Title: USING HIGH RESOLUTION QUICKBIRD SATELLITE IMAGERY FOR COTTON YIELD ESTIMATION
Yang, Chenghai - TX A&M EXP'T. STN. WESLAC
Submitted to: American Society of Agricultural Engineers Meetings Papers
Publication Type: Proceedings
Publication Acceptance Date: August 1, 2004
Publication Date: October 20, 2004
Citation: Yang, C., Everitt, J.H., Bradford, J.M. 2004. Using high resolution Quickbird satellite imagery for cotton yield estimation. American Society of Agricultural Engineers. Paper No. 04-1119. St. Joseph, Michigan. 2004 CD-ROM.
Interpretive Summary: High resolution satellite sensors offer the farming community more choices of remote sensing products, but little is known about the usefulness of this type of imagery for assessing crop growth conditions as compared with airborne imagery. This study was designed to compare QuickBird imagery with airborne imagery for mapping cotton growth and yield variability. Statistical analyses showed that cotton yield monitor data were significantly related to both types of imagery and that the QuickBird imagery had similar correlations with cotton yield as compared with the airborne imagery. These results indicate that QuickBird imagery can be useful for assessing crop growth conditions and for mapping crop yield.
High spatial resolution imagery from recently launched satellite sensors offers new opportunities for crop management and agricultural applications. 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 in four spectral bands (blue, green, red and near-infrared) was acquired from south Texas in the 2003 growing season. Airborne color-infrared imagery with submeter resolution was also collected from two cotton fields within the satellite scene. Yield data were collected at harvest from the two fields using a cotton yield monitor. Field images for the two fields were extracted from the QuickBird scene and both the satellite and airborne images were aggregated into 8.4 m, close to twice the harvest width. Vegetation indices including band ratios and normalized differences were calculated from the spectral bands, and cotton yield was related to the spectral bands and vegetation indices for both the satellite and airborne 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 image data and that the QuickBird imagery had similar correlations with yield as compared with 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.