Next Article in Journal
Effects of Category Aggregation on Land Change Simulation Based on Corine Land Cover Data
Next Article in Special Issue
Soil Organic Carbon Mapping from Remote Sensing: The Effect of Crop Residues
Previous Article in Journal
Automatic Classification of Cotton Root Rot Disease Based on UAV Remote Sensing
Previous Article in Special Issue
Hyperspectral Estimation of Soil Organic Matter Content using Different Spectral Preprocessing Techniques and PLSR Method
Open AccessArticle

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

1
Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
2
Soil and Water Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(8), 1308; https://doi.org/10.3390/rs12081308
Received: 30 March 2020 / Revised: 16 April 2020 / Accepted: 17 April 2020 / Published: 21 April 2020
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. View Full-Text
Keywords: on-line measurement; VNIR spectroscopy; Cubist; external parameters; spectral correction methods; soil organic carbon on-line measurement; VNIR spectroscopy; Cubist; external parameters; spectral correction methods; soil organic carbon
Show Figures

Graphical abstract

MDPI and ACS Style

Nawar, S.; Abdul Munnaf, M.; Mouazen, A.M. Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect. Remote Sens. 2020, 12, 1308.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop