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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >The Effect of Vegetation on the Remotely Sensed Soil Thermal Inertia and a Two-Source Normalized Soil Thermal Inertia Model for Vegetated Surfaces
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The Effect of Vegetation on the Remotely Sensed Soil Thermal Inertia and a Two-Source Normalized Soil Thermal Inertia Model for Vegetated Surfaces

机译:植被对遥感土壤热惯性的影响及植被表面两源归一化土壤热惯性模型

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摘要

The estimation of soil thermal inertia (STI) (P) on the vegetated surface is a challenging task due to the difficulty in acquiring soil temperature under vegetation. In most cases, mixed surface temperature (T) is used to replace soil temperature () to estimate P. Inevitably, errors are introduced because of the effect of vegetation. In this paper, on the basis of a simplified STI concept and an operational algorithm of surface temperature separation, the differences of STI estimated from , vegetation temperature () and T were quantified. When there is large difference between T and (as much as 10 K), the mean absolute percentage difference (MAPD) between STI estimated from () and STI estimated from T () can reach 60%. A normalized STI () to account for the vegetated surface was proposed in terms of the linear mixing theory, which can be used to estimate the relative soil water (SW) content. Under the condition that the wilting point of the soil moisture and the saturated soil moisture are known for an area, SW content can then be calculated from . The comparisons of the relationships between soil moisture from the advanced microwave scanning radiome- er-earth observing system and , and soil moisture estimated from show that is the best indicator of soil moisture, with the highest correlation coefficient of 0.64 and 0.75 for the two validation domains.
机译:由于难以获得植被下的土壤温度,因此估算植被表面的土壤热惯性(STI)(P)是一项艰巨的任务。在大多数情况下,用混合表面温度(T)代替土壤温度()来估算P.由于植被的影响,不可避免地会引入误差。本文基于简化的STI概念和表面温度分离的运算算法,量化了从,植被温度()和T估计的STI的差异。当T与(最大10 K)之间存在较大差异时,根据()估算的STI与根据T()估算的STI之间的平均绝对百分比差异(MAPD)可以达到60%。根据线性混合理论,提出了归因于植被表面的归一化STI(),可用于估计相对土壤水分(SW)含量。在已知某个地区土壤水分的枯萎点和饱和土壤水分的条件下,可以根据计算出SW含量。比较先进的微波扫描地对空观测系统的土壤水分与和估算的土壤水分之间的关​​系,表明这是土壤水分的最佳指标,两次验证的相关系数最高,分别为0.64和0.75。域。

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