Observation of sea-ice thickness in the sea of Okhotsk by using dual-frequency and fully polarimetric airborne SAR (pi-SAR) data

To investigate the possibilities of using dual-frequency, multipolarization synthetic aperture radar (SAR) data to monitor sea ice, we derived the relationship between various polarization characteristics and the physical parameters of sea ice. We discuss the frequency and polarization characteristics of the backscattering coefficients of sea ice and then characterize its thickness by comparing the corresponding backscattering coefficient for each polarization with the physical parameters of the ice. We first propose a methodology for classifying sea-ice types by using a polarimetric decomposition technique, before comparing an estimation of the sea-ice thickness with the corresponding dual-frequency, multipolarization SAR data. We utilized the backscattering ratio to estimate the thickness of the sea ice. This ratio canceled the effect of roughness on the backscattering. The method was validated using Pi-SAR (polarimetric and interferometric airborne SAR) observation data obtained at ground-truth sites.     

Focused synthetic aperture radar processing of ice-sounder datacollected over the Greenland ice sheet

The authors developed a synthetic aperture radar (SAR) processing algorithm for airborne/spaceborne ice-sounding radar systems and applied it to data collected in Greenland. By using focused SAR (phase-corrected coherent averaging), they improved along-track resolution by a factor of four and provided a 6-dB processing gain over unfocused SAR (coherent averaging without phase correction) based on a point-target analysis for a Greenland ice-sounding data set. Also, They demonstrated that the focused-SAR processing reduced clutter and enabled them to identify bedrock-interface returns buried in clutter. Using focused-SAR technique, they processed data collected over a key 360-km-long portion of the 2000-m contour line of southwest Greenland. To the best of their knowledge, these are the first high-quality radar ice thickness measurements over this key location. Moreover, these ice-thickness measurements have been used for improving mass-balance estimates of the Greenland ice sheet     

Polarimetric Characteristics of sea ice in the sea of Okhotsk observed by airborne L-band SAR

The Phased-Array L-Band SAR (PALSAR) aboard the Advanced Land Observing Satellite (ALOS) is capable of globally acquiring fully polarimetric data. In order to confirm the ability of L-band polarimetric synthetic aperture radar (SAR) to investigate sea ice before the ALOS launch, we conducted a field experiment using an airborne Polarimetric and Interferometric SAR (Pi-SAR) in the Sea of Okhotsk in 1999. This paper presents the analyzed results of data acquired in that experiment. The extracted polarimetric parameters of several ice types suggested that polarimetric coherences and phase differences between right-right (RR) and left-left (LL) are good candidates for discriminating ice types. The polarimetric anisotropy as well as the beta angle of the first eigenvector calculated in the polarimetric decomposition procedure are alternative parameters that are sensitive to ice type differences. Due to the low depolarization characteristics of open water, it could be discriminated from sea ice by scattering entropy in all incidence angle ranges. From the relation between ice thickness and the polarimetric parameters, we found that backscattering coefficients and vertical (VV) to horizontal (HH) backscattering ratio are highly correlated with ice thickness. Since the ratio is sensitive to ice surface dielectric constants, a simple simulation using the integral equation method surface model was conducted by using the physical parameters of typical sea ice. A two-dimensional ice thickness map was derived from an empirical relation between the VV-to-HH backscattering ratio and ice thickness.     

