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ISSN: 1023-5086

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ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

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DOI: 10.17586/1023-5086-2018-85-12-60-68

УДК: 631.421

Comparison of the effectiveness of various ways of preprocessing spectrometric data in order to predict the concentration of organic soil carbon

For Russian citation (Opticheskii Zhurnal):

Чинилин А.В., Савин И.Ю. Сравнение эффективности различных методов предварительной обработки данных спектрометрирования для прогнозирования содержания органического углерода почв // Оптический журнал. 2018. Т. 85. № 12. С. 60–68. http://doi.org/10.17586/1023-5086-2018-85-12-60-68

 

Chinilin A.V., Savin I.Yu. Comparison of the effectiveness of various ways of preprocessing spectrometric data in order to predict the concentration of organic soil carbon [in Russian] // Opticheskii Zhurnal. 2018. V. 85. № 12. P. 60–68. http://doi.org/10.17586/1023-5086-2018-85-12-60-68

For citation (Journal of Optical Technology):

A. V. Chinilin and I. Yu. Savin, "Comparison of the effectiveness of various ways of preprocessing spectrometric data in order to predict the concentration of organic soil carbon," Journal of Optical Technology. 85(12), 789-795 (2018). https://doi.org/10.1364/JOT.85.000789

Abstract:

This paper discusses the effectiveness of using a number of methods of preprocessing spectrometric data in the 325–1075-nm wavelength range in order to predict the concentration of organic soil carbon. Methods of preprocessing spectral data (moving-average filtering, Savitzky–Golay smoothing, calculating the first and second derivatives, and scaling) were consecutively applied to the spectral data of soils (with their natural texture and pulverized) to increase the reliability and effectiveness of the models. According to the criterion of maximizing the determination coefficient and minimizing the rms error as a cross check, the best method of predicting organic soil carbon turned out to be partial-least-squares regression with computation of the first derivatives of the original spectra (Rcv2=0.758RMSEcv=0.492).

Keywords:

soils spectroscopy, spectral reflectance, prediction, regression

Acknowledgements:

These studies were carried out with the support of Grant 18-016-00052 of the Russian Foundation for Basic Research, using the equipment of the Center for Collective Usage: “The functions and properties of soils and soil cover,” V. V. Dokuchaev Soil Institute.

OCIS codes: 280.4788, 300.6190, 300.6340, 300.6490, 300.6550

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