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

Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluatio...

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Bibliographic Details
Published in:PloS one Vol. 13; no. 4; p. e0196198
Main Authors: Sun, Wenjuan, Li, Xinju, Niu, Beibei
Format: Journal Article
Language:English
Published: United States Public Library of Science 20.04.2018
Public Library of Science (PLoS)
Subjects:
Agricultural engineering
Aluminum
Analysis
Biology and Life Sciences
Carbon
Carbon content
Coal mines
Coal mining
Computer and Information Sciences
Convolution
Ecology and Environmental Sciences
Engineering and Technology
Evaluation
I.R. radiation
Laboratories
Land reclamation
Land subsidence
Light
Measurement
Methods
Mine reclamation
Modelling
Multiple scattering
Near infrared radiation
Near-infrared spectroscopy
Noise reduction
Organic carbon
Organic soils
Physical Sciences
Preprocessing
Reclaimed land
Reflectance
Regression analysis
Regression models
Research and Analysis Methods
Scattering
Sensitivity
Smoothing
Soil carbon
Soil properties
Soil quality
Soil sciences
Soils
Spectra
Spectral reflectance
Spectral sensitivity
Spectroscopy
Transformation
China
ISSN:1932-6203, 1932-6203
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Summary:Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great significance. 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 soil organic carbon 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). In addition, the first-order differential of R combined with SG, MSC with R, SG together with MSC and R also produced better modeling results than other pretreatment combinations. Vis-NIR modeling with specific spectral preprocessing methods could predict SOC content effectively.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0196198