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Open AccessArticle

Artificial Neural Network Approach for Mapping Contrasting Tillage Practices

Department of Civil Engineering, Indian Institute of Technology Madras, India
USDA-ARS Conservation and Production Research Laboratory, PO Drawer 10, Bushland, TX 79012, USA
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
Author to whom correspondence should be addressed.
Remote Sens. 2010, 2(2), 579-590;
Received: 18 December 2009 / Revised: 5 January 2010 / Accepted: 8 February 2010 / Published: 23 February 2010
Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remote sensing data. However, these models require information about the complex nature of underlying watershed characteristics and processes. Unlike regression-based models, Artificial Neural Network (ANN) provides an efficient alternative to map complex nonlinear relationships between an input and output datasets without requiring a detailed knowledge of underlying physical relationships. Limited or no information currently exist quantifying ability of ANN models to identify contrasting tillage practices from remote sensing data. In this study, a set of Landsat TM-based ANN models was developed to identify contrasting tillage practices in the Texas High Plains. Observed tillage data from Moore and Ochiltree Counties were used to develop and evaluate the models, respectively. The overall classification accuracy for the 15 models developed with the Moore County dataset varied from 74% to 91%. Statistical evaluation of these models against the Ochiltree County dataset produced results with an overall classification accuracy varied from 66% to 80%. The ANN models based on TM band 5 or indices of TM Band 5 may provide consistent and accurate tillage information when applied to the Texas High Plains. View Full-Text
Keywords: tillage mapping; Landsat; Texas high plains tillage mapping; Landsat; Texas high plains
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Sudheer, K.P.; Gowda, P.; Chaubey, I.; Howell, T. Artificial Neural Network Approach for Mapping Contrasting Tillage Practices. Remote Sens. 2010, 2, 579-590.

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