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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >A survey of temporal decorrelation from spaceborne L-Band repeat-pass InSAR
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A survey of temporal decorrelation from spaceborne L-Band repeat-pass InSAR

机译:星载L波段重复通过InSAR的时间去相关研究

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In this paper we quantify the effects of temporal decorrelation in repeat pass synthetic aperture radar interferometry (InSAR). Temporal decorrelation causes significant uncertainties in vegetation parameter estimates obtained using various InSAR techniques, which are desired on a global scale. Because of its stochastic nature temporal decorrelation is hard to model and isolate. In this paper we analyze temporal decorrelation statistically as observed in a large swath of SIR-C L-Band InSAR data collected over the eastern United States, with a repeat pass duration of one day in October 1994 and a near zero perpendicular baseline. The very small baseline for this particular pair makes the effect of volumetric scattering on correlation magnitude statistics nearly imperceptible, allowing for a quantitative analysis of temporal effects alone. The swath analyzed in this paper spans more than a million hectares of terrain comprised primarily of deciduous and evergreen forests, agricultural land, water and urban areas. The relationships of these different land-cover types, phenology and weather conditions (i.e. precipitation and wind) on the measures of interferometric correlation is analyzed in what amounts to be the most geographically extensive analysis of this phenomenon to date.
机译:在本文中,我们量化了时间相关性在重复通过合成孔径雷达干涉测量(InSAR)中的影响。时间去相关导致使用各种InSAR技术获得的植被参数估计值存在很大的不确定性,这在全球范围内是需要的。由于其随机性,时间去相关很难建模和隔离。在本文中,我们从统计学上分析了在美国东部收集的大量SIR-C L波段InSAR数据中观察到的时间去相关性,1994年10月重复通过时间为一天,垂直基线接近零。该特定对的非常小的基线使得几乎看不到体积散射对相关量级统计的影响,从而允许仅对时间影响进行定量分析。本文分析的地带覆盖了超过一百万公顷的地形,主要由落叶和常绿森林,农业用地,水和城市地区组成。分析了这些不同的土地覆盖类型,物候和天气状况(即降水和风)与干涉测量相关性之间的关系,这是迄今为止对该现象最广泛的地理分析。

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