Dataset Information

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

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.

SUBMITTER: Sun W

PROVIDER: S-EPMC5909913 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Sun Wenjuan W Li Xinju X Niu Beibei B

PloS one 20180420 4

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 t ...[more]

PMID: 29677214

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Project description:Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy (350-2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis-NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0-20 cm) samples were collected for SOM analysis and scanned with a Vis-NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23-4.69 g kg-1; R2 (coefficient of determination) = 0.80-0.84; RPD (ratio of standard deviation to RMSE) = 2.19-2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88-3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55-3.49 g kg-1; R2 = 0.87-0.93; RPD = 2.67-3.12; RPIQ = 3.15-4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis-NIR spectroscopy.

Project description:Opencast coal mining results in high loss of soil organic carbon (SOC), which may be restored via recultivation. Common strategies include liming, topsoil application, and phytoremediation. It remains unclear, however, which parameters determine the effectiveness of these varying recultivation strategies especially regarding SOC sequestration. This meta-analysis analyses the effect of varying recultivation strategies on SOC sequestration under different climate and soil conditions (pH, texture, depth) as well as in relation to time, based on 404 data entries from 51 studies. All included climatic regions recorded increases in SOC stocks, with tropical soils showing the highest potential for relative gains at up to 637%. We demonstrate that loamy soils sequester twice as much newly introduced SOC than sand. Strategy-wise, the highest mean rate of SOC sequestration is achieved by forest after topsoil application (3.9 Mg ha-1 a-1), agriculture after topsoil application (2.3 Mg ha-1 a-1), and agriculture with topsoil and fertiliser application (1.9 Mg ha-1 a-1) with a response ratio of 304%, 281%, and 218%, respectively. Soils analysed to less then 40 cm depth show higher SOC sequestration rates (< 10 cm: 0.6 Mg ha-1 a-1, < 20 cm: 1.0 Mg ha-1 a-1, and 20-40 cm: 0.4 Mg ha-1 a-1; response ratio of 123%, 68%, and 73%, respectively) than those analysed to a depth of 41-80 cm (0.1 Mg ha-1 a-1; response ratio of 6%). In terms of pH, strongly acidic soils (pH < 4.5) and alkaline conditions (pH > 7) offer the most beneficial environment for SOC sequestration at 0.4 Mg ha-1 a-1 and 0.8 Mg ha-1 a-1, respectively (185% and 273% response). Given comparable SOC sequestration potentials of forest after topsoil application, agriculture without amendments, and forest without amendments, we recommend to weigh these strategies against each other. Potentially decisive aspects are short- vs. long-term economic gains, food security concerns, and-in case of agriculture-the risk of overintensification leading to losses in SOC. Our data suggests that amendments exert considerable influence on SOC sequestration and need to be introduced under careful consideration.

Project description:BackgroundAs a special type of wetland, the new wetland in the coal mining subsidence area is highly sensitive to environmental changes. In recent years, more and more attention has been paid to the studies of soil microbial diversity in newly born wetlands in coal mining subsidence areas. However, there are few reports on the seasonal variation of soil microbial diversity and its relationship with soil physical and chemical properties.MethodsIn this study, 16S rRNA gene sequencing technology was used to analyze the seasonal changes of soil microbial composition and functional diversity in newly formed wetlands in coal mining subsidence areas, and to determine the seasonal changes of soil nutrient elements and physical and chemical properties in coal mining subsidence areas, so as to analyze the correlation between soil microbial diversity and soil nutrient elements and physical and chemical properties in newly formed wetlands in coal mining subsidence areas.ResultsA total of 16,050 OTUs were obtained after sample gene noise reduction. Proteobacteria, Acidobacteriota and Bacteroidota were the highest abundance in the coal mining subsidence area of Jining. The two seasons gathered separately, and temperature (Temp), total phosphorus (TP), available phosphorus (AP), total organic carbon (TOC) and dry matter content (DMC) were the key factors for the seasonal change of soil microbial community in the wetland of the coal mining subsidence area of Jining. The contents of Temp, AP and TP were significantly correlated with the abundance of soil microorganisms in summer subsidence area, while the contents of DMC and TOC were significantly correlated with the abundance of soil microorganisms in winter subsidence area.ConclusionSoil microbial diversity in coal mining subsidence area was correlated with the seasons. Temp, TP, AP, TOC and DMC were the key factors for the seasonal change of soil microbial community in the wetland of the coal mining subsidence area of Jining.

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

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

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