Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29083
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Identificação da dinâmica espaço-temporal para estimar área cultivada de soja a partir de imagens MODIS no Rio Grande do Sul
Other Titles: Identification of the spatial and temporal dynamics for estimating soybean crop area from MODIS images in the Rio Grande do Sul, Brazil
Author(s): dos Santos, Juliana S
Fontana, Denise C
Silva, Thiago S F
Rudorff, Bernardo F T
Keywords: Crop yield predictive
Multitemporal imagery
Phenology
Remote sensing
Issue Date: 30-Jan-2014
Citation: dos Santos JS, Fontana DC, Silva TSF & Rudorff BFT (2014) Identificação da dinâmica espaço-temporal para estimar área cultivada de soja a partir de imagens MODIS no Rio Grande do Sul [Identification of the spatial and temporal dynamics for estimating soybean crop area from MODIS images in the Rio Grande do Sul, Brazil]. Revista Brasileira de Engenharia Agricola e Ambiental, 18 (1), pp. 54-63. https://doi.org/10.1590/S1415-43662014000100008
Abstract: The objective of this study was to define a method for estimating soybean crop area in the Northern Rio Grande do Sul state (Brazil). Overall, six different remote sensing methods were proposed based on spectral-temporal profile and minimum and maximum values of NDVI/MODIS related to the stages of sowing, maximum development and harvesting of soybean areas. The resulting estimates were compared to official crop area data provided by the Brazilian government, using statistical analysis and the fuzzy similarity method. The performance of each method depended on information such as crop size, type of crop management, and sowing/harvesting dates. Regression coefficients of determination and fuzzy agreement values were above 0.8 and 0.45, respectively, for all methods. For operational monitoring of soybean crop area, the empirical threshold applied to the image difference with inclusion of harvest image method was the most effective, producing estimates that matched closely the official data. For spatial analysis the application of multitemporal images classification method is recommended that generated a map of better quality. The efficiency of these methods should be evaluated in the areas of soybean expansion in the state.
DOI Link: 10.1590/S1415-43662014000100008
Rights: All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/deed.en).



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