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中文摘要：
【目的】针对黄土高原丘陵地多、地形复杂、有机质含量低、采样困难以及因采煤活动引起大面积土地损毁等问题,在土地复垦与综合整治过程中,为快速定量监测与评估复垦农田土壤质量提供一种新的方法。【方法】以山西省襄垣县复垦农田土壤为研究对象,选取由北向南土地损毁中间条带状区域采集样品152个,进行室内土壤农化分析、光谱测定,运用Par Les 3.1软件对光谱曲线进行多元散射校正（multipication scatter correction,MSC）、基线偏移（baseline offset correction,BOC）和Savitzky-Golay filter平滑去噪预处理。对土壤原始光谱反射率（raw spectral reflectance,R）作一阶微分（first order differential reflectance,D（R））和倒数的对数变换（inverse-lg reflectance,lg（1/R））,分析3种不同变换形式的光谱数据与土壤有机质含量的相关性,相关系数通过P=0.01水平显著性检验来确定显著性波段的范围。基于全波段（400—2400 nm）和显著性波段利用偏最小二乘回归（partial least squares regression,PLSR）分析方法建立该区域土壤有机质含量高光谱预测模型,通过模型精度评价指标：决定系数（coefficient of determination,R^2）、均方根误差（root mean square error,RMSE）和相对预测偏差（residual prediction deviation,PRD）确定最优模型。【结果】通过P=0.01水平显著性检验的波段范围为：R的400—1 800、1880—2 400 nm;D（R）的420—790、1 020—1 040、2 150—2 200 nm;lg（1/R）的400—1 830、1 860—2 400 nm。光谱与有机质含量的相关系数绝对值最大的波段是R的800 nm;D（R）的600 nm;lg（1/R）的760 nm。进行D（R）变换,光谱曲线的吸收特征更加明显,相关系数在可见光（400—800 nm）波段范围内有所增加,其最大值由0.72提高到了0.82;基于显著性波段的PLSR建模效果优于全波段,其中lg（1/R）变换的预测精度为最佳,具有很好的预测能力,其校正
英文摘要：
【Objective】 In terms of the problems in the Loess Plateau, such as many hills, complex topography, low soil organic matter content（SOMC）, sampling difficulties, large areas of land damage caused by mining activities and so on, the object of this study is to provide an alternative method for the rapidly quantitative monitoring and evaluation of the SOMC in the process of land reclamation and comprehensive renovation. 【Method】 Taking the cropland soil in the coal mining areas in Xiangyuan County, Shanxi Province was picked as research object, 152 soil samples were collected from the intermediate strip area of land destruction region in a north to south direction. The physical and chemical properties of the soil samples were analyzed. At the same time, the raw hyperspectral reflectance（R） of the soil samples was measured by the standard procedure with an ASD Field Spec 3 instrument equipped with a high intensity contact probe under the laboratory conditions. The raw spectral reflectance（R） were pretreated by the smoothing or denoising methods of multiplication scatter correction（MSC）, baseline offset correction（BOC） and Savitzky-Golay filter in the Par Les 3.1 software. And the raw spectral reflectance（R） was transformed into two types of spectra, which were first order differential reflectance（D（R）） and inverse-log reflectance（lg（1/R））, to analyze the correlation coefficients between the three spectra and their SOMC. Then the significant bands were extracted by the significant correlation coefficients（P=0.01） of the three spectra with the SOMC. Finally, based on the full bands（400-2 400 nm） and significant bands of the three spectra, the hyperspectral predicting models of the SOMC were established by the method of partial least squares regression（PLSR）. The optimal models were determined by the assessing indices of predicting accuracies, including coefficient of determination（R~2）, root mean square error（RMSE）, and residual prediction deviation（R
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