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Detection of Chlorophyll and Leaf Area Index Dynamics from Sub-weekly Hyperspectral ImageryTemporally 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 time series 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.
Document ID
20170007800
Document Type
Conference Paper
External Source(s)
Authors
Houborg, Rasmus (King Abdullah Univ. of Science and Technology (KAUST) Thuwal, Saudia Arabia)
McCabe, Matthew F. (King Abdullah Univ. of Science and Technology (KAUST) Thuwal, Saudia Arabia)
Angel, Yoseline (King Abdullah Univ. of Science and Technology (KAUST) Thuwal, Saudia Arabia)
Middleton, Elizabeth M. (NASA Goddard Space Flight Center Greenbelt, MD United States)
Date Acquired
August 17, 2017
Publication Date
September 26, 2016
Publication Information
Publication: SPIE Proceedings
Volume: 9998
Subject Category
Earth Resources and Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN45894
Meeting Information
SPIE Remote Sensing Conference(Edinburgh)
Distribution Limits
Public
Copyright
Other