The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems
Open accessAbstract
The development and uptake of field deployable hyperspectral imaging systems within environmental monitoring represents an exciting and innovative development that could revolutionize a number of sensing applications in the coming decades. In this article we focus on the successful miniaturization and improved portability of hyperspectral sensors, covering their application both from aerial and ground-based platforms in a number of environmental application areas, highlighting in particular the recent implementation of low-cost consumer technology in this context. At present, these devices largely complement existing monitoring approaches, however, as technology continues to improve, these units are moving towards reaching a standard suitable for stand-alone monitoring in the not too distant future. As these low-cost and light-weight devices are already producing scientific grade results, they now have the potential to significantly improve accessibility to hyperspectral monitoring technology, as well as vastly proliferating acquisition of such datasets.
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
Cites
Unmanned aerial systems for photogrammetry and remote sensing: A review
Colomina, I., & Molina, P. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.02.013 · Cited by 2,954
A survey of mobile phone sensing
Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5560598 · Cited by 2,292
Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. (2017) · Remote Sensing · doi:10.3390/rs9111110 · Cited by 1,318
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Modern Trends in Hyperspectral Image Analysis: A Review
Khan, M. J., Khan, H. S., Yousaf, A., Khurshid, K., & Abbas, A. (2018) · IEEE Access · doi:10.1109/access.2018.2812999 · Cited by 865
Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. J. (2018) · Remote Sensing · doi:10.3390/rs10071091 · Cited by 647
CubeSat evolution: Analyzing CubeSat capabilities for conducting science missions
Poghosyan, A., & Golkar, A. (2017) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2016.11.002 · Cited by 537
Govender, M., Chetty, K., & Bulcock, H. (2009) · Water SA · doi:10.4314/wsa.v33i2.49049 · Cited by 514
Honkavaara, E., Saari, H., Kaivosoja, J., Pölönen, I., Hakala, T., Litkey, P., Mäkynen, J., & Pesonen, L. (2013) · Remote Sensing · doi:10.3390/rs5105006 · Cited by 495
Aasen, H., Burkart, A., Bolten, A., & Bareth, G. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.08.002 · Cited by 466
Metadata
- Authors
- Stuart, M. B., McGonigle, A. J. S., & Willmott, J. R.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-07-11
- Volume
- 19
- Issue
- 14
- Pages
- 3071
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2961290969
- Cited by
- 420
- References
- 53
- FWCI
- 16.32
- Type
- journal article
- Language
- en
- Open access
- gold
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: Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. https://app.cochat.ai/papers/10.3390/s19143071
- The paper
- Stuart, M. B., McGonigle, A. J. S., & Willmott, J. R. (2019). Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors. https://doi.org/10.3390/s19143071
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.
