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

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          Abstract

          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 R 2 = 0.86, RMSE = 2.00 g/kg, verification R 2 = 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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          Most cited references 23

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          Using data mining to model and interpret soil diffuse reflectance spectra

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            High Dimensional Reflectance Analysis of Soil Organic Matter

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              Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy

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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Funding acquisition
                Role: InvestigationRole: SoftwareRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                20 April 2018
                2018
                : 13
                : 4
                Affiliations
                College of Resources and Environment, Shandong Agricultural University, Taian, China
                RMIT University, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-17-38676
                10.1371/journal.pone.0196198
                5909913
                29677214
                © 2018 Sun et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 4, Tables: 2, Pages: 10
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 41771324
                Award Recipient :
                This study was funded by the Natural Science Foundation of China (NSFC) (41771324). The funder has supported my work in data acquisition and analysis.
                Categories
                Research Article
                Computer and Information Sciences
                Software Engineering
                Preprocessing
                Engineering and Technology
                Software Engineering
                Preprocessing
                Physical Sciences
                Chemistry
                Environmental Chemistry
                Soil Chemistry
                Ecology and Environmental Sciences
                Environmental Chemistry
                Soil Chemistry
                Ecology and Environmental Sciences
                Soil Science
                Soil Chemistry
                Ecology and Environmental Sciences
                Soil Science
                Soil Ecology
                Engineering and Technology
                Energy and Power
                Fuels
                Fossil Fuels
                Coal
                Physical Sciences
                Materials Science
                Materials by Attribute
                Fuels
                Fossil Fuels
                Coal
                Physical Sciences
                Materials Science
                Organic Materials
                Coal
                Research and Analysis Methods
                Spectrum Analysis Techniques
                Absorption Spectroscopy
                Computer and Information Sciences
                Information Technology
                Data Mining
                Biology and Life Sciences
                Agriculture
                Agricultural Soil Science
                Ecology and Environmental Sciences
                Soil Science
                Agricultural Soil Science
                Physical Sciences
                Physics
                Electromagnetic Radiation
                Light
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized

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