Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges

Wang, Fenghe; Gao, Jay; Zha, Yong

doi:10.1016/j.isprsjprs.2017.12.003

Cited by 200 publications

(127 citation statements)

References 56 publications

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“…The soil heavy metals with low content are often difficult to directly estimate using soil spectral features. However, soil heavy metals, often absorbed or bounded, are characterized by spectrally active constituents depending on environmental conditions, which make it possible to estimate their contents and derive their spatial distributions using spectral variables from remote sensing data, especially hyperspectral data [49,50]. Previous studies have shown the availability for predicting soil heavy metal content by spectroscopic reflectance [49,50].…”

Section: Discussionmentioning

confidence: 99%

“…However, soil heavy metals, often absorbed or bounded, are characterized by spectrally active constituents depending on environmental conditions, which make it possible to estimate their contents and derive their spatial distributions using spectral variables from remote sensing data, especially hyperspectral data [49,50]. Previous studies have shown the availability for predicting soil heavy metal content by spectroscopic reflectance [49,50]. However, how to select the spectral variables that significantly contribute to the reduction of model fitting errors and increase of estimation accuracy but are not correlated with each other is critically important [15].…”

Section: Discussionmentioning

confidence: 99%

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Estimation of Soil Heavy Metal Content Using Hyperspectral Data

et al. 2019

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Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the Boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sample level and 51.55% and 36.34% at the regional scale. Moreover, the relationship of the DSSR/MSSR ratio with soil moisture content varied greatly before the wavelength of 1029 nm and became stable after that, which linked DSSR with MSSR and provided the possibility of applying the DSSR-based models to map the soil heavy metal content at the regional scale using the HJ-1A images. In addition, it was found that overall there were only a few soil samples with the content of heavy metals exceeding the health standards in Guangdong province, while in Conghua the seriously polluted areas were mainly distributed in the cities and of 26 croplands. This study implies that the new approach provides the potential to map the content of heavy metals in soil, but the estimation model of Cd was more accurate than those of As and Hg.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

et al. 2019

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“…Pure heavy metal elements have no direct absorption features in the infrared spectral region [10,30]. In general, heavy metal elements can be retained within a particular chemical or oxidizing environment.…”

Section: Introductionmentioning

confidence: 99%

“…The organic components form stable metal-organic complexes with a variety of metals, while clay minerals and oxides concentrate heavy metal ions through surface ion exchange and metal-complex surface adsorption [32]. Therefore, the prediction of the heavy metal content of soils based on the spectroscopic approach is made by indirect measurements of the heavy metals adsorbed on soil-constituting materials such as Fe-oxide, organic matter, and clay minerals, [5,10,15,30,[35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Spectral Responses of As and Pb Contamination in Tailings of a Hydrothermal Ore Deposit: A Case Study of Samgwang Mine, South Korea

Jeong

Wang

et al. 2018

Remote Sensing

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We analyzed chemical composition, mineralogy, and spectral characteristics of the tailings of a hydrothermal gold mine in South Korea. We measured spectral responses of tailings to arsenic (As) and lead (Pb) concentration and developed and validated a prediction model for As and Pb in the tailings. The tailing was heavily contaminated with heavy metal elements and composed of rock forming minerals, gangue minerals and hydrothermal alteration minerals. The spectral features of the tailing were closely related to hydrothermal alteration minerals. The spectral responses associated with As and Pb concentrations were detected in shortwave infrared (SWIR) region at absorption positions of the hydrothermal alteration minerals. The prediction models were constructed using spectral bands of absorption features of the hydrothermal alteration minerals and were statistically significant. We found distinctive differences in spectral characteristics and spectral response to heavy metal contamination between the tailings and soils in the mining area. While the spectral signals to heavy metal concentration of tailings were associated with the hydrothermal alteration minerals, those of soils in mining area were manifested by clay minerals originated from weathering processes. This infers that geological processes associated with formation of soils and tailings are the major controlling factors of spectral responses to heavy metal contamination. This study provides a rare reference for the estimation of As and Pb concentration in the tailings with similar types of ore deposit and host rock.

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“…To solve this problem, downsampling is applied to band 8 of every single date imagery to achieve the spatial resolution of 30m. PLSR is a particular form of multivariate linear regression (Wang et al, 2018),which is the most common method used in soil properties prediction (Pinheiro et al, 2017). PLSR is underpinned by the assumption that the dependent variable can be estimated via a linear combination of explanatory variables.The maximum number of latent variables in PLSR is set at 20 and the optimum number of latent variables are determined by 5-fold cross-validation.…”

Section: Satellite Datamentioning

confidence: 99%

Retrieval and Mapping of Heavy Metal Concentration in Soil Using Time Series Landsat 8 Imagery

Fang

Peng

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Heavy metal pollution is a critical global environmental problem which has always been a concern. Traditional approach to obtain heavy metal concentration relying on field sampling and lab testing is expensive and time consuming. Although many related studies use spectrometers data to build relational model between heavy metal concentration and spectra information, and then use the model to perform prediction using the hyperspectral imagery, this manner can hardly quickly and accurately map soil metal concentration of an area due to the discrepancies between spectrometers data and remote sensing imagery. Taking the advantage of easy accessibility of Landsat 8 data, this study utilizes Landsat 8 imagery to retrieve soil Cu concentration and mapping its distribution in the study area. To enlarge the spectral information for more accurate retrieval and mapping, 11 single date Landsat 8 imagery from 2013-2017 are selected to form a time series imagery. Three regression methods, partial least square regression (PLSR), artificial neural network (ANN) and support vector regression (SVR) are used to model construction. By comparing these models unbiasedly, the best model are selected to mapping Cu concentration distribution. The produced distribution map shows a good spatial autocorrelation and consistency with the mining area locations.

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Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges

Cited by 200 publications

References 56 publications

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Spectral Responses of As and Pb Contamination in Tailings of a Hydrothermal Ore Deposit: A Case Study of Samgwang Mine, South Korea

Retrieval and Mapping of Heavy Metal Concentration in Soil Using Time Series Landsat 8 Imagery

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