A Bayesian classification model for sea ice roughness fromscatterometer data

For sea ice in the Baltic Sea, surface scattering can be regarded as the dominant scattering mechanism at C-band. In this paper, a new statistical method is introduced for making statistical inferences about the underlying ice surface roughness on the basis of one-dimensional (1D) scatterometer data y. The central parameter in the hierarchical model applied in the context is a mixture parameter p, which indicates the degree of surface roughness in ice surface. Several questions related to the occurrence of different ice classes on a transect can be solved with the aid of the posterior distribution [p|y]. An empirical approximation for the posterior distribution is computed by using Markov Chain Monte Carlo methodology. The efficiency of the suggested approach is investigated by analyzing a C-band HH-polarization helicopter-borne HUTSCAT scatterometer data. The results provided by the statistical model show good agreement with a video-based ice type classification     

Determination of volume and surface scattering from saline iceusing ice sheets with precisely controlled roughness parameters

Experiments were performed at the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) in Hanover, NH, to precisely determine the relative contributions of surface and volume scattering from saline ice that has well-known surface roughness characteristics. The ice growth phase of the experiment made use of two 6-ft diameter tanks and a 6-ft diameter mold with known roughness statistical parameters of rms height=0.25 cm and Gaussian correlation (correlation length=2.0 cm). One tank was used for growing a moderately thick saline ice sheet with very smooth surface, and the other was used for growing a thin layer of freshwater ice over the surface mold. The latter resulted in a layer with one statistically known rough boundary and one smooth boundary. Wide-bandwidth, multiple incidence angle backscattering measurements were performed, first on the bare saline ice sheet and then on the same sheet after the thin freshwater ice sheet was placed on top of it. Results indicate that the surface scattering dominates over saline ice volume scattering at all frequencies for low incidence angles for both the very smooth and Gaussian rough surfaces. The significance of volume scattering depends strongly on angle of incidence, frequency, volume scattering albedo, surface roughness, and surface correlation function     

The effect of inhomogeneous roughness on radar backscattering fromslightly deformed sea ice

This paper focuses on the spatially varying backscattering signature of an area of refrozen brash ice observed by a ship based scatterometer. The measurements were carried out as part of the Baltic Experiment for ERS-1 in 1994. The scatterometer was operated at 5.4 GHz at different incidence angles and polarizations. By analysing the scatterometer data over azimuth scans, it was found that the backscattering variabilities are not only due to fading but also contain a textural component. Surface height profiles were measured using a laser. The observed ice surface roughness was nonstationary over the measurement area. The ice surface can be approximated by adjacent patches of stationary roughness with patch dimensions of about 4.5 m. From the roughness spectra of different stationary patches, two roughness classes can be distinguished. The implications of estimating the roughness parameters from relatively short profile lengths is discussed and the effect on theoretical predictions of the backscattering coefficient is investigated. The texture variance is evaluated theoretically on the basis of the simulated backscattering coefficients of the two observed roughness classes and is found to compare with the scatterometer data     

Analysis of surface roughness and morphology of first-year sea ice melt ponds: implications for microwave scattering

Variations in wind forcing over summer first-year sea ice (FYI) melt ponds occur at hourly to weekly scales and are a significant contributor to microwave backscatter (/spl sigma//spl deg/) variability observed from spaceborne synthetic aperture radar (SAR) platforms (e.g., ENVISAT-ASAR and RADARSAT-1). This variability impairs our ability to use SAR to derive information on summer sea ice thermodynamic state and energy balance parameters such as albedo and melt pond fraction. The surface roughness contribution of FYI melt ponds in the Canadian Arctic Archipelago to like-polarized, C-band /spl sigma//spl deg/ estimates is analyzed through a spectral and statistical analysis of surface wave height profiles for varying wind speeds, upwind fetch lengths, and melt pond depths. A unique derivation of melt pond surface wave height spectra is presented based on digital video of melt pond surface wave trains. Significant scale surface roughness was observed even at wind speeds of 3 m/spl middot/s/sup -1/, resulting in small perturbation model estimates of /spl sigma//spl deg/ (HH) ranging from -5 dB at 20/spl deg/ incidence to -22 dB at 50/spl deg/ incidence. Results from a multivariate linear regression analysis show that 53.5% of observed variance in /spl sigma//spl deg/ (HH or VV) can be explained by wind speed, upwind fetch from melt pond edges, and melt pond depth, with no appreciable difference in the relative contribution of explanatory variables. Modeled omnidirectional /spl sigma//spl deg/ as a function of wind speed and incidence angle for 100-m transects collected throughout the melt pond season act to elaborate the role of fetch and depth, as well as the modulating effect of hummocks, on /spl sigma//spl deg/.     