Backscattering properties of boreal forests at the C- and X-bands

The backscattering properties of boreal forests are studied using empirical airborne and spaceborne radar data from Finland. Airborne measurements were carried out in the summer of 1992 by the HUTSCAT scatterometer at the Teijo test area in southern Finland. The HUTSCAT scatterometer is an eight-channel helicopter-borne profiling radar operating at the C- and X-bands. The ranging capability of the HUTSCAT scatterometer was employed in the semiempirical modeling of forest backscatter. The backscatter profile information was used in the analysis of the canopy transmissivity and the canopy backscattering coefficient by distinguishing backscattering contributions from the canopy and the ground. Additionally, ERS-1 C-band satellite SAR measurements were obtained for the Teijo test area and for the reference test area in Sodankyla in northern Finland. The radar results were compared with operational ground-based forest assessment data on forest compartments (stands) of the area. The key parameter investigated was the stem (bole) volume per hectare. The results obtained show the behavior of the canopy transmissivity and the canopy backscatter as a function of stem volume (directly related to the forest biomass). The influence of seasonal and diurnal changes on, and the effects of the changes in soil moisture to the backscattering coefficient were also investigated     

SAR辐射定标精度设计与分析

合成孔径雷达（SAR）辐射定标是一项关联图像像素值和地物后向散射系数的重要工作。文中在分析SAR系统的误差源和辐射定标原理的基础上,研究了无源辐射定标和有源辐射定标的精度设计原理。以机载SAR、Pi-SAR定标结果为例,结合雷达散射计的测试结果,分析了相同条件相似地物的后向散射系数的差异及相应的原因。理论分析和数据比较表明了辐射定标精度设计和比较的合理性,为定标精度和结果分析提供了理论支持和技术途径。     

From Glacier Facies to SAR Backscatter Zones via GPR

We present a comparison between data acquired with frequency-modulated ground-penetrating radar (GPR) and satellite synthetic aperture radar (SAR). Both radars are polarimetric and operate at a center frequency of 5.3 GHz. The field site is the polythermal glacier Kongsvegen, Svalbard. Along glacier GPR profiles cover the ablation area and the accumulation area, where the latter consists of superimposed ice (SI) and firn. The glacier facies are clearly identifiable on the GPR profiles, although we show that the copolarized response is better for distinguishing different ice zones, whereas the SI–firn boundary is most obvious in the cross-polarized response. A calibrated backscatter coefficient is calculated for the GPR data and compared with the SAR backscatter coefficient. The SAR zones are in very good agreement with the GPR-derived glacier facies. We show that, in the ablation area, the SAR response is dominated by backscatter from the previous summer surface. In the SI and firn areas, it is dominated by sources below the previous summer surface.      

Radar backscattering model for multilayer mixed-species forests

A multilayer canopy scattering model is developed for mixed-species forests. The multilayer model provides a significantly enhanced representation of actual complex forest structures compared to the conventional canopy-trunk layer models. Multilayer Michigan Microwave Canopy Scattering model (Multi-MIMICS) allows overlapping layer configuration and a tapered trunk model applicable to forests of mixed species and/or mixed growth stages. The model is the first-order solution to a set of radiative transfer equations and includes layer interactions between overlapping layers. It simulates SAR backscattering coefficients based on input dimensional, geometrical, and dielectric variables of forest canopies. The Multi-MIMICS is an efficient realization of actual forest structures and can be shaped for specific interest of forest parameters. We present the model's application and validation in the paper. The model is parameterized using data collected from a 220,000-ha area of forests in central Queensland, Australia. Fifteen 50/spl times/50 m test sites representing the general forest diversity and growth stages are chosen as ground truth. Polarimetric backscattering airborne SAR (AIRSAR) data of the same area are acquired to validate the model simulations. The model predicts SAR backscattering coefficients of the test areas. Simulation results show a good agreement with AIRSAR data at most frequencies and polarizations. The simulated backscattering coefficient from the multilayer model and the standard MIMICS are also compared and significant improvements are observed.     

