Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites
Lithium (Li), a key element in green energy technologies, plays a pivotal role in achieving the United Nations Sustainable Development Goals (SDGs). Globally, spodumene-bearing pegmatite Li deposits are the primary source of Li. In this study, a series of spodumene thermal infrared indices (STIRI) w...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2448583 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841561195225546752 |
|---|---|
| author | Siyuan Li Nannan Zhang Yong Li Li Chen Hao Zhang Jinyu Chang Jintao Tao Jianpeng Jing |
| author_facet | Siyuan Li Nannan Zhang Yong Li Li Chen Hao Zhang Jinyu Chang Jintao Tao Jianpeng Jing |
| author_sort | Siyuan Li |
| collection | DOAJ |
| description | Lithium (Li), a key element in green energy technologies, plays a pivotal role in achieving the United Nations Sustainable Development Goals (SDGs). Globally, spodumene-bearing pegmatite Li deposits are the primary source of Li. In this study, a series of spodumene thermal infrared indices (STIRI) were derived via linear regression using thermal infrared (TIR) data from the SDGSAT-1, ASTER, and Landsat 8 satellites. These STIRI were combined with a band ratio (BR) index from the Landsat 8 visible near-infrared (VNIR) bands, and the matched filter (MF) index from the GF-5 shortwave infrared (SWIR) bands to construct a remote-sensing index cube. This cube was fed into a hybrid deep-learning model to identify spodumene in the 509 Li deposit in Dahongliutan, Xinjiang, China. The model combines a convolute onal neural network (CNN) and a graph convolutional network (GCN), integrating spatial and spectral features to enhance identification accuracy. The results showed a significant improvement in identification accuracy when TIR indices were incorporated alongside the VNIR and SWIR indices. Notably, SDGSAT-1 TIR data, with a 30 m spatial resolution and broader spectral range, proved highly effective in spodumene detection, offering new opportunities for Li ore prospecting in high-altitude, deeply eroded regions. |
| format | Article |
| id | doaj-art-826699bcaeeb4e54ac3277554c84f51a |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-826699bcaeeb4e54ac3277554c84f51a2025-01-03T05:27:15ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-12-0118110.1080/17538947.2024.2448583Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellitesSiyuan Li0Nannan Zhang1Yong Li2Li Chen3Hao Zhang4Jinyu Chang5Jintao Tao6Jianpeng Jing7State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaInstitute of Geology and Mineral Resources Exploration, Nonferrous Geological Exploration Bureau of Xinjiang Uygur Autonomous Region, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaLithium (Li), a key element in green energy technologies, plays a pivotal role in achieving the United Nations Sustainable Development Goals (SDGs). Globally, spodumene-bearing pegmatite Li deposits are the primary source of Li. In this study, a series of spodumene thermal infrared indices (STIRI) were derived via linear regression using thermal infrared (TIR) data from the SDGSAT-1, ASTER, and Landsat 8 satellites. These STIRI were combined with a band ratio (BR) index from the Landsat 8 visible near-infrared (VNIR) bands, and the matched filter (MF) index from the GF-5 shortwave infrared (SWIR) bands to construct a remote-sensing index cube. This cube was fed into a hybrid deep-learning model to identify spodumene in the 509 Li deposit in Dahongliutan, Xinjiang, China. The model combines a convolute onal neural network (CNN) and a graph convolutional network (GCN), integrating spatial and spectral features to enhance identification accuracy. The results showed a significant improvement in identification accuracy when TIR indices were incorporated alongside the VNIR and SWIR indices. Notably, SDGSAT-1 TIR data, with a 30 m spatial resolution and broader spectral range, proved highly effective in spodumene detection, offering new opportunities for Li ore prospecting in high-altitude, deeply eroded regions.https://www.tandfonline.com/doi/10.1080/17538947.2024.2448583Remote sensingSDGSAT-1thermal infraredspodumene identificationdeep learning |
| spellingShingle | Siyuan Li Nannan Zhang Yong Li Li Chen Hao Zhang Jinyu Chang Jintao Tao Jianpeng Jing Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites International Journal of Digital Earth Remote sensing SDGSAT-1 thermal infrared spodumene identification deep learning |
| title | Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites |
| title_full | Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites |
| title_fullStr | Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites |
| title_full_unstemmed | Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites |
| title_short | Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites |
| title_sort | identification of spodumene using a remote sensing index cube from sdgsat 1 and other satellites |
| topic | Remote sensing SDGSAT-1 thermal infrared spodumene identification deep learning |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2448583 |
| work_keys_str_mv | AT siyuanli identificationofspodumeneusingaremotesensingindexcubefromsdgsat1andothersatellites AT nannanzhang identificationofspodumeneusingaremotesensingindexcubefromsdgsat1andothersatellites AT yongli identificationofspodumeneusingaremotesensingindexcubefromsdgsat1andothersatellites AT lichen identificationofspodumeneusingaremotesensingindexcubefromsdgsat1andothersatellites AT haozhang identificationofspodumeneusingaremotesensingindexcubefromsdgsat1andothersatellites AT jinyuchang identificationofspodumeneusingaremotesensingindexcubefromsdgsat1andothersatellites AT jintaotao identificationofspodumeneusingaremotesensingindexcubefromsdgsat1andothersatellites AT jianpengjing identificationofspodumeneusingaremotesensingindexcubefromsdgsat1andothersatellites |