Abstract
Purpose
Rapid and accurate estimation of soil organic carbon (SOC) based on near-infrared spectroscopy can assist sustainable agricultural developments in the future.
Methods
In this study, based on the acquisition of soil spectra and SOC in field plot trials and farmland regional trials, the raw spectral data were processed by multi-class mathematical transformation to study the relationship between the raw spectra and their pre-processed spectra and SOC. Additionally, a near-infrared spectroscopy model has been constructed for the estimation of SOC based on multiple statistical regression (partial least squares, PLS; Support vector machine, SVM; successive projection algorithm-multiple linear regression, SPA-MLR) to resolve the optimal spectral pretreatment and modeling method suitable for SOC determination by Vis–NIR spectroscopy.
Results
The results revealed that the soil NIR spectra were negatively correlated with SOC, which suggested that the spectral reflectance decreased gradually with the increasing SOC content. A comparative analysis of the correlation between the original and pre-treatment spectra and SOC further revealed that except for the Savitzky-Golay smoothing treatment (T1), different spectral pretreatments significantly improved the correlation between soil spectra and SOC, and the maximum correlation coefficient could reach up to 0.8. Among the three types of multiple regression models based on different pretreatment conditions, the support vector machine and model of soil organic carbon based on Savitzky-Golay smoothing + first derivative (T7) achieved better estimation results (R2c = 0.9212, RMSEc = 1.4058, RPDc = 4.3403; R2v = 0.7556, RMSEv = 2.4589, RPDv = 2.0186).
Conclusions
Our findings provided some theoretical reference and practical application of the near-infrared spectroscopy-based estimation of organic carbon in agricultural soils.
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Funding
This work was funded by the National Natural Science Foundation of China (31871571, 31371572), the Outstanding Doctor Funding Award of Shanxi Province (SXYBKY2018040), the earmarked fund for Modern Agro-industry Technology Research System (2022-07) and the Higher education Project of Scientific and Technological Innovation in Shanxi (2020L0132). The project was also supported by the Scientific and Technological Innovation Fund of Shanxi Agricultural University (2018YJ17, 2020BQ32) and the Key Technologies R & D Program of Shanxi Province (201903D211002).
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Wang, Y., Yang, S., Yan, X. et al. Evaluation of data pre-processing and regression models for precise estimation of soil organic carbon using Vis–NIR spectroscopy. J Soils Sediments 23, 634–645 (2023). https://doi.org/10.1007/s11368-022-03337-2
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DOI: https://doi.org/10.1007/s11368-022-03337-2