Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/9750
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Título : Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery
Autor : Kempeneers, P., Zarco-Tejada, Pablo J., North, P. R. J., Backer, S. de, Delalieux, S., Sepulcre-Cantó, G., Morales Iribas, Fermín, Aardt, J. A. N. van, Sagardoy Calderón, Ruth, Coppin, P., Scheunders, P.
Palabras clave : Chlorophyll estimation
Model inversion
Fecha de publicación : 2008
Editor: Taylor & Francis
Resumen: This paper presents the results of estimation of leaf chlorophyll concentration through model inversion, from hyperspectral imagery of artificially treated orchard crops. The objectives were to examine model inversion robustness under changing viewing conditions, and the potential of multi-angle hyperspectral data to improve accuracy of chlorophyll estimation. The results were compared with leaf chlorophyll measurements from laboratory analysis and field spectroscopy. Two state-of-the-art canopy models were compared. The first is a turbid medium canopy reflectance model (MCRM) and the second is a 3D model (FLIGHT). Both were linked to the PROSPECT leaf model. A linear regression using a single band was also performed as a reference. The different techniques were able to detect nutrient deficiencies that caused stress from the hyperspectral data obtained from the airborne AHS sensor. However, quantitative chlorophyll retrieval was found largely dependent on viewing conditions for regression and the turbid medium model inversion. In contrast, the 3D model was successful for all observations. It offers a robust technique to extract chlorophyll quantitatively from airborne hyperspectral data. When multi-angular data were combined, the results for both the turbid medium and 3D model increased. Final RMSE values of 5.8 mg cm-2 (MCRM) and 4.7 mg cm-2 (FLIGHT) were obtained for chlorophyll retrieval on canopy level.
Versión del editor: http://dx.doi.org/10.1080/01431160802036458
URI : http://hdl.handle.net/10261/9750
ISSN: 0143-1161 (Print)
1366-5901 (Online)
DOI: 10.1080/01431160802036458
Citación : International Journal of Remote Sensing Vol. 29, Nos. 17–18, September 2008, 5093–5111
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