基于光谱分析的土壤游离铁预测研究  

Prediction of Soil Free Iron Oxide Content Based on Spectral Analysis

作　　者：魏昌龙[1,2] 赵玉国[1] 邬登巍[1,2] 陈吉科 

机构地区：[1]土壤与农业可持续发展国家重点实验室(中国科学院南京土壤研究所),南京210008 [2]中国科学院大学,北京100049

出　　处：《土壤》2014年第4期678-683,共6页Soils

基　　金：环境保护公益性行业科研专项(201209030);中国科学院战略性先导科技专项(XDA05050500);国家科技基础性工作专项(2008FY110600)资助

摘　　要：土壤游离铁含量的高低可作为土壤系统分类中判断土壤类型的诊断指标,同时也对土壤风化程度具有指示作用,并在一定程度上反映了土壤的成土环境。本研究调查了安徽宣城的91个土壤剖面,共398个土壤样品,采集了样品在350~2500 nm波段的漫反射光谱数据,并对游离铁含量进行化学分析。光谱数据包括反射率（R）、反射率一阶导数（FDR）和吸收度（Log（1/R））3种形式。本文采用偏最小二乘回归算法（PLSR）和反向神经网络（BPNN）建模预测游离铁含量,并分析不同形式光谱数据的建模预测效果。结果表明：当存在游离铁〈20 g/kg的样本时,传统建模方法不能准确预测游离铁含量（R2〈0.6,RPD〈1.5）,相对R和Log（1/R）两种光谱数据,以FDR作为自变量建模预测游离铁含量的效果最差。Free iron oxide content is an index of soil weathering degree, soil-forming condition and environment, soil classification and soil age estimation. This paper investigated 91 soil profiles containing 398 samples in XuanchengPrefecture of Anhui Province, the spectral reflectance of soil samples was measured over the range 350 - 2500 nm, the free iron oxide content was analyzed by chemical means. In order to determine the relationship between the free iron oxide and spectral data, the spectral data were transferred to first derivatives of the reflectance （FDR） and absorbance （Log （I/R）） other than reflectance （R）. This paper compared the performance of two calibration methods, namely partial least squares regression （PLSR） and back propagation neural network （BPNN） analyses for the accuracy of measurement of three spectral dataset, namely R, FDR and Log（I/R）, the spectral response of the free iron oxide also was determined. The results showed that traditional modeling methods could not accurately predict free iron content （R2〈0.6, RPD〈1.5） when its contents were lower than 20 g/kg. Compared to the R and log （I/R）, the FDR which was implemented in the models indicated a poor predictive ability.

关 键 词：土壤光谱 游离铁 PLSR BPNN 

分 类 号：S151.95[农业科学—土壤学]