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

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dc.contributorANA 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.creatorTORO, A. P. S. G. D. D.
dc.creatorBUENO, I. T.
dc.creatorWERNER, J. P. S.
dc.creatorANTUNES, J. F. G.
dc.creatorLAMPARELLI, R. A. C.
dc.creatorCOUTINHO, A. C.
dc.creatorESQUERDO, J. C. D. M.
dc.creatorMAGALHÃES, P. S. G.
dc.creatorFIGUEIREDO, G. K. D. A.
dc.date2023-03-20T11:50:46Z
dc.date2023-03-20T11:50:46Z
dc.date2023-03-20
dc.date2023
dc.date.accessioned2026-07-07T04:17:17Z
dc.descriptionIn 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.
dc.identifierRemote Sensing, v. 15, n. 4, 1130, Feb. 2023.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1152495
dc.identifierhttps://doi.org/10.3390/rs15041130
dc.identifier.urihttp://hdl.handle.net/123456789/456481
dc.languageeng
dc.rightsopenAccess
dc.subjectFloresta aleatória
dc.subjectAgricultura regenerativa
dc.subjectSistemas integrados lavoura-pecuária
dc.subjectAprendizado de máquina
dc.subjectAprendizado profundo
dc.subjectRegenerative agriculture
dc.subjectRandom forest
dc.subjectIntegrated Crop-livestock systems
dc.subjectICLS
dc.subjectLong short-term memory
dc.subjectLSTM
dc.subjectMultisource
dc.subjectTransformer
dc.subjectAgricultura
dc.subjectAgriculture
dc.titleSAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
dc.typeArtigo de periódico

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