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Remote Sensing for Mineral Exploration: Current Progress and Future Vision

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 18357

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Editors

Dr. Hengqian Zhao

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College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, China
Interests: hyperspectral remote sensing; mineral exploration; spectral analysis
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Prof. Dr. Zhifang Zhao

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School of Earth Sciences, Yunnan University, Kunming, China
Interests: remote sensing geology
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Dr. Joana Cardoso-Fernandes

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Institute of Earth Sciences, Faculty of Sciences, University of Porto, Rua Campo Alegre s/n, 4169-007 Porto, Portugal
Interests: remote sensing; machine learning algorithms; geological exploration; mineral deposits; Li mineralizations; geochemistry
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Dr. Deshuai Yuan

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: hyperspectral images; convolutional neural network; mining
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Special Issue Information

Dear Colleagues,

Remote sensing technology can rapidly, accurately, and non-destructively obtain information regarding the mineralization of surface and underground mineral deposits, becoming an efficient and economical detection method in mineral exploration. In recent years, due to the rapid development of sensor technology, aerospace technology, and computer technology, the spatial resolution, spectral resolution, and time resolution of remote sensing data have been enhanced continuously. Meanwhile, observation platforms are developing towards multi-platforms, including ground, aviation, and aerospace platforms. The successful application of remote sensing technology has been achieved in the field of mineral exploration, covering the identification of lithology, alteration extraction, the identification of fine minerals, and prospective prediction. In particular, hyperspectral remote sensing technology has shown significant advantages in the fine identification of rocks and minerals. With the application of advanced methods such as artificial intelligence and machine learning, the capabilities of remote sensing technology in mineral exploration have been significantly enhanced.

This Special Issue will outline the latest advances and trends in remote sensing-based mineral exploration, and contribute to the sustainable development of the global mining industry. We welcome the submission of articles related to this topic, including research based on new advances in laboratory hyperspectral reflectance and research performed using recent and emerging technologies such as hyperspectral imaging and deep learning.

Dr. Hengqian Zhao
Prof. Dr. Zhifang Zhao
Dr. Joana Cardoso-Fernandes
Dr. Deshuai Yuan
Guest Editors

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Keywords

remote sensing
mineral exploration
multispectral and hyperspectral
lithology
mineralization
deep learning

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Related Special Issue

Remote Sensing for Mineral Exploration: Current Progress and Future Vision (2nd Edition) in Remote Sensing

Published Papers (7 papers)

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Research

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35 pages, 17729 KB

Open AccessArticle

Integrating Multi-Source Geoscientific Data via Geologically Constrained Feature Engineering for Gold Prospectivity Mapping: A Case Study of Jiaoxibei, China

by Yajie Feng, Yongzhi Wang, Yigao Cheng, Jiahui Zheng, Shaohui Wang, Zhaofeng An and Zheng Ji

Remote Sens. 2026, 18(15), 2593; https://doi.org/10.3390/rs18152593 - 5 Aug 2026

Viewed by 404

Abstract

The Jiaoxibei gold cluster is one of the most significant gold-producing regions in China and retains substantial regional prospecting potential. However, the superposition of multiple mineralization events has resulted in strong spatial coupling, multi-scale variability, and substantial redundancy among structural, alteration, geophysical, and geochemical information, limiting the effective extraction of key ore-controlling features. This study developed a geologically constrained feature-engineering framework for regional mineral potential evaluation. An initial indicator system comprising 32 geologically meaningful factors was constructed from structural geometry, remote-sensing alteration, gravity, magnetic, and geochemical information. The previously developed SOMML method was extended by introducing borehole-derived geological constraints to construct G-SOMML and generate the comprehensive geochemical anomaly factor Chem_F. Correlation-based redundancy reduction, Random Forest importance evaluation, SHAP interpretation, and geological screening were then combined to identify nine core factors. The selected factors were subsequently transformed according to their mineralization-response directions and integrated through category-balanced fuzzy synthetic evaluation. By linking geology-guided feature construction, borehole-constrained geochemical anomaly extraction, data-driven feature diagnosis, and balanced evidence integration, the framework provides a reproducible and interpretable workflow for organizing heterogeneous geoscientific information in regional mineral potential evaluation. The resulting high-potential zones captured 21 of the 22 known gold occurrences in the Sanshandao and Jiaojia areas and 17 of the 23 occurrences in the other parts of the study area, yielding an overall deposit capture rate of 84.4%. Compared with the all-feature fuzzy synthetic evaluation model (AT-Fuzzy), feature engineering reduced the high-potential area ratio from 50.951% to 25.356% while increasing the deposit capture rate from 51.1% to 84.4%. At the map level, the proposed framework delineated a substantially smaller high-potential area than the Random Forest model while achieving higher deposit capture rates than both the Random Forest and Weights of Evidence models under the common evaluation domain and statistical thresholding criterion. These results support the applicability of the framework for interpretable regional mineral potential evaluation and target prioritization in complex metallogenic districts. Full article

