近红外光谱结合偏最小二乘法快速评估土壤质量  

RAPID EVALUATION OF SOIL QUALITY THROUGH A NEAR INFRARED-PARTIAL LEAST SQUARES(NIR-PLS) METHOD

作　　者：王昶[1] 黄驰超[1] 余光辉[1] 冉炜[1] 沈其荣[1] 

机构地区：[1]南京农业大学资源与环境科学学院,农业部长江中下游植物营养与施肥重点实验室,江苏省固体有机废弃物资源化高技术研究重点实验室,南京210095

出　　处：《土壤学报》2013年第5期881-890,共10页Acta Pedologica Sinica

基　　金：国家重点基础研究发展计划(973计划)(2011CB100503);高校"青蓝工程";111计划(B12009);博士后科学基金(1102079C)资助

摘　　要：以长江中下游粮食主产区水稻土为研究对象，采集17种不同施肥处理下共136个土壤样品在350 ～2 500 nm范围的近红外光谱，利用偏最小二乘回归分析结合交叉验证法建立了近红外漫反射光谱与传统化学分析方法测得的全碳、全氮、碳氮比、速效钾、速效磷、电导率、土壤pH等土壤指标之间的定量分析模型。模型的决定系数（R2）以及化学分析值标准差（SD）与模型的内部交叉验证均方差（RMSECV）的比值RSC用于判定建立的模型的好坏。结果表明：全碳、全氮、碳氮比和pH模型的R2和RSC分别为：R2=0.94，RSC=4.31；R2=0.95，RSC=4.35；R2=0.97，RSC=5.60；R2=0.92，RSC=3.37，说明上述土壤指标的预测结果很好。速效钾模型的R2和RSC分别为：R2=0.87，RSC=2.23，表明预测结果尚好。而速效磷和电导率模型的R2和RSC分别为：R2=0.18，RSC=1.16；R2=0.37，RSC=1.31，说明两者的预测结果均很不理想。综上所述，水稻土的土壤质量相关指标（全碳、全氮、碳氮比、速效钾和土壤pH）可以通过近红外光谱结合偏最小二乘法（NIR-PLS）快速评估。In this study, a total of 136 paddy soil samples were collected from 17 different fertilization treatments of two short-term field experiments in Jintan and Zhangjiagang, the main grain production region in the middle and lower reaches of the Yangtze River for near infrared （350 ～2 500 nm） -partial least squares （NIR-PLS） regression analysis. Based on the analysis coupled with the cross validation method, a model was established for quantitative analysis of the total carbon, total nitrogen, C/N ratio, available potassium, available phosphorus, electro-conductivity and soil pH obtained by near infrared diffuse reflectance spectroscopy and traditional chemical analysis. R2, determination coefficient value, and RSC, ratio of SD （standard deviation of chemical analysis）/RMSECV （root mean square error of cross validation） are two criteria for evaluation of the model. Results show that for the total carbon , total nitrogen , C/N ratio and pH, R2was 0.94, 0.95, 0.97 and 0.92 and RSC was 4.31, 4.35, 5.60 and 3.37, respectively, suggesting that the model is good in prediction. For available potassium, R2was 0.81 and RSC was 2.23, indicating that the model is good, however, for available phosphorus and electro-conductivity, R2 was 0.22 and 0.37 and RSC was 0.16 and 1.31, respectively, demonstrating that the model is not so ideal. To sum up, for paddy soil, relevant quality indices can be rapidly predicted through NIR-PLS regression analysis.

关 键 词：近红外光谱 偏最小二乘法 土壤质量 模型 

分 类 号：S123[农业科学—农业基础科学] S153.6