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...
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Main Authors: | , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2025-12-01
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Series: | International Journal of Digital Earth |
Subjects: | |
Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2448583 |
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Summary: | 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. |
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ISSN: | 1753-8947 1753-8955 |