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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING AND GIS FOR DETECTING AND MAPPING INVASIVE WEEDS IN RIPARIAN AND WETLAND ECOSYSTEMS Title: Airborne hyperspectral imagery for mapping crop yield variability

Author
item Yang, Chenghai

Submitted to: Geography Compass
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 24, 2009
Publication Date: November 5, 2009
Citation: Yang, C. 2009. Airborne hyperspectral imagery for mapping crop yield variability. Geography Compass. 3(5):1717-1731.

Interpretive Summary: Remote sensing has long been used to monitor crop growing conditions and to estimate crop yields. This article presents an overview on the use of airborne remote sensing imagery for mapping crop yield variability and illustrates how airborne hyperspectral imagery can be used for crop yield estimation. Some of the challenges and considerations on the use of hyperspectral imagery for yield mapping are discussed. The methodologies and techniques presented in this article will be useful for researchers and practitioners to use remote sensing imagery for crop management.

Technical Abstract: Information concerning the spatial variation in crop yield has become necessary for site-specific crop management. Traditional satellite imagery has long been used to monitor crop growing conditions and to estimate crop yields over large geographic areas. However, this type of imagery has limited use for assessing within-field yield variability because of its coarse spatial resolution. Therefore, high resolution airborne multispectral and hyperspectral imagery has been used for this purpose. This article presents an overview on the use of remote sensing imagery for mapping crop yield variability and illustrates how airborne hyperspectral imagery can be used for crop yield estimation based on the work conducted at the U.S. Department of Agriculture’s Kika de la Garza Subtropical Agricultural Research Center at Weslaco, Texas. Some of the challenges and considerations on the use of hyperspectral imagery for yield mapping are discussed.

Last Modified: 7/28/2014
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