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IMPROVEMENT OF BARE SURFACE SOIL MOISTURE ESTIMATION WITH L-BAND DUAL-POLARIZATION RADAR

机译:利用L波段双极化雷达改进裸表面土壤水分估计

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This study demonstrates a new algorithm development for estimating bare surface soil moisture using dual-polarization L-band backscattering measurements. Through our analyses on the numerically simulated surface backscattering database by Advanced Integral Equation Model (AIEM) with a wide range of soil moisture and surface roughness conditions, we found that the relative difference of the overall surface roughness parameters at the different co-polarizations can be well estimated through a roughness index. This new finding leads to an algorithm on estimation of bare surface soil moisture. We will demonstrate the theory and techniques of this algorithm through the AIEM simulated database and validate it with two field ground scatterometer experimental data. The results indicate that bare surface soil moisture can be estimated quite well with only co-polarized backscattering signals. It provides a solid support for Soil Moisture Active and Passive mission (SMAP).
机译:本研究展示了一种新的算法开发,用于使用双极化L波段反向散射测量估算裸表面土壤水分。通过通过高级整体方程模型(AIEM)对数值模拟表面反向散射数据库的分析,具有各种土壤水分和表面粗糙度条件,我们发现不同共偏振的整体表面粗糙度参数的相对差异可以是通过粗糙指数估计。这种新发现导致裸露表面土壤含水量的算法。我们将通过AIEM模拟数据库展示该算法的理论和技术,并用两个现场地面散射计实验数据验证。结果表明,只有共端的反向散射信号,可以估计裸露的表面土壤水分。它为土壤湿度活跃和被动任务(SMAP)提供了坚实的支持。

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