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dc.contributor.authorPARREIRAS, T. C.
dc.contributor.authorSANTOS, C. de O.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorSANO, E. E.
dc.contributor.authorLEANDRO, V. B. S.
dc.contributor.authorSILVA, G. B. S. da
dc.contributor.authorSILVA, L. A. P. da
dc.contributor.authorFURUYA, D. E. G.
dc.contributor.authorROMANI, L. A. S.
dc.contributor.authorMORTON, D.
dc.date.accessioned2025-09-12T19:48:35Z-
dc.date.available2025-09-12T19:48:35Z-
dc.date.created2025-09-12
dc.date.issued2025
dc.identifier.citationRemote Sensing. v. 17, n. 18, 3168, Sept. 2025.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1178755-
dc.descriptionCoffee demand continues to rise, while producing countries face increasing challenges and yield losses due to climate change. In response, farmers are adopting agricultural practices capable of boosting productivity. However, these practices increase intercrop variability, making coffee mapping more challenging. In this study, a novel approach is proposed to identify coffee cultivation considering four phenological stages: planting (PL), producing (PR), skeleton pruning (SK), and renovation with stumping (ST). A hierarchical classification framework was designed to isolate coffee pixels and identify their respective stages in one of Brazil’s most important coffee-producing regions. A dense time series of multispectral bands, spectral indices, and texture metrics derived from Harmonized Landsat Sentinel-2 (HLS) imagery, with an average revisit time of ~3 days, was employed.This data was combined with an ensemble learning approach based on decision-tree algorithms, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The results achieved unprecedented sensitivity and specificity for coffee plantation detection with RF, consistently exceeding 95%. The classification of coffee phenological stages showed balanced accuracies of 77% (ST) and from 93% to 95% for the other classes. These findings are promising and provide a scalable framework to monitor climate-resilient coffee management practices.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectLandsat Sentinel-2
dc.subjectCulturas perenes
dc.subjectClassificação
dc.subjectProdução de café
dc.subjectAprendizado de máquina
dc.subjectPerennial crops
dc.titleDense time series of harmonized Landsat Sentinel-2 and ensemble machine learning to map coffee production stages.
dc.typeArtigo de periódico
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroCoffea Arábica
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusClassification
riaa.ainfo.id1178755
riaa.ainfo.lastupdate2025-09-12
dc.identifier.doihttps://doi.org/10.3390/rs17183168
dc.contributor.institutionTAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; CLAUDINEI DE OLIVEIRA SANTOS, ACELEN RENOVÁVEIS; EDSON LUIS BOLFE, CNPTIA; EDSON EYJI SANO, CPAC; VICTÓRIA BEATRIZ SOARES LEANDRO, UNIVERSIDADE ESTADUAL DE CAMPINAS; GUSTAVO BAYMA SIQUEIRA DA SILVA, CNPMA; LUCAS AUGUSTO PEREIRA DA SILVA, UNIVERSIDADE ESTADUAL DA PARAÍBA; DANIELLE ELIS GARCIA FURUYA; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; DOUGLAS MORTON, NASA GODDARD SPACE FLIGHT CENTER.
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