Source: Remote Sensing Applications: Society and Environment. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, IMAGEAMENTO DE SATÉLITE, MAPEAMENTO DO SOLO, SENSORIAMENTO REMOTO, SÉRIES ESPAÇO-TEMPORAIS
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LUCIANO, Ana Cláudia dos Santos e CAMPAGNUCI, Bruna Cristina Gama e LE MAIRE, Guerric. Mapping 33 years of sugarcane evolution in São Paulo state, Brazil, using landsat imagery and generalized space-time classifiers. Remote Sensing Applications: Society and Environment, v. 26, p. 1-12, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.rsase.2022.100749. Acesso em: 30 set. 2024.APA
Luciano, A. C. dos S., Campagnuci, B. C. G., & le Maire, G. (2022). Mapping 33 years of sugarcane evolution in São Paulo state, Brazil, using landsat imagery and generalized space-time classifiers. Remote Sensing Applications: Society and Environment, 26, 1-12. doi:10.1016/j.rsase.2022.100749NLM
Luciano AC dos S, Campagnuci BCG, le Maire G. Mapping 33 years of sugarcane evolution in São Paulo state, Brazil, using landsat imagery and generalized space-time classifiers [Internet]. Remote Sensing Applications: Society and Environment. 2022 ; 26 1-12.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.rsase.2022.100749Vancouver
Luciano AC dos S, Campagnuci BCG, le Maire G. Mapping 33 years of sugarcane evolution in São Paulo state, Brazil, using landsat imagery and generalized space-time classifiers [Internet]. Remote Sensing Applications: Society and Environment. 2022 ; 26 1-12.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.rsase.2022.100749