Monitoring soil heavy metal content around a lead–zinc mining area in Yunnan, China using GF-5A and Sentinel-2B remote sensing with consideration of pollution sources.
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| Title: | Monitoring soil heavy metal content around a lead–zinc mining area in Yunnan, China using GF-5A and Sentinel-2B remote sensing with consideration of pollution sources. |
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| Authors: | Hu, Lin1,2 (AUTHOR) hl112365@163.com, Hu, Jiankai1 (AUTHOR), Gan, Shu1,2 (AUTHOR), Yuan, Xiping1,2 (AUTHOR), Li, Jie3 (AUTHOR), Qi, Yingtao1 (AUTHOR), Lu, Chengzhuo1 (AUTHOR) |
| Source: | International Journal of Remote Sensing. Jun2026, Vol. 47 Issue 11, p4641-4679. 39p. |
| Subjects: | Remote sensing, Pollution source apportionment, Zinc mining, Spectral sensitivity, Artificial satellites, Metal content of soils, Machine learning |
| Geographic Terms: | China, Yunnan Sheng (China) |
| Abstract: | To address the challenges of scarce exposed soils, strong surface heterogeneity, and insufficient synergistic monitoring using multi-source remote sensing in high-altitude Pb-Zn mining areas, this study used laboratory spectra as the reference and developed a workflow consisting of direct standardization (DS)-based correction of GF-5A bare-soil imagery, spectral enhancement, Boruta-based band selection, and XGBoost inversion. A parallel comparison with Sentinel-2B (S2B) was also conducted. By integrating SHapley Additive exPlanations (SHAP)-based feature attribution, Bootstrap uncertainty analysis, and the geographic detector to identify the sources of heavy metal contamination, an integrated analytical framework of 'inversion – reliability-source' was established. The results showed that DS tended to shift the spectra of-different land-cover types towards the 'soil spectral space' as a whole, while derivative transformation exhibited superior performance in enhancing heavy metal-sensitive bands. GF-5A provided more complete coverage over key diagnostic spectral regions, particularly within 2200–2450 nm, and achieved test-set R2 values of 0.694, 0.720, and 0.722 for Zn, Pb, and Ni, respectively, outperforming S2B overall. Although S2B yielded lower quantitative accuracy, it was more advantageous for delineating the fine details of pollution boundaries. Distance from the mining area was the dominant controlling factor for Zn and Pb contamination, whereas Ni was more strongly controlled by parent material background, and GF-5A exhibited lower overall uncertainty. This method is applicable to the remote sensing monitoring of heavy metals in bare soils of complex high-altitude mining areas and provides a basis for the integration of GF-5A and S2B. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194221566 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Monitoring soil heavy metal content around a lead–zinc mining area in Yunnan, China using GF-5A and Sentinel-2B remote sensing with consideration of pollution sources. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hu%2C+Lin%22">Hu, Lin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> hl112365@163.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Jiankai%22">Hu, Jiankai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gan%2C+Shu%22">Gan, Shu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Xiping%22">Yuan, Xiping</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jie%22">Li, Jie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qi%2C+Yingtao%22">Qi, Yingtao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Chengzhuo%22">Lu, Chengzhuo</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Remote+Sensing%22">International Journal of Remote Sensing</searchLink>. Jun2026, Vol. 47 Issue 11, p4641-4679. 39p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Pollution+source+apportionment%22">Pollution source apportionment</searchLink><br /><searchLink fieldCode="DE" term="%22Zinc+mining%22">Zinc mining</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+sensitivity%22">Spectral sensitivity</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Metal+content+of+soils%22">Metal content of soils</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Yunnan+Sheng+%28China%29%22">Yunnan Sheng (China)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the challenges of scarce exposed soils, strong surface heterogeneity, and insufficient synergistic monitoring using multi-source remote sensing in high-altitude Pb-Zn mining areas, this study used laboratory spectra as the reference and developed a workflow consisting of direct standardization (DS)-based correction of GF-5A bare-soil imagery, spectral enhancement, Boruta-based band selection, and XGBoost inversion. A parallel comparison with Sentinel-2B (S2B) was also conducted. By integrating SHapley Additive exPlanations (SHAP)-based feature attribution, Bootstrap uncertainty analysis, and the geographic detector to identify the sources of heavy metal contamination, an integrated analytical framework of 'inversion – reliability-source' was established. The results showed that DS tended to shift the spectra of-different land-cover types towards the 'soil spectral space' as a whole, while derivative transformation exhibited superior performance in enhancing heavy metal-sensitive bands. GF-5A provided more complete coverage over key diagnostic spectral regions, particularly within 2200–2450 nm, and achieved test-set R2 values of 0.694, 0.720, and 0.722 for Zn, Pb, and Ni, respectively, outperforming S2B overall. Although S2B yielded lower quantitative accuracy, it was more advantageous for delineating the fine details of pollution boundaries. Distance from the mining area was the dominant controlling factor for Zn and Pb contamination, whereas Ni was more strongly controlled by parent material background, and GF-5A exhibited lower overall uncertainty. This method is applicable to the remote sensing monitoring of heavy metals in bare soils of complex high-altitude mining areas and provides a basis for the integration of GF-5A and S2B. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/01431161.2026.2657534 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 39 StartPage: 4641 Subjects: – SubjectFull: Remote sensing Type: general – SubjectFull: Pollution source apportionment Type: general – SubjectFull: Zinc mining Type: general – SubjectFull: Spectral sensitivity Type: general – SubjectFull: Artificial satellites Type: general – SubjectFull: Metal content of soils Type: general – SubjectFull: Machine learning Type: general – SubjectFull: China Type: general – SubjectFull: Yunnan Sheng (China) Type: general Titles: – TitleFull: Monitoring soil heavy metal content around a lead–zinc mining area in Yunnan, China using GF-5A and Sentinel-2B remote sensing with consideration of pollution sources. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hu, Lin – PersonEntity: Name: NameFull: Hu, Jiankai – PersonEntity: Name: NameFull: Gan, Shu – PersonEntity: Name: NameFull: Yuan, Xiping – PersonEntity: Name: NameFull: Li, Jie – PersonEntity: Name: NameFull: Qi, Yingtao – PersonEntity: Name: NameFull: Lu, Chengzhuo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01431161 Numbering: – Type: volume Value: 47 – Type: issue Value: 11 Titles: – TitleFull: International Journal of Remote Sensing Type: main |
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