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On the Accuracy of Averaging Radar Backscattering Coefficients for Bare Soils Using the Finite-Element Method

机译:基于有限元方法的裸土平均雷达后向散射系数精度

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Radar backscattering coefficients for heterogeneous pixels are traditionally assumed to be the average of the coefficients for the constitutive homogeneous pixels. We investigate the validity of this assumption for bare rough surfaces by using the 2-D finite-element method to compute the ensemble averaged “true” coefficients for heterogeneous pixels and compare these values with the computed averages for a variety of surfaces. We quantify the impact of heterogeneity in both soil moisture and surface roughness on the averaging assumption. We find that the validity of the assumption rests crucially on the surface correlation type (exponential or Gaussian) and length. In particular, when considering pixels with either heterogeneous soil moisture or roughness, we find that for high-contrast pixels, the backscatter averaging assumption breaks down by as much as 11 dB for Gaussian correlated surfaces for the longest correlation lengths considered (regardless of the source of heterogeneity), whereas for exponentially correlated surfaces, it breaks down by 6 dB for pixels with heterogeneous roughness and 2 dB for pixels with heterogeneous moisture. We attribute this behavior to Gaussian correlated surfaces possessing higher cross-pixel coherent interactions. Furthermore, conditions of validity for the backscatter averaging assumption are identified.
机译:传统上将异构像素的雷达后向散射系数假定为本构均匀像素的系数的平均值。我们通过使用2-D有限元方法计算异构像素的整体平均“真实”系数,并将这些值与各种表面的计算平均值进行比较,从而研究了该假设对裸露粗糙表面的有效性。我们量化了土壤湿度和表面粗糙度的不均匀性对平均假设的影响。我们发现假设的有效性主要取决于表面相关类型(指数或高斯)和长度。特别是,当考虑具有不同土壤湿度或粗糙度的像素时,我们发现对于高对比度像素,对于考虑到最长相关长度的高斯相关表面,背向散射平均假设分解多达11 dB(无论来源如何)异质性),而对于指数相关的表面,粗糙度不均匀的像素分解为6 dB,水分不均匀的像素分解为2 dB。我们将此行为归因于具有较高跨像素相干交互作用的高斯相关曲面。此外,确定了反向散射平均假设的有效性条件。

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