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ASSESSING THE APPLICABILITY OF HYDROLOGIC INFORMATION FROM RADAR IMAGERY

机译:评估雷达影像水文信息的适用性

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During the last two decades, the potential of radar remote sensing in the retrieval of the water content of the near-surface unsaturated soil zone has been explored. This water content is usually referred to as soil moisture. The inversion of radar observations into soil moisture values has been hampered by an insufficient characterization of the soil roughness. However, some studies have focused on relating temporal changes of the radar signal to hydrologic relevant information. One result is presented here, where we show that variable source areas, which are mainly responsible for runoff in a catchment, can be visualized through a principal component analysis. A second part of this paper shows how soil moisture information obtained from radar imagery can be incorporated into hydrologic models. A first example uses an extended Kalman filtering technique, which adjusts the state variables from a hydrologic model. Through this technique, one-dimensional soil moisture profiles are retrieved with high accuracy. In a second example, we show a data-assimilation method which uses both the statistics and the spatial distribution of radar-retrieved soil moisture values, to adjust the modeled soil moisture profile. This methodology enables a better modeling of the rainfall-runoff behavior of the catchment.
机译:在过去的二十年中,已经探究了雷达遥感在恢复近地表非饱和土壤带水含量中的潜力。该水含量通常称为土壤水分。土壤粗糙度表征不足,阻碍了雷达观测值向土壤湿度值的转化。然而,一些研究集中在将雷达信号的时间变化与水文相关信息联系起来。此处给出了一个结果,其中我们表明可以通过主成分分析来可视化主要负责流域径流的可变源区域。本文的第二部分显示了如何将从雷达图像获得的土壤水分信息纳入水文模型。第一个示例使用扩展的卡尔曼滤波技术,该技术可根据水文模型调整状态变量。通过这种技术,可以高精度地检索一维土壤水分剖面。在第二个示例中,我们展示了一种数据同化方法,该方法使用统计数据和雷达获取的土壤水分值的空间分布来调整建模的土壤水分剖面。这种方法可以对流域的降雨-径流行为进行更好的建模。

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