The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress
Open accessAbstract
Thermal imaging is a potential tool for estimating plant temperature, which can be used as an indicator of stomatal closure and water deficit stress. In this study, a new method for processing and analysing thermal images was developed. By using remote sensing software, the information from thermal and visible images was combined, the images were classified to identify leaf area and sunlit and shaded parts of the canopy, and the temperature statistics for specific canopy components were calculated. The method was applied to data from a greenhouse water-stress experiment of Vicia faba L. and to field data for Vitis vinifera L. Vaseline-covered and water-sprayed plants were used as dry and wet references, respectively, and two thermal indices, based on temperature differences between the canopy and reference surfaces, were calculated for single Vicia faba plants. The thermal indices were compared with measured stomatal conductance. The temperature distributions of sunlit and shaded leaf area of Vitis vinifera canopies from natural rainfall and irrigation treatments were compared. The present method provides two major improvements compared with earlier methods for calculating thermal indices. First, it allows more accurate estimation of the indices, which are consequently more closely related to stomatal conductance. Second, it gives more accurate estimates of the temperature distribution of the shaded and sunlit parts of canopy, and, unlike the earlier methods, makes it possible to quantify the relationship between temperature variation and stomatal conductance.
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
Remote Sensing and Image Interpretation
Wright, J., Lillesand, T. M., & Kiefer, R. W. (1980) · The Geographical Journal · doi:10.2307/634969 · Cited by 7,238
Computer processing of remotely‐sensed images — An introduction
Mather, P. M. (1987) · Geocarto International · doi:10.1080/10106048709354125 · Cited by 1,086
Canopy Temperature and Crop Water Stress
Jackson, R. D. (1982) · Advances in Irrigation · doi:10.1016/b978-0-12-024301-3.50009-5 · Cited by 546
Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine
Jones, H. G. (2002) · Journal of Experimental Botany · doi:10.1093/jxb/erf083 · Cited by 541
Self-citationNon-water-stressed baselines: A key to measuring and interpreting plant water stress
Idso, S. B. (1982) · Agricultural Meteorology · doi:10.1016/0002-1571(82)90020-6 · Cited by 486
Jones, H. G. (1999) · Plant, Cell & Environment · doi:10.1046/j.1365-3040.1999.00468.x · Cited by 469
Self-citationUse of infrared thermal imaging to isolate Arabidopsis mutants defective in stomatal regulation
Merlot, S., Mustilli, A., Genty, B., North, H., Lefebvre, V., Sotta, B., Vavasseur, A., & Giraudat, J. (2002) · The Plant Journal · doi:10.1046/j.1365-313x.2002.01322.x · Cited by 366
Souza, C. R. D., Maroco, J. P., Santos, T. P. D., Rodrigues, M. L., Lopes, C. M., Pereira, J. S., & Chaves, M. M. (2003) · Functional Plant Biology · doi:10.1071/fp02115 · Cited by 185
Schuerger, A. C., Capelle, G. A., Benedetto, J. A. D., Mao, C., Thai, C. N., Evans, M. D., Richards, J. T., Blank, T. A., & Stryjewski, E. (2003) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00181-5 · Cited by 129
Cited by
Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review
Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., Miklas, P. N., Carter, A. H., Pumphrey, M. O., Knowles, N. R., et al. (2015) · European Journal of Agronomy · doi:10.1016/j.eja.2015.07.004 · Cited by 507
Calderón, R., Navas-Cortés, J. A., Lucena, C., & Zarco-Tejada, P. J. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.07.031 · Cited by 482
Coping with drought: stress and adaptive responses in potato and perspectives for improvement
Obidiegwu, J. E. (2015) · Frontiers in Plant Science · doi:10.3389/fpls.2015.00542 · Cited by 362
Zarco-Tejada, P. J., González-Dugo, V., Williams, L. E., Suárez, L., Berni, J. A. J., Goldhamer, D., & Fereres, E. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.07.024 · Cited by 329
Exploring Genetic Resources to Increase Adaptation of Wheat to Climate Change
Reynolds, M., Tattaris, M., Cossani, C. M., Ellis, M., Yamaguchi-Shinozaki, K., & Pierre, C. S. (2015) · Advances in Wheat Genetics: From Genome to Field · doi:10.1007/978-4-431-55675-6_41 · Cited by 62
Metadata
- Authors
- Leinonen, I., & Jones, H. G.
- Venue
- Journal of Experimental Botany
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-05-07
- Volume
- 55
- Issue
- 401
- Pages
- 1423-1431
- ISSN
- 0022-0957 (ISSN-L)
- 1460-2431
- OpenAlex
- W2097615691
- Cited by
- 351
- References
- 9
- FWCI
- 5.66
- 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: Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. https://app.cochat.ai/papers/10.1093/jxb/erh146
- The paper
- Leinonen, I., & Jones, H. G. (2004). Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. Journal of Experimental Botany. https://doi.org/10.1093/jxb/erh146
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.