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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Developing crop rotation maps from satellite imagery for use in modeling water quality in the Choptank River watershed

Authors
item Hively, Wells
item McCarty, Gregory
item Lang, Megan
item Bandaru, Varaprasad - UNIVERSITY OF MARYLAND
item Kim, Andrew - UNIVERSITY OF MARYLAND
item Sadeghi, Ali

Submitted to: National Sedimentaton Laboratory (NSL)- 50 Years of Soil & Water Research in a Changing Agricultural Environment
Publication Type: Proceedings
Publication Acceptance Date: May 2, 2008
Publication Date: September 15, 2008
Citation: Hively, W.D., McCarty, G.W., Lang, M.W., Bandaru, V., Kim, A., Sadeghi, A.M. 2008. Developing crop rotation maps from satellite imagery for use in modeling water quality in the Choptank River Watershed. In: Proceedings of the National Sedimentation Laboratory - 50 Years of Soil and Water Research in a Changing Agricultural Environment, September 3-5, 2008, Oxford, Mississippi. 2008 CDROM.

Technical Abstract: Current water quality models such as AnnAGNPS and SWAT run on input data layers that derive topographic features from realistic landscape maps, but land use coverage is often derived only from aggregated county-level statistics that are translated into landscape maps in a semi-random fashion. In the course of our studies in the Choptank River Watershed Conservation Effects Assessment Project (CEAP) we have accumulated a three-year set of quarterly SPOT satellite images that we are using to monitor crop rotation and agricultural practices on farms within our study area. Through image classification we are able to identify crop types in the landscape (maize; soybean; wheat/soybean double crop; vegetated fallow; bare fallow) with an initial classification accuracy of 77%. As the project advances, methods will be developed to improve classification accuracy. Resulting maps can be used to provide realistic, distributed input data layers that are expected to improve the accuracy of water quality models through the incorporation of spatially-precise landscape metrics particular to individual crop types, such as erodibility and application and loss of nutrients and pesticides. We are also using the data to investigate regional patterns of crop rotation and the temporal niches that farmers find suitable for growing winter cover crops. This approach can also be used to monitor changes in the rotation patterns caused by the distortions in commodity markets resulting from bioenergy production.

Last Modified: 9/20/2014
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