Seasonal dynamics of C-band backscatter of boreal forests withapplications to biomass and soil moisture estimation

The seasonal changes of the C-band backscattering properties of boreal forests are investigated by applying 1) a semiempirical forest backscattering model and 2) multitemporal ERS-1 SAR data from two test areas in Finland. The semiempirical modeling of forest canopy volume backscattering and extinction properties is based on high-resolution data from the authors' ranging scatterometer HUTSCAT. The response of ERS-1 SAR to forest stem volume (biomass) and other forest characteristics is investigated by employing the National Forest Inventory sample plots, stand-wise forest inventory data and LANDSAT- and SPOT-based digital land use maps. The results show that the correlation between the backscattering coefficient and forest stem volume (biomass) varies from positive to negative depending on canopy and soil moisture. Additionally, the seasonal snow cover and soil freezing/thawing effects cause drastic changes in the radar response. A novel method for the estimation of forest stem volume (biomass) is introduced. This technique is based on the use of: 1) multitemporal ERS-1 SAR data; 2) reference sample plot information; and 3) the semiempirical backscattering model. It is shown that the multitemporal ERS-1 SAR images can be successfully used for estimating the forest stem volume. The effects of soil moisture variations to ERS-1 SAR results have been analyzed using hydrological soil moisture model and in situ data. The results indicate that the semiempirical model can he used for predicting the soil and canopy moisture variations in ERS-1 images     

Automatic Extraction of Traffic Flows Using TerraSAR-X Along-Track Interferometry

Spaceborne synthetic aperture radar (SAR) offers great potential for the measurement of ground traffic flows. A SAR with multiple receiving apertures aligned in flight direction repeatedly images the same ground area with a short time lag. This allows for an effective detection of moving ground objects, whose range variation translates into an interferometric phase signal between the receiving channels. The high-resolution German SAR satellite TerraSAR-X offers several ways to create multiple along-track apertures. We exploit this to demonstrate satellite-based traffic-flow measurements using along-track interferometry (ATI) and Displaced Phase Center Array techniques. In this paper, we address the usage of different TerraSAR-X ATI modes for data acquisition and describe an automatic near-real-time processing chain for the extraction of traffic information. The performance of this TerraSAR-X traffic processor is significantly driven by incorporating a priori knowledge of road networks. We present examples of automatic traffic detection as well as empirical evaluations thereof using different kind of reference data.      

A three-dimensional radar backscatter model of forest canopies

A three-dimensional forest backscatter model, which takes full account of spatial position of trees in a forest stand is described. A forest stand was divided into cells according to arbitrary spatial resolution. The cells may include “crown”, “trunk”, and “gap” components, determined by the shape, size and position of the trees. The forest floor is represented by a layer of “ground” cells. A ray tracing method was used to calculate backscattering components of 1) direct crown backscatter, 2) direct backscattering from ground, 3) direct backscattering from trunk, 4) crown-ground scattering, and 5) trunk-ground scattering. Both the attenuation and time-delay of microwave signals within cells other than “gap” were also calculated from ray tracing. The backscattering Mueller matrices of these components within the same range intervals were incoherently added to yield the total backscattering of an image pixel. By assuming a zero-mean, multiplicative Gaussian noise for image speckle, the high-resolution images were aggregated to simulate a SAR image with a given spatial resolution and number of independent samples (looks). A well-characterized 150 m×200 m forest stand in Maine, USA, was used to parameterize the model. The simulated radar backscatter coefficients were compared with actual JPL SAR data. The model gives reasonable prediction of backscattering coefficients averaged over the entire stand with agreement between model and data within 1.35 dB for all channels. The correlations between simulated images and SAR data (10 by 15 pixels) were positive and significant at the 0.001 level for all frequencies (P, L, and C bands) and polarizations (HH, HV, and VV)     

Radar modeling of a boreal forest

The authors report on the use of microwave modeling, ground truth, and synthetic aperture radar (SAR) data to investigate the characteristics of forest stands. A mixed coniferous forest stand has been modeled at SAR frequencies (P-, L-, and C-bands). The extensive measurements of ground truth and canopy geometry parameters were performed in a 200 m-square hemlock-dominated plot inside a forest. Hemlock trees in the forest are modeled by characterizing tree trunks, branches, and needles (leaves) with randomly oriented, lossy dielectric cylinders whose area and orientation distributions are prescribed. The distorted Born approximation is used to compute the backscatter at P-, L-, and C-SAR frequencies     

