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首页> 外文期刊>Geoderma: An International Journal of Soil Science >High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures
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High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures

机译:在多变量回归建模过程中使用高光谱图像和现场数据进行高分辨率表层土制图

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摘要

The spatial variability of within field topsoil texture and organic matter was studied using airborne hyperspectral imagery so as to develop improved fine-scale soil mapping procedures. Two important topsoil variables for precision farming applications, soil organic matter and soil texture, were found to be correlated with spectral properties of the airborne HyMap scanner. The percentage sand, clay, organic carbon and total nitrogen content could be predicted quantitatively and simultaneously by a multivariate calibration approach using either partial least-square regression (PLSR) or multiple linear regression (MLR). The different topsoil parameters are determined simultaneously from the spectral signature contained in the single hyperspectral image, since the various variables were represented by varying combinations of wavebands across the spectra. The methodology proposed provides a means of simultaneously estimating topsoil organic matter and texture in a rapid and non-destructive manner, whilst avoiding the spatial accuracy problems associated with spatial interpolation. The use of high spatial resolution and hyperspectral remotely sensed data in the manner proposed in this paper can also be used to monitor and better understand the influence of management and land use practices on soil organic matter composition and content.
机译:利用机载高光谱成像技术研究了田间表层土壤质地和有机质的空间变异性,以开发改进的精细尺度土壤制图程序。发现用于精密农业的两个重要表土变量,土壤有机质和土壤质地与机载HyMap扫描仪的光谱特性相关。沙,粘土,有机碳和总氮含量的百分比可通过偏最小二乘回归(PLSR)或多元线性回归(MLR)的多元校准方法定量和同时预测。从单个高光谱图像中包含的光谱特征同时确定不同的表土参数,因为各种变量由光谱中各个波段的组合表示。所提出的方法提供了一种以快速且无损的方式同时估算表土有机质和质地的方法,同时避免了与空间插值相关的空间精度问题。以本文提出的方式使用高空间分辨率和高光谱遥感数据,也可以用于监测和更好地了解管理和土地利用实践对土壤有机质组成和含量的影响。

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