20190301

Author : 
Wenjuan Sun, Xinju Li, Beibei Niu

Year : 2018

Title : Prediction of soil organic carbon in a coal mining area by Vis-NIR spectroscopy

Journal :  PLOS one

Comment : 

 Visible-near infrared (Vis-NIR) spectroscopy has been shown to be a rapid, timely and efficient tool for the prediction of soil organic carbon (SOC). 

 In this study, 104 soil samples were collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and SOC content were then measured under laboratory conditions. 

 The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method or the multiple scattering correction (MSC) method, after which the spectral reflectance (R) was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. 

 Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). 

 The results showed that: 

(1) The SOC content in the mining area was generally low (at the below-average level) and exhibited great variability. 

(2) The spectral reflectance increased with the decrease of soil organic carbon content. In addition, the sensitivity of the spectrum to the change in SOC content, especially that in the near-infrared band of the original reflectance, decreased when the SOC content was low. 

(3) The modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R2 = 0.86, RMSE = 2.00 g/kg, verification R2 = 0.78, RMSE = 1.81 g/kg, and RPD = 2.69).