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Retrieving wheat Biomass by using a hyper-spectral device on UAV

  • L. Xia (a1) (a2), R. R. Zhang (a1) (a2), L. P. Chen (a1) (a2), Y. Wen (a1) (a2), F. Zhao (a3) and J. J. Hou (a1) (a2)...


In this study, the biomass of winter wheat was estimated by using hyperspectral data obtained from a hyperspectral camera on an Unmanned Aerial Vehicle (UAV). Every two bands from the hyperspectral data were selected to calculate two kinds of vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI). Linear models were established between winter wheat biomass and those indexes, and coefficient of determination R2 was used to draw the two-dimensional distribution of R2 values. The comparison between NDVI and RVI for pixel covered by soil and wheat showed that RVI is more efficient to mask the influence from soil than NDVI. For calculating the NDVI, optimal bands are located mainly around 820 nm and 725 nm to 750 nm. For assessing RVI, the wavelength range from 820 to 832 nm, 794 to 808 nm, 770 to 788 nm, 725 nm to 750 nm and 890 nm for RVI are most suitable. Those optimal bands can achieve a coefficient of determination R2 higher than 0.88 by using the linear regression model in the study.


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Retrieving wheat Biomass by using a hyper-spectral device on UAV

  • L. Xia (a1) (a2), R. R. Zhang (a1) (a2), L. P. Chen (a1) (a2), Y. Wen (a1) (a2), F. Zhao (a3) and J. J. Hou (a1) (a2)...


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