基于连续小波变换的土壤重金属镉含量的高光谱估测

Hyperspectral Estimation of Heavy Metal Cadmium Content in Soil based on Continuous Wavelet Transform

选择合理的处理方法可增强土壤光谱中有效信息特征,提高模型估测精度。以新疆渭-库绿洲土壤为研究对象,基于连续小波变换(continuous wavelet transformation,CWT)与传统数学变换相结合的方法进行光谱数据处理并提取特征波段,采用偏最小二乘回归(PLSR)、BP神经网络(BPNN)、随机森林回归(RFR)和支持向量机回归(SVMR)方法构建土壤重金属镉含量估测模型。结果表明:(1)土壤原始光谱曲线趋势基本一致,在600~2450 nm范围内,随着重金属镉含量增加,其光谱反射率降低,二者呈负相关。(2)CWT与原始光谱一阶微分(R′)相结合的处理效果最佳,|r|值可达到0.586,为极显著负相关(P<0.001),表明数学变换与连续小波变换相结合的处理方法可有效反应光谱细节特征。(3)对比各模型的反演结果,发现CWT-R′-SVMR模型的训练集和验证集的决定系数(R^(2))大于0.86,均方根误差(RMSE)小于0.02 mg/kg,相对分析误差(RPD)大于2,建模效果较好,可作为最优模型对研究区土壤重金属镉含量进行估测。结合数学变换的连续小波分解技术可有效提取土壤中的潜在信息,为土壤重金属镉含量的准确估算提供参考。

Selecting a reasonable processing method can enhance the effective information characteristics of soil spectrum and improve the estimation accuracy of the model.Taking the Xinjiang Wei-Ku oasis soil as the research object.The spectral data is processed by continuous wavelet transform(CWT)and mathematical transformation to extract characteristic bands.Partial least squares regression(PLSR),BP neural network(BPNN),random forest regression(RFR)and support vector machine regression(SVMR)methods are used to construct the soil heavy metal cadmium content estimation model.The results show that:(1)The trend of the original spectral curve of soil is basically the same.In the range of 600-2450 nm,the spectral reflectance decreased with the increase of heavy metal cadmium content,and both are negatively correlated.(2)The combination of CWT and first-order differential(R′)of the original spectrum has the best effect.Its|r|value can reach 0.586,which is a very significant negative correlation(P<0.001),indicating that the processing method combining mathematical transformation and continuous wavelet transform can effectively reflect the spectral details.(3)Comparing the results,it is found that the coefficient of determination(R^(2))of the CWT-R′-SVMR model is greater than 0.86,the root mean square error(RMSE)is less than 0.02 mg/kg,and the relative percent deviation(RPD)is greater than 2.In summary,this model is effective and it can be used as an optimal model to estimate the soil heavy metal cadmium content in the study area.The continuous wavelet decomposition technology combined with mathematical transformation can effectively extract the potential information in the soil,and provide a reference for the accurate estimation of soil cadmium content.