Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 1, 2009
Publication Date: October 2, 2009
Citation: Yang, C., Everitt, J.H., Bradford, J.M. 2009. Evaluating high resolution SPOT 5 satellite imagery for crop yield estimation. Precision Agriculture. 10(4):292-303.
Interpretive Summary: As high resolution satellite imagery is becoming available, it is necessary to evaluate this type of image data for assessing crop growth and yield conditions. This study examined SPOT 5 satellite imagery for estimating crop yield. A SPOT 5 image with 10-m spatial resolution and four spectral bands (green, red, near-infrared, and mid-infrared) was acquired in south Texas, and yield monitor data were collected from three grain sorghum fields in the imaging area. Statistical analysis showed that the SPOT 5 image data were significantly related to the yield monitor data and explained 68% of the variability in yield in the three fields. These results indicate that high resolution SPOT 5 satellite imagery can be useful for monitoring crop conditions and mapping yield variations.
High resolution satellite imagery has the potential for mapping within-field variability in crop growth and yield. This study examined SPOT 5 multispectral imagery for estimating grain sorghum yield. A SPOT 5 image with 10-m spatial resolution and four spectral bands (green, red, near-infrared, and mid-infrared) was acquired in south Texas, and yield monitor data were collected from three grain sorghum fields in the imaging area. Subset images covering the three fields were extracted from the satellite scene, and images with pixel sizes of 20 m and 30 m were also generated from the subset images to simulate coarser resolution satellite imagery. Vegetation indices and principle components were derived from the images at the three spatial resolutions. Grain yield was related to the vegetation indices, the four bands, and the principal components for each field and for all the fields combined. The effect of the mid-infrared band on yield estimation was examined by comparing the regression results from all four bands with those from the other three bands. Statistical analysis showed that the 10-m, four-band image and the aggregated 20-m and 30-m images explained respectively 68, 76, and 83% of the variability in yield for all the fields combined. The coefficient of determination between yield and the imagery increased with pixel size due to the smoothing effect. The inclusion of the mid-infrared band slightly improved the R-squared values. These results indicate that high resolution SPOT 5 multispectral imagery can be a useful data source for determining within-field yield variability for crop management.