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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| Vydáno v: | PloS one Ročník 13; číslo 4; s. e0196198 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
20.04.2018
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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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 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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| AbstractList | 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.sup.2 = 0.86, RMSE = 2.00 g/kg, verification R.sup.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. 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.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. 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. 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. |
| Audience | Academic |
| Author | Li, Xinju Sun, Wenjuan Niu, Beibei |
| AuthorAffiliation | College of Resources and Environment, Shandong Agricultural University, Taian, China RMIT University, AUSTRALIA |
| AuthorAffiliation_xml | – name: RMIT University, AUSTRALIA – name: College of Resources and Environment, Shandong Agricultural University, Taian, China |
| Author_xml | – sequence: 1 givenname: Wenjuan surname: Sun fullname: Sun, Wenjuan – sequence: 2 givenname: Xinju orcidid: 0000-0003-1017-9078 surname: Li fullname: Li, Xinju – sequence: 3 givenname: Beibei surname: Niu fullname: Niu, Beibei |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29677214$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1108_SR_08_2018_0207 crossref_primary_10_1002_saj2_70028 crossref_primary_10_3390_rs13071376 crossref_primary_10_1007_s11368_022_03337_2 crossref_primary_10_1080_10095020_2023_2211612 crossref_primary_10_3390_rs13091682 crossref_primary_10_3390_app13148412 crossref_primary_10_1002_tqem_22126 crossref_primary_10_1007_s11270_023_06274_z crossref_primary_10_3390_s22113990 crossref_primary_10_1016_j_gsd_2024_101359 crossref_primary_10_1016_j_geoderma_2020_114306 crossref_primary_10_1111_sum_13147 crossref_primary_10_1111_sum_12684 crossref_primary_10_1016_j_rsase_2023_100995 crossref_primary_10_3390_su12041626 crossref_primary_10_3390_su11030667 crossref_primary_10_1007_s40789_023_00588_3 crossref_primary_10_1016_j_ijbiomac_2025_147235 |
| Cites_doi | 10.1016/j.geoderma.2008.01.010 10.2136/sssaj1992.03615995005600030031x 10.1016/j.biosystemseng.2009.08.002 10.1111/insr.12116 10.1016/j.ecoleng.2011.07.013 10.1016/j.geoderma.2009.12.025 10.3390/ijgi6100308 10.1016/j.geoderma.2016.10.033 10.1016/j.biosystemseng.2016.04.018 10.1371/journal.pone.0140688 10.1016/j.catena.2013.06.013 10.1007/s12524-015-0458-0 10.1371/journal.pone.0099251 |
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| Copyright | COPYRIGHT 2018 Public Library of Science 2018 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2018 Sun et al 2018 Sun et al |
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| References | HM Bartholomeus (ref7) 2008; 145 ref11 RA Viscarra Rossel (ref27) 2010; 158 K Tan (ref19) 2014 R Srivastava (ref1) 2015; 43 M Fajardo (ref2); 263 NS Bao (ref23) 2017; 288 PS Li (ref17) 2005; 33 HR Dongmei (ref13) 2014; 9 HZ Chen (ref26) 2011; 28 HT Wang (ref24) 2014 L Bodlák (ref15) 2012; 43 AP Leone (ref4) 2013; 22 AX Yang (ref25) 2015; 31 YP Hou (ref16) 2015; 46 X Wang (ref14) 2016 SQ Wang (ref3) 2008; 28 TL Henderson (ref8) 1992; 56 M Febrero-Bande (ref20) 2017; 85 Y Liu (ref5) 2015; 10 DD Guo (ref18) 2014; 30 L Gao (ref22) 2017; 6 H Aïchi (ref10) 2009; 104 M Conforti (ref9) 2013; 110 A Morellos (ref6) 2016; 152 D Yang (ref12) 2016; 75 (ref21) 1998 24905998 - PLoS One. 2014 Jun 06;9(6):e99251 18619304 - Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Apr;28(4):808-12 25881431 - Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Dec;34(12):3317-22 26468645 - PLoS One. 2015 Oct 15;10(10):e0140688 |
| References_xml | – volume: 33 start-page: 61 issue: 7 year: 2005 ident: ref17 article-title: Coal DTG curve denoise based on Savitzky-Golay method publication-title: Journal of Huazhong University of Science and Technology(Nature Science) – volume: 145 start-page: 28 issue: 1 year: 2008 ident: ref7 article-title: Spectral reflectance based indices for soil organic carbon quantification publication-title: Geoderma doi: 10.1016/j.geoderma.2008.01.010 – volume: 56 start-page: 865 issue: 3 year: 1992 ident: ref8 article-title: High dimensional reflectance analysis of soil organic matter publication-title: Soil Science Society of America Journal doi: 10.2136/sssaj1992.03615995005600030031x – volume: 28 start-page: 518 issue: 5 year: 2011 ident: ref26 article-title: Combination optimization of multiple scatter correction and Savitzky-Golay smoothing modes applied to the near infrared spectroscopy analysis of soil organic matter publication-title: Computers and Applied Chemistry – year: 2014 ident: ref24 article-title: Predicting of forest soil organic carbon content based on Near Infrared Spectroscopy – volume: 46 start-page: 287 issue: 2 year: 2015 ident: ref16 article-title: Treatment effects on soil hyperspectral stability in laboratory test publication-title: Chinese Journal of Soil Science – volume: 104 start-page: 442 issue: 3 year: 2009 ident: ref10 article-title: Regional predictions of soil organic carbon content from spectral reflectance measurements publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2009.08.002 – year: 1998 ident: ref21 article-title: Chinese soil – ident: ref11 – volume: 85 start-page: 61 issue: 1 year: 2017 ident: ref20 article-title: Functional principal component regression and functional Partial Least-squares regression: An overview and a comparative study publication-title: International Statistical Review doi: 10.1111/insr.12116 – start-page: 80 issue: 5 year: 2016 ident: ref14 article-title: The status and prospect of soil organic carbon in Southwest China publication-title: Tillage and Cultivation – volume: 43 start-page: 53 year: 2012 ident: ref15 article-title: SOC content—An appropriate tool for evaluating the soil quality in a reclaimed post-mining landscape publication-title: Ecological Engineering doi: 10.1016/j.ecoleng.2011.07.013 – volume: 30 start-page: 192 issue: 21 year: 2014 ident: ref18 article-title: Comparative analysis of various hyperspectral prediction models of fluvo-aquic soil organic matter publication-title: Transactions of the Chinese Society of Agricultural Engineering – volume: 158 start-page: 46 issue: 1–2 year: 2010 ident: ref27 article-title: Using data mining to model and interpret soil diffuse reflectance spectra publication-title: Geoderma doi: 10.1016/j.geoderma.2009.12.025 – volume: 6 start-page: 308 issue: 10 year: 2017 ident: ref22 article-title: Exploring the role of the spatial characteristics of visible and near-Infrared reflectance in predicting soil organic carbon density publication-title: ISPRS International Journal of Geo-Information doi: 10.3390/ijgi6100308 – volume: 288 start-page: 47 year: 2017 ident: ref23 article-title: Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory publication-title: GEODERMA doi: 10.1016/j.geoderma.2016.10.033 – volume: 31 start-page: 162 issue: 18 year: 2015 ident: ref25 article-title: Comparative assessment of two methods for estimation of soil organic carbon content by Vis-NIR spectra in Xinjiang Ebinur Lake Wetland publication-title: Transactions of the Chinese Society of Agricultural Engineering – volume: 152 start-page: 104 year: 2016 ident: ref6 article-title: Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2016.04.018 – volume: 22 start-page: 1230 issue: 4B year: 2013 ident: ref4 article-title: An applicantion of VIS-NIR reflectance spectroscopy and artificial neural neworks to the prediction of soil organic carbon content in southern Italy publication-title: Fresenius Environmental Bulletin – volume: 10 start-page: e0140688 issue: 10 year: 2015 ident: ref5 article-title: Predicting soil salinity with Vis-NIR spectra after removing the effects of soil moisture using external parameter orthogonalization publication-title: Plos One doi: 10.1371/journal.pone.0140688 – volume: 110 start-page: 44 issue: 2 year: 2013 ident: ref9 article-title: Studying the relationship between water-induced soil erosion and soil organic matter using Vis–NIR spectroscopy and geomorphological analysis: A case study in southern Italy publication-title: Catena doi: 10.1016/j.catena.2013.06.013 – volume: 75 start-page: 1 issue: 8 year: 2016 ident: ref12 article-title: Impact on soil physical qualities by the subsidence of coal mining: a case study in Western China publication-title: Environmental Earth Sciences – volume: 43 start-page: 751 issue: 4 year: 2015 ident: ref1 article-title: Development of hyperspectral model for rapid monitoring of soil organic carbon under precision farming in the Indo-Gangetic Plains of Punjab, India publication-title: Journal of the Indian Society of Remote Sensing doi: 10.1007/s12524-015-0458-0 – volume: 263 start-page: 244 ident: ref2 article-title: Fuzzy clustering of Vis-NIR spectra for the objective recognition of soil morphological horizons in soil profiles publication-title: Geoderma. 2015 – volume: 9 start-page: e99251 issue: 6 year: 2014 ident: ref13 article-title: Long Term Effect of Land Reclamation from Lake on Chemical Composition of Soil Organic Matter and Its Mineralization publication-title: Plos One doi: 10.1371/journal.pone.0099251 – start-page: 3317 issue: 12 year: 2014 ident: ref19 article-title: Estimation of Heavy Metal Concentrations in Reclaimed Mining Soils Using Reflectance Spectroscopy publication-title: Spectroscopy and Spectral Analysis – volume: 28 start-page: 808 issue: 4 year: 2008 ident: ref3 article-title: In-site total n content prediction of soil with Vis-NIR Spectroscopy publication-title: Spectroscopy and Sectral Analysis – reference: 25881431 - Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Dec;34(12):3317-22 – reference: 26468645 - PLoS One. 2015 Oct 15;10(10):e0140688 – reference: 24905998 - PLoS One. 2014 Jun 06;9(6):e99251 – reference: 18619304 - Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Apr;28(4):808-12 |
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| SubjectTerms | 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 |
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