Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1182998
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dc.contributor.authorNOVAIS, J. J. M.
dc.contributor.authorMELO, B. M. D.
dc.contributor.authorNEVES JUNIOR, A. F.
dc.contributor.authorCAVALCANTE LIMA, R. H.
dc.contributor.authorSOUZA, R. E. de
dc.contributor.authorMELO, V. F.
dc.contributor.authorAMARAL, E. F. do
dc.contributor.authorTZIOLAS, N.
dc.contributor.authorDEMATTÊ, J. A. M.
dc.date.accessioned2025-12-17T15:48:40Z-
dc.date.available2025-12-17T15:48:40Z-
dc.date.created2025-12-17
dc.date.issued2025
dc.identifier.citationJournal of Environmental Management, v. 375, article 124155, Feb. 2025.
dc.identifier.issn0301-4797
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1182998-
dc.descriptionAnalyzing soil in large and remote areas such as the Amazon River Basin (ARB) is unviable when it is entirely performed by wet labs using traditional methods due to the scarcity of labs and the significant workforce requirements, increasing costs, time, and waste. Remote sensing, combined with cloud computing, enhances soil analysis by modeling soil from spectral data and overcoming the limitations of traditional methods. We verified the potential of soil spectroscopy in conjunction with cloud-based computing to predict soil organic carbon (SOC) and particle size (sand, silt, and clay) content from the Amazon region.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectCarbono orgânico
dc.subjectAnálisis del suelo
dc.subjectEspectroscopia de reflectancia
dc.subjectCarbono orgánico del suelo
dc.subjectFracción de arcilla
dc.subjectFracción limo
dc.subjectFracción de arena
dc.subjectAmazon basin
dc.subjectBacia Amazônica
dc.titleOnline analysis of Amazon’s soils through reflectance spectroscopy and cloud computing can support policies and the sustainable development.
dc.typeArtigo de periódico
dc.subject.thesagroAnálise do Solo
dc.subject.thesagroPropriedade Físico-Química
dc.subject.thesagroFração do Solo
dc.subject.thesagroArgila
dc.subject.thesagroAreia
dc.subject.thesagroLodo Residual
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroPrograma de Computador
dc.subject.nalthesaurusSoil analysis
dc.subject.nalthesaurusPhysicochemical properties
dc.subject.nalthesaurusSoil organic carbon
dc.subject.nalthesaurusSilt fraction
dc.subject.nalthesaurusClay fraction
dc.subject.nalthesaurusSand fraction
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusReflectance spectroscopy
dc.subject.nalthesaurusComputer software
riaa.ainfo.id1182998
riaa.ainfo.lastupdate2025-12-17
dc.identifier.doihttps://doi.org/10.1016/j.jenvman.2025.124155
dc.contributor.institutionJEAN JESUS MACEDO NOVAIS, UNIVERSIDADE DE SÃO PAULO; BORGES MARFRANN DIAS MELO, UNIVERSIDADE DE SÃO PAULO; AFRÂNIO FERREIRA NEVES JUNIOR, UNIVERSIDADE FEDERAL DO AMAZONAS; RAIMUNDO HUMBERTO CAVALCANTE LIMA, UNIVERSIDADE FEDERAL DO AMAZONAS; RENATO EPIFÂNIO DE SOUZA, INSTITUTO FEDERAL DE EDUCAÇÃO, CIÊNCIA E TECNOLOGIA DO ACRE; VALDINAR FERREIRA MELO, UNIVERSIDADE FEDERAL DE RORAIMA; EUFRAN FERREIRA DO AMARAL, CPAF-AC; NIKOLAOS TZIOLAS, UNIVERSITY OF FLORIDA; JOSÉ ALEXANDRE MELO DEMATTÊ, UNIVERSIDADE DE SÃO PAULO.
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