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Corn and soybean Landsat MSS classification performance as a function of scene characteristicsIn order to fully utilize remote sensing to inventory crop production, it is important to identify 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 sampling the U.S. Corn Belt were classified using a Gaussian maximum likelihood classifier on multitemporally registered data from two key acquisition periods. 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 drainage, slope, soil order, long-term average soybean yield, maximum yield, relative position of the segment in the Corn Belt, weather, and crop development stage.
Document ID
19840030286
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Batista, G. T.
(Purdue Univ. West Lafayette, IN, United States)
Hixson, M. M.
(Purdue Univ. West Lafayette, IN, United States)
Bauer, M. E.
(Purdue University West Lafayette, IN, United States)
Date Acquired
August 12, 2013
Publication Date
January 1, 1982
Subject Category
Earth Resources And Remote Sensing
Accession Number
84A13073
Funding Number(s)
CONTRACT_GRANT: NAS9-15466
Distribution Limits
Public
Copyright
Other

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