A tomographic formulation of spotlight-mode synthetic aperture radar

Spotlight-mode synthetic aperture radar (spotlight-mode SAR) synthesizes high-resolution terrain maps using data gathered from multiple observation angles. This paper shows that spotlight-mode SAR can be interpreted as a tomographic reeonstrution problem and analyzed using the projection-slice theorem from computer-aided tomograpy (CAT). The signal recorded at each SAR transmission point is modeled as a portion of the Fourier transform of a central projection of the imaged ground area. Reconstruction of a SAR image may then be accomplished using algorithms from CAT. This model permits a simple understanding of SAR imaging, not based on Doppler shifts. Resolution, sampling rates, waveform curvature, the Doppler effect, and other issues are also discussed within the context of this interpretation of SAR.     

Rectangular building extraction from stereoscopic airborne Radar images

From the recent availability of images recorded by synthetic aperture radar (SAR) airborne systems, automatic results of digital elevation models (DEMs) on urban structures have been published lately. This paper deals with automatic extraction of three-dimensional (3-D) buildings from stereoscopic high-resolution images recorded by the SAR airborne RAMSES sensor from the French Aerospace Research Center (ONERA). On these images, roofs are not very textured whereas typical strong L-shaped echoes are visible. These returns generally result from dihedral corners between ground and structures. They provide a part of the building footprints and the ground altitude, but not the building heights. Thus, we present an adapted processing scheme in two steps. First is stereoscopic structure extraction from L-shaped echoes. Buildings are detected on each image using the Hough transform. Then they are recognized during a stereoscopic refinement stage based on a criterion optimization. Second, is height measurement. As most of previous extracted footprints indicate the ground altitude, building heights are found by monoscopic and stereoscopic measures. Between structures, ground altitudes are obtained by a dense matching process. Experiments are performed on images representing an industrial area. Results are compared with a ground truth. Advantages and limitations of the method are brought out.     

Estimating Snow Accumulation From InSAR Correlation Observations

Snow accumulation in remote regions, such as Greenland and Antarctica, is a key factor for estimating the Earth's ice mass balance. In situ data are sparse; hence, they are useful to derive snow accumulation from remote sensing observations, such as microwave thermal emission and radar brightness. These data are usually interpreted using electromagnetic models in which volume scattering is the dominant mechanism. The main limitation of this approach is that microwave brightness is not well related to backscatter if the ice sheet is layered. Because larger grain size and thicker annual layers both increase radar image brightness, with the first corresponding to lower accumulation rate and the second to higher accumulation rate, models of radar brightness alone cannot accurately reflect accumulation. Consideration of correlation measurements can also resolve this ambiguity. We introduce an interferometric ice scattering model that relates the interferometric synthetic aperture radar correlation and radar brightness to both ice grain size and hoar layer spacing in the dry-snow zone of Greenland. We use this model and the European Remote Sensing satellite radar observations to derive several parameters related to snow accumulation rates in a small area in the dry-snow zone. These parameters show agreement with four in situ core accumulation rate measurements in this area, whereas models using only radar brightness data do not match the observed variation in accumulation rates     

A new statistical model for Markovian classification of urban areas in high-resolution SAR images

We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (K, Weibull, Log-normal, Nakagami-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolution. The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground, buildings, and vegetation.     

