Título:	Optimum combination of spectral variables for crop mapping in heterogeneous landscapes based on Sentinel-2 time series and machine learning.
Autoria:	OLIVEIRA JÚNIOR, J. G. de ESQUERDO, J. C. D. M. LAMPARELLI, R. A. C.
Afiliação:	JOSÉ GALDINO DE OLIVEIRA JÚNIOR, UNIVERSIDADE ESTADUAL DE CAMPINAS; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; RUBENS AUGUSTO CAMARGO LAMPARELLI, UNIVERSIDADE ESTADUAL DE CAMPINAS.
Ano de publicação:	2024
Referência:	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. X-3-2024, p. 85-92, 2024.
Conteúdo:	This 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.
Thesagro:	Sensoriamento Remoto Uso da Terra
NAL Thesaurus:	Time series analysis Remote sensing Land cover Land use
Palavras-chave:	Monitoramento agrícola Séries temporais Aprendizado de máquina Cobertura da terra Agricultural monitoring Random forest SITS Red Edge
ISSN:	2194-9050
Digital Object Identifier:	https://doi.org/10.5194/isprs-annals-X-3-2024-85-2024
Notas:	Edition of proceedings of the ISPRS TC III mid-term symposium “Beyond the canopy: technologies and applications of remote sensing”, Belém, Brazil, 2024.
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPTIA)