辽宁省黄土状母质发育土壤有机质含量高光谱预测模型的构建

A Hyperspectral Prediction Model for Organic Matter Content in Soil Developed from Loess-like Parent Material in Liaoning Province

【目的】建立辽宁省黄土状母质发育土壤有机质含量的高光谱预测模型,以便快速获取土壤样品的有机质含量。【方法】对省域内黄土状母质发育土壤进行了样品采集,获取样品有机质含量和高光谱数据;选择原始光谱及其一阶微分、二阶微分、倒数对数、倒数对数一阶微分、倒数对数二阶微分6种光谱变换数据作为自变量,与土壤有机质含量进行相关分析,选取特征波段,分别建立多元逐步线性回归(SMLR)、偏最小二乘回归(PLSR)和主成分回归(PCR)3种土壤有机质高光谱线性预测模型,并进行了支持向量机(SVM)方法的非线性模型拟合。【结果】土壤有机质含量与其光谱反射率呈负相关关系,对光谱进行不同的数学变换,可以提高土壤有机质含量与光谱反射率的相关性,其中一阶微分和二阶微分的提升效果最佳;相同光谱数据在不同模型中建模精度存在显著差异,以原始光谱反射率一阶微分为自变量的PLSR模型精度最高,建模集和验证集的决定系数(R2)分别为0.958和0.976;3种线性方法建立的最佳预测模型的检验精度为:PLSR>SMLR>PCR。【结论】PLSR模型是辽宁省黄土状母质发育土壤有机质含量的最佳高光谱预测模型,且基于特征波段的建模效果优于全波段;SVM非线性模型的预测精度较低。

[Objective] A hyperspectral prediction model for organic matter content of soil developed from loess-like parent material in Liaoning Province was established for rapid acquiring contents of soil organic matter(SOM).[Method] Samples were collected from soils developed from loess-like parent material, and their SOM contents and hyperspectral data were determined. The original spectra and its six spectral transformations of first-order differential,second-order differential, inverse logarithmic, inverse logarithmic first-order differential and inverse logarithmic second-order differential were selected as independent variables, to conduct correlation analysis with SOM content.The characteristic bands in the spectra data were selected, and three linear models for hyperspectral prediction of SOM content were developed by using multiple stepwise linear regression(SMLR), partial least squares regression(PLSR)and principal component regression(PCR), respectively. While nonlinear model fitting by support vector machine(SVM) was also performed. [Results] The SOM content was negatively correlated with spectral reflectance. The different mathematical treatments of the spectra could improve the correlation between SOM content and spectral reflectance, especial for the first-order differential and second-order differential treatments. the model accuracy of the same spectral in different models differed significantly, the PLSR model with the first-order differentiation of the original spectral reflectance as the independent variable had the highest accuracy, and the coefficients of determination(R~2) of the modeling set and validation set were 0.958 and 0.976. The test accuracies of the best prediction models established by the three linear methods were: PLSR > SMLR > PCR. [Conclusion] The PLSR model was the optimal model for predicting the organic matter content of soil developed from loess-like parent material in Liaoning Province,and the model based on the characteristic bands was better than that based on full bands. The prediction accuracy of SVM nonlinear model was lower.