Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice

Incidence angle dependence of three statistical parameters-the mean of the backscattering coefficient (/spl sigma//spl deg/), standard deviation, and autocorrelation coefficient of texture (/spl sigma//sub T/ and /spl rho//sub T/)-of the C-band horizontal-horizontal (HH) polarization backscattering signatures of the Baltic Sea ice are investigated using RADARSAT ScanSAR Narrow images and helicopter-borne Helsinki University of Technology Scatterometer (HUTSCAT) data. The analysis of the large amount of data shows that the relationship between the mean /spl sigma//spl deg/ in decibel scale and the incidence angle in the range from 19/spl deg/ to 46/spl deg/ is usually well described by a linear model. In general, the RADARSAT and HUTSCAT results agree with each other, and they are also supported by theoretical backscattering model calculations; the more deformed the ice, the smaller the slope between /spl sigma//spl deg/ and the incidence angle, and the higher the moisture content of snow or ice, the larger the slope. The derived /spl sigma//spl deg/ incidence angle dependencies can be used to roughly compensate the /spl sigma//spl deg/ incidence angle variation in the SAR images to help their visual and automated classification. The variability of /spl sigma//sub T/ and /spl rho//sub T/ with the increasing incidence angle is insignificant compared to the variability within each ice type. Their average changes with the incidence angle are so small that, in practice, their trends do not need to be compensated. The results of this study can be utilized when developing classification algorithms for the RADARSAT ScanSAR and ENVISAT HH-polarization Wide Swath images of the Baltic Sea ice.

[1]  Robin Berglund,et al.  Demonstration of operational sea-ice monitoring in the Baltic Sea with ERS-l SAR , 1995 .

[2]  R. Johansson Detection And Characterization Of Ice Ridges In The Baltic Sea Using CV-580 Sar Imagery , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[3]  A. Carlstrom,et al.  Validation of backscatter models for level and deformed sea-ice in ERS-l SAR images , 1995 .

[4]  Martti Hallikainen,et al.  Classification of Sea Ice Types with Radar , 1992, 1992 22nd European Microwave Conference.

[5]  A. T. Manninen,et al.  Surface roughness of Baltic sea ice , 1997 .

[6]  Juha Karvonen,et al.  Classification of First-Year Sea Ice Using Pulse-Coupled Neural Network , 1999 .

[7]  F. Ulaby,et al.  Dielectric properties of snow in the 3 to 37 GHz range , 1986 .

[8]  Jan Askne,et al.  Multifrequency scatterometer measurements of Baltic Sea ice during EMAC-95 , 1996 .

[9]  A. Fung Microwave Scattering and Emission Models and their Applications , 1994 .

[10]  A. T. Manninen Effects of ice ridge properties on calculated surface backscattering in BEPERS-88 , 1992 .

[11]  Juha Hyyppä,et al.  A helicopter-borne eight-channel ranging scatterometer for remote sensing. I. System description , 1993, IEEE Trans. Geosci. Remote. Sens..

[13]  E. Rignot,et al.  Characterization of spatial statistics of distributed targets in SAR data , 1993, International Journal of Remote Sensing.

[14]  Juha Hyyppä,et al.  Classification of low-salinity sea ice types by ranging scatterometer , 1992 .

[15]  Juha A. Karvonen,et al.  Pulse-coupled neural network for sea ice SAR image segmentation and classification , 1999, Other Conferences.

[16]  M Lepparanta,et al.  FIELD MEASUREMENTS OF THE STRUCTURE AND STRENGTH OF FIRST-YEAR ICE RIDGES IN THE BALTIC SEA , 1989 .