Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms (2022)
- Authors:
- USP affiliated authors: FERREIRA, TIAGO OSORIO - ESALQ ; DEMATTE, JOSE ALEXANDRE MELO - ESALQ ; MELLO, FELLIPE ALCANTARA DE OLIVEIRA - ESALQ
- Unidade: ESALQ
- DOI: 10.1016/j.catena.2022.106370
- Subjects: ALGORITMOS; APRENDIZADO COMPUTACIONAL; SENSOR; SOLOS
- Keywords: Argiluviação; Ferratização
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MELLO, Danilo Cesar de et al. Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms. Catena, v. 216, p. 1-15, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.catena.2022.106370. Acesso em: 12 fev. 2026. -
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. (2022). Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms. Catena, 216, 1-15. doi:10.1016/j.catena.2022.106370 -
NLM
Mello DC de, Ferreira TO, Veloso GV, Lana MG de, Mello FA de O, Di Raimo LADL, Schaefer CEGR, Francelino MR, Fernandes-Filho EI, Dematte JAM. Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms [Internet]. Catena. 2022 ; 216 1-15.[citado 2026 fev. 12 ] Available from: https://doi.org/10.1016/j.catena.2022.106370 -
Vancouver
Mello DC de, Ferreira TO, Veloso GV, Lana MG de, Mello FA de O, Di Raimo LADL, Schaefer CEGR, Francelino MR, Fernandes-Filho EI, Dematte JAM. Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms [Internet]. Catena. 2022 ; 216 1-15.[citado 2026 fev. 12 ] Available from: https://doi.org/10.1016/j.catena.2022.106370 - Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning
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Informações sobre o DOI: 10.1016/j.catena.2022.106370 (Fonte: oaDOI API)
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