Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/356393
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampColtri, Priscila Pereira-
dc.typeArtigopt_BR
dc.titleA spectral agrometeorological model for estimating soybean grain productivity in Mato Grosso, Brazilpt_BR
dc.contributor.authorSarmiento, Christiany M.-
dc.contributor.authorColtri, Priscila P.-
dc.contributor.authorAlves, Marcelo de C.-
dc.contributor.authorCarvalho, Luiz G. de-
dc.subjectModelagem matemáticapt_BR
dc.subjectAgricultura - Sensoriamento remotopt_BR
dc.subject.otherlanguageMathematical modelingpt_BR
dc.subject.otherlanguageAgriculture - Remote sensingpt_BR
dc.description.abstractThis 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, respectivelypt_BR
dc.relation.ispartofEngenharia agrícolapt_BR
dc.publisher.cityJaboticabal, SPpt_BR
dc.publisher.countryBrasilpt_BR
dc.publisherAssociação Brasileira de Engenharia Agrícola / Brazilian Agricultural Engineering Societypt_BR
dc.date.issued2020-
dc.date.monthofcirculationMay/Junept_BR
dc.language.isoengpt_BR
dc.description.volume40pt_BR
dc.description.issuenumber3pt_BR
dc.rightsAbertopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0100-6916pt_BR
dc.identifier.eissn1809-4430pt_BR
dc.identifier.doi10.1590/1809-4430-eng.agric.v40n3p405-412/2020pt_BR
dc.identifier.urlhttps://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300405pt_BR
dc.date.available2021-02-24T14:25:29Z-
dc.date.accessioned2021-02-24T14:25:29Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2021-02-24T14:25:29Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-05-24T19:06:39Z : No. of bitstreams: 1 000547377900018.pdf: 1131778 bytes, checksum: ef00b5520caf6dea30f570214ad70b13 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-02-24T14:25:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2020en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/356393-
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.unidadeCentro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agriculturapt_BR
dc.subject.keywordMathematical Modelingpt_BR
dc.subject.keywordCrop Monitoringpt_BR
dc.identifier.source000547377900018pt_BR
dc.creator.orcid0000-0002-0807-3410pt_BR
dc.type.formArtigopt_BR
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