Crop classification in the US Corn Belt using MODIS imagery

被引：16

作者：

Doraiswamy, Paul C. ^{[1
]}

Stern, Alan J. ^{[1
]}

Akhmedov, Bakhyt ^{[2
]}

机构：

[1] ARS, Hydrol & Remote Sensing Lab, USDA, Beltsville, MD USA

[2] Inc, Sci Syst & Appl, Lanham, MD USA

来源：

IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET | 2007年

关键词：

MODIS classification; data filtering; crop classification;

D O I：

10.1109/IGARSS.2007.4422920

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：809 / +

页数：2

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