Applications of Airborne Hyperspectral Imagery in Rare Earth Element Exploration: A Case Study of the World-Class Bayan Obo Deposit, China.
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| Title: | Applications of Airborne Hyperspectral Imagery in Rare Earth Element Exploration: A Case Study of the World-Class Bayan Obo Deposit, China. |
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| Authors: | Liu, Cai1,2 (AUTHOR), Qiu, Junting2,3 (AUTHOR), Yu, Junchuan2,3 (AUTHOR) yujunchuan@mail.cgs.gov.cn, Zhao, Yanbo4 (AUTHOR), Xu, Yuanquan1,2 (AUTHOR), Zhang, Xin2,4 (AUTHOR), Chen, Bin2,3 (AUTHOR), Xu, Rong2,4 (AUTHOR), Ma, Qianli2 (AUTHOR), Liu, Gang2 (AUTHOR), Yang, Jinzhong2 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1110. 13p. |
| Subjects: | Monazite, Airborne-based remote sensing, Remote sensing, Rare earth metals, Prospecting, Absorption spectra, Hyperspectral imaging systems |
| Geographic Terms: | China, Inner Mongolia (China) |
| Abstract: | Highlights: What are the main findings? This study proposed a hyperspectral workflow for REE identification, that enabled the detection of REE-bearing minerals regardless of the host rock types. The absorption features of the REE-bearing mineral monazite within the 400–1000 nm range can be ascribed to Nd3+. What are the implications of the main findings? Airborne hyperspectral imagery enables effective identification of REEs information on the Earth's surface. The CASI-1500h imagery performed most effectively in identifying the locations of REEs among the three sensors. Rare earth elements (REEs) play an important role in emerging renewable energy technology, the production of advanced materials, energy conservation, and high-end manufacturing industries, making them an irreplaceable strategic resource. The diagnostic spectral absorption features of REEs in the visible and near-infrared spectrum can be effectively used for identifying the occurrences of REEs on the Earth's surface. This study systematically compared three airborne hyperspectral sensors—HyMap, CASI-1500h, and AisaFENIX 1K—for detecting REEs in the Bayan Obo area of Inner Mongolia, China. The CASI-1500h imagery performed most effectively in identifying the locations of REEs among the three sensors evaluated here. Additionally, this study proposed a hyperspectral workflow for REE identification, which enabled the detection of REE-bearing minerals regardless of the host rock types—including carbonatites and associated dikes, fenite-syenites, and metamorphic feldspar-quartz sandstone. Laboratory-based spectroscopy and mineral chemistry analyses indicated that the absorption features of the REE-bearing mineral monazite within the 400–1000 nm range can be ascribed to Nd3+. This study demonstrates the potential of airborne hyperspectral technology for efficient and large-scale exploration of REE deposits. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI 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.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 193435589 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Applications of Airborne Hyperspectral Imagery in Rare Earth Element Exploration: A Case Study of the World-Class Bayan Obo Deposit, China. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Cai%22">Liu, Cai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Junting%22">Qiu, Junting</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Junchuan%22">Yu, Junchuan</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> yujunchuan@mail.cgs.gov.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yanbo%22">Zhao, Yanbo</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Yuanquan%22">Xu, Yuanquan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xin%22">Zhang, Xin</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Bin%22">Chen, Bin</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Rong%22">Xu, Rong</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Qianli%22">Ma, Qianli</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Gang%22">Liu, Gang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jinzhong%22">Yang, Jinzhong</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2026, Vol. 18 Issue 8, p1110. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Monazite%22">Monazite</searchLink><br /><searchLink fieldCode="DE" term="%22Airborne-based+remote+sensing%22">Airborne-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Rare+earth+metals%22">Rare earth metals</searchLink><br /><searchLink fieldCode="DE" term="%22Prospecting%22">Prospecting</searchLink><br /><searchLink fieldCode="DE" term="%22Absorption+spectra%22">Absorption spectra</searchLink><br /><searchLink fieldCode="DE" term="%22Hyperspectral+imaging+systems%22">Hyperspectral imaging systems</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Inner+Mongolia+%28China%29%22">Inner Mongolia (China)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? This study proposed a hyperspectral workflow for REE identification, that enabled the detection of REE-bearing minerals regardless of the host rock types. The absorption features of the REE-bearing mineral monazite within the 400–1000 nm range can be ascribed to Nd3+. What are the implications of the main findings? Airborne hyperspectral imagery enables effective identification of REEs information on the Earth's surface. The CASI-1500h imagery performed most effectively in identifying the locations of REEs among the three sensors. Rare earth elements (REEs) play an important role in emerging renewable energy technology, the production of advanced materials, energy conservation, and high-end manufacturing industries, making them an irreplaceable strategic resource. The diagnostic spectral absorption features of REEs in the visible and near-infrared spectrum can be effectively used for identifying the occurrences of REEs on the Earth's surface. This study systematically compared three airborne hyperspectral sensors—HyMap, CASI-1500h, and AisaFENIX 1K—for detecting REEs in the Bayan Obo area of Inner Mongolia, China. The CASI-1500h imagery performed most effectively in identifying the locations of REEs among the three sensors evaluated here. Additionally, this study proposed a hyperspectral workflow for REE identification, which enabled the detection of REE-bearing minerals regardless of the host rock types—including carbonatites and associated dikes, fenite-syenites, and metamorphic feldspar-quartz sandstone. Laboratory-based spectroscopy and mineral chemistry analyses indicated that the absorption features of the REE-bearing mineral monazite within the 400–1000 nm range can be ascribed to Nd3+. This study demonstrates the potential of airborne hyperspectral technology for efficient and large-scale exploration of REE deposits. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI 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: 10.3390/rs18081110 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1110 Subjects: – SubjectFull: Monazite Type: general – SubjectFull: Airborne-based remote sensing Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Rare earth metals Type: general – SubjectFull: Prospecting Type: general – SubjectFull: Absorption spectra Type: general – SubjectFull: Hyperspectral imaging systems Type: general – SubjectFull: China Type: general – SubjectFull: Inner Mongolia (China) Type: general Titles: – TitleFull: Applications of Airborne Hyperspectral Imagery in Rare Earth Element Exploration: A Case Study of the World-Class Bayan Obo Deposit, China. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Cai – PersonEntity: Name: NameFull: Qiu, Junting – PersonEntity: Name: NameFull: Yu, Junchuan – PersonEntity: Name: NameFull: Zhao, Yanbo – PersonEntity: Name: NameFull: Xu, Yuanquan – PersonEntity: Name: NameFull: Zhang, Xin – PersonEntity: Name: NameFull: Chen, Bin – PersonEntity: Name: NameFull: Xu, Rong – PersonEntity: Name: NameFull: Ma, Qianli – PersonEntity: Name: NameFull: Liu, Gang – PersonEntity: Name: NameFull: Yang, Jinzhong IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 8 Titles: – TitleFull: Remote Sensing Type: main |
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