In order to utilize remote sensing fully to inventory crop production, it is important to identify and quantify the effects of the factors that affect the accuracy of LANDSAT classifications. The objective of this study was to investigate the effect of scene characteristics involving crop, soil and weather variables on the accuracy of LANDSAT classifications of corn and soybeans. Segments of multitemporally registered LANDSAT MSS data from two key acquisition periods sampling the U.S. Corn Belt were classified using a Gaussian maximum likelihood classifier. Field size had a strong effect on classification accuracy with small fields tending to have low accuracies even when the effect of mixed pixels was eliminated. Other scene characteristics accounting for variability in classification accuracy included proportions of corn and soybeans, crop diversity index, proportion of all field crops, soil order, soil drainage class, percentage of slope, long-term average soybean yield, maximum yield, relative position of the segment in the Corn Belt, weather and crop development stage.
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Acknowledgments This research was sponsored by the NASA Johnson Space Center, contract NAS9-15466, while the authors were with the Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, Indiana.
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