Paper Info
Title
Applying linear spectral unmixing to airborne hyperspectral imagery for mapping crop yield variability
Abstract
This study evaluated linear spectral unmixing techniques for mapping the variation in crop yield. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery recorded from a grain sorghum field and a cotton field. A pair of plant and soil spectra derived from each image was used as endmember spectra to generate unconstrained and constrained plant and soil cover fractions. Yield was positively related to plant fractions and negatively related to soil fractions. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Plant fractions provided better correlations with yield than the majority of the NDVIs. These results indicate that plant cover fraction maps derived from hyperspectral imagery can be used as relative yield maps to characterize crop yield variability.
Year
DOI
Venue
2009
10.1109/WHISPERS.2009.5289022
WHISPERS
Keywords
Field
DocType
agriculture,crops,image processing,airborne hyperspectral imagery,cotton field,crop yield variability,grain sorghum field,narrow-band normalized difference vegetation indices,plant cover fraction map,plant fraction,soil cover fraction,unconstrained linear spectral unmixing model,linear spectral unmixing,hyperspectral imagery,narrow-band ndvi,yield variability,cotton,hyperspectral imaging,plant cover,crop yield,pixel,correlation
Endmember,Vegetation,Crop yield,Plant cover,Remote sensing,Image processing,Hyperspectral imaging,Environmental science,Pixel,Sorghum
Conference
ISBN
Citations 
PageRank 
978-1-4244-4687-2
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Chenghai Yang100.34
Everitt, J.H.200.34
Bradford, J.M.300.34