结合光谱变换和Kennard-Stone算法的水稻土全氮光谱估算模型校正集构建策略研究

摘要

土壤组分光谱估算过程中校正样本集的构建会影响模型的预测精度.当前结合反射光谱和Kennard-Stone (KS)算法的校正样本集构建策略忽视了土壤反射光谱是土壤属性的综合反映,构建的样本集通常无法很好地代表目标土壤组分的变异.光谱变换方法可以突出目标组分的光谱特征,为此,本文以湖北省江汉平原滨湖地区水稻土为研究对象,结合包括一阶微分(FD)、Savitzky-Golay(SG)、Haar小波变换、标准正态变量变换(SNV)和多元散射校正(MSC)在内的光谱变换方法和KS算法进行校正样本集建构,通过对比不同样本集构建策略对使用偏最小二乘回归(PLSR)建立的土壤全氮含量光谱估算模型预测精度的影响,研究光谱变换是否有助于提高基于KS算法构建的校正样本集的代表性.结果表明:不同光谱变换会影响校正样本集的构建.反射光谱经过SG或Haar小波变换后,再使用KS算法构建校正样本集与直接基于反射光谱使用KS算法构建的校正样本集相同,建立的估算模型精度不变,相对分析误差(RPD)分别为1.41和1.27.结合FD,SNV或MSC变换和KS算法构建的校正集与基于反射光谱使用KS算法构建的校正集不同,建立的估算模型RPD分别从0.95,1.48和1.42提高到1.13、1.78和2.20.研究表明SNV和MSC等光谱变换方法可以提高基于KS算法构建的校正样本集的代表性,并可有效提高模型预测精度.%Visible and near infrared (VNIR) has been widely used to estimate various soil properties .The construction of cali-bration dataset during the VNIR estimation of soil properties not only influences the representative of the calibration dataset ,but also on prediction accuracy of models .Current strategy for calibration dataset construction combines VNIR spectra with Ken-nard-Stone (KS) algorithm .However ,this strategy neglects the fact that soil reflectance spectra are a comprehensive reflection of soil properties rather than a specific component .As a result ,the constructed dataset from this approach may be not good enough to represent the relationships between soil spectra and the target soil component .Given that different spectral transfor-mations could be helpful to highlight the spectral characteristics of target component ,they might be also useful in the selection of samples for model calibration .The aim of the study is to explore the potentials of the combined approach of different spectral transformations and KS algorithm in the construction of calibration dataset and the VNIR estimation of soil total nitrogen (TN) .It is hypothesized that the proposed approach could help to better select samples for calibration ,which are more representative comparing with those selected by KS algorithm using sample reflectance spectra .A total of 100 samples have been collected from paddy soil in Jianghan Plain of Hubei Province .Five transformation methods ,namely the first derivative (FD) ,Savitzky-Golay (SG) ,Standard Normal Variate (SNV) ,Multiple Scatter Correction (MSC) and Harr Wavelet transform have been employed for spectral transformations .Thereafter ,KS algorithm is used to construct representative calibration datasets based on the dif-ferently transformed spectra .Partial least square regression (PLSR) is then used for model calibration .Whether the different spectral transformation methods can improve the representative of the calibration dataset constructed by KS algorithm is exam-ined .The results illustrate that different spectral transformations can exert different effects on the construction of calibration dataset .The SG and Wavelet spectral transformations do not make a difference for the calibration dataset constructed by KS al-gorithm using reflectance spectra ,with ratio of performance to standard deviate (RPD) of 1.41and 1.27 respectively .The spec-tral transformations of FD ,SNV or MSC do improve the calibration dataset constructed by KS algorithm ,with the RPDs im-prove from 0.95 ,1.48 and 1.42 to 1.13 ,1.78 and 2.20 respectively .The study indicates that such spectral transformations as SNV and MSC could change the way that KS algorithm constructs calibration dataset and improve its representative relationships between soil spectra and soil TN .Therefore ,we conclude that the proposed strategy for calibration dataset construction holds great pontentials to improve the model prediction capability in the VNIR estimation of soil TN .