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Remote Sens. 2012, 4(10), 3127-3142;

Algorithm for Soybean Classification Using Medium Resolution Satellite Images

Center for Remote Sensing and Meteorological Research, Federal University of Rio Grande do Sul, Porto Alegre-RS, CP15044, Brazil
Environmental Engineering, University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo-RS, CP275, Brazil
Astronomy Department, Federal University of Rio Grande do Sul, Porto Alegre-RS, CP15044, Brazil
Author to whom correspondence should be addressed.
Received: 18 August 2012 / Revised: 27 September 2012 / Accepted: 29 September 2012 / Published: 18 October 2012
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An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales. View Full-Text
Keywords: remote sensing; classification; crop area; reflectance remote sensing; classification; crop area; reflectance
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Gusso, A.; Ducati, J.R. Algorithm for Soybean Classification Using Medium Resolution Satellite Images. Remote Sens. 2012, 4, 3127-3142.

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