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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING AND GIS FOR DETECTING AND MAPPING INVASIVE WEEDS IN RIPARIAN AND WETLAND ECOSYSTEMS Title: Comparison of airborne multispectral and hyperspectral imagery for estimating grain sorghum yield

Authors
item Yang, Chenghai
item Everitt, James
item Bradford, Joe
item Murden, Dale - RIO FARMS,MONTE ALTO,TX

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 8, 2009
Publication Date: April 30, 2009
Citation: Yang, C., Everitt, J.H., Bradford, J.M., Murden, D. 2009. Comparison of airborne multispectral and hyperspectral imagery for estimating grain sorghum yield. Transactions of the ASABE. 52(2):641-649.

Interpretive Summary: Both multispectral and hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare the differences between these two types of imagery for crop yield estimation. This study used different spectral and statistical analysis techniques to compare airborne multispectral imagery with airborne hyperspectral imagery for mapping yield variability in grain sorghum fields. Analysis results showed that the hyperspectral imagery accounted for more variability in yield than multispectral imagery. These results indicate that hyperspectral imagery has the potential to provide better results for crop yield estimation.

Technical Abstract: Both multispectral and hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare the differences between these two types of imagery for assessing crop growth and yields. The objective of this study was to compare airborne multispectral imagery with airborne hyperspectral imagery for mapping yield variability in grain sorghum fields. Airborne color-infrared (CIR) imagery acquired from a three-band imaging system and hyperspectral imagery acquired from a 128-band hyperspectral imaging system along with yield monitor data collected from four fields were used in this study. Three-band imagery with wavebands corresponding to the collected CIR imagery and four-band imagery with wavebands similar to QuickBird satellite imagery were generated from the hyperspectral imagery. All four types of imagery (two original and two simulated) were aggregated to increase pixel size to match the yield data resolution. Principle components and normalized difference vegetation indices (NDVIs) were derived from each type of imagery and related to yield. Statistical analysis showed that the hyperspectral imagery accounted for more variability in yield than the other three types of multispectral imagery and that best narrow-band NDVIs derived from the hyperspectral imagery explained more variability than best broad-band NDVIs derived from the multispectral imagery. These results indicate that hyperspectral imagery has the potential for improving yield estimation accuracy.

Last Modified: 10/30/2014
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