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首页> 外文期刊>Eurasian Soil Science >The application of the piecewise linear approximation to the spectral neighborhood of soil line for the analysis of the quality of normalization of remote sensing materials
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The application of the piecewise linear approximation to the spectral neighborhood of soil line for the analysis of the quality of normalization of remote sensing materials

机译:分段线性近似对土线谱距分析遥感材料标准化分析的应用

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The concept of soil line can be to describe the temporal distribution of spectral characteristics of the bare soil surface. In this case, the soil line can be referred to as the multi-temporal soil line, or simply temporal soil line (TSL). In order to create TSL for 8000 regular lattice points for the territory of three regions of Tula oblast, we used 34 Landsat images obtained in the period from 1985 to 2014 after their certain transformation. As Landsat images are the matrices of the values of spectral brightness, this transformation is the normalization of matrices. There are several methods of normalization that move, rotate, and scale the spectral plane. In our study, we applied the method of piecewise linear approximation to the spectral neighborhood of soil line in order to assess the quality of normalization mathematically. This approach allowed us to range normalization methods according to their quality as follows: classic normalization > successive application of the turn and shift > successive application of the atmospheric correction and shift > atmospheric correction > shift > turn > raw data. The normalized data allowed us to create the maps of the distribution of a and b coefficients of the TSL. The map of b coefficient is characterized by the high correlation with the ground-truth data obtained from 1899 soil pits described during the soil surveys performed by the local institute for land management (GIPROZEM).
机译:土壤线的概念可以是描述裸土表面的光谱特性的时间分布。在这种情况下,土线可以称为多颞土线,或简称颞土线(TSL)。为了为Tula Oblast的三个地区的境内创建8000个常规格点的TSL,我们在某些转化后的1985年至2014年期间获得了34个Landsat图像。由于Landsat图像是光谱亮度值的矩阵,这种转换是矩阵的标准化。有几种归一化方法,移动,旋转和缩放光谱平面。在我们的研究中,我们将分段线性近似的方法应用于土线的光谱邻域,以评估数学上的归一化质量。这种方法使我们能够根据其质量进行测距,如下所示:经典归一化>转弯和换档的连续应用>连续应用大气校正和换档>大气校正> Shift>转向>原始数据。归一化数据允许我们创建TSL的A和B系数的分布的地图。 B系数的地图的特征在于与在由当地土地管理研究所(Giprozem)执行的土壤调查期间从1899年描述的土壤坑中获得的地面真理数据的高相关性。

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