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Open AccessArticle

Algorithm for Soybean Classification Using Medium Resolution Satellite Images

1
Center for Remote Sensing and Meteorological Research, Federal University of Rio Grande do Sul, Porto Alegre-RS, CP15044, Brazil
2
Environmental Engineering, University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo-RS, CP275, Brazil
3
Astronomy Department, Federal University of Rio Grande do Sul, Porto Alegre-RS, CP15044, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2012, 4(10), 3127-3142; https://doi.org/10.3390/rs4103127
Received: 18 August 2012 / Revised: 27 September 2012 / Accepted: 29 September 2012 / Published: 18 October 2012
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
MDPI and ACS Style

Gusso, A.; Ducati, J.R. Algorithm for Soybean Classification Using Medium Resolution Satellite Images. Remote Sens. 2012, 4, 3127-3142. https://doi.org/10.3390/rs4103127

AMA Style

Gusso A, Ducati JR. Algorithm for Soybean Classification Using Medium Resolution Satellite Images. Remote Sensing. 2012; 4(10):3127-3142. https://doi.org/10.3390/rs4103127

Chicago/Turabian Style

Gusso, Anibal; Ducati, Jorge Ricardo. 2012. "Algorithm for Soybean Classification Using Medium Resolution Satellite Images" Remote Sens. 4, no. 10: 3127-3142. https://doi.org/10.3390/rs4103127

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