NDLI: On the retrival of vegetation parameters from multi-angular hyperspectral remote sensing data

Content Provider	IEEE Xplore Digital Library
Author	Baoxin Hu Frank Zhang Jianguo Wang
Copyright Year	2009
Description	Author affiliation: Dept. of Earth and Space Science and Engineering, York University, 4700Keele St., Toronto, Canada (Baoxin Hu; Frank Zhang; Jianguo Wang)
Abstract	In this study, a new algorithm was developed to effectively use multi-angular hyperspectral remote sensing data in the retrieval of vegetation parameters based on a coupled leaf and canopy reflectance model. Since the observations acquired at different viewing angles tend to have different noise levels, the posterior variance factors of the observations at different angles were estimated and they were then used to construct the observations' weight factors in model inversion. The developed method was validated using simulated data. The results show that the posterior variance factor can be used to characterize the uncertainty in the data from different sources and thus provides a means to weight these data in the model inversion.
Starting Page	451
Ending Page	455
File Size	490924
Page Count	5
File Format	PDF
ISBN	9781424438778
DOI	10.1109/TIC-STH.2009.5444458
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral sensors Model inversion Artificial neural networks Information retrieval Table lookup Remote sensing Noise level Reflectivity Vegetation parameters Vegetation mapping Multi-angular Data fusion Remote monitoring Hyperspectral imaging
Content Type	Text
Resource Type	Article

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