Inversion of the surface properties of ice sheets from satellite microwave data

Electromagnetic models are used as the basis for a least squares inversion technique to estimate the dry snow zone surface properties of the terrestrial ice sheets from active and passive microwave satellite data. Retrieved parameters include grain size, density, layer thickness, and accumulation rate. The prime motivation is to provide information of direct value to the Cryosat altimeter mission. The derived parameters can be used to convert from elevation change to snow mass change. They can also be used to predict geophysical retracking errors in altimeter data and to estimate the resulting uncertainty in the altimeter elevation measurement. With this technique, snow accumulation rate can also be estimated using passive microwave data. These data can then be compared to historical European Remote Sensing (ERS) satellite altimeter data in order to assess the impact of interannual variability in accumulation rate on the significance of rates of elevation change. The technique is in the preliminary stages of assessment, but is demonstrated using ERS-2 altimeter data in conjunction with spatio-temporally colocated Special Sensor Microwave/Imager (SSM/I) and QuikSCAT data. It is planned to apply the technique ultimately to Cryosat.     

Multisensor approach to automated classification of sea ice image data

A multisensor data fusion algorithm based on a multilayer neural network is presented for sea ice classification in the winter period. The algorithm uses European Remote Sensing (ERS), RADARSAT synthetic aperture radar (SAR), and low-resolution television camera images and image texture features. Based on a set of in situ observations made at the Kara Sea, a neural network is trained, and its structure is optimized using a pruning method. The performance of the algorithm with different combinations of input features (sensors) is assessed and compared with the performance of a linear discriminant analysis (LDA)-based algorithm. We show that for both algorithms a substantial improvement can be gained by fusion of the three different types of data (91.2% for the neural network) as compared with single-source ERS (66.0%) and RADARSAT (70.7%) SAR image classification. Incorporation of texture increases classification accuracy. This positive effect of texture becomes weaker with increasing number of sensors (from 8.4 to 6.4 percent points for the use of two and three sensors, respectively). In view of the short training time and smaller number of adjustable parameters, this result suggests that semiparametric classification methods can be considered as a good alternative to the neural networks and traditional parametric statistical classifiers applied for the sea ice classification.     

On the dimensionality of multiparameter microwave image data fromthin sea ice in the Labrador Sea

Thin ice is typically defined as comprising the World Meteorological Organization's young and new ice categories, referring to sea ice that is less than 0.3 m thick. This ice type is an extremely important factor in both the thermodynamic and dynamic properties of both polar and marginal ice covers. In the LIMEX'89 experiment, spatially and temporally registered multipolarization data from 5.3 and 9.25 GHz SARs and from 37 and 90 GHz imaging radiometers were acquired over a region containing a wide range of new ice growth stages in the Labrador ice pack. The temperatures at the data acquisition time were -10 and correspond to ice growth conditions. In this paper, the dimensionality of the multifrequency, multipolarization active and passive data set is examined to determine the complementarity of the sensor parameters and sensor types for thin ice measurements. Principal component analysis is used to provide estimates of the information content of individual measurement channels and their combinations. Various measurement subspaces are examined. Criteria for channel redundance are proposed and tested and the classification potential of the multidimensional measurement set is tested for thin ice growth stages that are known to present classification difficulties for microwave sensors. Given six nominally independent SAR measurement channels, the information space dimension of this subspace is shown to be greater than five. The four radiometer channels are shown to have information space dimension two and are redundant in frequency. The combined, ten element, SAR/radiometer measurement space was shown to have information space dimension eight under the criteria used when the scattering polarization ratios are included     

