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Geostatistical Characterization of Snow-Depth Structures on Sea Ice Near Point Barrow, Alaska—A Contribution to the AMSR-Ice03 Field Validation Campaign

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3 Author(s)
U. C. Herzfeld ; Cooperative Inst. for Res. in Environ. Sci., Colorado Univ., Boulder, CO ; J. A. Maslanik ; M. Sturm

The objective of this paper is to characterize spatial properties of snow-depth structures and their role as indicators of sea-ice properties and sea-ice-morphogenetic processes, and to provide quantitative measures of sea-ice properties that may be utilized in analyses of passive-microwave data. Snow-depth data collected near Point Barrow, Alaska, as part of the AMSRIce03 Field Validation Campaign for Advanced Microwave Scanning Radiometer (AMSR)-E-Sea-Ice Products from NASA earth-observing-systems satellite AQUA, are analyzed and compared to P-3 polarimetric scanning radiometer (PSR) data, a proxy for AMSR-E brightness temperatures. The approach taken in the analysis is geostatistical characterization. Various functions of first and second order are calculated for the snow-depth profiles, then geostatistical classification parameters are extracted and combined into feature vectors, on which the characterization is based. The complexity of sea ice requires a generalization of the method by introduction of the hyperparameter concept. Results include a quantitative characterization of sea-ice provinces from field transects in the Beaufort Sea, Chukchi Sea, and Elson Lagoon, which represent a good subset of Arctic sea-ice types, an internal segmentation of the longer profiles, and a derivation of surface-roughness length and of sea-ice-type complexity. PSR data reflect complexity of spatial snow-depth structures as captured in multidimensional feature vectors and, less directly, snow-depth and surface-roughness length. These results indicate that passive-microwave data in general may be affected by spatial snow depth and surface roughness, with a dependence on scale and quantified by geostatistical classification

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:44 ,  Issue: 11 )