The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Hyperspectral remote sensing of plant pigments
Open accessAbstract
The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.
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
[34] Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes
Lichtenthaler, H. K. (1987) · Methods in Enzymology · doi:10.1016/0076-6879(87)48036-1 · Cited by 13,616
Sims, D. A., & Gamon, J. A. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00010-x · Cited by 3,476
Gitelson, A. A., Gritz †, Y., & Merzlyak, M. N. (2003) · Journal of Plant Physiology · doi:10.1078/0176-1617-00887 · Cited by 2,683
Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance
Daughtry, C. (2000) · Remote Sensing of Environment · doi:10.1016/s0034-4257(00)00113-9 · Cited by 2,543
PROSPECT: A model of leaf optical properties spectra
Jacquemoud, S., & Baret, F. (1990) · Remote Sensing of Environment · doi:10.1016/0034-4257(90)90100-z · Cited by 2,400
A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency
Gamon, J. A., Peñuelas, J., & Field, C. B. (1992) · Remote Sensing of Environment · doi:10.1016/0034-4257(92)90059-s · Cited by 2,184
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00018-4 · Cited by 2,029
Graps, A. (1995) · IEEE Computational Science and Engineering · doi:10.1109/99.388960 · Cited by 1,765
Broge, N. H., & Leblanc, E. (2001) · Remote Sensing of Environment · doi:10.1016/s0034-4257(00)00197-8 · Cited by 1,760
The role of xanthophyll cycle carotenoids in the protection of photosynthesis
Demmig-Adams, B., & Adams, W. W. (1996) · Trends in Plant Science · doi:10.1016/s1360-1385(96)80019-7 · Cited by 1,673
Cited by
Close range hyperspectral imaging of plants: A review
Mishra, P., Asaari, M. S. M., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2017.09.009 · Cited by 319
Montes, J. M., Technow, F., Dhillon, B. S., Mauch, F., & Melchinger, A. E. (2011) · Field Crops Research · doi:10.1016/j.fcr.2010.12.017 · Cited by 144
Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants
Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C., & Kersting, K. (2016) · Scientific Reports · doi:10.1038/srep22482 · Cited by 118
Hyperspectral imaging of snow algae and green algae from aeroterrestrial habitats
Holzinger, A., Allen, M. C., & Deheyn, D. D. (2016) · Journal of Photochemistry and Photobiology B: Biology · doi:10.1016/j.jphotobiol.2016.07.001 · Cited by 32
Metadata
- Authors
- Blackburn, G. A.
- Venue
- Journal of Experimental Botany
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-11-30
- Volume
- 58
- Issue
- 4
- Pages
- 855-867
- ISSN
- 0022-0957 (ISSN-L)
- 1460-2431
- OpenAlex
- W2102273661
- Cited by
- 722
- References
- 68
- FWCI
- 5.78
- Type
- journal article
- Language
- en
- Open access
- bronze
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 remote sensing of plant pigments. https://app.cochat.ai/papers/10.1093/jxb/erl123
- The paper
- Blackburn, G. A. (2006). Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany. https://doi.org/10.1093/jxb/erl123
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.
