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Research article - Peer-reviewed, 2015

Using visible and near infrared spectroscopy to estimate carbonates and gypsum in soils of arid and subhumid regions of Isfahan, Iran

Khayamim, Fatemeh; Wetterlind, Johanna; Khademi, Hossein; Robertson, A. H. J.; Faz Cano, Angel; Stenberg, Bo

Abstract

Soils in arid and semi-arid regions are strongly affected by the accumulation of carbonates, gypsum and other, more soluble, salts. Carbonates and gypsum both have a considerable influence on soil properties, especially the chemical properties of the soil solution. The development of reliable, fast and inexpensive methods to quantify the amounts of carbonates and gypsum in soil is therefore important. Visible and near infrared (vis-NIR) spectroscopy is a non-destructive, rapid and cheap method for measuring several soil properties simultaneously. However, research on vis-NIR spectroscopy in quantifying carbonates and gypsum is limited. Therefore, this study evaluated the efficiency of vis-NIR spectroscopy in quantifying carbonates and gypsum in surface soils using partial least-squares regression (PLSR) compared with standard laboratory methods and compared PLSR with a feature-specific method using continuum removal (CR). Carbonates and gypsum in a total of 251 sieved and air-dried topsoil samples from Isfahan Province in central Iran were measured by standard laboratory methods and vis-NIR spectroscopy (350-2500nm wavelength range). In parallel, PLSR and the feature-specific method based on CR spectra were used to predict carbonates and gypsum. The PLSR model efficiency (E) for carbonates and gypsum in the validation set was 0.52 and 0.80, respectively. The PLSR model resulted in better predictions than the feature-specific method for both soil properties. Because of the unique absorption features of gypsum, which did not overlap with other soil properties, predictions of gypsum resulted in higher Evalues and lower errors than predictions of carbonates.

Keywords

gypsum; carbonates; vis-NIR spectroscopy; continuum removal; partial least-squares regression (PLSR)

Published in

Journal of Near Infrared Spectroscopy
2015, Volume: 23, number: 3, pages: 155-165