275 research outputs found
Impact of Sea Ice Drift Retrieval Errors, Discretization and Grid Type on Calculations of Sea Ice Deformation
We studied two issues to be considered in the calculation of parameters characterizing sea
ice deformation: the effect of uncertainties in an automatically retrieved sea ice drift field, and the
influence of the type of drift vector grid. Sea ice deformation changes the local ice mass balance
and the interaction between atmosphere, ice, and ocean, and constitutes a hazard to marine traffic
and operations. Due to numerical effects, the results of deformation retrievals may predict, e.g.,
openings and closings of the ice cover that do not exist in reality. We focus specifically on fields
of ice drift obtained from synthetic aperture radar (SAR) imagery and analyze the Propagated
Drift Retrieval Error (PDRE) and the Boundary Definition Error (BDE). From the theory of error
propagation, the PDRE for the calculated deformation parameters can be estimated. To quantify the
BDE, we devise five different grid types and compare theoretical expectation and numerical results
for different deformation parameters assuming three scenarios: pure divergence, pure shear, and a
mixture of both. Our findings for both sources of error help to set up optimal deformation retrieval
schemes and are also useful for other applications working with vector fields and scalar parameters
derived therefrom
Analysis of Sea Ice Roughness and Thickness Variation for Improvement of SAR Ice Type Classification
Sea ice local surface topography from single-pass satellite InSAR measurements: a feasibility study
Quantitative parameters characterizing the sea ice surface topography are needed in geophysical investigations
such as studies on atmosphere–ice interactions or sea ice mechanics.Recently, the use of space-borne single-pass interferometric synthetic aperture radar (InSAR) for retrieving the ice surface topography has attracted notice among geophysicists. In this paper the potential of InSAR measurements is examined for several satellite configurations and radar frequencies, considering statistics of heights and widths of ice
ridges as well as possible magnitudes of ice drift. It is shown that, theoretically, surface height variations can be retrievedwith relative errors < 0.5 m. In practice, however, the sea ice drift and open water leads may contribute significantly to the measured interferometric phase. Another essential factor is
the dependence of the achievable interferometric baseline on the satellite orbit configurations. Possibilities to assess the influence of different factors on the measurement accuracy are demonstrated: signal-to-noise ratio, presence of a snow layer, and the penetration depth into the ice. Practical examples of
sea surface height retrievals from bistatic SAR images collectedduring the TanDEM-X Science Phase are presented
A method to improve high-resolution sea ice drift retrievals in the presence of deformation zones
Source at: http://doi.org/10.3390/rs9070718 Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial
resolution are valuable for understanding kinematic behavior and deformation processes of the
ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in
sequences of SAR images; which makes it difficult to retrieve ice displacement with algorithms based
on correlation and feature identification techniques. Here, we propose two extensions to a pattern
matching algorithm, with the objective to improve the reliability of the retrieved sea ice drift field at
spatial resolutions of a few hundred meters. Firstly, we extended a reliability assessment proposed
in an earlier study, which is based on analyzing texture and correlation parameters of SAR image
pairs, with the aim to reject unreliable pattern matches. The second step is specifically adapted to the
presence of deformation features to avoid the erasing of discontinuities in the drift field. We suggest
an adapted detection scheme that identifies linear deformation features (LDFs) in the drift vector field,
and detects and replaces outliers after considering the presence of such LDFs in their neighborhood.
We validate the improvement of our pattern matching algorithm by comparing the automatically
retrieved drift to manually derived reference data for three SAR scenes acquired over different sea ice
covered regions
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