Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/356393
Type: Artigo
Title: A spectral agrometeorological model for estimating soybean grain productivity in Mato Grosso, Brazil
Author: Sarmiento, Christiany M.
Coltri, Priscila P.
Alves, Marcelo de C.
Carvalho, Luiz G. de
Abstract: This study used spectral data integrated with the agrometeorological model by Doorenbos and Kassam to estimate soybean grain productivity in the state of Mato Grosso, Brazil. In the developed model, spectral data were used instead of meteorological data and biophysical parameters of the crop. For this purpose, the products of real and potential evapotranspiration (MOD16), normalized difference vegetation index – NDVI (MOD13Q1), and leaf area index (MOD15A2H) from the MODIS satellite were used, in addition to sunstroke data obtained by using the visible channel from the satellite GOES IMAGER. The results obtained showed that, with the proposed methodology, it was possible to follow the development of soybean cultivation throughout the cycle and to estimate production and productivity in the study area. Willmott's agreement index was 0.99 and 0.96 and Pearson's correlation coefficient was 0.99 and 0.84 for production and productivity, respectively
Subject: Modelagem matemática
Agricultura - Sensoriamento remoto
Country: Brasil
Editor: Associação Brasileira de Engenharia Agrícola / Brazilian Agricultural Engineering Society
Rights: Aberto
Identifier DOI: 10.1590/1809-4430-eng.agric.v40n3p405-412/2020
Address: https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300405
Date Issue: 2020
Appears in Collections:Cepagri- Artigos e Outros Documentos

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