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Assessment of red-edge vegetation indices for crop leaf area index estimation
Assessment of red-edge vegetation indices for crop leaf area index estimation
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- dc.contributor.author
- Dong, Taifeng
- Liu, Jiangui
- Shang, Jiali
- Qian, Budong
- Ma, Baoluo
- Kovacs, John M.
- Walters, Dan
- Jiao, Xianfeng
- Geng, Xiaoyuan
- Shi, Yichao
- dc.date.accessioned
- 2023-03-22T17:51:23Z
- dc.date.available
- 2023-03-22T17:51:23Z
- dc.date.issued
- 2018-12-29
- dc.description - en
- N/A
- dc.description.abstract - en
- This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.
- dc.identifier.citation
- Dong, T., Liu, J., Shang, J., Qian, B., Ma, B., Kovacs, J. M., Walters, D., Jiao, X., Geng, X., & Shi, Y. (2018). Assessment of red-edge vegetation indices for crop leaf area index estimation. Remote Sensing of Environment, 222, 133–143. https://doi.org/10.1016/j.rse.2018.12.032
- dc.identifier.doi
- https://doi.org/10.1016/j.rse.2018.12.032
- dc.identifier.issn
- 1879-0704
- dc.identifier.uri
- https://open-science.canada.ca/handle/123456789/109
- dc.language.iso
- en
- dc.publisher - en
- Elsevier
- dc.publisher - fr
- Elsevier
- dc.rights.openaccesslevel - en
- Green
- dc.rights.openaccesslevel - fr
- Vert
- dc.subject - en
- Agriculture
- dc.subject - fr
- Agriculture
- dc.subject.en - en
- Agriculture
- dc.subject.fr - fr
- Agriculture
- dc.title - en
- Assessment of red-edge vegetation indices for crop leaf area index estimation
- dc.title.fosrctranslation - fr
- Assessment of red-edge vegetation indices for crop leaf area index estimation
- dc.type - en
- Accepted manuscript
- dc.type - fr
- Manuscrit accepté
- local.article.journaltitle
- Remote Sensing of Environment
- local.article.journalvolume
- 222
- local.article.pagination
- 133-143
- local.peerreview - en
- Yes
- local.peerreview - fr
- Oui
- local.requestdoi - en
- No
- local.requestdoi - fr
- No
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