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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

(abstract) Examining Sea Ice SAR Signatures in the Arctic

This research examines the seasonal changes of the sea ice cover in the Arctic Basin as it responds to atmospheric and oceanic conditions. Monitoring this process provides a means of determining the onset and extent of the annual seasonal stages, which is thought to be an indicator for detecting climate change in the polar regions. Much of the response of sea ice to seasonal conditions results in changes in the phase of water (both in the ice and snow cover), surface roughness, and internal properties such as air bubbles. Imagery from SAR has proven to be an important tool for revealing these changes since radar backscatter is affected by both surface roughness and dielectric properties of water and salt. The major ice types and ice features may have unique SAR backscatter signatures because of the inherent variations in surface roughness, salinity, and internal properties in each category.

change surface roughness dielectric properties↗
NASA NTRS1987

Image-analysis techniques for determination of morphology and kinematics in arctic sea ice

SAR data have been used to study sea ice with respect to its motion and formation/deformation. With the prospect of the Alaska SAR Facility development in the near future, there is a great need for robust and efficient sea-ice analysis techniques. This paper presents a sea-ice motion analysis technique that can be used for: (1) local motion analysis of a selected ice patch, and (2) a global ice motion over the entire image area. In order to meet the operational speed requirement (over fifty images per day) a sea-ice motion analysis technique has been developed which requires very little human interaction. The proposed technique uses a subset of easily distinguishable features to predict global motion characteristics. The developed technique is applied to two pairs of SEASAT SAR images, one pair with a minor motion of 'ice pack' and another with a larger and discontinuous motion of 'fast ice'. The new approach enables the development of a set of computer-aided tools for feature selection and registration and the implementation of an optimal search strategy for automatic template matching via a motion prediction model.

Lee, Meemong↗
NASA NTRS1991

Automated extraction of pressure ridges from SAR images of sea ice - Comparison with surface truth

The authors estimate the characteristics of ridges and leads in sea ice from SAR (synthetic aperture radar) images. Such estimates are based on the hypothesis that bright filamentary features in SAR sea ice images correspond with pressure ridges. A data set collected in the Greenland Sea in 1987 allows this hypothesis to be evaluated for X-band SAR images. A preliminary analysis of data collected from SAR images and ice elevation (from a laser altimeter) is presented. It is found that SAR image brightness and ice elevation are clearly related. However, the correlation, using the data and techniques applied, is not strong.

Vesecky, J. F.↗
NASA NTRS1993

Observation of pressure ridges in SAR images of sea ice: Scattering theory and comparison with observations

Ridges and keels (hummocks and bummocks) in sea ice flows are important in sea ice research for both scientific and practical reasons. Sea ice movement and deformation is driven by internal and external stresses on the ice. Ridges and keels play important roles in both cases because they determine the external wind and current stresses via drag coefficients. For example, the drag coefficient over sea ice can vary by a factor of several depending on the fluid mechanical roughness length of the surface. This roughness length is thought to be strongly dependent on the ridge structures present. Thus, variations in ridge and keel structure can cause gradients in external stresses which must be balanced by internal stresses and possibly fracture of the ice. Ridging in sea ice is also a sign of fracture. In a practical sense, large ridges form the biggest impediment to surface travel over the ice or penetration through sea ice by ice-strengthened ships. Ridges also play an important role in the damage caused by sea ice to off-shore structures. Hence, observation and measurement of sea ice ridges is an important component of sea ice remote sensing. The research reported here builds on previous work, estimating the characteristics of ridges and leads in sea ice from SAR images. Our objective is to develop methods for quantitative measurement of sea ice ridges from SAR images. To make further progress, in particular, to estimate ridge height, a scattering model for ridges is needed. Our research approach for a ridge scattering model begins with a survey of the geometrical properties of ridges and a comparison with the characteristics of the surrounding ice. For this purpose we have used airborne optical laser (AOL) data collected during the 1987 Greenland Sea Experiment. These data were used to generate a spatial wavenumber spectrum for height variance for a typical ridge - the typical ridge is the average over 10 large ridges. Our first-order model radar scattering includes both the quasi-specular and Bragg resonant scatter mechanisms. This model is extended to include contributions from volume scatter and scatter from discrete objects. Geometrical characteristics from the AOL survey and model calculations imply that for radar wavelengths and observation geometries that are dominated by the quasi-specular scattering mechanism radar backscatter from a ridge is a measure of peak ridge height. We present scattering model results and compare them with ridges observed during the LEADEX experiment of March-April 1992 when both X, C, and L-band aircraft SAR and the ERS-1 satellite SAR observed a region in the Beaufort Sea near 86 deg N, 10 deg W. Data were also collected documenting ridge characteristics on the surface. The surface data are used to generate a SAR signature via the scattering model described above. The predicted SAR signatures compare well with the SAR observations.

