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dc.contributor.authorFAGUNDES, R.
dc.contributor.authorKAYSER, L. C.
dc.contributor.authorAMARAL, L. de P.
dc.contributor.authorBENEDETTI, A. C.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorPARREIRAS, T. C.
dc.contributor.authorRAMOS-OSPINA, M.
dc.contributor.authorMARULANDA-TOBÓN, A.
dc.date.accessioned2025-07-07T11:47:58Z-
dc.date.available2025-07-07T11:47:58Z-
dc.date.created2025-07-07
dc.date.issued2025
dc.identifier.citationGeomatics, v. 5, n. 2, 19, June 2025.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1177070-
dc.descriptionAbstract: Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), derived from the Red Edge band of MSI/Sentinel-2A (B05, B06, B07), are critical for coffee monitoring. To align the Red Edge band (20 m resolution) with the NIR band (10 m resolution), the nearest neighbor, bilinear, cubic and Lanczos resampling methods were used, available in the Terra package in the R software(4.4.0). This study evaluates these methods using two original B05 images from 24 November 2023, and 21 September 2023, covering the “Ouro Verde” (15 ha) and “Canto do Rio” (45 ha) farms in Bahia, Brazil. A total of 500 random points were analyzed using PSF, linear models, and cross-validation with R2, MAE, and RMSE. PSF analysis confirmed data integrity, and the cubic method demonstrated the best performance (R2 = 0.996, MAE = 0.008 and RMSE = 0.012 in the “Ouro Verde” Farm and R2 = 0.995, MAE = 0.007 and RMSE = 0.011 in the “Canto do Rio” Farm). The results highlight the importance of selecting appropriate resampling methods for precise remote sensing in coffee cultivation, ensuring accurate digital processing aligned with study objectives.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectÍndices espectrais
dc.subjectProcessamento digital
dc.subjectSpectral indices
dc.titleAnalysis of resampling methods for the red edge band of MSI/Sentinel-2A for coffee cultivation monitoring.
dc.typeArtigo de periódico
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroAgricultura de Precisão
dc.subject.thesagroCafé
dc.subject.thesagroCoffea Arábica
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusImage analysis
riaa.ainfo.id1177070
riaa.ainfo.lastupdate2025-07-07
dc.identifier.doihttps://doi.org/10.3390/geomatics5020019
dc.contributor.institutionROZYMARIO FAGUNDES, UNIVERSIDADE FEDERAL DE SANTA MARIA; LUIZ PATRIC KAYSER, UNIVERSIDADE FEDERAL DE SANTA MARIA; LÚCIO DE PAULA AMARAL, UNIVERSIDADE FEDERAL DE SANTA MARIA; ANA CAROLINE BENEDETTI, UNIVERSIDADE FEDERAL DE SANTA MARIA; EDSON LUIS BOLFE, CNPTIA; TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; MANUELA RAMOS-OSPINA, UNIVERSIDAD EAFIT; ALEJANDRO MARULANDA-TOBÓN, UNIVERSIDAD EAFIT.
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