Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1119120
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dc.contributor.authorPADILHA, M. C. C.eng
dc.contributor.authorVICENTE, L. E.eng
dc.contributor.authorDEMATTÊ, J. A. M.eng
dc.contributor.authorLOEBMANN, D. G. dos S. W.eng
dc.contributor.authorURBINA SALAZAR, D.eng
dc.contributor.authorKOGA-VICENTE, A.eng
dc.contributor.authorARAUJO, L. S. deeng
dc.contributor.authorMANZATTO, C. V.eng
dc.date.accessioned2020-01-21T18:23:26Z-
dc.date.available2020-01-21T18:23:26Z-
dc.date.created2020-01-21
dc.date.issued2019
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos. Anais... São José dos Campos: INPE, 2019. Ref. 96042.eng
dc.identifier.isbn978-85-17-00097-3eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1119120-
dc.descriptionAbstract: The quantification of soil organic carbon (SOC) is essential to agriculture and sustainable use of the land. However, there are difficulties to estimate it in large areas due to high cost of soil sample extraction, and laboratory preparations. There are approaches that may facilitate the estimation of SOC, such as the use of satellite imagery and the application of statistical models based on the spectral bands of the satellite under study. In July of 2017, this study proposed a prediction statistical model from optical-orbital data of the series Landsat, OLI sensor for estimating SOC content.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectLinear regressioneng
dc.subjectLandsat OLIeng
dc.titlePrediction statistical model for soil organic carbon mapping in crop areas using the Landsat/OLI sensor.eng
dc.typeArtigo em anais e proceedingseng
dc.date.updated2020-01-21T18:23:26Z
dc.subject.thesagroCarbonoeng
dc.subject.thesagroSoloeng
dc.subject.thesagroRegressão Lineareng
dc.subject.thesagroSatéliteeng
dc.subject.nalthesaurusSoil organic carboneng
dc.subject.nalthesaurusPredictioneng
dc.subject.nalthesaurusRegression analysiseng
dc.subject.nalthesaurusLinear modelseng
dc.format.extent2p. 1-4.eng
riaa.ainfo.id1119120eng
riaa.ainfo.lastupdate2020-01-21
dc.contributor.institutionMANUELA CORRÊA DE CASTRO PADILHA, ESALQ-USP; LUIZ EDUARDO VICENTE, CNPMA; JOSÉ ALEXANDRE MELO DEMATTÊ, ESALQ-USP; DANIEL GOMES DOS SANTOS W LOEBMANN, CNPMA; DIEGO URBINA SALAZAR, ESALQ-USP; ANDREA KOGA-VICENTE; LUCIANA SPINELLI DE ARAUJO, CNPMA; CELSO VAINER MANZATTO, CNPMA.eng
Aparece nas coleções:Artigo em anais de congresso (CNPMA)

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