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Appl. Sci. 2017, 7(7), 708; doi:10.3390/app7070708

Potential Model Overfitting in Predicting Soil Carbon Content by Visible and Near-Infrared Spectroscopy

1
Department of Soils and Natural Resources, Faculty of Agronomy, Universidad de Concepción,Vicente Méndez 595, Casilla 537, Chillán 3812120, Chile
2
Doctoral Program in Agronomic Sciences, Faculty of Agronomy, Universidad de Concepción,Vicente Méndez 595, Casilla 537, Chillán 3812120, Chile
3
Department of Silviculture, Faculty of Forest Sciences, Universidad de Concepción, Victoria 631,Casilla 160-C, Concepción 4030000, Chile
Current address: Facultad de Ingeniería Agrícola, Universidad Técnica de Manabí, Casilla 82, Lodana, Manabí, Ecuador.
*
Author to whom correspondence should be addressed.
Received: 13 June 2017 / Revised: 5 July 2017 / Accepted: 5 July 2017 / Published: 8 July 2017
(This article belongs to the Section Optics and Lasers)
View Full-Text   |   Download PDF [1462 KB, uploaded 19 July 2017]   |  

Abstract

Soil spectroscopy is known as a rapid and cost-effective method for predicting soil properties from spectral data. The objective of this work was to build a statistical model to predict soil carbon content from spectral data by partial least squares regression using a limited number of soil samples. Soil samples were collected from two soil orders (Andisol and Ultisol), where the dominant land cover is native Nothofagus forest. Total carbon was analyzed in the laboratory and samples were scanned using a spectroradiometer. We found evidence that the reflectance was influenced by soil carbon content, which is consistent with the literature. However, the reflectance was not useful for building an appropriate regression model. Thus, we report here intriguing results obtained in the calibration process that can be confusing and misinterpreted. For instance, using the Savitzky–Golay filter for pre-processing spectral data, we obtained R2 = 0.82 and root-mean-squared error (RMSE) = 0.61% in model calibration. However, despite these values being comparable with those of other similar studies, in the cross-validation procedure, the data showed an unusual behavior that leads to the conclusion that the model overfits the data. This indicates that the model should not be used on unobserved data. View Full-Text
Keywords: chemometrics; SOC; spectral diffuse reflectance; partial least squares regression; cross-validation chemometrics; SOC; spectral diffuse reflectance; partial least squares regression; cross-validation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Reyna, L.; Dube, F.; Barrera, J.A.; Zagal, E. Potential Model Overfitting in Predicting Soil Carbon Content by Visible and Near-Infrared Spectroscopy. Appl. Sci. 2017, 7, 708.

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