首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于正交信号校正的Vis-NIR光谱土壤质地预测
引用本文:王德彩,蔚霖,张俊辉,杨红震,黄家荣,孙孝林.基于正交信号校正的Vis-NIR光谱土壤质地预测[J].河南农业大学学报,2017,51(3).
作者姓名:王德彩  蔚霖  张俊辉  杨红震  黄家荣  孙孝林
作者单位:1. 河南农业大学林学院,河南郑州,450002;2. 河南农业大学资源与环境学院,河南郑州,450002;3. 中山大学地理科学与规划学院,广东广州,510275
摘    要:为提高基于VIS-NIR光谱的土壤质地预测精度,引入了正交信号校正(OSC)光谱预处理算法。分别用原始光谱、微分处理、OSC处理光谱,建立偏最小二乘回归(PLSR)模型。结果表明,OSC-PLSR模型验证精度高于其他两种方法所建模型,砂粒含量OSC-PLSR模型的RMSEp为5.94,粘粒含量OSC-PLSR模型RMSEp为1.25,相比PLSR模型,分别降低22.22%和9.42%。OSC算法在土壤质地的VIS-NIR反演中能有效消除不相关因素的影响,提高模型预测精度。

关 键 词:Vis-NIR光谱  土壤质地  正交信号校正  偏最小二乘回归

Prediction of soil texture using Vis-NIR spectra based on orthogonal signal correction
WANG Decai,WEI Lin,ZHANG Junhui,YANG hongzhen,HUANG Jiarong,SUN Xiaolin.Prediction of soil texture using Vis-NIR spectra based on orthogonal signal correction[J].Journal of Henan Agricultural University,2017,51(3).
Authors:WANG Decai  WEI Lin  ZHANG Junhui  YANG hongzhen  HUANG Jiarong  SUN Xiaolin
Abstract:In order to improve the prediction accuracy soil texture based on VIS-NIR spectra this paper introduces the orthogonal signal correction (OSC) spectra pretreatment method.Separate partial least squares regression (PLSR) model was established using the original spectrum,derivative analysis spectra,and OSC processing spectra respectively.The results showed that OSC-PLSR model validation's accuracy is higher than that of the other two models.The RMSEp of prediction from the OSC-PLSR models of content of sand and clay separates were 5.94 and 1.25,which were 22.22% and 9.42% lower than of the PLSR model respectively.The results of this research showed OSC algorithm can effectively eliminate the influence of unrelated factors and improve the accuracy of prediction when using Vis-NIR spectra to predict soil texture.
Keywords:Vis-NIR spectroscopy  soil texture  orthogonal signal correction  partial least squares regression
本文献已被 CNKI 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号