Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1145300
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dc.contributor.authorPARREIRAS, T. C.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorCHAVES, M. E. D.
dc.contributor.authorDEL'ARCO SANCHES, I.
dc.contributor.authorSANO, E. E.
dc.contributor.authorVICTORIA, D. de C.
dc.contributor.authorBETTIOL, G. M.
dc.contributor.authorVICENTE, L. E.
dc.date.accessioned2022-08-05T20:20:15Z-
dc.date.available2022-08-05T20:20:15Z-
dc.date.created2022-08-05
dc.date.issued2022
dc.identifier.citationRemote Sensing, v. 14, n. 15, 3736, Aug. 2022.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1145300-
dc.descriptionAbstract. The Brazilian Savanna presents a complex agricultural dynamic and cloud cover issues; therefore, there is a need for new strategies for more detailed agricultural monitoring. Using a hierarchical classification system, we explored the Harmonized Landsat Sentinel-2 (HLS) dataset to detect soybean in western Bahia, Brazil. Multispectral bands (MS) and vegetation indices (VIs) from October 2021 to March 2022 were used as variables to feed Random Forest models, and the performances of the complete HLS time-series, HLSS30 (harmonized Sentinel), HLSL30 (harmonized Landsat), and Landsat 8 OLI (L8) were compared. At Level 1 (agricultural areas × native vegetation), HLS, HLSS30, and L8 produced identical models using MS + VIs, with 0.959 overall accuracies (OA) and Kappa of 0.917. At Level 2 (annual crops × perennial crops × pasturelands), HLS and L8 achieved an OA of 0.935 and Kappa > 0.89 using only VIs. At Level 3 (soybean × other annual crops), the HLS MS + VIs model achieved the best performance, with OA of 0.913 and Kappa of 0.808. Our results demonstrated the potential of the new HLS dataset for medium-resolution mapping initiatives at the crop level, which can impact decision-making processes involving large-scale soybean production and agricultural sustainability.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectMonitoramento agrícola
dc.subjectMultisensor
dc.subjectHarmonized Landsat Sentinel-2
dc.subjectHLS
dc.subjectAgriculture monitoring
dc.titleHierarchical classification of soybean in the Brazilian Savanna based on Harmonized Landsat Sentinel data.
dc.typeArtigo de periódico
dc.subject.thesagroSoja
dc.subject.thesagroCerrado
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroGlycine Max
dc.subject.nalthesaurusSoybeans
dc.subject.nalthesaurusAgriculture
dc.subject.nalthesaurusRemote sensing
riaa.ainfo.id1145300
riaa.ainfo.lastupdate2022-08-05
dc.identifier.doihttps://doi.org/10.3390/rs14153736
dc.contributor.institutionTAYA CRISTO PARREIRAS, IG/UNICAMP; EDSON LUIS BOLFE, CNPTIA, IG/UNICAMP; MICHEL EUSTÁQUIO DANTAS CHAVES, INPE; IARA DEL´ARCO SANCHES, INPE; EDSON EYJI SANO, CPAC; DANIEL DE CASTRO VICTORIA, CNPTIA; GIOVANA MARANHAO BETTIOL, CPAC; LUIZ EDUARDO VICENTE, CNPMA.
Aparece nas coleções:Artigo em periódico indexado (CNPTIA)

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