Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 20, 2006
Publication Date: December 15, 2006
Citation: Yang, C., Everitt, J.H., Bradford, J.M. 2006. Evaluating high resolution QuickBird satellite imagery for estimating cotton yield. Transactions of the ASABE. 49(5):1599-1606.
Interpretive Summary: Recently launched satellite sensors provide high resolution remote sensing imagery, but little is known about the usefulness of this type of image data for assessing crop conditions. The objective of this study was to evaluate QuickBird satellite imagery for mapping plant growth and yield variability in cotton fields. Statistical analyses showed that cotton yield monitor data were significantly related to QuickBird data and that QuickBird imagery had similar correlations with yield to airborne imagery. These results indicate that QuickBird imagery can be a useful data source for identifying plant growth patterns and estimating crop yield.
High spatial resolution imagery from recently launched satellite sensors offers new opportunities for crop management. The objective of this study was to evaluate QuickBird satellite imagery for mapping plant growth and yield variability in cotton fields. A QuickBird image scene with 2.8 m resolution and four spectral bands (blue, green, red and near-infrared) was acquired from an intensively cropped area in south Texas. Airborne color-infrared imagery was also collected from two cotton fields within the satellite scene. Yield data were collected from the two fields using a cotton yield monitor. Images for the two fields were extracted from the QuickBird scene, and both the satellite and airborne images were degraded to a pixel resolution of 8.4 m, about twice the harvester swath. Vegetation indices including band ratios and normalized differences were calculated from the spectral bands, and cotton yield was related to the bands and vegetation indices for both types of imagery. The extracted QuickBird images were classified into 2-10 zones using unsupervised classification, and mean yields among the zones were compared. Results showed that cotton yield was significantly related to both types of imagery and that the QuickBird imagery had similar correlations with yield to the airborne imagery. Moreover, the unsupervised classification maps effectively differentiated cotton production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for identifying plant growth patterns and estimating crop yield.