weather Prediction (NWP), flood forecasting and other time-critical applications. In this paper we provide information about the status of the ASCAT Level 2 soil moisture processor, review first published validation and application studies and discuss plans for further improvements.

RADARSAT-2 is a follow-up to RADARSAT-1 and is an all weather Earth observation satellite with fully polarimetric imaging capability. The synthetic aperture radars (SARs) onboard both RADARSATs are C-band imaging radars and they are well suited for Earth's ecosystem monitoring and maritime surveillance, because of the near polar orbit and their unique all weather imaging capability, independent of solar illumination. In this paper, RADARSAT-2 is first introduced and several applications of various modes of SAR data to coastal zone problems are discussed, including the coastal surface wind, waterline mapping, and polarimetric SAR data inversion for topographic and geological parameters of tidal flats. Coastal zones, the important interface between the land and the ocean, where a large proportion of the world's population inhabits, continuously change and evolve. The dynamic interaction of coastal winds, coupled with the coastal waves and currents, continuously erode rocks and land mass, and move and deposit various sediments on a continuous basis, along with the tides. Estimation of wind speeds and directions in coastal areas are empirically formulated and can further be improved with the available fully polarimetric data from RADARSAT-2. The water line mapping critically depends on the SAR frequency, or the wavelength of the SAR data used, and RADARSAT-2 SAR data using C-band should map waterlines more accurately than the longer wavelength L- or P-band SAR systems. The roughness parameters and partial information on the tidal flat compositions can be obtained from fully polarimetric SAR data. Some results obtained from NASA AIRSAR(2000) L-band data and RADARSAT-2(2008) C-band data do not fully agree with field measurements and further investigation is in progress. The inversion of polarimetric SAR data is a very complex problem and critically depends on the SAR signal frequency and model functions. RADARSAT-2 is an imaging radar, which is very flexible and powerful tool for potential coastal zone applications. Key RADARSAT-2 features and potential coastal zone application capabilities are also briefly reviewed.

The tidal flats are largely developed along the western and southern coast of Korean peninsula. The simulation results of tidal flats through analytical scattering models could not be fully assessed by using only surface roughness (RMS height and correlation length) and soil moisture. The tidal flats are usually saturated with saline sea water and partially covered with remnant water in depressions. This may cause lower backscatters in SAR images due to the partial specular reflection.

The coastal zones of the Korean peninsula are well known for their large tide ranges and vast expanse of intertidal flats. In this paper, methods of extracting the roughness of the scattering surface of intertidal mudflats from polarimetric synthetic aperture radar (SAR) data have been investigated. The L-band NASA/Jet Propulsion Laboratories airborne SAR data, which were acquired in the intertidal zone during PACRIM-II Korea campaign, were used to estimate the roughness of intertidal mudflats. Surface roughness can be utilized as a useful parameter to monitor the fishery activities in intertidal flats as well as the changes in textural characteristics of surface sediments. In order to retrieve roughness parameters, such as the rms height and the correlation length, of intertidal mudflats, three types of roughness inversion algorithms, based on the Integral Equation Method (IEM), semiempirical, and extended-Bragg models, have been investigated and developed. The inversion algorithms based on the IEM and semiempirical models can be applied to the dual-polarized SAR, while the extended-Bragg model-based inversion approach is also applicable to the fully polarimetric SAR observations. Results indicate the fully polarimetric approach is more pertinent to monitor geophysical parameters from space than the dual polarimetric approach, even if it is possible to reduce the number of unknown surface variables in the specific case of inversion problems.

Tidal flats form a unique ecosystem, playing important roles in the environmental assessment of coastal zones. This study aims to derive surface characteristics of tidal flats from fully polarimetric SAR data sets. The microwave surface scattering models were employed to measure the surface roughness on tidal flats from air-borne and space-borne polarimetric SAR data, such as NASA/JPL AIRSAR and ALOS PALSAR. Surface roughness parameters were estimated from the circular polarization coherence and co-polarized scattering elements. Recent changes in costal wetlands will be continually monitored by using the PALSAR data sets together with other newly developed space-borne SAR systems, such as RADARSAT-2 and TerraSAR-X.

Statistical properties of rough surfaces can be affected by the spatial resolution of the radar sensor. This study aims to understand effects of the spatial resolution on the statistical description of surface roughness properties and on the surface scattering characteristics of polarimetric radar signal. A new expression for the surface autocovariance function and its corresponding roughness spectrum is proposed in order to characterize surface scattering. Results represent increases in the backscattering coefficients in accordance with decreases in the resolution cell size. Conventional continuous spectrum in the theoretical scattering model could underestimate the radar response in case of the surface defined by high correlation length.

