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Machine learning based on-line prediction of soil organic carbon after removal of soil moisture effect

Said Nawar (UGent) , Muhammad Abdul Munnaf (UGent) and Abdul Mouazen (UGent)
(2020) REMOTE SENSING. 12(8).
Author
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Abstract
It is well-documented in the visible and near-infrared reflectance spectroscopy (VNIRS) studies that soil moisture content (SMC) negatively affects the prediction accuracy of soil attributes. This work was undertaken to remove the negative effect of SMC on the on-line prediction of soil organic carbon (SOC). A mobile VNIR spectrophotometer with a spectral range of 305-1700 nm and spectral resolution of 1 nm (CompactSpec, Tec5 Technology, Germany) was used for the spectral measurements at four farms in Flanders, Belgium. A total of 381 fresh soil samples were collected and divided into a calibration set (264) and a validation set (117). The validation samples were processed (air-dried and grind) and scanned with the same spectrophotometer in the laboratory. Three SMC correction methods, namely, external parameter orthogonalization (EPO), piecewise direct standardization (PDS), and orthogonal signal correction (OSC) were used to correct the on-line fresh spectra based-on its corresponding laboratory spectra. Then, the Cubist machine learning method was used to develop calibration models of SOC using the on-line spectra (after correction) of the calibration set. Results indicated that the EPO-Cubist outperformed the PDS-Cubist and the OSC-Cubist, with considerable improvements in the prediction results of SOC (coefficient of determination (R-2) = 0.76, ratio of performance to deviation (RPD) = 2.08, and root mean square error of prediction (RMSEP) = 0.12%), compared with the corresponding uncorrected on-line spectra (R-2 = 0.55, RPD = 1.24, and RMSEP = 0.20%). It can be concluded that SOC can be accurately predicted on-line using the Cubist machine learning method, after removing the negative effect of SMC with the EPO method.
Keywords
General Earth and Planetary Sciences, on-line measurement, VNIR spectroscopy, Cubist, external parameters, spectral correction methods, soil organic carbon, NEAR-INFRARED SPECTROSCOPY, EXTERNAL PARAMETER ORTHOGONALIZATION, IN-SITU, REFLECTANCE SPECTROSCOPY, INORGANIC CARBON, VISNIR SPECTRA, TOTAL NITROGEN, LEAST-SQUARES, CLAY CONTENT, EPO-PLS

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Citation

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MLA
Nawar, Said, et al. “Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect.” REMOTE SENSING, vol. 12, no. 8, 2020, doi:10.3390/rs12081308.
APA
Nawar, S., Munnaf, M. A., & Mouazen, A. (2020). Machine learning based on-line prediction of soil organic carbon after removal of soil moisture effect. REMOTE SENSING, 12(8). https://doi.org/10.3390/rs12081308
Chicago author-date
Nawar, Said, Muhammad Abdul Munnaf, and Abdul Mouazen. 2020. “Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect.” REMOTE SENSING 12 (8). https://doi.org/10.3390/rs12081308.
Chicago author-date (all authors)
Nawar, Said, Muhammad Abdul Munnaf, and Abdul Mouazen. 2020. “Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect.” REMOTE SENSING 12 (8). doi:10.3390/rs12081308.
Vancouver
1.
Nawar S, Munnaf MA, Mouazen A. Machine learning based on-line prediction of soil organic carbon after removal of soil moisture effect. REMOTE SENSING. 2020;12(8).
IEEE
[1]
S. Nawar, M. A. Munnaf, and A. Mouazen, “Machine learning based on-line prediction of soil organic carbon after removal of soil moisture effect,” REMOTE SENSING, vol. 12, no. 8, 2020.
@article{8663948,
  abstract     = {{It is well-documented in the visible and near-infrared reflectance spectroscopy (VNIRS) studies that soil moisture content (SMC) negatively affects the prediction accuracy of soil attributes. This work was undertaken to remove the negative effect of SMC on the on-line prediction of soil organic carbon (SOC). A mobile VNIR spectrophotometer with a spectral range of 305-1700 nm and spectral resolution of 1 nm (CompactSpec, Tec5 Technology, Germany) was used for the spectral measurements at four farms in Flanders, Belgium. A total of 381 fresh soil samples were collected and divided into a calibration set (264) and a validation set (117). The validation samples were processed (air-dried and grind) and scanned with the same spectrophotometer in the laboratory. Three SMC correction methods, namely, external parameter orthogonalization (EPO), piecewise direct standardization (PDS), and orthogonal signal correction (OSC) were used to correct the on-line fresh spectra based-on its corresponding laboratory spectra. Then, the Cubist machine learning method was used to develop calibration models of SOC using the on-line spectra (after correction) of the calibration set. Results indicated that the EPO-Cubist outperformed the PDS-Cubist and the OSC-Cubist, with considerable improvements in the prediction results of SOC (coefficient of determination (R-2) = 0.76, ratio of performance to deviation (RPD) = 2.08, and root mean square error of prediction (RMSEP) = 0.12%), compared with the corresponding uncorrected on-line spectra (R-2 = 0.55, RPD = 1.24, and RMSEP = 0.20%). It can be concluded that SOC can be accurately predicted on-line using the Cubist machine learning method, after removing the negative effect of SMC with the EPO method.}},
  articleno    = {{1308}},
  author       = {{Nawar, Said and Munnaf, Muhammad Abdul and Mouazen, Abdul}},
  issn         = {{2072-4292}},
  journal      = {{REMOTE SENSING}},
  keywords     = {{General Earth and Planetary Sciences,on-line measurement,VNIR spectroscopy,Cubist,external parameters,spectral correction methods,soil organic carbon,NEAR-INFRARED SPECTROSCOPY,EXTERNAL PARAMETER ORTHOGONALIZATION,IN-SITU,REFLECTANCE SPECTROSCOPY,INORGANIC CARBON,VISNIR SPECTRA,TOTAL NITROGEN,LEAST-SQUARES,CLAY CONTENT,EPO-PLS}},
  language     = {{eng}},
  number       = {{8}},
  pages        = {{19}},
  title        = {{Machine learning based on-line prediction of soil organic carbon after removal of soil moisture effect}},
  url          = {{http://doi.org/10.3390/rs12081308}},
  volume       = {{12}},
  year         = {{2020}},
}
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