Submitted to: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)
Publication Type: Proceedings
Publication Acceptance Date: 7/28/2002
Publication Date: 8/30/2002
Citation: Yang, C., Everitt, J.H., Bradford, J.M. 2002. Airborne hyperspectral imaging and yield monitoring of grain sorghum yield variability. Agricultural Engineering International Conference. CDROM. Interpretive Summary: Limited research has been conducted to evaluate the usefulness of hyperspectral imagery for crop monitoring and precision agriculture applications. This study examined airborne hyperspectral imagery for mapping grain sorghum yield variability as compared with yield monitor data. Statistical analyses indicated that grain yield was significantly related to hyperspectral data. Hyperspectral imagery can be used to identify the optimum spectral bands (wavelengths) and band combinations for mapping yield variability.
Technical Abstract: As hyperspectral imagery is becoming more available, it is necessary to evaluate its potential for crop monitoring and precision agriculture applications. In this study, airborne hyperspectral imagery was examined for mapping grain sorghum yield variability as compared with yield monitor data. Hyperspectral images were acquired using a CCD camera-based hyperspectral imaging system from two grain sorghum fields during the 2000 growing season, and yield data were also collected from the fields using a yield monitor. The hyperspectral images contained 128 bands covering a spectral range from 457.2 to 921.7 nm with a band width of 3.63 nm. The images had a swath width of 640 pixels and each pixel had a gray level between 0 and 4095. The raw hyperspectral images were corrected for the geometric distortion caused by the motion of the aircraft flying the imaging system. The corrected images were rectified to the UTM coordinate system with 1 m resolution and the raw digital numbers were converted to reflectance. The calibrated image data were then aggregated into images with a cell size of 9 m, close to the combine=s effective cutting width. Correlation analysis showed that grain yield was significantly related to the image data for all the bands except for a few in the transitional range from the red to the near-infrared region. Principal components analysis indicated that the first few principal components of the hyperspectral images accounted for 99% of variability in the image data. Regression analysis based on the principal components effectively quantified the amount of yield variability explained by image data. Stepwise regression analysis performed directly on the yield and hyperspectral data identified the optimum bands and band combinations for mapping yield variability, though the optimum bands differed between the two fields.