Improvements of polarimetric inversion algorithms is presented in this paper. The confounding influence of roughness and vegetation cover on the estimation of the soil moisture contents is considered in the inversion algorithm that estimates volumetric moisture contents and roughness parameters simultaneously from the pertinent combination of polarization measurements. The estimation of the soil moisture contents from polarimetric SAR data is investigated using L- and P-band AIRSAR polarimetric SAR data collected over the Jeju Island, Korea. Results indicate that the estimation of the soil moisture contents can be expanded to a wider range of terrain types by using both L- and P-band polarimetric SAR data.

Waterline detection in the intertidal areas was investigated through synthetic aperture radar (SAR) images and field measurements. Two valuable facts were found in this letter: 1) A discrepancy of waterlines between L- and P-band airborne SAR images was discovered and investigated through precise global positioning system measurements and the theory of the SAR imaging mechanism. In the intertidal areas having low slopes, the Bragg waves resonant with the radar signal can reside in different depths depending on the radar frequency, with the result that the boundary between water and land can be mapped differently in the respective SAR images. 2) Intertidal areas covered with a water film present low radar backscatter in SAR images, which can cause mislocation of waterlines

The eigenvalue-eigenvector-based approach for understanding the scattering mechanisms of polarimetric synthetic aperture radar (POLSAR) data leads to noisy classification results due to arbitrarily fixed zone boundaries in the$H/\overline{\alpha}$plane. In this paper, a new classification scheme that can address the inherent vagueness of class boundaries in the$H/\overline{\alpha}$plane was tested in order to improve the unsupervised classification of the microwave scattering mechanism by introducing concepts related to fuzzy sets. A 2-D fuzzy membership function was developed for the fuzzification of the 2-D$H/ \overline{\alpha}$plane. The proposed fuzzy$H/\overline{ \alpha}$classifier is composed of three steps: fuzzification of the$H/\overline{\alpha}$plane, iterative refinement of membership degrees using the$c$-means algorithm, and defuzzification for the final decision process. The performance of this new approach for the L-band NASA/Jet Propulsion Laboratory's Airborne SAR data obtained during the PACRIM-II experiment was shown to be consistently improved. This new classification technique can be applied to POLSAR data without anya prioriinformation. The fuzzification of the zone boundaries can be further applied to the interpretation of the POLSAR data, e.g., multifrequency classification, retrieval of bio- and geophysical parameters, etc. In order to propose another implementation of the fuzzy boundary representation, we exploited the combination of the$H/\overline{\alpha}$state space and anisotropy information.

Large portion of western coast of Korean peninsular has been reclaimed. Because the reclaimed land stands on weak foundation, the subsidence monitoring is required for civil protection. Newly developed PSInSAR technique is applied to monitor surface deformation on reclaimed land in Incheon Port. There was long lasting subsidence on test area up to 30 mm/year. APS and DEM error are also analyzed for the confirmation of the result.

In this paper an improvement of radiative transfer models that accounts for the depolarizing ground scattering and tilted scattering surface is presented to model vegetation scattering mechanisms. This study is implemented with the real L-band NASA (JPL) AIRSAR data in Jeju volcanic island. The vegetation scattering model in sloping surface is adequate to assess slight changes in microwave scattering mechanisms and to interpret the physical characteristics of eigen-parameters of the coherency matrix in sloping vegetated area.

Tidal flats form a unique ecosystem, providing the habitat for various species of fauna and flora which play an important role in the essential function of water purification. Tidal flats have particular significance for coastal zone environmental assessment. This study aims to derive physical properties and surface characteristics of tidal flats from L-band NASA/JPL fully polarimetric AIRSAR data which was acquired over Southern coast of Korean peninsula in September, 2000, as part of the PACRIM-Ⅱ mission. Surface roughness is an essential parameter prescribed by surface sand ripples in tidal flat. We employed the Extended-Bragg (X-Bragg) model to measure the surface roughness on tidal flats from polarimetric SAR data. There are two approaches of X-Bragg model, which are circular polarization roughness and Anisotropy methods. The circular polarization coherence RRLL ρ was developed to investigate terrain surface roughness. Anisotropy method was developed to separate the surface roughness from surface dielectric constant by utilizing the scattering entropy H, the anisotropy A and the alpha angle The surface roughness was calculated and analyzed using the polarimetric anisotropy and the circular polarization coherence, RRLL ρ . I. INTRODUCTION ρ and the related polarimetric decomposition parameter, the anisotropy, was performed. In this study, the surface roughness parameters were estimated using these two methods which are based on the relation with circular polarization coherence and surface roughness. Four study sites in the intertidal study area in the Southern coast of Korean peninsula were selected for this study. The SAR data analyzed in this paper is multi-look images (MLC), acquired by the fully polarized NASA/JPL Airborne SAR (AIRSAR) survey during the PACRIM-Ⅱ campaign.

