Optimum combination of spectral variables for crop mapping in heterogeneous landscapes based on Sentinel-2 time series and machine learning.

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dc.contributorJOSÉ GALDINO DE OLIVEIRA JÚNIOR, UNIVERSIDADE ESTADUAL DE CAMPINAS; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; RUBENS AUGUSTO CAMARGO LAMPARELLI, UNIVERSIDADE ESTADUAL DE CAMPINAS.
dc.creatorOLIVEIRA JÚNIOR, J. G. de
dc.creatorESQUERDO, J. C. D. M.
dc.creatorLAMPARELLI, R. A. C.
dc.date2024-11-14T11:55:06Z
dc.date2024-11-14T11:55:06Z
dc.date2024-11-14
dc.date2024
dc.date.accessioned2026-07-07T04:17:53Z
dc.descriptionThis article aimed to determine a workflow for more efficient large-scale crop mapping using a time series of images from the Sentinel-2 Satellite, statistical methods of attribute selection, and machine learning. The proposed methodology explores the best possible combination of spectral variables related to vegetation (16 vegetation indices in the RGB, NIR, SWIR, and Red Edge regions) to characterize different spectro-temporal profiles of Land Use and Land Cover (LULC) in spatially heterogeneous landscapes.
dc.descriptionEdition of proceedings of the ISPRS TC III mid-term symposium “Beyond the canopy: technologies and applications of remote sensing”, Belém, Brazil, 2024.
dc.identifierISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. X-3-2024, p. 85-92, 2024.
dc.identifier2194-9050
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1169127
dc.identifierhttps://doi.org/10.5194/isprs-annals-X-3-2024-85-2024
dc.identifier.urihttp://hdl.handle.net/123456789/456874
dc.languageeng
dc.rightsopenAccess
dc.subjectMonitoramento agrícola
dc.subjectSéries temporais
dc.subjectAprendizado de máquina
dc.subjectCobertura da terra
dc.subjectAgricultural monitoring
dc.subjectRandom forest
dc.subjectSITS
dc.subjectRed Edge
dc.subjectSensoriamento Remoto
dc.subjectUso da Terra
dc.subjectTime series analysis
dc.subjectRemote sensing
dc.subjectLand cover
dc.subjectLand use
dc.titleOptimum combination of spectral variables for crop mapping in heterogeneous landscapes based on Sentinel-2 time series and machine learning.
dc.typeArtigo de periódico

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