Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/20/2012
Publication Date: 1/15/2013
Citation: Luo, B., Yang, C., Chanussot, J. 2013. Crop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 51:162-173. Interpretive Summary: Hyperspectral imagery can be useful for estimating crop yield and its spatial variation in precision agriculture. This study evaluated new spectral techniques, unsupervised linear unmixing, to estimate crop yield from hyperspectral images taken on multiple dates. Statistical analysis showed crop yield was significantly related to the crop abundance maps. By combining the vegetation abundances extracted on the two dates, the correlations with yield are improved. These results indicate that unsupervised linear unmixing is a useful tool to convert hyperspectral imagery of crop fields to relative yield maps.
Technical Abstract: 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 combination of the spectra of the vegetation and the bare soil. Recently-developed linear unmixing approaches are evaluated in this paper, which automatically extract 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).