Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

Abstract

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area.

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

“…The application of the derivative method in VIS-NIR spectra is able to eliminate background noises, sharpen spectral features, improve the spectral resolution of overlapped peaks and extract important spectral information [13,54,55]. Generally, 1-order derivative stands for the slope of the reflectance spectra, and 2-order derivative stands for the curvature of the reflectance spectra [56].…”

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy

“…SOC was one of most important controlling components of soil spectral features. With SOC content of 2% as a boundary, that is, when SOM content exceeded 2%, the SOC played a principal role in masking out the spectral features, while the SOC content was less than 2%, it became less effective ( Wang et al, 2017 ). Many studies have used VIS/NIR spectroscopy to study, estimate and monitor SOC content, but mainly on Alfisols, Entisols, Ultisols ( Chang et al, 2001 ; Summers et al, 2011 ; Vasques, Grunwald & Harris, 2010 ), and Mollisols ( Araújo et al, 2015 ; Hong et al, 2018 ).…”

Peer Review #2 of "Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy (v0.1)"

“…It is imperative to develop SOC was one of most important controlling components of soil spectral features. With SOC content of 2% as a boundary, that is, when SOM content exceeded 2%, the SOC played a principal role in masking out the spectral features, while the SOC content was less than 2%, it became less effective (Wang et al 2017). Many studies have used VIS/NIR spectroscopy to study, estimate and monitor SOC content, but mainly on Alfisols, Entisols, Ultisols (Chang et al 2001;Summers et al 2011;Vasques et al 2010), and Mollisols (Araújo et al 2015;Hong et al 2017).…”