高光谱反演耕地土壤质量评价元素含量方法研究

A method for hyperspectral inversion of element contents for soil-quality evaluation of cultivated land

为探讨利用高光谱快速估算耕地土壤质量元素镉(Cd)、砷(As)含量的可行性和准确度,该文针对元素光谱特征波段提取及高光谱定量反演建模开展研究。使用一阶/二阶微分(first derivative/second derivative,FD/SD)、倒数对数(logarithm reciprocal,LR)、包络线去除(continuum removal,CR)4种光谱变换与竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)、相关性分析(Pearson correlation analysis,PCC)2种特征筛选相组合的多种方法提取光谱特征波段。在此基础上,分别利用偏最小二乘回归(partial least squares regression,PLSR)和粒子群改进的随机森林回归(particle swarm optimization-random forest regression,PSO-RFR)2种回归模型来反演元素含量并进行精度验证。结果表明,实验区土壤元素Cd和As预测的最佳模型均为FD-CARS-PLSR,Cd和As元素模型的决定系数R^(2)最高分别为0.863和0.959,相对分析误差分别为2.799和5.119。FD/SD光谱变换结合CARS特征筛选能够提升PLSR反演模型的精度。研究成果可以为土壤Cd和As元素含量的快速估算提供参考。

To explore the feasibility and accuracy of the method of utilizing hyperspectral data to estimate the contents of elements Cd and As for soil quality elevation of cultivated land,this study delves into the extraction of characteristic bands of the spectra of both elements and the modeling of quantitative hyperspectral inversion.The characteristic bands of spectra were extracted using multiple methods derived from the combination of four spectral transformations and two feature selection methods,with the former comprising first-order/second-order differential(FD/SD),reciprocal logarithm(LR),and continuum removal(CR)and the latter consisting of the competitive adaptive reweighted sampling(CARS)method and the Pearson correlation coefficient(PCC)analysis.Based on this,the element content inversion was conducted using the partial least squares regression(PLSR)and the particle swarm optimization optimized random forest regression(PSO-RFR),followed by the verification of inversion accuracy.The results indicate that the FD-CARS-PLSR inversion model exhibited the best prediction effect for both elements,with maximum determination coefficients R^(2)of 0.863 and 0.959 and relative percent differences(RPDs)of 2.799 and 5.119 for Cd and As,respectively.The FD and SD spectral transformations combined with the CARS method can improve the accuracy of the PLSR inversion model.The results of this study can provide a reference for the rapid estimation of the contents of Cd and As in soil.