Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning (2021)
- Authors:
- USP affiliated authors: DEMATTE, JOSE ALEXANDRE MELO - ESALQ ; AMORIM, MERILYN TAYNARA ACCORSI - ESALQ ; MELLO, FELLIPE ALCANTARA DE OLIVEIRA - ESALQ
- Unidade: ESALQ
- DOI: 10.1080/10106049.2021.1996639
- Subjects: SOLOS; VARIABILIDADE ESPACIAL; MAPEAMENTO DO SOLO; PRODUÇÃO AGRÍCOLA; AGRICULTURA SUSTENTÁVEL; SENSORIAMENTO REMOTO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Geocarto International
- ISSN: 1010-6049
- Volume/Número/Paginação/Ano: online, November 2021
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
NAIMI, Salman et al. Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning. Geocarto International, 2021Tradução . . Disponível em: https://doi.org/10.1080/10106049.2021.1996639. Acesso em: 23 abr. 2024. -
APA
Naimi, S., Ayoubi, S., Demattê, J. A. M., Zeraatpisheh, M., Amorim, M. T. A., & Mello, F. A. de O. (2021). Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning. Geocarto International. doi:10.1080/10106049.2021.1996639 -
NLM
Naimi S, Ayoubi S, Demattê JAM, Zeraatpisheh M, Amorim MTA, Mello FA de O. Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning [Internet]. Geocarto International. 2021 ;[citado 2024 abr. 23 ] Available from: https://doi.org/10.1080/10106049.2021.1996639 -
Vancouver
Naimi S, Ayoubi S, Demattê JAM, Zeraatpisheh M, Amorim MTA, Mello FA de O. Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning [Internet]. Geocarto International. 2021 ;[citado 2024 abr. 23 ] Available from: https://doi.org/10.1080/10106049.2021.1996639 - Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning
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Informações sobre o DOI: 10.1080/10106049.2021.1996639 (Fonte: oaDOI API)
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