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Journal of Arid Land  2022, Vol. 14 Issue (1): 102-114    DOI: 10.1007/s40333-022-0050-8
Geography, geology and natural resources in Central Asia (Guest Editorial Board Member: Prof. Dr. XIAO Wenjiao)     
Optimal bandwidth selection for retrieving Cu content in rock based on hyperspectral remote sensing
MA Xiumei1,2,3,4, ZHOU Kefa1,2,3,4, WANG Jinlin1,2,3,4,*, CUI Shichao1,2,3,4, ZHOU Shuguang1,2,3,4, WANG Shanshan1,2,3,4, ZHANG Guanbin5
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3Xinjiang Research Centre for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
4University of Chinese Academy of Sciences, Beijing 100049, China
5Xinjiang Academy of Science and Technology for Development Strategy, Urumqi 830011, China
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Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands, high resolution, and abundant information. Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element contents in rocks, the influence of bandwidth on the inversion accuracy are ignored. In this study, we collected 258 rock samples in and near the Kalatage polymetallic ore concentration area in the southwestern part of Hami City, Xinjiang Uygur Autonomous Region, China and measured the ground spectra of these samples. The original spectra were resampled with different bandwidths. A Partial Least Squares Regression (PLSR) model was used to invert Cu contents of rock samples and then the influence of different bandwidths on Cu content inversion accuracy was explored. According to the results, the PLSR model obtains the highest Cu content inversion accuracy at a bandwidth of 35 nm, with the model determination coefficient (R2) of 0.5907. The PLSR inversion accuracy is relatively unaffected by the bandwidth within 5-80 nm, but the accuracy decreases significantly at 85 nm bandwidth (R2=0.5473), and the accuracy gradually decreased at bandwidths beyond 85 nm. Hence, bandwidth has a certain impact on the inversion accuracy of Cu content in rocks using the PLSR model. This study provides an indicator argument and theoretical basis for the future design of hyperspectral sensors for rock geochemistry.

Key wordshyperspectral remote sensing      Cu element      bandwidth      Partial Least Squares Regression      inversion accuracy      Kalatage polymetallic ore concentration area     
Received: 30 October 2020      Published: 31 January 2022
Corresponding Authors: * WANG Jinlin (E-mail:
About author: First author contact:

The third author and the seventh author contributed equally to this work.

Cite this article:

MA Xiumei, ZHOU Kefa, WANG Jinlin, CUI Shichao, ZHOU Shuguang, WANG Shanshan, ZHANG Guanbin. Optimal bandwidth selection for retrieving Cu content in rock based on hyperspectral remote sensing. Journal of Arid Land, 2022, 14(1): 102-114.

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Fig. 1 Geological map and alteration zone of the Yudai porphyry Cu (Au) deposit (revised according to Mao et al. (2017))
Fig. 2 Distribution of sampling points. YD, Yudai.
Fig. 3 Original spectral curves of some rocks (a) and spectral curves of some rocks after the removal of water vapour absorption bands (b)
Fig. 4 The 5 nm custom spectral response function (a) and spectral curves of the original rock resampled to 5 nm (b)
Fig. 5 Values of the prediction error sum of squares (PRESS) corresponding to the number of different principal components detected at 35 nm bandwidth using the Partial Least Squares Regression (PLSR) method
Element Spectral
Number of samples Bandwidth (nm) Number of bands PLSR model
R2 PRESS (×105) Principal components
Cu Reflectivity 258 5 339 0.5872 1.2865 8
10 169 0.5857 1.2855 8
15 113 0.5858 1.2802 8
20 85 0.5852 1.2790 8
25 68 0.5851 1.2746 8
30 57 0.5864 1.2729 8
35 49 0.5907 1.2725 8
40 43 0.5878 1.2785 8
45 38 0.5832 1.2774 8
50 34 0.5794 1.2763 8
55 31 0.5843 1.2754 8
60 28 0.5832 1.2785 8
65 26 0.5799 1.2896 8
70 24 0.5861 1.2806 8
75 23 0.5778 1.2803 8
80 21 0.5856 1.2783 8
85 20 0.5473 1.3047 7
90 19 0.5465 1.3076 8
95 18 0.5416 1.3164 7
100 17 0.5693 1.2746 8
150 12 0.5234 1.3611 7
200 9 0.5058 1.3990 7
Table 1 Accuracy of Cu content prediction with different bandwidths based on the partial least squares regression (PLSR) model
Fig. 6 Accuracy curve of the PLSR model with different bandwidths for Cu content prediction
Fig. 7 Cu content at a bandwidth of 35 nm. It should be noted that due to the limitations of the PLSR model, negative values of Cu content will inevitably appear. LOOCV, leave-one-out cross validation.
Fig. 8 Equation and accuracy of Cu content predication with a bandwidth of 35 nm
Fig. 9 Accuracy and fitting results of the LOOCV method on Cu content prediction with a bandwidth of 35 nm
Fig. 10 Trend of Cu content for different samples. The x-axis indicates the sample number.
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