14 September 2021 Estimation of soil copper content in mining area using ZY1-02D satellite hyperspectral data
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Abstract

The content of soil heavy metal elements such as copper (Cu) is a crucial indicator for soil environmental monitoring and evaluation. However, traditional field sampling and laboratory chemical analysis methods are time-consuming and labor-intensive. The newly launched ZY1-02D satellite has a large imaging width, nanometer-level spectral resolution, and almost continuous spectral curve, providing a promising means for large-scale rapid estimation of soil heavy metal content. For the first time, our study explores the application potential of ZY1-02D satellite hyperspectral data in estimating soil Cu content in mining areas. A comprehensive approach combining correlation analysis, feature selection, and a regression model is proposed to predict the soil Cu content. First, the correlations between sampling soil Cu content and spectral features (including dozens of spectral indices and different transformed spectra calculated based on the pixel spectrum) were analyzed. Then the random frog algorithm was chosen to reduce the spectral features’ dimensionality and select the proper predictors. Several regression models of soil Cu content, including Gaussian process regression (GPR), boosted trees, bagged trees, and support vector machine, were established using these predictors. The model comparison shows that the GPR model has the best agreement with the sampling data. Furthermore, the accuracy of the GPR model with different numbers of predictors was also analyzed. The results show that the proposed estimation approach based on correlation analysis, random frog algorithm, and GPR model has a good performance compared to sampling data (R2  =  0.83, adjusted R2  =  0.81, RMSE  =  364.61  mg  /  kg, and RPD  =  1.83). And the results further indicate the great potential of AHSI/ZY1-02D hyperspectral data in predicting soil Cu content in mining areas.

© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2021/$28.00 © 2021 SPIE
Kun Shang, Chenchao Xiao, Fuping Gan, Hongyan Wei, and Changkun Wang "Estimation of soil copper content in mining area using ZY1-02D satellite hyperspectral data," Journal of Applied Remote Sensing 15(4), 042607 (14 September 2021). https://doi.org/10.1117/1.JRS.15.042607
Received: 1 June 2021; Accepted: 20 August 2021; Published: 14 September 2021
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Cited by 11 scholarly publications.
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KEYWORDS
Copper

Soil science

Data modeling

Satellites

Mining

Metals

General packet radio service

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