(This article belongs to the Special Issue Remote Sensing for Mineral Exploration: Current Progress and Future Vision)

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30 pages, 40815 KB

Open AccessArticle

Integrated Geoscientific Data with Sampling Bias Correction for Porphyry Copper Prospectivity Mapping

by Muhammad Atif Bilal, Kateryna Hlyniana, Yongzhi Wang, Muhammad Pervez Akhter and Shiting Sheng

Remote Sens. 2026, 18(13), 2091; https://doi.org/10.3390/rs18132091 - 26 Jun 2026

Cited by 1 | Viewed by 779

Abstract

Multisource remote sensing and Earth observation (EO) products provide scalable covariates for regional mineral prospectivity mapping, but their integration with incomplete and preferentially sampled occurrence records can produce biased prediction maps. We present a bias-aware machine learning workflow for porphyry copper prospectivity mapping that integrates satellite-derived alteration proxies, topographic variables, regional geology, structural context, and accessibility-related EO layers on a harmonized 1 km grid. The workflow separates remote sensing/geological predictors from survey-effort proxies and combines this decomposition with positive-unlabeled learning, stacked ensembling, rank-optimized blending, fold-wise calibration, and spatial block cross-validation. The case study covers the eastern Central Asian Orogenic Belt (CAOB) and uses porphyry Cu occurrences together with covariates derived from ASTER short-wave infrared information, Landsat 8 reflectance, SRTM topography, VIIRS night-time lights, GHSL population data, geological units, and active fault information. Across held-out spatial folds, the final RO-BAB ensemble provides a modest but exploration-relevant improvement in ranking relative to the all-covariate XGBoost baseline, increasing PR-AUC from 0.0297 to 0.0364 and recovering 26.75% of known deposits within the top 5% of ranked cells. The resulting maps delineate coherent remote sensing-supported prospective corridors while exposing regions where predictions may be influenced by historical accessibility and recording effort. The study demonstrates how machine learning that accounts for sampling bias can improve the reliability and interpretability of remote sensing mineral prospectivity products in the presence of only reference data. Full article

(This article belongs to the Special Issue Remote Sensing for Mineral Exploration: Current Progress and Future Vision)

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31 pages, 11304 KB

Open AccessArticle

Geo-U-Mamba: A Mamba-Based Framework for Mineral Prospectivity Mapping of Gold Exploration Using Multi-Source Geoscientific Data

by Yuheng Zhou, Yongzhi Wang, Shibo Wen, Guangpeng Zhang and Yong Li

Remote Sens. 2026, 18(13), 2068; https://doi.org/10.3390/rs18132068 - 24 Jun 2026

Viewed by 680

Abstract

Modern mineral exploration faces the pivotal challenge of detecting concealed mineral deposits in complex geology, as depleting outcropping ores have driven global exploration to depths where 1000 m deep mining is now commonplace. To address this, this study proposes Geo-U-Mamba, an unsupervised deep learning framework for gold mineral prospectivity mapping. The model integrates multi-source geoscientific data, encompassing geochemistry, remote sensing alteration indicators, topography, and structural distance fields. By incorporating a Mamba-driven four-directional cross-scan mechanism into a U-Net architecture, the framework effectively models the complex nonlinear mapping relationships between metallogenic elements and the geological environment. This approach recognizes gold geochemical anomalies with an 86.11% deposit capture rate, decoupling environmental noise by reconstructing the geochemical background field and extracting anomalies in combination with C-A fractal theory. When applied to China’s Hatu gold belt in Xinjiang, Geo-U-Mamba achieved an AUC of 0.83, consistently outperforming classical baselines such as CAE, U-Net, and ViT. Ultimately, the findings indicate that this framework provides a reliable and high-precision tool for modern mineral exploration, successfully separating mineralization signals from geological backgrounds in complex metallogenic belts to facilitate exploration targeting. Full article

