Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield

Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth... Applications of hyperspectral remote sensing data to derive relevant properties for precision agriculture are described. Green leaf area index, fraction of senescent material and grain yield are retrieved from the hyperspectral data. Two sensors were used to obtain these data; the airborne visible/infrared imaging spectrometer AVIS and the space-borne compact high-resolution imaging spectrometer CHRIS; they show the applicability of the methods to different spatial scales. In addition, the bi-directional observation capability of the CHRIS sensor is used to derive information about the average leaf angle of the canopies which are used to link canopy structure with phenological development. Derivation of the canopy properties, green leaf area index and fraction of senescent material was done with the radiative transfer model, SLC (soil–leaf–canopy). The results were used as input into the crop growth model PROMET-V to calculate grain yield. Two years of data from the German research project preagro are presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield

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Publisher
Springer US
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-009-9104-6
Publisher site
See Article on Publisher Site

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