Assessment of heavy metal pollution in agricultural soil around a gold mining area in Yitong County, China, based on satellite hyperspectral imagery

Wu, Fuyu; Wang, Xue; Liu, Zhaoxian; Ding, Jianwei; Tan, Kun; Chen, Yu

doi:10.1117/1.jrs.15.042613

Cited by 4 publications

(3 citation statements)

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“…For example, clouds may change the illumination regime from directed illumination to diffuse illumination conditions and affect hyperspectral measurements [ 27 ]. By using statistical models and machine learning algorithms to study the correlation between soil heavy metal element concentrations and hyperspectral reflectance data, a commendable level of accuracy in estimating soil heavy metal content can also be achieved based on laboratory hyperspectral data [ 28 ], airborne hyperspectral imagery [ 29 ], and spaceborne hyperspectral imagery [ 28 , 30 ]. The airborne hyperspectral imagery can offer finer details [ 29 ], laboratory hyperspectral data is less susceptible to interference and can provide higher accuracy [ 28 ], and spaceborne hyperspectral imagery is more suitable for cost-effective large-scale pollution assessments [ 28 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

“…By using statistical models and machine learning algorithms to study the correlation between soil heavy metal element concentrations and hyperspectral reflectance data, a commendable level of accuracy in estimating soil heavy metal content can also be achieved based on laboratory hyperspectral data [ 28 ], airborne hyperspectral imagery [ 29 ], and spaceborne hyperspectral imagery [ 28 , 30 ]. The airborne hyperspectral imagery can offer finer details [ 29 ], laboratory hyperspectral data is less susceptible to interference and can provide higher accuracy [ 28 ], and spaceborne hyperspectral imagery is more suitable for cost-effective large-scale pollution assessments [ 28 , 30 ]. However, regarding the problem of redundant and unstable spectral features due to interaction effects among soil elements, the optimisation of different elemental feature bands or spectral indices is not often considered in the inversion of heavy metal concentrations.…”

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: Introductionmentioning

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

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“…This method has high accuracy, but it takes a long time and costs a lot. Now, the development of hyperspectral technology provides technical support for nondestructive, real-time, broad-scale monitoring of the Cd content in crops [18]. Only the spectral data of plant leaves are needed, and the Cd content model can be established according to sensitive bands or a spectral index [19].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Cadmium Content in the Leaves of Field Pepper and Eggplant in a Karst Area Using Hyperspectral Remote Sensing Data

Wen

Lan

et al. 2023

Sustainability

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The ability to quickly and non-destructively monitor the cadmium (Cd) content in agricultural crops is the basic premise of effective prevention and control of Cd contamination in agricultural products. Hyperspectral technology provides a solution for this issue. The potential capability for the spectral prediction of the Cd content in the leaves of pepper and eggplant in the field was explored, and a spectral prediction model of the Cd content in these leaves was established. In this study, based on the indoor spectrum, the sensitive wavebands for predicting the Cd content in leaves were determined preliminarily by correlation analysis. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were used to establish spectral prediction models, and the final sensitive wavebands were determined by the size of the model index. The results show that the SVMR model exhibited higher prediction accuracy than the PLSR model. The RPDp (relative percent different of prediction set) values of the best SVMR prediction models for the pepper leaves and the eggplant leaves were 1.82 and 1.49, respectively. The values of Rp2 (coefficient of determination of prediction set), which can quantitatively estimate the Cd content in leaves, were 0.897 (p < 0.01) and 0.726 (p < 0.01), respectively. This study demonstrated that the leaf spectra of pepper and eggplant in the field can be used to predict the Cd content in leaves, providing a reference for monitoring the Cd content in the fruits of pepper and eggplant in the future.

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Estimation of the distribution patterns of heavy metal in soil from airborne hyperspectral imagery based on spectral absorption characteristics

Tan,

Chen,

Wang

et al. 2023

Journal of Environmental Management

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Assessment of heavy metal pollution in agricultural soil around a gold mining area in Yitong County, China, based on satellite hyperspectral imagery

Cited by 4 publications

References 0 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

Monitoring Cadmium Content in the Leaves of Field Pepper and Eggplant in a Karst Area Using Hyperspectral Remote Sensing Data

Estimation of the distribution patterns of heavy metal in soil from airborne hyperspectral imagery based on spectral absorption characteristics

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