Crop Yield Estimation Based on Unsupervised Linear Unmixing of Multidate Hyperspectral Imagery

Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hypers...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2013-01, Vol.51 (1), p.162-173
Main Authors: Bin Luo, Chenghai Yang, Chanussot, J., Liangpei Zhang
Format: Article
Language:English
Subjects:
Agriculture
Airborne hyperspectral imagery
Applied geophysics
Computer Science
Correlation
crop yield
Earth sciences
Earth, ocean, space
Engineering Sciences
Exact sciences and technology
grain sorghum field
Hyperspectral imaging
Internal geophysics
multidate
Signal and Image processing
Soil
unmixing
Vegetation mapping
Yield estimation
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Summary:Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hyperspectral image is considered as a linear combinations of the spectra of the vegetation and the bare soil. Recently developed linear unmixing approaches are evaluated in this paper, which automatically extracts the spectra of the vegetation and bare soil from the images. The vegetation abundances are then computed based on the extracted spectra. In order to reduce the influences of this uncertainty and obtain a robust estimation results, the vegetation abundances extracted on two different dates on the same fields are then combined. The experiments are carried on the multidate hyperspectral images taken from two grain sorghum fields. The results show that the correlation coefficients between the vegetation abundances obtained by unsupervised linear unmixing approaches are as good as the results obtained by supervised methods, where the spectra of the vegetation and bare soil are measured in the laboratory. In addition, the combination of vegetation abundances extracted on different dates can improve the correlations (from 0.6 to 0.7).
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2012.2198826