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Proceedings Paper

Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery
Author(s): Rasmus Houborg; Matthew F. McCabe; Yoseline Angel; Elizabeth M. Middleton
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Paper Abstract

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Paper Details

Date Published: 25 October 2016
PDF: 11 pages
Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 999812 (25 October 2016); doi: 10.1117/12.2241345
Show Author Affiliations
Rasmus Houborg, King Abdullah Univ. of Science and Technology (Saudi Arabia)
Matthew F. McCabe, King Abdullah Univ. of Science and Technology (Saudi Arabia)
Yoseline Angel, King Abdullah Univ. of Science and Technology (Saudi Arabia)
Elizabeth M. Middleton, NASA Goddard Space Flight Ctr. (United States)


Published in SPIE Proceedings Vol. 9998:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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