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Soil Moisture Retrieval by Means of Adaptive Polarimetric Two-Scale Two-Component Model with Fully Polarimetric ALOS-2 Data

机译:利用全极化ALOS-2数据的自适应极化两尺度两分量模型反演土壤水分

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In this paper, we attempt to develop the soil moisture retrieval method taking into account a wide variety of vegetation variations for the sparsely vegetated area. The method is constructed by introducing generalized volume scattering model into the polarimetric two-scale two-component model (PTSTCM). The proposed method was applied to L-band fully polarimetric ALOS-2 SAR datasets obtained over tropical peatland, Indonesia. The retrieved results were validated by simultaneously measured in-situ soil moisture. The proposed method yields more improved results than original PTSTCM with specific types of volume model (i.e., randomly, horizontally, and vertically oriented volume models) regarding the root-mean-square-error (RMSE) as well as inversion rate.
机译:在本文中,我们尝试开发一种考虑到植被稀疏地区的各种植被变化的土壤水分反演方法。该方法是通过将广义体积散射模型引入极化两尺度两分量模型(PTSTCM)中而构造的。将该方法应用于印度尼西亚热带泥炭地获得的L波段全极化ALOS-2 SAR数据集。通过同时测量原位土壤水分验证了检索到的结果。与原始PTSTCM相比,对于均方根误差(RMSE)和反转率的特定类型的体积模型(即随机,水平和垂直定向的体积模型),该方法产生的结果要好得多。

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