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Soil Moisture Inversion with Sparse Vegetation Coverage Areas and Analysis of Spatial Characteristics Based on RADARSAT-2

机译:土壤水分反演与稀疏植被覆盖区域及基于雷达拉特2的空间特征分析

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Backscatter coefficient of radar imagery is mainly affected by the surface soil moisture and roughness.This paper was based on the RADARSAT-2 polarimetric SAR image data,field measured soil moisture data,surface roughness,soil particle composition and soil bulk density data.The soil moisture from SAR was retrieved by using 0h2004 semi empirical model inversion method,0h2004 semi empirical model LUT method,AIEM theory model which is based on the effective correlation length and AIEM theory model on account of the effective RMS height.After soil moisture retrieval of the study area sampling points at 0-6cm depth,this study analyzed the corresponding to the four kinds of methods of the optimal filtering window size,made a comparative analysis of the four methods and determined the optimum inversion model of the study area soil moisture microwave remote sensing at 0-6cm depth.This article used the best model for retrieving soil moisture in depth of 0-6cm,made the distribution map of soil moisture at 0-6cm depth,and analyzed spatial distribution characteristics.
机译:雷达图像的后向散射系数主要受表面土壤湿度和roughness.This纸是基于RADARSAT-2极化SAR图像数据,场测量土壤湿度数据,表面粗糙度,土壤颗粒组成和容重data.The土壤从SAR湿气通过使用0h2004半经验模型反演方法,0h2004半经验模型LUT方法,该方法是基于帐户的有效RMS height.After土壤湿度检索的有效相关长度和AIEM理论模型AIEM理论模型中检索研究区在0-6cm深度的采样点,本研究分析了相应于四种滤波窗口大小的最佳方法,提出的四种方法的比较分析和远程确定的研究区土壤水分微波的最佳反演模型感测在0-6cm depth.This物品中使用的最好的模型中的0-6cm深度检索土壤水分,制成土壤米的分布图oisture在0-6cm的深度,并进行分析空间分布特性。

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