02255naa a2200229 a 450000100080000000500110000800800410001910000140006024500830007426000090015750001180016652014810028465000500176565000190181565000150183465000160184965000140186565000250187970000140190470000180191877300890193620680412017-05-16       2017    bl uuuu     u00u1 u    #d1 aGUSSO, A.  aModel for soybean production forecast based on prevailing physical conditions.  c2017  aTítulo em português: Modelo para previsão da produção de soja baseado em condições físicas predominantes.  aThe objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R2=0.96 (p?0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R2=0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast.  aModerate resolution imaging spectroradiometer  aRemote sensing  aSatellites  aAgricultura  aSatélite  aSensoriamento remoto1 aARVOR, D.1 aDUCATI, J. R.  tPesquisa Agropecuária Brasileira, Brasília, DFgv. 52, n. 2, p. 95-103, fev. 2017.