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Soil copper concentration map in mining area generated from AHSI remote sensing imagery.

Weichao Sun1, Shuo Liu1, Mengfei Wang2

  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China.

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Summary

Hyperspectral remote sensing effectively maps soil heavy metal (copper) concentration. A new variable weighting method improves prediction accuracy and eliminates abnormal values in bare soil mapping.

Keywords:
Abnormal prediction valuesGF-5 AHSI imageryHeavy metal concentration in soilHyperspectral remote sensingVariable weighting

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Area of Science:

  • Environmental Science
  • Geoscience
  • Remote Sensing

Background:

  • Hyperspectral remote sensing offers advantages for mapping soil heavy metal concentrations compared to traditional methods.
  • Accurate quantitative analysis using hyperspectral data is challenged by varying band contributions and misclassified bare soil, leading to abnormal prediction values.
  • Soil heavy metal pollution, particularly from mining activities, poses significant environmental risks requiring effective monitoring solutions.

Purpose of the Study:

  • To develop and validate a variable weighting method for hyperspectral bands to improve the accuracy of soil heavy metal concentration prediction.
  • To mitigate abnormal prediction values in quantitative hyperspectral remote sensing by enhancing bare soil image classification.
  • To generate a soil copper concentration map using satellite-based hyperspectral imagery and advanced modeling techniques.

Main Methods:

  • A variable weighting method utilizing absorption depths from continuum removal was applied to hyperspectral bands.
  • A probability threshold was employed to refine bare soil image classification.
  • Genetic algorithm and partial least squares regression (PLSR) were used for model calibration and prediction of soil copper (Cu) concentration.
  • Advanced Hyperspectral Imager (AHSI) data from the Geofen-5 (GF-5) satellite were utilized.

Main Results:

  • The variable weighting method improved the prediction of soil copper concentration, reducing the root mean square error (RMSE) from 21.59 mg kg⁻¹ to 18.33 mg kg⁻¹ and increasing the coefficient of determination (R²) from 0.60 to 0.71.
  • The improved bare soil classification successfully eliminated negative prediction values in the copper concentration map derived from AHSI imagery.
  • Kriging spatial interpolation was used to generate a detailed soil copper concentration map.

Conclusions:

  • The proposed variable weighting method is effective for enhancing hyperspectral band contributions in quantitative soil heavy metal prediction.
  • Improved bare soil image classification significantly mitigates the problem of abnormal prediction values in hyperspectral remote sensing applications.
  • The study demonstrates the potential of satellite-based hyperspectral remote sensing, combined with advanced data processing, for accurate environmental monitoring of soil pollution.