Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 14, 2006
Publication Date: April 15, 2007
Citation: Yang, C., Everitt, J.H., Bradford, J.M. 2007. Using multispectral imagery and linear spectral unmixing techniques for estimating crop yield variability. Transactions of the ASABE. 50(2):667-674.
Interpretive Summary: Vegetation indices calculated from remote sensing imagery are commonly used to estimate crop plant growth and yield, but most of these indices use only two spectral bands in the image. This study evaluated linear spectral unmixing techniques, which can use all the bands in the image, for estimating yield variability from airborne multispectral imagery. Linear spectral unmixing models were applied to five time-sequential airborne images collected from a grain sorghum field to generate crop plant and soil abundance images. Plant and soil abundance data were significantly related to grain yield with equivalent or better correlations compared with the normalized difference vegetation index. These results indicate that linear spectral unmixing techniques can be used alone or in conjunction with traditional vegetation indices for quantifying crop canopy cover and mapping crop yield.
Vegetation indices derived from multispectral imagery are commonly used to extract crop growth and yield information. Spectral unmixing techniques provide an alternative approach to quantifying crop canopy abundance within each image pixel and have the potential for mapping crop yield variability. The objective of this study was to apply linear spectral unmixing techniques to airborne multispectral imagery for estimating grain sorghum yield variability. Five time-sequential airborne multispectral images and yield monitor data collected from a grain sorghum field were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the images to generate crop plant and soil abundances for each image and all 26 multi-image combinations of the five images. Correlation analysis showed that yield was significantly related to unconstrained and constrained plant and soil abundances. For comparison, yield was also related to the normalized difference vegetation index (NDVI) and the green NDVI (GNDVI). Results showed that unconstrained plant abundance had better correlations with yield than NDVI for all five images. Although GNDVI had better correlations with yield for the first three images, unconstrained plant abundance derived from the fourth image provided the best overall correlation with yield (r=0.88). Moreover, multi-image combinations generally improved the correlations with yield over single images and the best three-image combination resulted in the highest overall correlation (r=0.90) between yield and unconstrained plant abundance. These results indicate that linear spectral unmixing techniques can be a useful tool for quantifying crop canopy abundance and mapping crop yield.