SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.

Detalhes bibliográficos
Autor(a) principal: TORO, A. P. S. G. D. D.
Data de Publicação: 2023
Outros Autores: BUENO, I. T., WERNER, J. P. S., ANTUNES, J. F. G., LAMPARELLI, R. A. C., COUTINHO, A. C., ESQUERDO, J. C. D. M., MAGALHÃES, P. S. G., FIGUEIREDO, G. K. D. A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1152495
https://doi.org/10.3390/rs15041130
Resumo: In this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested.
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spelling SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.Floresta aleatóriaAgricultura regenerativaSistemas integrados lavoura-pecuáriaAprendizado de máquinaAprendizado profundoRegenerative agricultureRandom forestIntegrated Crop-livestock systemsICLSLong short-term memoryLSTMMultisourceTransformerAgriculturaAgricultureIn this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested.ANA P. S. G. D. D. TORO, UNIVERSIDADE ESTADUAL DE CAMPINAS; INACIO T. BUENO, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOÃO PAULO SAMPAIO WERNER, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; RUBENS AUGUSTO DE CAMARGO LAMPARELLI, UNIVERSIDADE ESTADUAL DE CAMPINAS; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; PAULO S. G. MAGALHÃES, UNIVERSIDADE ESTADUAL DE CAMPINAS; GLEYCE KELLY DANTAS ARAÚJO FIGUEIREDO, UNIVERSIDADE ESTADUAL DE CAMPINAS.TORO, A. P. S. G. D. D.BUENO, I. T.WERNER, J. P. S.ANTUNES, J. F. G.LAMPARELLI, R. A. C.COUTINHO, A. C.ESQUERDO, J. C. D. M.MAGALHÃES, P. S. G.FIGUEIREDO, G. K. D. A.2023-03-20T11:50:46Z2023-03-20T11:50:46Z2023-03-202023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleRemote Sensing, v. 15, n. 4, 1130, Feb. 2023.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1152495https://doi.org/10.3390/rs15041130enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2023-03-20T11:50:46Zoai:www.alice.cnptia.embrapa.br:doc/1152495Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-03-20T11:50:46Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
title SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
spellingShingle SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
TORO, A. P. S. G. D. D.
Floresta aleatória
Agricultura regenerativa
Sistemas integrados lavoura-pecuária
Aprendizado de máquina
Aprendizado profundo
Regenerative agriculture
Random forest
Integrated Crop-livestock systems
ICLS
Long short-term memory
LSTM
Multisource
Transformer
Agricultura
Agriculture
title_short SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
title_full SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
title_fullStr SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
title_full_unstemmed SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
title_sort SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
author TORO, A. P. S. G. D. D.
author_facet TORO, A. P. S. G. D. D.
BUENO, I. T.
WERNER, J. P. S.
ANTUNES, J. F. G.
LAMPARELLI, R. A. C.
COUTINHO, A. C.
ESQUERDO, J. C. D. M.
MAGALHÃES, P. S. G.
FIGUEIREDO, G. K. D. A.
author_role author
author2 BUENO, I. T.
WERNER, J. P. S.
ANTUNES, J. F. G.
LAMPARELLI, R. A. C.
COUTINHO, A. C.
ESQUERDO, J. C. D. M.
MAGALHÃES, P. S. G.
FIGUEIREDO, G. K. D. A.
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv ANA P. S. G. D. D. TORO, UNIVERSIDADE ESTADUAL DE CAMPINAS; INACIO T. BUENO, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOÃO PAULO SAMPAIO WERNER, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; RUBENS AUGUSTO DE CAMARGO LAMPARELLI, UNIVERSIDADE ESTADUAL DE CAMPINAS; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; PAULO S. G. MAGALHÃES, UNIVERSIDADE ESTADUAL DE CAMPINAS; GLEYCE KELLY DANTAS ARAÚJO FIGUEIREDO, UNIVERSIDADE ESTADUAL DE CAMPINAS.
dc.contributor.author.fl_str_mv TORO, A. P. S. G. D. D.
BUENO, I. T.
WERNER, J. P. S.
ANTUNES, J. F. G.
LAMPARELLI, R. A. C.
COUTINHO, A. C.
ESQUERDO, J. C. D. M.
MAGALHÃES, P. S. G.
FIGUEIREDO, G. K. D. A.
dc.subject.por.fl_str_mv Floresta aleatória
Agricultura regenerativa
Sistemas integrados lavoura-pecuária
Aprendizado de máquina
Aprendizado profundo
Regenerative agriculture
Random forest
Integrated Crop-livestock systems
ICLS
Long short-term memory
LSTM
Multisource
Transformer
Agricultura
Agriculture
topic Floresta aleatória
Agricultura regenerativa
Sistemas integrados lavoura-pecuária
Aprendizado de máquina
Aprendizado profundo
Regenerative agriculture
Random forest
Integrated Crop-livestock systems
ICLS
Long short-term memory
LSTM
Multisource
Transformer
Agricultura
Agriculture
description In this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested.
publishDate 2023
dc.date.none.fl_str_mv 2023-03-20T11:50:46Z
2023-03-20T11:50:46Z
2023-03-20
2023
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Remote Sensing, v. 15, n. 4, 1130, Feb. 2023.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1152495
https://doi.org/10.3390/rs15041130
identifier_str_mv Remote Sensing, v. 15, n. 4, 1130, Feb. 2023.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1152495
https://doi.org/10.3390/rs15041130
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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