A new three-band spectral and metal element index for estimating soil arsenic content around the mining area

Fu, Pingjie; Yang, Keming; Meng, Fei; Zhang, Wei; Cui, Yu Yan; Feng, Fuping; Yao, Guangchun

doi:10.1016/j.psep.2021.10.028

Cited by 15 publications

(5 citation statements)

References 45 publications

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“…Analysis of the existing results revealed that among the soil spectral pre-processing methods, excellent results were achieved by the derivative processing technique, which effectively eliminated the environmental background interference [ 11 ]. The first-order derivative data of the spectra were used to construct highly accurate three-band spectral indices with the inversion of soil heavy metal concentrations [ 18 ]. First, the collected soil spectra were processed for first-order derivatives.…”

Section: Methodsmentioning

confidence: 99%

“…Basically, the characteristic bands of target elements are obtained by correlation analysis of the target heavy metal elements with spectral transform data and are then used for the estimation of elemental concentrations [ 14 , 15 , 16 , 17 ]. Later, other studies found that the construction of multi-band spectral indices using the characteristic bands of target elements could improve the inversion of soil heavy metal concentrations [ 18 , 19 ]. Peng et al conducted a Pearson’s correlation analysis on the spectra and soil heavy metal concentration, and a total of 13 feature bands were determined for the inversion model [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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

Fu,

Zhang,

Yuan

et al. 2024

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Fu,

Zhang,

Yuan

et al. 2024

Sensors

Self Cite

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“…In addition, the continuous improvement of band selection algorithms and the application of different modeling methods are reported in many research results on soil heavy metal estimation. For example, in a study of arsenic concentration in the soil of an open-pit coal mine, a three-band spectral index was constructed by combining the spectral information and soil clay minerals, which led to the enhancement of arsenic estimation accuracy with the highest correlation coefficient and the lowest RMSE (r = 0.9732, RMSE = 0.0703) [12]. In another study, it was proposed that algorithms such as wavelet transform and random forest can effectively improve the inversion accuracy [13,14], and all of these results show the applicability of hyperspectral technology in the evaluation of soil heavy metal pollution [15,16].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Inversion of Chromium Content in Soils of the Tangshan Iron Tailings Area

Jia,

Gan,

Bai

et al. 2024

Pol. J. Environ. Stud.

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In this paper, a method of heavy metal chromium estimation was proposed, which can be used for soil heavy metal pollution evaluation in Anshan-type iron tailings. The content and spectral characteristics of chromium in 43 field samples were determined, and the optimal characteristic spectrum and model were obtained.Our research shows that SPA selects 18 band points of the full spectrum, RMSEP is 6.2, RPD is 2.5, and the SPA method for heavy metal chromium had the best prediction performance. Moreover, the inversion model obtained via successive projections algorithm-multiple linear regression (SPA-MLR) was found to be the best inversion model. Collectively, the findings of the present study provide a basis for the estimation of soil heavy metal content in the Anshan tailings area.

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“…To further improve the reliability of spectral index extraction bands, using fractional derivatives combined with band combination algorithms effectively mines more soil Cu spectral information [21]. In addition, the three-band metal element index (TSMEI) developed by Fu [22] can better monitor arsenic concentration in soil. However, due to the complexity of soil composition, low Cu concentrations, and weak spectral information, it is still unclear which spectral indices are the most effective for estimating soil Cu.…”

Section: Introductionmentioning

confidence: 99%

Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas

Meng,

Hong,

et al. 2024

Sustainability

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Soil copper (Cu) contamination in mining areas poses a serious threat to the surrounding environment and human health. Timely determination of Cu concentrations is crucial for the ecological protection of mining areas. Hyperspectral remote sensing technology, with its non-destructive monitoring advantages, is essential for monitoring soil Cu pollution and achieving sustainable agricultural development. Using the hyperspectral technique for assessing soil Cu concentration, four machine learning models (support vector regression (SVR), random forest (RF), partial least squares regression (PLSR), and artificial neural network (ANN)), combined with three types of input variables (the full-band, sensitive bands, and optimized spectral indices (Opt-TBIs)) were employed. The hyperspectral reflectance of 647 soil samples from an abandoned tailings mine in western Inner Mongolia, China was collected. The sensitive bands were extracted using the successive projections algorithms (SPA), and 12 Opt-TBIs were selected. Results showed that the regions with higher soil Cu concentration extracted by SPA and Opt-TBIs were concentrated in the red edge and near-infrared regions. Compared with the full spectrum and SPA-sensitive bands, models based on Opt-TBIs successfully predicted soil Cu concentrations. The Opt-TBIs-RF model provided higher accuracy in estimating soil Cu among the four models. Using only four Opt-TBIs as input variables, the model maintained a stable performance in estimating Cu concentrations in different mining areas (R2Val = 0.72, RPDVal = 1.90). In conclusion, Opt-TBIs as input variables demonstrate good predictive capabilities for soil Cu concentrations in the study area, providing a basis for the formulation of sustainable strategies for soil reclamation and environmental protection in Inner Mongolia.

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A new three-band spectral and metal element index for estimating soil arsenic content around the mining area

Cited by 15 publications

References 45 publications

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

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

Hyperspectral Inversion of Chromium Content in Soils of the Tangshan Iron Tailings Area

Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas

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