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Classification of corn and soybeans using multitemporal Thematic Mapper dataThe multitemporal classification approach based on the greenness profile derived from Landsat Multispectral Scanner (MSS) spectral bands has proved successful in effectively separating and identifying corn, soybean, and other ground cover classes. Features derived from these profiles have been shown to carry virtually all the information contained in the original data and, in addition, have been shown to be stable over a large geographic area of the United States. The objective of this investigation was to determine if the same features derived from multitemporal Thematic Mapper (TM) data would also prove effective in separating these two crop types, and, in fact, if algorithms developed for MSS could be directly applied to TM. It is shown that this is indeed the case. In addition, because of greater spatial and spectral resolution, the accuracy of TM classifications is better than in MSS.
Document ID
19850030824
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Badhwar, G. D.
(NASA Johnson Space Center Houston, TX, United States)
Date Acquired
August 12, 2013
Publication Date
October 1, 1984
Publication Information
Publication: Remote Sensing of Environment
Volume: 16
ISSN: 0034-4257
Subject Category
Earth Resources And Remote Sensing
Accession Number
85A12975
Distribution Limits
Public
Copyright
Other

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