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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
19/11/2021 |
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Data da última atualização: |
26/04/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLDONI, L. V.; MERCANTE, E.; ANTUNES, J. F. G.; CATTANI, C. E. V.; SILVA JUNIOR, C. A. da; CAON, I. L.; PRUDENTE, V. H. R. |
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Afiliação: |
LUCAS VOLOCHEN OLDONI, INPE; ERIVELTO MERCANTE, UNIOESTE; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; CARLOS EDUARDO VIZZOTTO CATTANI, UNIOESTE; CARLOS ANTONIO DA SILVA JUNIOR, UNEMAT; IVÃ LUIZ CAON, UNIOESTE; VICTOR HUGO ROHDEN PRUDENTE, INPE. |
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Título: |
Extraction of crop information through the spatiotemporal fusion of OLI and MODIS images. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Geocarto International, 2021. |
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DOI: |
https://doi.org/10.1080/10106049.2021.2000648 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT. Spatiotemporal data fusion algorithms have been developed tofuse satellite imagery from sensors with different spatial and tempoporal resolutions and generate predicted imagery. In this study, we compare the predictions of three spatiotemporal data fusion algorithms in blending Landsat-8/OLI and Terra-Aqua/MODIS images for mapping soybean and corn under five classification scenarios. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and Flexible Spatiotemporal Data Fusion (FSDAF) algorithms were compared to generate images for the 2016/2017 summer crop-year. Classifications including phenological metrics extracted from FSDAF- and STARFM-predicted EVI time series had overalls accuracies higher than the other scenarios, 93.11% and 91.33%, respectively. The results show that phenological metrics extracted from predicted images are an interesting alternative to overcome cloud cover frequency limitations for soybean and corn mapping in tropical areas. |
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Palavras-Chave: |
Algoritmos de fusão de dados espaço-temporal; ESTARM; FSDAF; Séries temporais; STARFM; Time series. |
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Thesagro: |
Fenologia; Sensoriamento Remoto. |
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Thesaurus Nal: |
Phenology; Remote sensing. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02008naa a2200325 a 4500
001 2136350
005 2022-04-26
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1080/10106049.2021.2000648$2DOI
100 1 $aOLDONI, L. V.
245 $aExtraction of crop information through the spatiotemporal fusion of OLI and MODIS images.$h[electronic resource]
260 $c2021
520 $aABSTRACT. Spatiotemporal data fusion algorithms have been developed tofuse satellite imagery from sensors with different spatial and tempoporal resolutions and generate predicted imagery. In this study, we compare the predictions of three spatiotemporal data fusion algorithms in blending Landsat-8/OLI and Terra-Aqua/MODIS images for mapping soybean and corn under five classification scenarios. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and Flexible Spatiotemporal Data Fusion (FSDAF) algorithms were compared to generate images for the 2016/2017 summer crop-year. Classifications including phenological metrics extracted from FSDAF- and STARFM-predicted EVI time series had overalls accuracies higher than the other scenarios, 93.11% and 91.33%, respectively. The results show that phenological metrics extracted from predicted images are an interesting alternative to overcome cloud cover frequency limitations for soybean and corn mapping in tropical areas.
650 $aPhenology
650 $aRemote sensing
650 $aFenologia
650 $aSensoriamento Remoto
653 $aAlgoritmos de fusão de dados espaço-temporal
653 $aESTARM
653 $aFSDAF
653 $aSéries temporais
653 $aSTARFM
653 $aTime series
700 1 $aMERCANTE, E.
700 1 $aANTUNES, J. F. G.
700 1 $aCATTANI, C. E. V.
700 1 $aSILVA JUNIOR, C. A. da
700 1 $aCAON, I. L.
700 1 $aPRUDENTE, V. H. R.
773 $tGeocarto International, 2021.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
