Dr Luke Brown L.A.Brown4@salford.ac.uk
Lecturer
Deriving leaf area index reference maps using temporally continuous data: a comparison of upscaling approaches
Brown, LA; Ogutu, BO; Camacho, F; Fuster, B; Dash, J
Authors
BO Ogutu
F Camacho
B Fuster
J Dash
Abstract
To further progress the validation of global leaf area index (LAI) products, temporally continuous reference data are a key requirement, as periodic field campaigns fail to adequately characterize temporal dynamics. Progress in cost-effective automated measurement techniques has been made in recent years, but appropriate upscaling methodologies are less mature. Recently, the use of multitemporal transfer functions has been proposed as a potential solution. Using data collected during an independent field campaign, we evaluated the performance of both vegetation index-based multitemporal transfer functions and a radiative transfer model (RTM)-based upscaling approach. Whether assessed using cross validation or data from the independent field campaign, the RTM-based approach provided the best performance (r 2 ≥ 0.88, RMSE ≤ 0.41, NRMSE <; 13%). For upscaling temporally continuous in situ data, the ability of RTM-based approaches to account for seasonal changes in sun-sensor geometry is a key advantage over vegetation index-based multitemporal transfer functions.
Citation
Brown, L., Ogutu, B., Camacho, F., Fuster, B., & Dash, J. (2020). Deriving leaf area index reference maps using temporally continuous data: a comparison of upscaling approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 624-630. https://doi.org/10.1109/JSTARS.2020.3040080
Journal Article Type | Article |
---|---|
Publication Date | Nov 24, 2020 |
Deposit Date | Oct 28, 2022 |
Publicly Available Date | Oct 28, 2022 |
Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Print ISSN | 1939-1404 |
Electronic ISSN | 2151-1535 |
Publisher | Institute of Electrical and Electronics Engineers |
Volume | 14 |
Pages | 624-630 |
DOI | https://doi.org/10.1109/JSTARS.2020.3040080 |
Publisher URL | https://doi.org/10.1109/JSTARS.2020.3040080 |
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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