(This article belongs to the Special Issue Remote Sensing for Mineral Exploration: Current Progress and Future Vision)

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23 pages, 13143 KB

Open AccessArticle

Method of Convolutional Neural Networks for Lithological Classification Using Multisource Remote Sensing Data

by Zixuan Zhang, Yuanjin Xu and Jianguo Chen

Remote Sens. 2026, 18(1), 29; https://doi.org/10.3390/rs18010029 - 22 Dec 2025

Cited by 4 | Viewed by 1426

Abstract

Xinfeng County, Shaoguan City, Guangdong Province, China, is a typical vegetation-covered area that suffers from severe attenuation of rock and mineral spectral information in remote sensing images owing to dense vegetation. This situation limits the accuracy of traditional lithological mapping methods, making them unable to meet geological mapping demands under complex conditions, and thus necessitating a tailored lithological identification model. To address this issue, in this study, the penetration capability of microwave remote sensing (for extracting indirect textural features of lithology) was combined with the spectral superiority of hyperspectral remote sensing (for capturing lithological spectral features), resulting in a dual-branch deep-learning framework for lithological classification based on multisource remote sensing data. The framework independently extracts features from Sentinel-1 imagery and Gaofen-5 data, integrating three key modules: texture feature extraction, spatial–spectral feature extraction, and attention-based adaptive feature fusion, to realize deep and efficient fusion of heterogeneous remote sensing information. Ablation and comparative experiments were conducted to evaluate each module’s contribution. The results show that the dual-branch architecture effectively captures the complementary and discriminative characteristics of multimodal data, and that the encoder–decoder structure demonstrates strong robustness under complex conditions such as dense vegetation. The final model achieved 97.24% overall accuracy and 90.43% mean intersection-over-union score, verifying its effectiveness and generalizability in complex geological environments. The proposed multi-source remote sensing–based lithological classification model overcomes the limitations of single-source data by integrating indirect lithological texture features containing vegetation structural information with spectral features, thereby providing a viable approach for lithological mapping in vegetated regions. Full article

(This article belongs to the Special Issue Remote Sensing for Mineral Exploration: Current Progress and Future Vision)

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24 pages, 6356 KB

Open AccessArticle

Tectonic Rift-Related Manganese Mineralization System and Its Geophysical Signature in the Nanpanjiang Basin

by Daman Cui, Zhifang Zhao, Wenlong Liu, Haiying Yang, Yun Liu, Jianliang Liu and Baowen Shi

Remote Sens. 2025, 17(15), 2702; https://doi.org/10.3390/rs17152702 - 4 Aug 2025

Cited by 1 | Viewed by 1898

Abstract

The southeastern Yunnan region in the southwestern Nanpanjiang Basin is one of the most important manganese enrichment zones in China. Manganese mineralization is mainly confined to marine mud–sand–carbonate interbeds of the Middle Triassic Ladinian Falang Formation (T₂f), which contains several medium to large deposits such as Dounan, Baixian, and Yanzijiao. However, the geological processes that control manganese mineralization in this region remain insufficiently understood. Understanding the tectonic evolution of the basin is therefore essential to unravel the mechanisms of Middle Triassic metallogenesis. This study investigates how rift-related tectonic activity influences manganese ore formation. This study integrates global gravity and magnetic field models (WGM2012, EMAG2v3), audio-frequency magnetotelluric (AMT) profiles, and regional geological data to investigate ore-controlling structures. A distinct gravity low–magnetic high belt is delineated along the basin axis, indicating lithospheric thinning and enhanced mantle-derived heat flow. Structural interpretation reveals a rift system with a checkerboard pattern formed by intersecting NE-trending major faults and NW-trending secondary faults. Four hydrothermal plume centers are identified at these fault intersections. AMT profiles show that manganese ore bodies correspond to stable low-resistivity zones, suggesting fluid-rich, hydrothermally altered horizons. These findings demonstrate a strong spatial coupling between hydrothermal activity and mineralization. This study provides the first identification of the internal rift architecture within the Nanpanjiang Basin. The basin-scale rift–graben system exerts first-order control on sedimentation and manganese metallogenesis, supporting a trinity model of tectonic control, hydrothermal fluid transport, and sedimentary enrichment. These insights not only improve our understanding of rift-related manganese formation in southeastern Yunnan but also offer a methodological framework applicable to similar rift basins worldwide. Full article