Vesecky, J. F.↗
NASA NTRS1993

On evaluating the accuracy of SAR sea-ice classification using multifrequency polarimetric AIRSAR data

We investigate how multifrequency and polarimetric synthetic aperture radar (SAR) imagery enhances present capability to discriminate different ice conditions in single-frequency, single-polarization satellite SAR data. Frequencies considered are C- (lambda = 5.6cm), L- (lambda = 24cm) and P- (lambda = 68cm) band. Radar backscatter characteristics of six radiometrically and polarimetrically distinct ice types are selected from a cluster analysis of the multifrequency polarimetric SAR data and used to classify SAR images. Validation of these ice conditions is based on information provided by aerial photos, weather and ice surface measurements acquired at an ice camp, together with airborne passive microwave imagery, and visual analysis of the SAR data. The six identified sea-ice types are: (1) multiyear sea-ice; (2) compressed first year ice; (3) first year rubble and ridges; (4) first year rough ice; (5) first year smooth ice; and (6) thin ice. Open water is absent in all analyzed data. Classification of the SAR imagery into those six ice types is performed using a Bayesian Maximum A Posteriori classifier. Two complete scenes acquired at different dates in different locations are classified. The scenes were chosen such that they are representative of typical ice conditions in the Beaufort Sea in March 1988 and because ancillary information is available for validating the segmentation of various ice surface conditions.

Drinkwater, Mark R.↗
NASA NTRS1989

The discrimination of sea ice types using SAR backscatter statistics

X-band (HH) synthetic aperture radar (SAR) data of sea ice collected during the Marginal Ice Zone Experiment in March and April of 1987 was statistically analyzed with respect to discriminating open water, first-year ice, multiyear ice, and Odden. Odden are large expanses of nilas ice that rapidly form in the Greenland Sea and transform into pancake ice. A first-order statistical analysis indicated that mean versus variance can segment out open water and first-year ice, and skewness versus modified skewness can segment the Odden and multilayer categories. In additions to first-order statistics, a model has been generated for the distribution function of the SAR ice data. Segmentation of ice types was also attempted using textural measurements. In this case, the general co-occurency matrix was evaluated. The textural method did not generate better results than the first-order statistical approach.

Shuchman, Robert A.↗
NASA NTRS1983

Spaceborne SAR and sea ice

A number of remote sensing systems deployed in satellites to view the Earth which are successful in gathering data on the behavior of the world's snow and ice covers are described. Considering sea ice which covers over 10% of the world ocean, systems that have proven capable to collect useful data include those operating in the visible, near-infrared, infrared, and microwave frequency ranges. The microwave systems have the essential advantage in observing the ice under all weather and lighting conditions. Without this capability data are lost during the long polar night and during times of storm passage, periods when ice activity can be intense. The margins of the ice pack, a region of particular interest, is shrouded in cloud between 80 and 90% of the time.

Weeks, W. F.↗
NASA NTRS1995

Computing Thickness Of Sea Ice From Polarimetric SAR Data

Artificial neural network developed for computing thickness of sea ice from polarimetric synthetic-aperture-radar (SAR) data. Once neural network trained, no further intervention needed: when stimulated with SAR scattering data, quickly responds with estimate of thickness of sea ice.

Huynh, Dave D.↗
NASA NTRS1993

Characterization of spatial statistics of distributed targets in SAR data

A statistical approach to the analysis of spatial statistics in polarimetric multifrequency SAR data, which is aimed at extracting the intrinsic variability of the target by removing variability from other sources, is presented. An image model, which takes into account three sources of spatial variability, namely, image speckle, system noise, and the intrinsic spatial variability of the target or texture, is described. It is shown that the presence of texture increases the image variance-to-mean square ratio and introduces deviations of the image autocovariance function from the expected SAR system response. The approach is exemplified by sea-ice SAR imagery acquired by the Jet Propulsion Laboratory three-frequency polarimetric airborne SAR. Data obtained indicate that, for different sea-ice types, the spatial statistics seem to vary more across frequency than across polarization and the observed differences increase in magnitude with decreasing frequency.

