Abstract.Although spaceborne scatterometers such as the NASA scatterometer have inherently low spatial resolution, resolution enhancement techniques can be used to increase the utility of scatterometer data in monitoring sea-ice extent in the polar regions, a key parameter in the global climate. The resolution enhancement algorithm produces images of A and B, where A is the normalized radar backscatter coefficient o at 40Њ incidence and B is the incidence angle dependence of o . Dual-polarization A and B parameters are used to identify sea ice and ocean pixels in composite images. The A copolarization ratio and vertically polarized B are used as primary classification parameters to discriminate between sea ice and open ocean. Estimates of the sea-ice extent are obtained using linear and quadratic (Mahalanobis distance) discriminant boundaries. The distribution parameters needed for the quadratic estimate are taken from the linear estimate. The o error variance is used to reduce errors in the linear and Mahalanobis ice/ocean classifications. Noise reduction is performed through binary image region growing and erosion/dilation techniques. The resulting edge closely matches the NASA Team algorithm special sensor microwave imager derived 30% ice concentration edge. A 9-month data set of global sea-ice extent maps is produced with one 6-day average map every 3 days.

Abstract-Polar sea ice is an important input to global climate models and is considered to be a sensitive indicator of climate change. While originally designed only for wind estimation, radar backscatter measurements collected by wind scatterometers have proven useful for estimating the extent of sea ice. During the Quick Scatterometer (QuikSCAT) mission, SeaWinds data were used to operationally map the sea ice extent. The resulting sea ice maps were used to mask near-surface winds to support SeaWinds' primary mission of measuring near-surface winds over the ocean. This paper describes the operational SeaWinds sea ice extent mapping algorithm, provides validation comparisons, and presents results from the ten-year data product. Starting with enhanced resolution horizontal polarization and vertical polarization backscatter images, the algorithm employs an iterative maximum-likelihood classifier with fixed thresholds to segment sea ice and open ocean pixels. Residual classification errors are reduced through binary image processing techniques and sea ice growth/retreat constraint methods. The algorithm results are compared with sea ice concentrations derived from Special Sensor Microwave/Imager data and with RADARSAT synthetic aperture radar imagery. The results suggest differences in the sensitivities of active and passive products given their channel sets and specific algorithms. Derived sea ice extents over the full decade-long QuikSCAT mission data set are analyzed to show important trends in sea ice extent for the Antarctic and Arctic regions.

Abstract-Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes governing climate changes. Sea ice can be grouped into a number of general classes with different characteristics. Multisensor data from NSCAT, ERS-2, and SSM/I are reconstructed into enhanced resolution imagery for use in ice-type classification. The resulting twelve-dimensional data set is linearly transformed through principal component analysis to reduce data dimensionality and noise levels. An iterative statistical data segmentation algorithm is developed using maximum likelihood (ML) and maximum a posteriori (MAP) techniques. For a given ice type, the conditional probability distributions of observed vectors are assumed to be Gaussian. The cluster centroids, covariance matrices, and a priori distributions are estimated from the classification of a previous temporal image set. An initial classification is produced using centroid training data and a weighted nearestneighbor classifier. Though validation is limited, the algorithm results in an ice classification that is judged to be superior to a conventional -means approach.

No abstract

Abstract-Polar sea ice characteristics provide important inputs to models of several geophysical processes. Microwave scatterometers are ideal for monitoring these regions due to their sensitivity to ice properties and insensitivity to atmospheric distortions. Many forward electromagnetic scattering models have been proposed to predict the normalized radar cross section ( ) from sea ice characteristics. These models are based on very small scale ice features and generally assume that the region of interest is spatially homogeneous. Unfortunately, spaceborne scatterometer footprints are very large (5-50 km) and usually contain very heterogeneous mixtures of sea ice surface parameters. In this paper, we use scatterometer data in a large-scale inverse modeling experiment. Given the limited data resolution, we adopt a simple geometric optics for