|Yang, Chenghai - TX A&M UNIV., WESLACO|
|Murden, Dale - RIO FARMS, INC.|
Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 31, 2003
Publication Date: December 20, 2003
Citation: Yang, C., Everitt, J.H., Bradford, J.M., Murden, D. 2004. Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability. Precision Agriculture 5(5):445-461. Interpretive Summary: Limited research has been conducted to evaluate the usefulness of hyperspectral imagery for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton yield variability as compared with yield monitor data. Statistical analyses indicated that cotton yield was significantly related to hyperspectral data. Therefore, hyperspectral imagery can be a useful remote sensing data source for mapping crop yield variability.
Technical Abstract: Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne hyperspectral imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands covering a spectral range from 457 to 922 nm with a bandwidth of 3.6 nm. Geometric distortions in the raw images were corrected using a reference line approach. The corrected images were georeferenced and resampled to 1 m resolution and the digital numbers of the images were converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton picker's cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified optimum bands and band combination for mapping yield variability for the two fields. The stepwise regression models explained 61% and 69% of the variability in cotton yield for the two fields, respectively. These results indicate that hyperspectral imagery can be useful for mapping crop yield variability.