NDLI: Crop classification in the U.S. Corn Belt using MODIS imagery

Content Provider	IEEE Xplore Digital Library
Author	Doraiswamy, P.C. Stern, A.J. Akhmedov, B.
Copyright Year	2007
Description	Author affiliation: ARS-Hydrology & Remote Sensing Lab, Beltsville (Doraiswamy, P.C.; Stern, A.J.)
Abstract	Landcover classification is essential in studies of landcover change, climate, hydrology, carbon sequestration, and yield prediction. The potential for using NASA's MODIS sensor at 250-meter resolution was investigated for USDA's operational programs. This research was conducted over Iowa and Illinois to classify corn and soybean crops. Multitemporal 8-day composite 250-meter-resolution surface reflectance product time series were used to generate the NDVI data, which were used to differential between corn and soybean crops in the U.S. Corn Belt. The results of the MODIS-based classification were compared with the Landsat-based classification for the 2-year period. The overall classification accuracy for Iowa was 82%, and for Illinois 75%. In conclusion, this method has been used successively during the 2002-2006 years to develop crop classifications and products for crop conditions and potential yield maps for Iowa and Illinois.
Starting Page	809
Ending Page	812
File Size	779803
Page Count	4
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4422920
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Crops Belts MODIS Classification tree analysis Vegetation mapping Decision trees Reflectivity Filtering Error correction Remote sensing Crop Classification MODIS Classification Data filtering
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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