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.
Highlights
Traditional methods for the determination of soil organic carbon (SOC) content are time consuming and laborious and need high cost and exhibit poor real-time performance [1]
In terms of the statistical characteristics of the sample collectivity, the SOC content ranged from 0.79 g/kg to 27.72 g/kg, with an average value of 11.34 g/kg, indicating that the SOC content in the study area was generally low
In accordance with the classification standard of soil organic matter in the second national soil survey in China [21], the soil samples were divided into 6 groups based on their SOC content
Summary
Traditional methods for the determination of soil organic carbon (SOC) content are time consuming and laborious and need high cost and exhibit poor real-time performance [1]. Prediction of SOC in coal mining area by Vis-NIR spectroscopy information hidden in the soil. It was widely used in analysis of soil physical and chemical properties, such as soil humus structure [2], soil nutrients [3,4], soil salinity [5] and soil moisture content [6]. Aïchi et al [10] established a model that was proven to be valid over a range of 0.90–5.20% of organic carbon content through the original spectral absorbency of 400~950 nm, which included the near-infrared band. Few study has been done to improve the prediction accuracy using a combination of various spectral preprocessing methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
