Soil oil content hyperspectral model in Gudong Oilfield

doi:10.11834/jrs.20121004

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Soil oil content hyperspectral model in Gudong Oilfield
Vol. 16, Issue 2, Pages: 378-389(2012)
Published：2012
DOI： 10.11834/jrs.20121004
稿件说明：

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[1]樊彦国,张磊.孤东油田土壤石油类含量的高光谱反演模型[J].遥感学报,2012,16(02):378-389. DOI： 10.11834/jrs.20121004.

Soil oil content hyperspectral model in Gudong Oilfield[J]. Journal of Remote Sensing, 2012, 16(2): 378-389. DOI： 10.11834/jrs.20121004.

摘要

土壤中石油类含量的检测对石油污染的预防与治理具有重要的实际意义。本文首先进行孤东油田土壤样品高光谱反射率的室内测定及石油含量的检测

然后利用单变量预测模型和逐步回归方法分析了土壤光谱特征参数与石油类含量之间的线性和非线性关系

结果表明:包络线分析的第三折线段斜率与石油类含量相关性最好

该段斜率的三次曲线函数为石油类含量的最佳单变量估算模型。标准正态变量变换对光谱的预处理效果最好

利用变换后光谱建立多元模型

其调整的判定系数R2是0.826

总均方根RMSE是0.531

且自变量个数较少

为最优预测模型。本文提出的利用高光谱数据检测土壤中石油类含量的方法

为土壤石油类污染检测提供了一种有效的新思路。

Abstract

The detection of oil content in soil has an important practical significance in oil pollution prevention and control. We measure the hyperspectral reflectivity and the oil content for soil samples in Gudong Oilfield. Using variable forecast model and stepwise regression method

we analyze the linear and nonlinear relationships between soil spectral characteristic parameters and oil content. The experiment shows that there is a significant correlation between the third broken line segment slope of envelope line analysis and the oil content. The cubic function of this section slope is the best single variate estimation model. The standard normal variate transformation has the best effect on spectrum pretreatment. When the transformed spectral are used to build multivariate regression model

the adjusted coefficient of determination is 0.826

and the total RMSE is 0.531

which is the best forecast model. The method of using hyperspectral data to detect the oil content will provide an effective new way for detecting the oil pollution in soil.