Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 24, 2005
Publication Date: March 25, 2006
Citation: Yang, C., Everitt, J.H., Bradford, J.M. 2006. Comparison of QuickBird satellite imagery for mapping grain sorghum yield patterns. Precision Agriculture. 7:33-33. Interpretive Summary: High resolution satellite imagery is becoming commercially available, but little research has been conducted to evaluate this type of imagery for crop management. The objectives of this study were to examine QuickBird imagery for mapping grain sorghum growth and yield patterns and to compare QuickBird imagery with airborne multispectral imagery for yield estimation. Results showed that grain yield was significantly related to both types of image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery. These results indicate that QuickBird imagery can be a useful data source for mapping crop yield variability and for assessing crop conditions.
Technical Abstract: Timely and accurate information on crop conditions obtained during the growing season is of vital importance for crop management. High spatial resolution satellite imagery has the potential for mapping crop growth variability and identifying problem areas within fields. The objectives of this study were to use QuickBird satellite imagery for mapping plant growth and yield patterns within grain sorghum fields as compared with airborne multispectral image data. A QuickBird 2.8-m four-band image covering a cropping area in south Texas was acquired in the 2003 growing season. Airborne three-band imagery with submeter resolution was also collected from two grain sorghum fields within the satellite scene. Yield data were collected from the two fields using a grain yield monitor. Grain yield was related to the spectral bands and vegetation indices derived from the bands for both the satellite and airborne imagery. The extracted QuickBird images for the two fields were then classified into multiple zones using unsupervised classification, and mean yields among the zones were compared. Results showed that grain yield was significantly related to both types of image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery. Moreover, the unsupervised classification maps effectively differentiated grain production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for determining plant growth and yield patterns for within-field crop management.