Vol. 3 No. 1 (2023)
Articles

Vis-NIR Spectroscopy Coupled with Machine Learning Algorithms to Predict and Identify the Key Wavelengths of Soil Gypsum Content in Fars Province, Southern Iran

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  • Monireh Mina,
  • Mahrooz Rezaei,
  • Leila Hossein Abadi ,
  • Abdolmajid Sameni
Monireh Mina
shiraz university
Mahrooz Rezaei
2Wageningen University & Research P.O. Box 47, AA, 6700, Meteorology and Air Quality Department, Wageningen, the Netherlands

Published 2023-03-24

Keywords

  • Key wavelength,
  • PLSR model,
  • Savitzky-Golay filter,
  • Spectral reflectance

How to Cite

Mina, M., Rezaei, M., Hossein Abadi , L. ., & Sameni , A. . (2023). Vis-NIR Spectroscopy Coupled with Machine Learning Algorithms to Predict and Identify the Key Wavelengths of Soil Gypsum Content in Fars Province, Southern Iran. Advanced Geomatics, 3(1), 9–15. Retrieved from https://publish.mersin.edu.tr/index.php/geomatics/article/view/423

Copyright (c) 2023 Advanced Geomatics

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

The use of soil spectral reflectance, which has been introduced as a new method in soil science, is widely used in estimating the physicochemical properties of soil. The purpose behind this research was estimating the amount of gypsum in surface soils of Fars province. Based on random sampling method, 100 soil samples were collected and measured by standard method. Spectral analysis of soil samples was performed using a spectrophotometer between the range of 2500-400 nm. After this stage, various preprocessing methods were evaluated and finally the percentage of soil gypsum was modeled using two models of partial least squares regression (PLSR) and support vector regression (SVR). Our results illustrated that best results for estimating the percentage of soil gypsum are related to the SVR model with Preprocessing Savitzky- Golay Filter with the first derivative. Also, according to RPIQ statistics, the estimation of PLSR model for the percentage of soil gypsum in the weak class is 1.02% and for the SVR model in the moderate class is 1.54%. In the present study, key wavelengths were defined as wavelengths which ranged around 750, 1400, 1570, 1750-1800, 2100, 2200 and 2338 nm and showed the highest correlation with gypsum content in soil.

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