Optimal sample selection for measurement of soil organic carbon using on-line vis-NIR spectroscopy

2018 ◽  
Vol 151 ◽  
pp. 469-477 ◽  
Author(s):  
Said Nawar ◽  
Abdul M. Mouazen
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sample Selection ◽  
Nir Spectroscopy ◽  
Optimal Sample ◽  
On Line ◽  
Selection For

Strategies for the efficient estimation of soil organic carbon at the field scale with vis-NIR spectroscopy: Spectral libraries and spiking vs. local calibrations

Geoderma ◽  
2019 ◽  
Vol 354 ◽  
pp. 113856 ◽  
Author(s):  
Michael Seidel ◽  
Christopher Hutengs ◽  
Bernard Ludwig ◽  
Sören Thiele-Bruhn ◽  
Michael Vohland
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Nir Spectroscopy ◽  
Efficient Estimation ◽  
Field Scale ◽  
Spectral Libraries

scholarly journals Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

2014 ◽  
Vol 6 (4) ◽  
pp. 2699-2717 ◽  
Author(s):  
Xiaoting Peng ◽  
Tiezhu Shi ◽  
Aihong Song ◽  
Yiyun Chen ◽  
Wenxiu Gao
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Nir Spectroscopy

scholarly journals Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect

2020 ◽  
Vol 12 (8) ◽  
pp. 1308 ◽  
Author(s):  
Said Nawar ◽  
Muhammad Abdul Munnaf ◽  
Abdul Mounem Mouazen
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
External Parameter ◽  
Machine Learning Method ◽  
Learning Method ◽  
Orthogonal Signal Correction ◽  
Negative Effect ◽  
On Line

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 (R2) = 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 (R2 = 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.

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