(This article belongs to the Special Issue Remote Sensing for Mineral Exploration: Current Progress and Future Vision)

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23 pages, 8890 KB

Open AccessArticle

Alteration Information Extraction and Mineral Prospectivity Mapping in the Laozhaiwan Area Using Multisource Remote Sensing Data

by Qi Chen, Dayu Cai, Zhifang Zhao, Xiaoguang Yang, Yilong Wang, Xiao Jiang, Lei Xu, Haichuan Duan, Yang He, Xiaoxiao Zhang, Yiyang Wang and Ting Xu

Remote Sens. 2025, 17(13), 2178; https://doi.org/10.3390/rs17132178 - 25 Jun 2025

Cited by 9 | Viewed by 4464

Abstract

Gold is a vital strategic resource for many countries. The Laozhaiwan area is an important gold resource base in Yunnan Province and even nationwide. Conducting mineral resource exploration in this region to increase gold reserves is of great significance. The application of remote sensing technology in mineral resource exploration is a green and efficient technical approach, which has been widely utilized in the field of mineral resource prospecting. This study selects the Laozhaiwan area in the southeastern part of Yunnan Province as the research region. Linear and ring structures were extracted using the remote sensing visual interpretation method based on Sentinel-2A multispectral data. Additionally, Sentinel-2A, ASTER, and ZY1-02D data were used to extract iron-stained, hydroxyl, silicification, and limonite alteration information through Principal Component Analysis (PCA) and Spectral Angle Mapper (SAM) methods. Additionally, 50 linear structures and 12 ring structures were extracted. A comprehensive analysis of geological data reveals that alteration minerals and linear-ring structures are closely related to mineralization, providing valuable indicators for mineral resource exploration. By comprehensively analyzing the alteration information and remote sensing interpretation results of the linear-ring structures, two prospective areas for mineral exploration were delineated. Field investigations and petrographic studies confirmed the reliability of remote sensing technology in mineral exploration. The mineral exploration method based on multi-source remote sensing technology can clearly reflect various alteration information and linear-ring structural data. It provides remote sensing geological insights for geological survey work and has great application potential in the field of mineral resource exploration. Full article

(This article belongs to the Special Issue Remote Sensing for Mineral Exploration: Current Progress and Future Vision)

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Review

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34 pages, 4933 KB

Open AccessReview

Current Progress in and Future Visions of Key Technologies of UAV-Borne Multi-Modal Geophysical Exploration for Mineral Exploration: A Scoping Review

by Xin Wu, Guo-Qiang Xue, Yan-Bo Wang and Song Cui

Remote Sens. 2025, 17(15), 2689; https://doi.org/10.3390/rs17152689 - 3 Aug 2025

Cited by 3 | Viewed by 5163

Abstract

For mineral exploration, an increasing number of geophysical instruments have adopted unmanned aerial vehicles (UAVs) as their carrier platforms. The effective fusion of multi-modal geophysical information will be conducive to further enhancing the reliability of exploration results. However, the integration degree of UAVs and geophysical equipment is still low, and the advantages of UAVs as robots have not been fully exploited. In addition, the existing fusion methods are still difficult to use to establish the spatial distribution model of ore-bearing rock. Therefore, we reviewed the development status of UAVs and the geophysical instruments. We believe that only by integrating the system, designing the observation plan in accordance with the requirements of the fusion method, and treating the hardware part as an external extension of the algorithm, can high-matching data be provided for fusion. Subsequently, we analyzed the progress of the fusion methods, leading us to believe that the cross-dimensional and cross-abstract-level issues are major challenges in the algorithm aspect. Meanwhile, the fusion should be carried out simultaneously with the generation of the ore-bearing rock model, that is, to establish an integrated system of fusion and generation. It is hoped that this research can promote the development of UAV-borne multi-modal observation technology. Full article

(This article belongs to the Special Issue Remote Sensing for Mineral Exploration: Current Progress and Future Vision)

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