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. 2025 Jul 31;30(15):3215.
doi: 10.3390/molecules30153215.

A Rapid Intelligent Screening of a Three-Band Index for Estimating Soil Copper Content

Shiyao Liu  1   2   3 , Shichao Cui  1   2   3 , Rengui Wang  1   2   3 , Minming Han  1   2   3 , Jingtao Kou  1   2   3
Affiliations

A Rapid Intelligent Screening of a Three-Band Index for Estimating Soil Copper Content

Shiyao Liu et al. Molecules. .

Abstract

Research has widely validated three-band spectral index as a simple, valid, and highly accurate method of estimating the copper content of soil. However, selecting the best band combination from hundreds of thousands, even millions of candidate combinations in hyperspectral data, is a very complicated problem. To address this issue, this study collected a total of 170 soil samples from the Aktas copper-gold mining area in Fuyun County, Xinjiang, China. Then, two algorithms including Competitive Weighted Resampling (CARS) and Stepwise Regression Analysis (STE) were applied to pick the bands from the original and first-order derivative spectra, respectively. A three-band index model was developed using the selected feature bands to estimate soil copper content. Results showed the first-order derivative spectrum transforms the spectral curve into a sharper one, with more peaks and valleys, which is beneficial for increasing the correlation between bands and copper content compared with the original spectrum. Moreover, integrating first-order derivative spectroscopy with CARS makes it possible to precisely identify key spectral bands and outperforms the dimensionality-reduction capabilities compared with the integration of STE. This strategy drastically reduces the time spent screening and is proven to have similar model accuracy, as compared to the individual group lifting method. Specifically, it reduces the duration of an 8 h task down to a mere 2 s. An intelligent screening of three-band indices is proposed in this study as a method of rapidly estimating copper content in soil.

Keywords: CARS; copper content; estimation model; three-band spectral index.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.

Figures

Figure 1
Figure 1
Spatial distribution map of sampling points.
Figure 2
Figure 2
Mean and standard deviation of reflectance for the 170 soil samples after preprocessing. The black line represents the average reflectance of collected samples, and the pink area represents the standard deviation.
Figure 3
Figure 3
Statistical histogram of copper content in soil samples: (a) training set; (b) validation set.
Figure 4
Figure 4
Correlation coefficients between different spectra and copper content.
Figure 5
Figure 5
The performance comparison of the quantitative estimation model for copper content, which is constructed by selecting characteristic spectral bands through CARS to develop a three-band index: (a) original spectrum and TVI-1; (b) original spectrum and TVI-2; (c) original spectrum and TVI-3; (d) first-order derivative spectrum and TVI-1; (e) first-order derivative spectrum and TVI-2; (f) first-order derivative spectrum and TVI-3. The black scatter points represent paired data points of measured and estimated values, the black solid line is the fitting line between the two, and the red solid line is the 1:1 reference line.
Figure 6
Figure 6
The performance comparison of the quantitative estimation model for copper content, which is constructed by selecting characteristic spectral bands through STE to develop a three-band index: (a) original spectrum and TVI-1; (b) original spectrum and TVI-2; (c) original spectrum and TVI-3; (d) first-order derivative spectrum and TVI-1; (e) first-order derivative spectrum and TVI-2; (f) first-order derivative spectrum and TVI-3. The black scatter points represent paired data points of measured and estimated values, the black solid line is the fitting line between the two, and the red solid line is the 1:1 reference line.
Figure 7
Figure 7
Comparison of the accuracy of copper content quantitative estimation models: between original and first-order derivative spectra, and between CARS and STE methods.
See this image and copyright information in PMC

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