Rignot, E.↗
NASA NTRS1990

Extraction of lead and ridge characteristics from SAR images of sea ice

Image-processing techniques for extracting the characteristics of lead and pressure ridge features in SAR images of sea ice are reported. The methods are applied to a SAR image of the Beaufort Sea collected from the Seasat satellite on October 3, 1978. Estimates of lead and ridge statistics are made, e.g., lead and ridge density (number of lead or ridge pixels per unit area of image) and the distribution of lead area and orientation as well as ridge length and orientation. The information derived is useful in both ice science and polar operations for such applications as albedo and heat and momentum transfer estimates, as well as ship routing and offshore engineering.

Vesecky, John F.↗
NASA NTRS1986

Automated remote sensing of sea ice using synthetic aperture radar

Two techniques for automated sea ice tracking: image pyramids (hierarchical correlation) and feature tracking were applied to a pair of SEASAT SAR sea ice images. The results compare well with each other and manually tracked estimates of the ice velocity field. Refinement of the image pyramid technique should include removal of a small number of erroneous velocity vectors using constraints dependent on correlation coefficient, near neighbor velocities, and velocity gradients. Estimates of sea ice velocity are successfully obtained over limited areas using the boundary segment tracking technique. Refinement of the feature tracking technique should include use of pressure ridge features, constrained search, and relaxation techniques.

Vesecky, J. F.↗
NASA NTRS1992

Measurement of pressure ridges in SAR images of sea ice - Preliminary results on scattering theory

Sea ice ridges and keels (hummocks and bummocks) are important in sea ice research for both scientific and practical reasons. A long-term objective is to make quantitative measurements of sea ice ridges using synthetic aperture radar (SAR) images. The preliminary results of a scattering model for sea ice ridge are reported. The approach is through the ridge height variance spectrum Psi(K), where K is the spatial wavenumber, and the two-scale scattering model. The height spectrum model is constructed to mimic height statistics observed with an airborne optical laser. The spectrum model is used to drive a two-scale scattering model. Model results for ridges observed at C- and X-band yield normalized radar cross sections that are 10 to 15 dB larger than the observed cross sections of multiyear ice over the range of angles of incidence from 10 to 70 deg.

Vesecky, J. F.↗
NASA NTRS2001

Calibration of Sea Ice Motion from QuikSCAT with those from SSM/I and Buoy

QuikSCAT backscatter and DMSP SSM/I radiance data are used to derive sea ice motion for both the Arctic and Antarctic region using wavelet analysis method. This technique provides improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing satellite data from Synthetic Aperture Radar (SAR). Sea ice motion of the Arctic for the period from October 1999 to March 2000 derived from QuikSCAT and SSM/I data agrees well with that derived from ocean buoys quantitatively. Thus the ice tracking results from QuikSCAT and SSM/I are complement to each other, Then, three sea-ice drift daily results from QuikSCAT, SSM/I, and buoy data can be merged to generate composite maps with more complete coverage of sea ice motion than those from single data source. A series of composite sea ice motion maps for December 1999 show that the major circulation patterns of sea ice motion are changing and shifting significantly within every four days and they are dominated by wind forcing. Sea-ice drift in the summer can not be derived from NSCAT and SSM/I data. In later summer of 1999 (in September), however, QuikSCAT data can provide good sea ice motion information in the Arctic. QuiksCAT can also provide at least partial sea ice motion information until June 15 in early summer 1999. For the Antarctic, case study shows that sea ice motion derived from QuikSCAT data is predominantly forced by and is consistent with wind field derived from QuikSCAT around the polar region. These calibrated/validated results indicate that QuikSCAT, SSM/I, and buoy merged daily ice motion are suitably accurate to identify and closely locate sea ice processes, and to improve our current knowledge of sea ice drift and related processes through the data assimilation of ocean-ice numerical model.

Liu, Antony K.↗
NASA NTRS1998

Polarimetric C-band SAR Observations of Sea Ice in the Greenland Sea

The fully polarimetric EMISAR acquired C-band radar signatures of sea ice in the Greenland Sea during a campaign in March 1995. We present maps of polarimetric signatures over an area containing various kinds of ice and discuss the use of polarimetric SAR for identification of ice types and their geophysical characteristics.

Polarimetry↗