Radar measurements of snow: experiment and analysis

This paper considers two specific types of experiments conducted to improve the authors' understanding of radar backscatter from snow-covered ground surfaces. The first experiment involves radar backscatter measurements at Cand X-band of artificial snow of varying depths. The relatively simple target characteristics, combined with an exhaustive ground truth effort, make the results of this experiment especially amenable to comparison with predictions based on theoretical methods for modeling volume-scattering media. It is shown that both conventional and dense-medium radiative transfer models fail to adequately explain the observed results. A direct polarimetric inversion approach is described by which the characteristics of the snow medium are extracted from the measured data. The second type of experiment examined in this study involves diurnal backscatter measurements that were made contemporaneously with detailed measurements of the snow-wetness depth profiles of the observed scene. These data are used to evaluate the capability of a recently proposed algorithm for snow wetness retrieval from polarimetric synthetic aperture radar (SAR) measurements, which has hithertofore been applied only to data from very complex and extended mountainous terrains     

Soil moisture estimation from ERS/SAR data: toward an operational methodology

Previous studies have shown the possibility of using European Remote Sensing/synthetic aperture radar (ERS/SAR) data to monitor surface soil moisture from space. The linear relationships between soil moisture and the SAR signal have been derived empirically and, thus, were a priori specific to the considered watershed. In order to overcome this limit, this study focused on two objectives. The first one was to validate over two years of data the empirical sensitivity of the radar signal to soil moisture, in the case of three agricultural watersheds with different soil compositions and land cover uses. The slope of the observed relationship was very consistent. Conversely, the offset could change, making the soil moisture retrieval only relative (and not absolute). The second one was to propose an "operational" methodology for soil moisture monitoring based on ERS/SAR data. The implementation of this methodology is based on two steps: the calibration period and the operational period. During the calibration period, ground truth campaigns are performed to measure vegetation parameters (to correct the SAR signal from the vegetation effect), and the ERS/SAR data is processed only once a field land cover map is established. In contrast, during the operational period, no vegetation field campaigns are performed, and the images are processed as soon as they are available. The results confirm the relevance of this operational methodology, since no loss of performance (in soil moisture retrieval) is observed between the calibration and operational periods.     

SAR image statistics related to atmospheric drag over sea ice

The possibility of using synthetic aperture radar (SAR) data to distinguish sea-ice regions with different atmospheric drag is explored. Both the amplitude of the radar return and statistics derived from SAR image data are examined. Roughness statistics data from several pack-ice areas are used in a backscatter model to predict the return from surfaces with measured drag coefficients. The results suggest that the scattering coefficient for typical radar wavelengths is insensitive to the roughness elements responsible for the observed drag coefficient variations over pack ice free of major ridges. For marginal ice zones, where ice concentration and floe deformation contribute to atmospheric drag, a simple model for the atmospheric boundary layer is used to provide qualitative relationships between drag coefficient and regional ice properties (ice concentration, floe size distribution, floe edge density) derivable from SAR data. Simple algorithms to produce maps of ice concentration and edge density are outlined and applied to 23.5-cm SAR digital image data     

Quantitative analysis of RADARSAT SAR data over a sparse forest canopy

This article studies the behavior of the backscattering coefficient of a sparse forest canopy composed of relatively short black spruce trees. Qualitative analysis of the multiangular data measured by the RADARSAT synthetic aperture radar (SAR) sensor shows a good agreement with surface and vegetation volume scattering fundamental behaviors. For a quantitative analysis, allometric equations and measurements of tree components collected within the framework of the Extended Collaboration to Link Ecophysiology and Forest Productivity (ECOLEAP) project are used, in an existing multilayer radiative transfer model for forest canopies, to simulate the RADARSAT SAR data. In our approach, the fractional cover of trees estimated from aerial photographs is used as a weighting parameter to adapt the closed-canopy backscattering model to the sparse forest under study. Our objective is to analyze the sensitivity of the backscattering coefficient as a function of sensor configuration, soil wetness, forest cover, and forest structural properties in order to determine the suitable soil, vegetation, and sensor parameters for a given thematic application. For the entire incidence angle domain (20/spl deg/ to 50/spl deg/) of the sensor, simulations show that over a sparse forest composed of mature trees the monitoring of the ground surface is possible only under very wet soil conditions. Therefore, this article informs about the ability of the RADARSAT SAR sensor in monitoring wetlands.