Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1186853
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dc.contributor.authorFURUYA, D. E. G.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorPARREIRAS, T. C.
dc.contributor.authorSOARES, V. B.
dc.contributor.authorSILVEIRA, F. da
dc.contributor.authorBARBEDO, J. G. A.
dc.contributor.authorSANTOS, T. T.
dc.contributor.authorGEBLER, L.
dc.date.accessioned2026-05-14T13:49:10Z-
dc.date.available2026-05-14T13:49:10Z-
dc.date.created2026-05-14
dc.date.issued2026
dc.identifier.citationRemote Sensing, v. 18, n. 10, 1465, May 2026.
dc.identifier.issn2072-4292
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186853-
dc.descriptionApple orchards are increasingly adopting anti-hail nets to mitigate climate risks; however, these structures alter canopy reflectance and pose challenges for remote sensing. This study presents an operational framework to map apple orchards under different netting conditions in Vacaria, Brazil. Multisensor surface reflectance data from Sentinel-2 and Harmonized Landsat and Sentinel-2 were used to generate dense spectral index time series combined with field observations. Five spectral indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Bare Soil Index (BSI), were evaluated individually and in combination within a hierarchical classification framework. Random Forest (RF) and one-dimensional convolutional neural networks (1DCNN) were applied as complementary machine learning approaches. RF showed more stable performance across hierarchical levels, while indices contributed differently depending on scale: BSI and NDVI were more effective at broader levels, whereas EVI and SAVI were critical for discriminating net colors. To our knowledge, this is the first study applying multisensor time series and machine learning to map anti-hail net systems in apple orchards.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectFloresta aleatória
dc.subjectCobertura da terra
dc.subjectPomar de maçã
dc.subjectAprendizado de máquina
dc.subjectMachine learning
dc.subjectRandom forest
dc.subject1DCNN
dc.subjectHarmonized Landsat and Sentinel-2 (HLS)
dc.subjectAgricultural production
dc.titleMapping anti-hail net systems in apple orchards using multisensor time series and machine learning.
dc.typeArtigo de periódico
dc.subject.thesagroProdução Agrícola
dc.subject.thesagroUso da Terra
dc.subject.thesagroSensoriamento Remoto
dc.subject.nalthesaurusLand use
dc.subject.nalthesaurusLand cover
dc.subject.nalthesaurusRemote sensing
riaa.ainfo.id1186853
riaa.ainfo.lastupdate2026-05-14
dc.identifier.doihttps://doi.org/10.3390/rs18101465
dc.contributor.institutionDANIELLE ELIS GARCIA FURUYA; EDSON LUIS BOLFE, CNPTIA; TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; VICTÓRIA BEATRIZ SOARES, UNIVERSIDADE ESTADUAL DE CAMPINAS; FRANCO DA SILVEIRA; JAYME GARCIA ARNAL BARBEDO, CNPTIA; THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO GEBLER, CNPUV.
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