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Title: AIRBORNE HYPERSPECTRAL IMAGERY AND YIELD MONITOR DATA FOR ESTIMATING GRAIN SORGHUM YIELD VARIABILITY

Author
item YANG, CHENGHAI - TX A&M-WESLACO
item Everitt, James
item Bradford, Joe

Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/11/2004
Publication Date: 8/28/2004
Citation: Yang, C., Everitt, J.H., Bradford, J.M. 2004. Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Transactions of the American Society for Agricultural Engineers. 47(3):915-924.

Interpretive Summary: Hyperspectral imagery brings a wealth of new data in the spectral domain and has the potential for better differentiation and estimation of biophysical parameters than multispectral imagery. This study evaluated 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 a useful remote sensing data source for monitoring crop conditions and 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 estimating grain sorghum yield variability as compared with yield monitor data. Hyperspectral images containing 128 spectral bands in the visible to near-infrared region were acquired using an airborne hyperspectral imaging system from two grain sorghum fields during the 2000 growing season, and yield data were collected from the two fields using a yield monitor. The raw hyperspectral images were geometrically corrected, georeferenced and resampled to 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. Stepwise regression analysis based on the first ten principal components revealed that five significant components explained 68% and 80% of the variability in grain yield for fields 1 and 2, respectively. Stepwise regression analysis performed directly on the yield and hyperspectral data identified four optimum bands for field 1 and seven for field 2. The best four-band combination accounted for 69% of the variability in yield for field 1, while the best seven-band combination explained 82% of the variability for field 2. These results demonstrate that hyperspectral imagery can be a useful remote sensing data source for crop yield.