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Sensitivity of microwave remotely-sensed soil moisture to soil properties

机译:微波遥感土壤水分对土壤特性的敏感性

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摘要

Surface soil moisture is important in a number of disciplines including agricultural scheduling, water resource management, and weather forecasting. While conventional methods of surface soil moisture determination are labor intensive and of limited spatial scale, ground- and aircraft-based microwave remote sensing systems provide repetitive measurements over large areas. However, it is unclear how the remotely-sensed soil moisture reflects the variations in soil parameters used as input variables in soil moisture retrieval algorithms. Data from a multi-frequency passive microwave remote sensing experiment were used to study the sensitivity of an algorithm to soil texture, bulk density and surface roughness. The algorithm was relatively insensitive to texture, slightly sensitive to bulk density and highly sensitive to surface roughness (RMS). For the most part, the deviations in volumetric soil moisture from the baseline were more pronounced under wet conditions than under dry conditions, and these deviations were also independent of wavelength. This study implies that areal average values of texture variables and bulk density can serve as convenient inputs into soil moisture algorithms.
机译:地表土壤水分在许多学科中都很重要,包括农业调度,水资源管理和天气预报。传统的表面土壤水分测定方法需要大量劳动并且空间规模有限,而基于地面和基于飞机的微波遥感系统却可以在大面积上进行重复测量。但是,尚不清楚遥感土壤水分如何反映土壤参数的变化,这些参数在土壤水分检索算法中用作输入变量。利用来自多频无源微波遥感实验的数据来研究该算法对土壤质地,堆积密度和表面粗糙度的敏感性。该算法对纹理相对不敏感,对体积密度稍敏感,对表面粗糙度(RMS)高度敏感。在大多数情况下,在潮湿条件下土壤水分相对于基线的偏差比在干燥条件下更为明显,并且这些偏差也与波长无关。这项研究表明,质地变量和堆积密度的面积平均值可以作为土壤水分算法的便捷输入。

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