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. 2024 Feb 25;24(5):1492.
doi: 10.3390/s24051492.

Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

Pingjie Fu  1 , Jiawei Zhang  1   2 , Zhaoxian Yuan  3 , Jianfei Feng  1 , Yuxuan Zhang  1 , Fei Meng  1 , Shubin Zhou  4   5
Affiliations

Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

Pingjie Fu et al. Sensors (Basel). .

Abstract

In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.

Keywords: Catboost; entisols; heavy metals; hyperspectral remote sensing; spectral index.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Location of the study area and sampling site distribution.
Figure 2
Figure 2
Flowchart of the analysis process.
Figure 3
Figure 3
Preferred combination of spectral indices for heavy metal elements based on RCBP.
Figure 4
Figure 4
Schematic diagram of Catboost algorithm.
Figure 5
Figure 5
Radar maps of correlation coefficients between the characteristic bands of the target elements and other elements. (a) Target element = Pb, (b) Target element = As, (c) Target element = Zn, (d) Target element = Mn.
Figure 5
Figure 5
Radar maps of correlation coefficients between the characteristic bands of the target elements and other elements. (a) Target element = Pb, (b) Target element = As, (c) Target element = Zn, (d) Target element = Mn.
Figure 6
Figure 6
Comparison of the measured concentrations and those estimated by the Catboost algorithm for each heavy metal in the test set (As: p-value < 0.05; Mn: p-value < 0.01; Pb: p-value < 0.05; Zn: p-value < 0.05).
Figure 7
Figure 7
Pearson correlation matrix of elemental concentrations (Fe, Pb, As, Zn, and Mn) in soils from entisols in a metal mining-impacted area in China.
Figure 8
Figure 8
Correlations between characteristic bands of Fe and those of Pb, As, Zn, and Mn based on different spectral index combinations.
Figure 9
Figure 9
Spatial distributions of soil heavy metal contents (measured and predicted values).
Figure 10
Figure 10
Spatial distributions of clusters of predicted and real concentrations of the four soil heavy metals.
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