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Title: MAPPING COTTON YIELD VARIABILITY USING AIRBORNE HYPERSPECTRAL IMAGERY AND YIELD MONITOR DATA

Author
item YANG, CHENGHAI - TX AG EXP STN-WESLACO
item Everitt, James
item Bradford, Joe
item MURDEN, DALE - RIO FARMS-MONTE ALTO, TX

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/15/2003
Publication Date: 8/30/2003
Citation: Yang, C., Everitt, J.H., Bradford, J.M., Murden, D. 2003. Mapping cotton yield variability using airborne hyperspectral imagery and yield monitor data. International Conference on Precision Agriculture Abstracts & Proceedings. CD-ROM.

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 grain yield was significantly related to hyperspectral data. Hyperspectral imagery can be used to identify the optimum wavelengths and band combinations for mapping cotton yield variability.

Technical Abstract: Increased availability of airborne 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 a charge-coupled device camera-based 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 hyperspectral images were corrected using a reference line approach. The corrected images were georeferenced and the digital numbers of the images were converted to reflectance. 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 the optimum bands and band combinations for mapping yield variability, though the optimum bands differed between the two fields.