Spectral response feature bands extracted from near standard soil samples for estimating soil Pb in a mining area

Zou, Bin; Zou, Bin; Tu, Yulong; Feng, Huihui; He, Chencheng; Ma, Xuying; Ning, Jing

doi:10.1080/10106049.2022.2076921

Cited by 13 publications

(8 citation statements)

References 52 publications

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“…The maximum correlation and response wavelengths for the concentration of soil heavy metals and different spectral are showed in Table 3 . Results showed that spectral transformation effectively improves the spectral response of soil heavy metals, especially the second derivative spectral, which might help extract relevant information for the rare components [ 58 ]. After the continuum removed treatment, the soil spectral absorption characteristics were highlighted, and the absolute values of the maximum correlation coefficients of Hg and Cu with the spectra in visible and near-infrared band ranges reached to 0.827 and 0.688, respectively.…”

Section: Resultsmentioning

confidence: 99%

Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods

Yang,

Han,

Liu

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods

Yang,

Han,

Liu

2023

Heliyon

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“…On the one hand, estimating Zn content in soils using hyperspectral remote sensing is a cost-efficient method but challenging due to the effects of natural environmental conditions and soil properties [27] . On the other hand, high-data dimensionality is a common problem in hyperspectral data processing, so the inversion accuracy of the constructed model is biased by redundant spectra and noise [23,28] .…”

Section: Discussion Of Optimal Prediction Modelsmentioning

confidence: 99%

Hyperspectral Inversion and Analysis of Zinc Concentration in Urban Soil in the Urumqi City of China

Zhong,

Eziz,

Ainiwaer

et al. 2023

j. of environ. & earth. sci.

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Excessive accumulation of zinc (Zn) in urban soil can lead to environmental pollution and pose a potential threat to human health and the ecosystem. How to quickly and accurately monitor the urban soil zinc content on a large scale in real time and dynamically is crucial. Hyperspectral remote sensing technology provides a new method for rapid and nondestructive soil property detection. The main goal of this study is to find an optimal combination of spectral transformation and a hyperspectral estimation model to predict the Zn content in urban soil. A total of 88 soil samples were collected to obtain the Zn contents and related hyperspectral data, and perform 18 transformations on the original spectral data. Then, select important wavelengths by Pearson's correlation coefficient analysis (PCC) and CARS. Finally, establish a partial least squares regression model (PLSR) and random forest regression model (RFR) with soil Zn content and important wavelengths. The results indicated that the average Zn content of the collected soil samples is 60.88 mg/kg. Pearson's correlation coefficient analysis (PCC) and CARS for the original and transformed wavelengths can effectively improve the correlations between the spectral data and soil Zn content. The number of important wavelengths selected by CARS is less than the important wavelengths selected by PCC. Partial least squares regression model based on first-order differentiation of the reciprocal by CARS (CARS-RTFD-PLSR) is more stable and has the highest prediction ability (R2 = 0.937, RMSE = 8.914, MAE = 2.735, RPD = 3.985). The CARS-RTFD-PLSR method can be used as a means of prediction of Zn content in soil in oasis cities. The results of the study can provide technical support for the hyperspectral estimation of the soil Zn content.

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“…The hyperspectral inversion of soil Ni content is influenced by two aspects; on the one hand, estimating Ni content in soil using hyperspectral remote sensing is a cost-efficient method, but challenging due to the effects of natural environmental conditions and soil properties [41]. On the other hand, high-data dimensionality is a common problem in hyperspectral data processing [32], so the inversion accuracy of the constructed model is biased by redundant spectra and noise [42]. Wang et al [40] have pointed out that spectral transformation is an effective approach for identifying the highly correlated spectral bands.…”

Section: Discussion Of Optimal Prediction Modelsmentioning

confidence: 99%

Application of a Hyperspectral Remote Sensing Model for the Inversion of Nickel Content in Urban Soil

Zhong,

Eziz,

Sawut

et al. 2023

Sustainability

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Hyperspectral remote sensing technology can provide a rapid and nondestructive method for soil nickel (Ni) content detection. In order to select a high-effective method for estimating the soil Ni content using a hyperspectral remote sensing technique, 88 soil samples were collected in Urumqi, northwest China, to obtain Ni contents and related hyperspectral data. At first, 12 spectral transformations were used for the original spectral data. Then, Pearson’s correlation coefficient analysis (PCC) and the CARS method were used for selecting important wavelengths. Finally, partial least squares regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) models were used to establish the hyperspectral inversion models of the Ni content in the soil using the important wavelengths. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were selected to evaluate the inversion effects of the models. The results indicated that using the PCC and CARS method for the original and transformed wavebands can effectively improve the correlations between the spectral data and Ni content of the soil in the study area. The random forest regression model, based on the first-order differentiation of the reciprocal (RTFD–RFR), was more stable and had the best inversion effects, with the highest predictive ability (R2 = 0.866, RMSE = 1.321, MAE = 0.986, RPD = 2.210) for determining the Ni content in the soil. The RTFD–RFR methods can be used as a means of the inversion of the Ni content in urban soil. The results of the study can provide a technical support for the hyperspectral estimation of the Ni content of urban soil.

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Spectral response feature bands extracted from near standard soil samples for estimating soil Pb in a mining area

Cited by 13 publications

References 52 publications

Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods

Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods

Hyperspectral Inversion and Analysis of Zinc Concentration in Urban Soil in the Urumqi City of China

Application of a Hyperspectral Remote Sensing Model for the Inversion of Nickel Content in Urban Soil

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