A numerical scattering model for artificial metal structure based on physical optics approximation is developed to identify the height of phase center, and the result is compared with interferometric SAR DEM. The interferometric SAR data were gathered by AIRSAR during PACRIM-Ⅱ campaign on Jeju Island. Power transmission towers on piedmont pasture along the slopes of Mt. Halla look like elliptic risings in TOPSAR DEM. The heights of risings are quantitatively analyzed using a scattering model in the way of achieving the height of phase centers of power transmission towers. A numerical algorithm is developed on the basis of physical optics approximation. The structure of power transmission tower was decomposed into hundreds of rectangular metal plates, of which the scattering matrix is known in analytic form, and the calculated scattering fields were summed coherently. The effect of direct backscattering component, ground-scatterer component and scatterer-ground component are decomposed and computed individually for each rectangular metal plate. The Δk- radar equivalent was used to calculate height of phase center of the scatterer. The heights of a selected power transmission tower and scattering algorithm results give existence and location of the transmission towers but not actual tower heights.

Discrepancy of waterline extracted from L- and P-band airborne SAR images was investigated in the intertidal zone through the field measurements and theory of SAR imaging mechanism. In the intertidal zone which has low slope, the Bragg waves resonant with each radar frequency can reside in different depth of surf zone, resulting in the boundary between water and land can be mapped differently in SAR images. I. INTRODUCTION Most large modern cities are located in coastal zones. It is essential to map and monitor changes in waterlines in a timely. Airborne or space-borne synthetic aperture radar (SAR) image provide an efficient aid to monitor and map the waterlines due to high resolution synoptic views of water extent. The west and south coast of the Korean Peninsula is famous for its large tidal range and vast tidal flats. Continuous observation of waterline is highly required in these areas. The extraction of waterlines from SAR images has been studied (1-5). Most of these studies were based on gray level thresholding, segmentation or tracing along the boundary between land and water directly shown in SAR images. However, images of waterlines generated by SAR suffer from a number of speckle effect and meteorological conditions. The return from the wind roughened and wave modulated water can frequently equal or exceed the return from a nearby land area, resulting in inadequate contrast for unambiguous separation. Furthermore, waterline itself estimated from SAR images can vary depending on the sea surface states. Radar frequency or wavelength is important parameter in ocean SAR imaging. Different radar frequency of signal will interact with different length of ocean waves - Bragg scattering (6-8). In the coastal region, the water depth is shallow and rapidly changing, and surface waves can feel the bottom, causing breaking waves. The interaction of these breaking waves with radar signals is extremely complex and very difficult to model. The means by which waves break depends on the nature of the bottom and the characteristics of the wave. For very mildly sloping beaches like tidal flat, typically the waves are spilling breakers - numerous waves occur within the surf zone and these waves begin to lose their energy through friction with the bottom (9). In this paper, we investigate the characteristics of (breaking) surface waves in surf zone in order to explain the discrepancy of waterlines extracted from multiple frequency SAR data in intertidal zone.

In this paper, a new classification scheme which can address the inherent vagueness of class boundaries in the entropy- alpha plane is tested for improvements by introducing the fuzzy concept. A two-dimensional fuzzy membership function is developed for fuzzification of the entropy-alpha plane. Proposed classification approach preserves the information on polarimetric scattering mechanisms and overcomes difficulties in dealing with class type mixing along the boundary of different scattering types. I. INTRODUCTION One of the key applications of polarimetric Synthetic Aperture Radar (POLSAR) image is classification of terrain cover types. In 1997, Cloude and Pottier (1) proposed an unsupervised classification algorithm based on another target decomposition theory (2). An eigen-analysis of the coherency matrix provides the polarimetric scattering mechanisms with the matrix characterizing parameters such as the polarimetric entropy H, the polarimetric anisotropy A, and the averaged polarimetric scattering angle α . Using the two-dimensional α

The potential of using the eigen-analysis of the coherency matrix is discussed for improved understanding of microwave interaction mechanism in vegetated areas. Using a nonsymmetric vegetation scattering model, a theoretical target matrix is generated to compare the eigen-parameters of the coherency matrix with vegetated scattering mechanisms.

Two types of polarimetric Target Decomposition (TD) theories were used to investigate the effects of surface slopes on the physical scattering mechanisms over mountainous scattering surfaces. This study is implemented with the L- & P-band NASA (JPL) airborne SAR (AIRSAR) data, which was acquired during PACRIM-II Korea campaign in September 2000. Although the dominant scattering mechanism of mountainous forest area is volume scattering in pine forest scattering surface, detailed scattering mechanism varies with respect to the local incidence angle variations due to the local topography and wavelength.