Application of near-infrared reflectance for quantitative assessment of soil properties

Mohamed, Elsayed Said; Saleh, Ahmed M.; Belal, Abdel-Aziz; Gad, A.

doi:10.1016/j.ejrs.2017.02.001

Cited by 94 publications

(65 citation statements)

References 53 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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“…This spectrum encodes information able to provide information to derive qualitative and quantitative information of soil properties [17]. VNIR-SWIR spectroscopy is based on characteristic vibrations of chemical bonds in molecules [18]. Particularly, in the visible region (400-700 nm) the electronic transitions generate wide absorption bands related to chromophores that affect soil colour, while in the NIR-SWIR (700-2500 nm) weak overtones and combinations of these vibrations occur due to stretching and bending of the N-H, O-H, and C-H bonds [19,20].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

et al. 2019

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Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end.

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“…The spectroscopic test provides valuable information on the spectral responses associated with heavy metal contamination, and thus, the test results give a band combination and spectral signatures useful for remote sensing approaches. The visible-near infrared-shortwave infrared (VNIR-SWIR) spectral ranges (350 to 2500 nm) have been used to study the physicochemical properties of mineral objects and the associated environmental changes [12,[16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

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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Application of near-infrared reflectance for quantitative assessment of soil properties

Cited by 94 publications

References 53 publications

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

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

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