Hyperspectral Estimation of Heavy Metal Pb Concentration in Vineyard Soil in Turpan Basin

doi:10.11873/j.issn.1004-0323.2021.2.0362

Remote Sensing Technology and Application

2021, Vol. 36

Issue (2): 362-371 DOI: 10.11873/j.issn.1004-0323.2021.2.0362

Hyperspectral Estimation of Heavy Metal Pb Concentration in Vineyard Soil in Turpan Basin

Aynur Matnuri¹(

),Mamattursun Eziz^1,²(

),Marhaba Turgun¹,Xinguo Li^1,²

^1.College of Geographical Science and Tourism，Xinjiang Normal University，Urumqi 830054，China
^2.Xinjiang Laboratory of Arid Zone Lake Environment and Resources，Xinjiang Normal University，Urumqi 830054，China

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Abstract

As a product of the development of modern industry and mining， soil heavy metal lead pollution has gradually invaded agricultural production and agricultural products. Hyperspectral technology has become an important method for monitoring heavy metals in soil due to its macroscopic， rapid and efficient characteristics. This study takes the Pb element of vineyard soil in Xinjiang Turpan Basin as the research object， analyzes the relationship between soil spectral reflectance data and soil Pb content under 15 spectral transformations including the original soil spectrum， and constructs a partial least square regression of soil Pb content （ PLSR） model and geographic weighted re-regression （GWR） model， comparative analysis and discussion of the feasibility of using soil hyperspectral to estimate the vineyard soil Pb content. The results show that the original spectral reflectance of the soil can effectively enhance the spectral characteristics of the vineyard soil Pb element and the estimation accuracy of the model through spectral transformation. Among them， the SRSD transformation PLSR model and GWR model have the best estimation capabilities. The GWR model is better than the PLSR model to explain the hyperspectral estimation of the heavy metal Pb content in vineyard soil. From the perspective of model stability and accuracy， in the SRSD differential transformation， the GWR model R² is increased from 0.262 of the PLSR model to 0.866， and the RMSE is reduced by 1.009. Using GWR model can effectively improve the accuracy of estimating the Pb content of vineyard soil. This study provides a useful reference for the study of soil heavy metal pollution and soil environmental safety in Chinese vineyard bases.

Key words: Soil Heavy metal Pb Hyperspectral Spectral transformation Turpan Basin

Received: 15 August 2020 Published: 24 May 2021

ZTFLH:

TP79

Corresponding Authors: Mamattursun Eziz E-mail: AynurER@126.com;oasiseco@126.com

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Cite this article:

Aynur Matnuri,Mamattursun Eziz,Marhaba Turgun,Xinguo Li. Hyperspectral Estimation of Heavy Metal Pb Concentration in Vineyard Soil in Turpan Basin. Remote Sensing Technology and Application, 2021, 36(2): 362-371.

URL:

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2021.2.0362 OR http://www.rsta.ac.cn/EN/Y2021/V36/I2/362

Fig.1 Location of the research area and sampling sites

Fig. 2 Reflectance spectra of soil samples in vineyard soils in Turpan Basin

Fig.3 Correlations between Pb concentration and its soil reflectance of vineyard soils in Turpan Basin

Table 1 Characteristic ban and estimation model for Pb concentration under different spectral transformation

Fig.4 Validation of Pb concentration of vineyard soil based on PLSR model

Table 2 Prediction accuracy of the GWR regression model for heavy metal in vineyard soil

Fig.5 Validation of Pb concentration of vineyard soil based on GWR model

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