The rapid decline of sea ice in the Arctic has resulted in a variable sea ice roughness that necessitates improved methods for efficient observation using high-resolution spaceborne radar. The utility of C-band polarimetric backscatter, coherences, and ratios as a discriminator of ice surface roughness is evaluated. An existing one-dimensional backscatter model has been modified to two-dimensions (2D) by considering deviation in the orientation (i.e., the slopes) in azimuth and range direction of surface roughness simultaneously as an improvement in the model. It is shown theoretically that the circular coherence (
Arctic sea ice is going through a rapid decline [
The use of polarimetric synthetic aperture radar (pol-SAR) represents a promising approach for satellite-based monitoring of surface roughness and, concurrently, discriminating sea ice types within a MIZ. A pol-SAR records the amplitude and phase information of backscattered energy for four transmit-receive polarizations (HH, HV, VH, and VV), thereby facilitating the derivation of the full polarimetric response of the target. It is recognizable that the diversity in polarization achievable by pol-SARs or even by dual-polarization SAR systems provides more complete inference of target features (e.g., sea ice) than conventional, single channel SARs. Furthermore, recently launched pol-SARs are capable of higher spatial resolution (<10 m) imaging, leading to enhanced potential for monitoring complex ice environments.
Discrimination of ice types using SAR has been conventionally achieved by utilizing different combinations of linearly polarized backscattering coefficients [
In this study, ship-based observations of co- (linear) and crosspolarized backscatter, circular polarimetric coherences ( to investigate the performance of polarimetric to evaluate the utility of C-band polarimetric backscatter, coherences, and polarization ratios as a discriminator of surface roughness or ice type in a MIZ during fall freeze-up.
The study area is located in the southeastern Beaufort Sea and Amundsen Gulf regions in the western Canadian Arctic (Figure
Geographic map of study area showing sampling locations.
Photographs of ice types used in the study. (a) snow-covered first-year ice (SCFYI), (b) deformed first-year ice (DFYI), (c) consolidated pancake ice (PI), (d) snow-covered frost flower (SCFF), and (e) dense frost flower (DFF).
Sea ice is a distributed radar target, and the conditions of stationarity and homogeneity seldom hold for dynamically changing ice in a MIZ. The radar backscattering is therefore analyzed using temporally and spatially varying stochastic processes. Backscatter from sea ice is incoherent and either partially or completely polarized, as described by the polarimetric covariance matrix. The electric field vector of an incident (
The coherency matrices can be derived as copolarized (
C-band polarimetric backscattering data were collected using a completely stationary ship-mounted scatterometer system developed by ProSensing Inc., (Amherst, MA, USA) and mounted 7.56 m above the mean sea level on the port side of the
Technical properties and specifications of C-band scatterometer.
RF output frequency | 5.25–5.75 GHz |
Transmit power at bulkhead connector | 12 dBm |
Antenna diameter | 0.61 m |
Transmit bandwidth | 500 MHz |
Antenna beamwidth | 5.5° |
Antenna gain | 28 dB, nominal |
Crosspolarization isolation | >30 dB, measured at the peak of the beam |
Transmit-receive polarizations | Linear, vertical, and horizontal |
Sensitivity, minimum NRCS at 15 m range | –40 dB m2/m2 |
Towards objective 2, scan data for each ice type were grouped by incidence angle representing near (20–25°), mid (35–40°), and far (55–60°) range groupings. These groupings best represent the diversity of scattering mechanisms available across the acquired incidence angle range. In the near range, surface scattering is expected to dominate the measured C-band backscatter, while surface-volume scattering is increasingly expected to influence C-band backscatter beyond approximately 30°, that is, mid to far ranges [
In pursuit of objective 1, a polarimetric backscattering model is used which is mainly a Bragg backscattering (coherent scattering) model modified for surface roughness considering the surface slope by slightly changing the tilt of the surface from the horizontal. Microwave measurements of surface roughness using co- or crosspolarization backscattered power are most successful in flat areas. In sea ice microwave remote sensing, the dielectric constant and topography (slope in range and azimuth) are important. According to
The rotation matrix [
Illustration of scattering plane geometry with slight deviations in the orientation angles in azimuth (
The new rotation matrix
The
Given the above, the
Now, the slope-induced roughness is examined in the range direction only. Lee et al. [
The relationship between slopes in azimuth and range direction is further demonstrated. Corresponding shifts and radar incidence angle are given by (see Appendix
The date and hour of scatterometer data acquisitions corresponding to each sea ice type, as well as coincident meteorological parameters, namely, wind speed, air temperature, and relative humidity, are provided in Table
Meteorological parameters associated with each ice type on different dates.
Sea ice type | Wind speed (m/s) | Air temperature (°C) | Relative humidity % | |
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Nov. 15, 2007 |
SCFYI |
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Nov. 19, 2007 |
DFYI |
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Nov. 20, 2007 |
PI |
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Nov. 21, 2007 |
SCFF |
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Nov. 25, 2007 |
DFF |
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Figure
Co- (HH and VV) and cross- (HV) polarization backscatter intensities of snow-covered first-year sea ice (SCFYI), deformed first-year sea ice (DFYI), consolidated pancake ice (PI), snow-covered frost flowers (SCFF), and dense frost flowers (DFF).
Mean coherences and polarization ratios for each ice type as a function of incidence angle grouping are documented in Table
Mean C-band polarimetric coherences and ratios of selected ice types, for near (N), middle (M), and far (F) range incidence angle groupings (also shown graphically in Figure
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Looking at polarization ratios in Table
Figure
Matrix of significance values from non-parametric Kruskal-Wallis tests for independence between ice types based on polarimetric parameters and near (N), middle (M), and far (F) range groupings. The number of data samples is: SCFYI,
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SCFYI | DFYI | PI | SCFF | DFF | SCFYI | DFYI | PI | SCFF | DFF | SCFYI | DFYI | PI | SCFF | DFF | |
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Box plots of coherences and polarization ratios of ice types based on near, middle, and far range incidence angle groupings. Significance values are provided in Table
It is demonstrated using theory that lower values of
The one-dimensional backscatter model of Schuler et al. [
The conclusions with reference to objective 1 are as follows. It was shown theoretically that the
The utility of C-band polarimetric coherences and ratios is addressed in the light of objective 2 as follows: for coherences,
The knowledge obtained through surface-based polarimetric coherences and ratios can readily be extended to discriminate sea ice roughness on small scales using C-band microwave satellites (currently in orbit RADARSAT-2, RISAT-1). Future work will be to develop an algorithm combining all polarimetric coherences and ratios to discriminate individual ice type in a MIZ. These observations may become particularly useful for satellite measurements once planned SAR constellations (Sentinel series) systems are available, as currently planned by National Aeronautics and Space Administration and European Space Agency.
To understand how to extract best information from the scattering matrix
The
Here, the relationship between slope in azimuth and ground range, radar look angle, shift in azimuth, and shift in ground range is derived. The slope equation given by Lee et al. [
The main funding for the project was provided by the IPY-Canada, the Natural Sciences and Engineering Research Council (NSERC), and the Canada Research Chairs (CRC) Program, each to DGB. Authors gratefully thank icebreaker