地球信息科学学报 ›› 2012, Vol. 14 ›› Issue (2): 258-264.doi: 10.3724/SP.J.1047.2012.00258

• 遥感技术与应用 • 上一篇    下一篇

土壤有机质含量估测及其影响因素的光谱分析

胡文生1, 任红艳*1 2, 庄大方1, 史学正3, 刘绍贵4, 黄耀欢1, 于信芳1   

  1. 1. 中国科学院地理科学与资源研究所 资源环境与信息系统国家重点实验室, 北京 100101;
    2. 华东师范大学地理信息科学教育部重点实验室, 上海 200062;
    3. 中国科学院南京土壤研究所, 南京 210008;
    4. 江苏省扬州市土壤肥料站, 扬州 225101
  • 收稿日期:2012-04-24 修回日期:2012-04-24 出版日期:2012-04-25 发布日期:2012-04-24
  • 作者简介:胡文生(1973),女,博士。主要研究方向为资源环境与遥感应用。Email: huwsh@lreis.ac.cn
  • 基金资助:

    地理信息科学教育部重点实验室开放研究基金资助项目(KLGIS2011A13)和国家自然科学基金项目(41001279)。

Effects on Application of Spectroscopy in Estimating of Soil Organic Matter Content

HU Wensheng1, REN Hongyan1 2, ZHUANG Dafang1, SHI Xuezheng3, LIU Shaogui4, HUANG Yaohuan1, YU Xinfang1   

  1. 1. State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200062, China;
    3. Institute of Soil Science, CAS, Nanjing 210008, China;
    4. Yangzhou Soil and Fertilizer Extension Service Center, Yangzhou 225101, China
  • Received:2012-04-24 Revised:2012-04-24 Online:2012-04-25 Published:2012-04-24
  • Contact: 任红艳 (1979-),男,博士,助理研究员,主要研究方向为环境遥感应用。E-mail: renhy@lreis.ac.cn E-mail:renhy@lreis.ac.cn

摘要: 土壤颗粒大小差异使土壤反射光谱产生相应变化,影响土壤有机质含量等属性的光谱预测精度。本研究准备了颗粒粒径分别为2、0.25和0.15mm的土样,测定土壤有机质(Soil Organic Matter,SOM)含量,并于室内模拟条件下测定其反射光谱。通过分析不同粒径土样的原始(Raw)、多次散射校正(Multiple scattering correction, Msc)、一阶微分(First derivative, Fd)、连续统去除(Continuum removal, Cr)光谱与SOM含量之间的关系,筛选出与SOM含量相关性最强的Fd光谱单波段(2250nm, r=0.82, P<0.01),并建立线性回归模型;利用全波段光谱反射率,以偏最小二乘回归(Partial least square regression, PLSR)方法,确立2mm土样Msc处理光谱的PLSR模型为最优模型(RPD=3.56、R2=0.90、RMSEP=1.96g/kg)。土壤颗粒粒径对土壤光谱反射率变化有明显影响,但二者之间并非简单的线性关系,可能存在一个转折点;单变量(单波段光谱反射率)线性回归模型的预测能力,明显低于全波段反射光谱(Msc处理)-PLSR模型;土样样本容量对SOM含量预测精度有显著影响。因此,根据样本容量大小,选择合适的土壤颗粒粒径与光谱预处理方法组合可以提高预测精度。

关键词: 土壤颗粒粒径, 反射光谱, 有机质含量, 光谱预处理

Abstract: Reflectance spectra of soil responds to the differences of soil particle sizes, which affects directly the capability of predicting soil organic matter (SOM) content by spectral reflectance. With soil samples of different particle sizes at 2mm, 0.25mm and 0.15mm, reflectance spectra was collected under the condition of simulated sunshine in the laboratory and SOM contents were acquired. Multiple scattering correction (Msc), first derivative (Fd) and continuum removal (Cr) were used as data pretreatment methods to raise the Signal-to-Noise of raw spectra (Raw) before analyzing the correlation between spectral reflectance and SOM contents. Single waveband at 2250nm is selected from Fd-treated reflectance spectra because of its maximal linear correlation coefficient (r=0.82, P<0.01) responding to SOM contents, and then a linear regression is built with a moderate precision (R2=0.69). Furthermore, partial least square (PLS) was adopted to extract several principal components (PC) of full wavelength from 350 to 2500nm and to build prediction models. Only PLSR model of Msc-treated reflectance spectra of soil samples at 2mm particle size yielded better prediction for SOM content (RPD=3.56, R2=0.90, RMSEP=1.96g/kg). It indicates that particle size of soil sample poses obvious effects on soil reflectance spectra. There is a conceivable turning point of particle size because the correlation between particle size and reflectance is not simple linear relation. Capability of predicting SOM content by univariate linear regression model is markedly lower than that of PLSR model. In addition, prediction precision is significantly affected by capacity of soil samples. Hence, satisfying prediction precision of SOM content can be acquired by effective combination of moderate particle size (2mm) and proper spectral pretreatment (Msc).

Key words: soil particle size, reflectance spectra, soil organic matter content, spectral pretreatment.