English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/10635
logo share SHARE   Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:


Leaf Chlorophyll a+b and canopy LAI estimation in crops using R-T models and Hyperspectral Reflectance Imagery

AuthorsZarco-Tejada, Pablo J. ; Haboudane, D.; Miller, John R.; Tremblay, Nicolas; Dextraze, L.
Issue Date2002
AbstractRecent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing reflectance in the assessment of vegetation biophysical variables both in forestry (Zarco- Tejada et al., 2001) and agriculture (Haboudane et al., 2002). Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as leaf area index (LAI), leaf angle distribution function (LADF), leaf chlorophyll a+b content (Cab), canopy shadows, background reflectance, and illumination-observational conditions. Of particular significance to precision agriculture is Cab, which is related to the nitrogen concentration and serves as a measure of crop response to nitrogen application. We present a modeling approach to predict Cab from hyperspectral remote sensing while minimizing soil reflectance (ρs), shadow effects, and considering LAI variations. This method was developed using simulated data, followed by assessment using hyperspectral airborne imagery. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer (RT) models and developing optical indices that minimize bi-directional and soil background effects.
DescriptionIn Proceedings of the VII Congress of the European Society for Agronomy, Cordoba, Spain, 15-18th July, 2002.
Appears in Collections:(IAS) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
26.pdf139,24 kBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.