Retrieval of Heavy Metal Content in Soil Using GF-5 Satellite Images Based on GA-XGBoost Model

Han Bai; Yun Yang; Qinfang Cui; Peng Jia; Lixia Wang

doi:10.3788/LOP202259.1230001

Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1230001(2022)

Retrieval of Heavy Metal Content in Soil Using GF-5 Satellite Images Based on GA-XGBoost Model

Han Bai¹, Yun Yang^1,2、*, Qinfang Cui³, Peng Jia⁴, and Lixia Wang¹

Author Affiliations

¹College of Geology Engineering and Surveying, Chang’an University, Xi’an 710054, Shaanxi , China

²Key laboratory of Degraded and Unused Land Consolidation Engineering, Ministry of Natural Resources, Xi’an 710016, Shaanx , China

³Technologies Co., Ltd., Xi’an 710001, Shaanxi , China

⁴Changqing Engineering Design Co., Ltd., Xi’an 710018, Shaanxi , China

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Figures & Tables(14)

Fig. 1. Mean-standard deviation distribution of MCCV method

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Fig. 2. Spectral curves of different kinds of spectral transformations. (a) R; (b) CR; (c) log R^-1

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Fig. 3. Correlation analysis of different spectral transformations and heavy metal content

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Fig. 4. Scatter diagrams of prediction results of CFS-XGBoost. (a) R; (b) log R^-1; (c) CR

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Fig. 5. Scatter diagram of prediction results of GA-XGBoost. (a) R; (b) log R^-1; (c) CR

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Fig. 6. Results of correlation coefficient feature selection and GA feature selection

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Fig. 7. Feature importance scores given by XGBoost

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Fig. 8. Spatial distribution of Cu content

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Table 1. Comparison of hyperspectral satellite parameters

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Table 1. Comparison of hyperspectral satellite parameters

Satellite and its sensor	Band	Wavelength /nm	Spectral resolution /nm	Spatial resolution /m
GF-5（AHSI）	330	450‒2500	5（VNIR），10（SWIR）	30
HJ-1-A（HSI）	105	450‒1050	2‒9	100
EO-1（Hyperion）	220	400‒2500	10	30

Table 2. Comparison between measured Cu content and regional background value
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Table 2. Comparison between measured Cu content and regional background value
Item Minimum Median Maximum Mean
Background 6.8 19.5 43.6 21.4
Sample 24.0 46.0 75.0 48.2

Table 3. Statistics of correlation coefficient between original spectrum and its two transformations with Cu content
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Table 3. Statistics of correlation coefficient between original spectrum and its two transformations with Cu content
Spectrum transform Maximum absolute correlation coefficient Band Number of significant bands
R 0.464^** R₂₃₄₄ 77
CR 0.590^** R₂₃₄₄ 95
log R^-1 0.453^** R₂₃₄₄ 186

Table 4. Statistical characteristics of training set and testing set

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Table 4. Statistical characteristics of training set and testing set

Sample set	Sample size	Maximum /（mg·kg^-1）	Minimum /（mg·kg^-1）	Mean /（mg·kg^-1）	Standard deviation /（mg·kg^-1）
Total set	39	75.0	24.0	48.2	13.38
Training set	31	75.0	26.0	49.1	13.5
Testing set	8	64.0	24.0	45.0	13.1

Table 5. Precision comparison of XGBoost model based on GA and CFS feature selection methods

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Table 5. Precision comparison of XGBoost model based on GA and CFS feature selection methods

method Selection	Model	Training set			Testing set			Number of bands
method Selection	Model	R²	RMSE	RPD	R²	RMSE	RPD	Number of bands
CFS	R-CFS-XGB	0.95	3.05	4.1	0.51	8.57	1.1	77
	CR-CFS-XGB	0.92	3.77	3.0	0.61	7.59	1.6	95
	log R^-1-CFS-XGB	0.94	3.12	3.9	0.53	8.36	1.3	186
GA	R-GA-XGB	0.95	3.07	3.8	0.61	7.60	1.5	139
	CR-GA-XGB	0.94	3.38	3.3	0.84	4.85	2.0	137
	log R^-1-GA-XGB	0.91	3.86	2.7	0.65	7.48	1.7	110

Table 6. Statistics of copper content estimation results
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Table 6. Statistics of copper content estimation results
Content of copper /（mg·kg^-1） 19.79‒21.40 21.40‒38.76 38.76‒45.02 45.02‒51.83 51.83‒66.75
Percentage /% 0.07 53.06 21.07 17.86 7.94

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Han Bai, Yun Yang, Qinfang Cui, Peng Jia, Lixia Wang. Retrieval of Heavy Metal Content in Soil Using GF-5 Satellite Images Based on GA-XGBoost Model[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1230001

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