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Artigo de periodico
Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning (2023)
Mello, Danilo César de ; Ferreira, Tiago Osório ; Veloso, Gustavo Vieira ; Lana, Marcos Guedes de; Mello, Fellipe Alcantara de Oliveira ; Di Raimo, Luis Augusto Di Loreto ; Cabrero, Diego Ribeiro Oquendo; Souza, José João Lelis Leal de ; Fernandes-Filho, Elpídio Inácio ; Francelino, Márcio Rocha ; Demattê, José Alexandre Melo
Source: Journal of South American Earth Sciences. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, ESPECTROMETRIA, IMAGEAMENTO DE SATÉLITE, INTEMPERISMO, MAPEAMENTO DO SOLO, MORFOLOGIA DO SOLO, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MELLO, Danilo César de et al. Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning. Journal of South American Earth Sciences, v. 128, p. 1-15, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.jsames.2023.104449. Acesso em: 06 nov. 2024.
APA
Mello, D. C. de, Ferreira, T. O., Veloso, G. V., Lana, M. G. de, Mello, F. A. de O., Di Raimo, L. A. D. L., et al. (2023). Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning. Journal of South American Earth Sciences, 128, 1-15. doi:10.1016/j.jsames.2023.104449
NLM
Mello DC de, Ferreira TO, Veloso GV, Lana MG de, Mello FA de O, Di Raimo LADL, Cabrero DRO, Souza JJLL de, Fernandes-Filho EI, Francelino MR, Demattê JAM. Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning [Internet]. Journal of South American Earth Sciences. 2023 ; 128 1-15.[citado 2024 nov. 06 ] Available from: https://doi.org/10.1016/j.jsames.2023.104449
Vancouver
Mello DC de, Ferreira TO, Veloso GV, Lana MG de, Mello FA de O, Di Raimo LADL, Cabrero DRO, Souza JJLL de, Fernandes-Filho EI, Francelino MR, Demattê JAM. Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning [Internet]. Journal of South American Earth Sciences. 2023 ; 128 1-15.[citado 2024 nov. 06 ] Available from: https://doi.org/10.1016/j.jsames.2023.104449
Artigo de periodico
Soil apparent electrical conductivity survey in different pedoenvironments by geophysical sensor EM38: a potential tool in paedology and pedometry studies (2022)
Mello, Danilo César de ; Safanelli, José Lucas ; Poppiel, Raul Roberto ; Veloso, Gustavo Vieira ; Cabrero, Diego Ribeiro Oquendo; Greschuk, Lucas Tadeu ; Mello, Fellipe Alcantara de Oliveira ; Francelino, Márcio Rocha ; Ker, João Carlos; Leite, Emilson Pereira; Fernandes-Filho, Elpídio Inácio ; Schaefer, Carlos Ernesto Gonçalves Reynaud; Demattê, Jose Alexandre Melo
Source: Geocarto International. Unidade: ESALQ
Subjects: CONDUTIVIDADE ELÉTRICA, GÊNESE DO SOLO, MAPEAMENTO DO SOLO, PEDOLOGIA, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MELLO, Danilo César de et al. Soil apparent electrical conductivity survey in different pedoenvironments by geophysical sensor EM38: a potential tool in paedology and pedometry studies. Geocarto International, p. 1-22, 2022Tradução . . Disponível em: https://doi.org/10.1080/10106049.2022.2076913. Acesso em: 06 nov. 2024.
APA
Mello, D. C. de, Safanelli, J. L., Poppiel, R. R., Veloso, G. V., Cabrero, D. R. O., Greschuk, L. T., et al. (2022). Soil apparent electrical conductivity survey in different pedoenvironments by geophysical sensor EM38: a potential tool in paedology and pedometry studies. Geocarto International, 1-22. doi:10.1080/10106049.2022.2076913
NLM
Mello DC de, Safanelli JL, Poppiel RR, Veloso GV, Cabrero DRO, Greschuk LT, Mello FA de O, Francelino MR, Ker JC, Leite EP, Fernandes-Filho EI, Schaefer CEGR, Demattê JAM. Soil apparent electrical conductivity survey in different pedoenvironments by geophysical sensor EM38: a potential tool in paedology and pedometry studies [Internet]. Geocarto International. 2022 ; 1-22.[citado 2024 nov. 06 ] Available from: https://doi.org/10.1080/10106049.2022.2076913
Vancouver
Mello DC de, Safanelli JL, Poppiel RR, Veloso GV, Cabrero DRO, Greschuk LT, Mello FA de O, Francelino MR, Ker JC, Leite EP, Fernandes-Filho EI, Schaefer CEGR, Demattê JAM. Soil apparent electrical conductivity survey in different pedoenvironments by geophysical sensor EM38: a potential tool in paedology and pedometry studies [Internet]. Geocarto International. 2022 ; 1-22.[citado 2024 nov. 06 ] Available from: https://doi.org/10.1080/10106049.2022.2076913
Artigo de periodico
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes (2022)
Mello, Danilo César de ; Veloso, Gustavo Vieira ; Lana, Marcos Guedes de; Mello, Fellipe Alcantara de Oliveira ; Poppiel, Raul Roberto ; Cabrero, Diego Ribeiro Oquendo; Di Raimo, Luis Augusto Di Loreto ; Schaefer, Carlos Ernesto Gonçalves Reynaud; Fernandes Filho, Elpídio Inácio ; Leite, Emilson Pereira; Demattê, José Alexandre Melo
Source: Geoscientific Model Development. Unidade: ESALQ
Subjects: ALGORITMOS, APRENDIZADO COMPUTACIONAL, FRAMEWORKS, GEOFÍSICA, MODELAGEM DE DADOS, SENSOR, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MELLO, Danilo César de et al. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes. Geoscientific Model Development, v. 15, p. 1219–1246, 2022Tradução . . Disponível em: https://doi.org/10.5194/gmd-15-1219-2022. Acesso em: 06 nov. 2024.
APA
Mello, D. C. de, Veloso, G. V., Lana, M. G. de, Mello, F. A. de O., Poppiel, R. R., Cabrero, D. R. O., et al. (2022). A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes. Geoscientific Model Development, 15, 1219–1246. doi:10.5194/gmd-15-1219-2022
NLM
Mello DC de, Veloso GV, Lana MG de, Mello FA de O, Poppiel RR, Cabrero DRO, Di Raimo LADL, Schaefer CEGR, Fernandes Filho EI, Leite EP, Demattê JAM. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes [Internet]. Geoscientific Model Development. 2022 ; 15 1219–1246.[citado 2024 nov. 06 ] Available from: https://doi.org/10.5194/gmd-15-1219-2022
Vancouver
Mello DC de, Veloso GV, Lana MG de, Mello FA de O, Poppiel RR, Cabrero DRO, Di Raimo LADL, Schaefer CEGR, Fernandes Filho EI, Leite EP, Demattê JAM. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes [Internet]. Geoscientific Model Development. 2022 ; 15 1219–1246.[citado 2024 nov. 06 ] Available from: https://doi.org/10.5194/gmd-15-1219-2022

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