Synergy of active and passive satellite microwave data for the study of first-year sea ice in the Caspian and Aral seas

The paper discusses application of active and passive microwave data for assessment of time and space variations of first-year ice cover. The Caspian and Aral seas are chosen as main study areas. The Caspian Sea evolution is primarily climate driven, while for the Aral Sea there is a mix of anthropic and climate factors. We analyze ice cover conditions using a novel method that combines active and passive satellite measurements for ice discrimination. This method uses the synergy of simultaneous data from active (radar altimeter) and passive (radiometer) microwave instruments onboard the TOPEX/Poseidon (T/P) satellite, launched in 1992. The benefits, drawbacks, and potential of ice cover studies using the proposed method are discussed. We analyze in detail how this method is influenced by the difference in footprints of the T/P sensors and by the radiometric properties of ice and snow at different stages of ice cover evolution. In order to link the T/P-derived results to historical observations that end in the mid-1980s, long time series of passive microwave data from SMMR and SSM/I sensors have also been analyzed. Satellite time series of ice cover extent and duration of ice period have been obtained for the Caspian and Aral seas since 1978. A good agreement is obtained between historical and satellite data, with significant spatial and temporal variability of ice conditions. There is a marked decrease of both duration of ice season and ice extent during the winters 1998/1999-2001/2002. These satellite-derived time series of sea ice parameters are very valuable in view of the heterogeneous and mostly unpublished data on ice conditions over the Caspian and Aral seas since the mid-1980s.     

On the utility of SeaWinds/QuikSCAT data for the estimation of the thermodynamic state of first-year sea ice

The thermodynamic state of sea ice is important to accurately and remotely monitor in order to provide improved geophysical variable parameterizations in sea ice thermodynamic models. Operationally, monitoring the thermodynamic state of sea ice can facilitate eased ship navigation routing. SeaWinds/QuikSCAT (QuikSCAT) dual-polarization [i.e., horizontal (HH) and vertical (VV)] active microwave data are available at a sufficiently large spatial scale and high temporal resolution to provide estimates of sea ice thermodynamics. This analysis evaluated the temporal evolution of the backscatter coefficient (/spl sigma//spl deg/) and VV/HH copolarization ratio from QuikSCAT for estimating sea ice thermodynamics. QuikSCAT estimates were compared against RADARSAT-1 synthetic aperture radar (SAR) imagery and the Canadian Ice Service (CIS) prototype operational ice strength algorithm. In situ data from the Collaborative Interdisciplinary Cryospheric Experiment (C-ICE) for 2000, 2001, and 2002 were used as validation. Results indicate that the temporal evolution of /spl sigma//spl deg/ from QuikSCAT is analogous to RADARSAT-1. The QuikSCAT /spl sigma//spl deg/ temporal evolution has the ability to identify winter, snow melt, and ponding thermodynamic states. Moreover, the copolarization VV/HH ratio of QuikSCAT could provide a second estimate of the ponding state in addition to identifying the drainage state that is difficult to detect by single-polarization SAR. QuikSCAT detected thermodynamic states that were found to be in reasonable agreement to that of in situ observations at the C-ICE camp for all years. Operational implications of this analysis suggest QuikSCAT is a more effective and efficient medium for monitoring ice decay compared to RADARSAT-1 and can be utilized to provide more robust modeled ice strength thresholds.     

Validation of sea ice motion from QuikSCAT with those from SSM/I and buoy

Arctic sea ice motion for the period from October 1999 to March 2000 derived from QuikSCAT and ocean buoy observations.Special Sensor Microwave/Imager (SSM/I) data using the wavelet analysis method agrees well with ocean buoy observations. Results from QuikSCAT and SSM/I are compatible when compared with buoy observations and complement each other. Sea ice drift merged from daily results from QuikSCAT, SSM/I, and buoy data gives more complete coverage of sea ice motion. Based on observations of six months of sea ice motion maps, the sea ice motion maps in the Arctic derived from QuikSCAT data appear to have smoother (less noisy) patterns than those from NSCAT, especially in boundary areas, possibly due to constant radar scanning incidence angle. For late summer, QuikSCAT data can provide good sea ice motion information in the Arctic as early as the beginning of September. For early summer, QuikSCAT can provide at least partial sea ice motion information until mid-June. In the Antarctic, a case study shows that sea ice motion derived from QuikSCAT data is consistent with pressure field contours.     

XPath语义特性及其对XML数据操作的应用研究

给出了XPath语义的简洁定义，使用XPath标准的语法格式说明查询步骤，并通过一个具体实例说明定位路径的方法，实现了XPath与XSLT结合的XML查询，同时对XPath在．NET中的应用进行了研究。     

Electron channeling and EBIC studies of edge-supported pulling silicon sheets

The dominant grain structure in edge-supported pulling silicon sheets has been studied by electron channeling in a scanning electron microscope. For unseeded polycrystalline silicon sheets in which equilibrium grain structures have been attained, we found that the dominant grain structure is long, narrow grains with surface normals less than twenty degrees off the [011] direction. The plane that is parallel to the growth direction and perpendicular to the surfaces for most of these grains is very close to     

Surface roughness reduction for AlCu-based metallization: implications from an integrative point of view

For an AlCu-based metallization utilized in a state-of-the-art production environment we demonstrate that, beyond the more traditional electromigration properties, also surface roughness needs to be considered carefully in order to achieve optimal yields. Through experiments carried out on blanket wafers, mechanisms are proposed which account for a reduced surface roughness of aluminum grown on titanium rather than on a sandwich of titanium and titanium nitride. The evolution of aluminum grain growth and thus roughness is studied for the two different metal stacks. The practical implications of a reduced surface roughness for integration purposes is subsequently shown by considering etch behavior, short yield as well as signals depending on the metal’s reflectivity.     

Arctic sea ice, cloud, water, and lead classification using neural networks and 1.6-/spl mu/m data

Polar sea ice plays a critical role in regulating the global climate. Seasonal variation in sea ice extent, however, coupled with the difficulties associated with in situ observations of polar sea ice, makes remote sensing the only practical way to estimate this important climatic variable on the space and time scales required. Unfortunately, accurate retrieval of sea ice extent from satellite data is a difficult task. Sea ice and high cold clouds have similar visible reflectance, but some other types of clouds can appear darker than sea ice. Moreover, strong atmospheric inversions and isothermal structures, both common in winter at some polar locations, further complicate the classification. This paper uses a combination of feed-forward neural networks and 1.6-/spl mu/m data from the new Chinese Fengyun-1C satellite to mitigate these difficulties. The 1.6-/spl mu/m data are especially useful for detecting illuminated water clouds in polar regions because 1) at 1.6 /spl mu/m, the reflectance of water droplets is significantly higher than that of snow or ice and 2) 1.6-/spl mu/m data are unaffected by atmospheric inversions. Validation data confirm the accuracy of the new classification technique. Application to other sensors with 1.6-/spl mu/m capabilities also is discussed.     

A wavelet transform coder supporting browsing and transmission of sea ice SAR imagery

A wavelet-transform-based algorithm for sea ice synthetic aperture radar (SAR) image data compression is presented. Compression of the relatively low resolution (100 m) SAR data is necessary to enable the transmission of such images from the Finnish Ice Service to users in the Baltic Sea. On board the ships, the SAR images are used for navigation purposes. Hence, in the visual appearance of the compressed image, special attention must be paid to the sharpness of small details. Several target-dependent features are incorporated in the compression scheme developed. The addition of these features was motivated by an examination of the wavelet coefficient statistics for the SAR data. In the quantization phase, the sensitivity characteristics of the human visual system were taken into account. As a result, the proposed algorithm deviates in several respects from the standard procedures used in wavelet-based compression. The algorithm requires the setting up of multiple user-defined parameters to satisfy the requirements of the users and data. This requires supervision of an expert, but also makes it flexible for many kinds of data and user requirements. The algorithm presented gives satisfactory results with a compression ratio of 20:1. Since only the location of ice-covered areas is of significance for ship traffic, the algorithm introduced contains an option to mask off open sea areas with the aid of an automatic open-water detection procedure. We also report the results of a user evaluation, in which the proposed algorithm is compared to the algorithm currently used by the Finnish Ice Service, as well as to the JPEG standard. The results of the evaluation favor the use of the proposed algorithm.     

