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

Abstract

Hyperspectral remote sensing has the advantages to predict and map soil heavy metal concentration over conventional monitoring methods and multispectral remote sensing. In quantitative applications of hyperspectral remote sensing imagery, the contribution of hyperspectral bands is different, and abnormal prediction values resulted from incorrectly classified bare soil images are a major problem. In this study, a variable weighting method was proposed to weight the hyperspectral bands, and a probability threshold was used to improve the classification to mitigate the problem of abnormal prediction values. The variable weighting was conducted by using the absorption depths obtained by continuum removal. Soil samples were collected from a mining area in southwestern China. Hyperspectral remote sensing imagery was acquired by the Advanced Hyperspectral Imager (AHSI) abroad on Geofen-5 (GF-5) satellite. Genetic algorithm and partial least squares regression (PLSR) were adopted to calibrate prediction models. In prediction of soil copper (Cu) concentration, root mean square error (RMSE) and coefficient of determination (R2) were 21.59 mg kg−1 and 0.60 for the prediction using raw reflectance spectra, and the values were improved to 18.33 mg kg−1 and 0.71 by using the weighted reflectance spectra. The developed prediction model was applied to the AHSI imagery to predict Cu concentration in bare soil areas. In prediction of Cu concentration using the AHSI imagery, negative prediction values were eliminated by using the bare soil image extracted by the improved classification. Based on the prediction, soil Cu concentration map was generated by kriging spatial interpolation. The result indicates that the proposed variable weighting method is effective and the problem of abnormal prediction values could be mitigated by using improved bare soil images. Further analysis indicates that some indices with proper thresholds also could be used to get improved bare soil images.