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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >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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Geostatistical Characterization of Snow-Depth Structures on Sea Ice Near Point Barrow, Alaska—A Contribution to the AMSR-Ice03 Field Validation Campaign

机译:阿拉斯加点巴罗附近海冰上雪深结构的地统计特征-对AMSR-Ice03现场验证活动的贡献

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摘要

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
机译:本文的目的是表征雪深结构的空间特性及其在指示海冰特性和海冰形态发生过程中的作用,并提供可用于海冰特征分析的定量测量方法。无源微波数据。对美国宇航局地球观测系统卫星AQUA进行的AMSRIce03先进微波扫描辐射计(AMSR)-E-海冰产品AMSRIce03现场验证活动的一部分进行了分析,并将雪深数据与P进行了比较。 -3偏振扫描辐射计(PSR)数据,是AMSR-E亮度温度的代理。分析中采用的方法是地统计特征。计算雪深剖面的一阶和二阶各种函数,然后提取地统计分类参数并将其组合到特征向量中,以此为特征。海冰的复杂性要求通过引入超参数概念来推广该方法。结果包括从Beaufort Sea,Chukchi Sea和Elson Lagoon的野外横断面对海冰省份进行定量表征,它们代表了北极海冰类型的良好子集,较长剖面的内部分段以及地表的推导粗糙度长度和海冰类型的复杂性。 PSR数据反映了多维特征向量中捕获的空间积雪深度结构的复杂性,而不是积雪深度和表面粗糙度长度的直接反映。这些结果表明,无源微波数据通常可能受到空间积雪深度和表面粗糙度的影响,并取决于规模,并通过地统计学分类进行了量化

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