Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect
Open Access
- 20 April 2020
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 12 (8), 1308
- https://doi.org/10.3390/rs12081308
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 (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.Keywords
Funding Information
- FWO (Nr. G0F9216N)
This publication has 57 references indexed in Scilit:
- On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learningSoil and Tillage Research, 2019
- Comparison between Random Forests, Artificial Neural Networks and Gradient Boosted Machines Methods of On-Line Vis-NIR Spectroscopy Measurements of Soil Total Nitrogen and Total CarbonSensors, 2017
- Modeling Soil Organic Carbon Change across Australian Wheat Growing Areas, 1960–2010PLOS ONE, 2013
- Sensing Soil Properties in the Laboratory, In Situ, and On-LinePublished by Elsevier BV ,2012
- Visible and Near Infrared Spectroscopy in Soil SciencePublished by Elsevier BV ,2010
- On-the-go VisNIR: Potential and limitations for mapping soil clay and organic carbonComputers and Electronics in Agriculture, 2009
- Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopyComputers and Electronics in Agriculture, 2008
- On-line measurement of some selected soil properties using a VIS–NIR sensorSoil and Tillage Research, 2007
- Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil propertiesGeoderma, 2005
- Soil Surface Structure Stabilization by Municipal Waste Compost ApplicationSoil Science Society of America Journal, 2001