The DOI matches its Crossref record.
Remote sensing for mineral exploration
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Deep learning and its application in geochemical mapping
Zuo, R., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019) · Earth-Science Reviews · doi:10.1016/j.earscirev.2019.02.023 · Cited by 426
A review of machine learning in processing remote sensing data for mineral exploration
Shirmard, H., Farahbakhsh, E., Müller, R. D., & Chandra, R. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112750 · Cited by 420
Potential of ESA's Sentinel-2 for geological applications
van der Meer, F. D., van der Werff, H. M. A., & van Ruitenbeek, F. J. A. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.03.022 · Cited by 289
Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
Cardoso-Fernandes, J., Teodoro, A. C., Lima, A., Perrotta, M., & Roda-Robles, E. (2020) · Applied Sciences · doi:10.3390/app10051785 · Cited by 71
Framework for remote sensing and modelling of lithium-brine deposit formation
Rossi, C., Bateson, L., Bayaraa, M., Butcher, A., Ford, J., & Hughes, A. (2022) · Remote Sensing · doi:10.3390/rs14061383 · Cited by 20
Metadata
- Authors
- Sabins, F. F.
- Venue
- Ore Geology Reviews
- Publisher
- Elsevier BV
- Published
- 1999-09
- Volume
- 14
- Issue
- 3-4
- Pages
- 157-183
- ISSN
- 0169-1368 (ISSN-L)
- OpenAlex
- W2077865152
- Cited by
- 925
- References
- 0
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Remote sensing for mineral exploration. https://app.cochat.ai/papers/10.1016/s0169-1368(99)00007-4
- The paper
- Sabins, F. F. (1999). Remote sensing for mineral exploration. Ore Geology Reviews. https://doi.org/10.1016/s0169-1368(99)00007-4
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
