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The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas. O objetivo deste trabalho foi avaliar a aplicação do método de classificação por superfícies de resposta espectro-temporal (STRS) em imagens do sensor Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) para estimar áreas de plantio de soja no Estado de Mato Grosso, Brasil. A classificação foi realizada usando o algoritmo de máxima verossimilhança (MLA) adaptado ao algoritmo STRS. Trinta segmentos de 30x30 km foram escolhidos ao longo das principais regiões agrícolas do estado, com dados da safra de verão de 2005/2006 (outubro a março), e mapeados com base em dados de campo e de imagens orbitais TM/Landsat-5 e CCD/CBERS-2. Cinco classes temáticas foram consideradas: Soja, Floresta, Cerrado, Pastagem e Solos Expostos. A classificação pelo método das STRS foi feita com base em uma área interseccionada por um subconjunto de segmentos de 30x30 km. O STRS superestimou os valores de referência em 21,31% em regiões com predomínio da cultura da soja e em 132,37% em regiões nas quais a soja era menos predominante. A exatidão global da classificação foi de 80%. As imagens MODIS e o algoritmo STRS mostraram-se promissores para a classificação da soja em regiões com predominância de grandes fazendas. Entretanto, os resultados para áreas fragmentadas em fazendas menores foram menos eficientes, superestimando as áreas de soja

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil