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

  • 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 date: 2012-04-24

  Revised date: 2012-04-24

  Online published: 2012-04-24


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).

Cite this article

HU Wensheng, REN Hongyan , ZHUANG Dafang, SHI Xuezheng, LIU Shaogui, HUANG Yaohuan, YU Xinfang . Effects on Application of Spectroscopy in Estimating of Soil Organic Matter Content[J]. Journal of Geo-information Science, 2012 , 14(2) : 258 -264 . DOI: 10.3724/SP.J.1047.2012.00258


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