The discrete wavelet transform and the scale analysis of thesurface properties of sea ice

The formalism of the one-dimensional discrete wavelet transform (DWT) based on Daubechies wavelet filters is outlined in terms of finite vectors and matrices. Both the scale-dependent wavelet variance and wavelet covariance are considered and confidence intervals for each are determined. The variance estimates are more accurately determined with a maximal-overlap version of the wavelet transform. The properties of several Daubechies wavelet filters and the associated basis vectors are discussed. Both the Mallat orthogonal-pyramid algorithm for determining the DWT and a pyramid algorithm for determining the maximal-overlap version of the transform are presented in terms of finite vectors. As an example, the authors investigate the scales of variability of the surface temperature and albedo of spring pack ice in the Beaufort Sea. The data analyzed are from individual lines of a Landsat TM image (25-m sample interval) and include both reflective (channel 3, 30-m resolution) and thermal (channel 6, 120-m resolution) data. The wavelet variance and covariance estimates are presented and more than half of the variance is accounted for by scales of less than 800 m. A wavelet-based technique for enhancing the lower-resolution thermal data using the reflected data is introduced. The simulated effects of poor instrument resolution on the estimated lead number density and the mean lead width are investigated using a wavelet-based smooth of the observations     

Beaufort-Chukchi seas summer and fall ice margin data from Seasat:conditions with similarities to the Labrador Sea

The margin of the sea-ice pack of the Beaufort and Chukchi seas is examined using the microwave data from Seasat taken during the summer to early fall, July 4 through October 8, 1978, and the observations are compared to the analogous observations taken in LIMEX'87. The sensors used are synthetic-aperture radar (SAR), the Seasat-A scatterometer system, and the Scanning Multichannel Microwave Radiometer     

A review of sea ice and ocean modeling relevant to the Labrador andNewfoundland shelves

Numerical and analytical models of sea ice and ocean circulation are reviewed from a viewpoint of their application to the Labrador and Newfoundland shelves. Seasonal variabilities of sea ice and ocean circulation have not been reported in numerical models. The difficulty comes from the importance of both stratification and bottom topography. Sea ice should be included for seasonal cycle calculations. Individual mechanisms of variabilities with time scales ranging from a day to a month have been studied extensively using various models so that the coupling of these models will make it possible to test the predictability of sea ice and ocean circulation. The data collected during LIMEX'87 and '89 provide verification of the models     

Surface roughness and optoelectronic properties of intrinsic and doped nc-Si:H prepared by Rf-magnetron sputtering at low temperature

The correlation between the surface roughness and optoelectronic properties of a series of intrinsic and doped nanocrystalline silicon samples deposited by rf-magnetron sputtering at low temperature has been deduced from atomic force microscopy, spectroscopic ellipsometry, optical transmission and reflection and Raman spectroscopy measurements. Atomic force microscopy observations and spectroscopic ellipsometry analysis of the surface layers reveal that the Root Mean Square (rms) surface roughness for the doped samples increases with increasing sample thickness, while for the intrinsic samples we obtain lower rms surface roughness values which are found to be independent of the film thickness. The surface roughness is related to the microstructure of crystalline grains at the layer surface as verified by analysis of the experimental pseudo-dielectric function. However optical reflectance measurements obtained show that the film thickness affects the surface roughness, but not significantly the complex refractive index.     

Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery

This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures.