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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
08/02/2017 |
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Data da última atualização: |
10/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CEDDIA, M. B.; GOMES, A. S.; VASQUES, G. M.; PINHEIRO, E. F. M. |
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Afiliação: |
MARCOS BACIS CEDDIA, UFRRJ; ANDRÉA S. GOMES, UFRRJ; GUSTAVO DE MATTOS VASQUES, CNPS; ÉRIKA F. M. PINHEIRO, UFRRJ. |
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Título: |
Soil carbon stock and particle size fractions in the Central Amazon predicted from remotely sensed relief, multispectral and radar data. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Remote Sensing, v. 9, n. 2, Feb. 2017. |
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DOI: |
https://doi.org/10.3390/rs9020124 |
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Idioma: |
Inglês |
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Conteúdo: |
Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps. The objectives of this study were to: (a) evaluate the addition of relief, and vegetation covariates derived from multispectral images with distinct spatial and spectral resolutions (Landsat 8 and RapidEye) and L-band radar (ALOS PALSAR) for the prediction of soil organic carbon stock (CS) and particle size fractions; and (b) evaluate the performance of four geostatistical methods to map these soil properties. Overall, the results show that, even under forest coverage, the Normalized Difference Vegetation Index (NDVI) and ALOS PALSAR backscattering coefficient improved the accuracy of CS and subsurface clay content predictions. The NDVI derived from RapidEye sensor improved the prediction of CS using isotopic cokriging, while the NDVI derived from Landsat 8 and backscattering coefficient were selected to predict clay content at the subsurface using regression kriging (RK). The relative improvement of applying cokriging and RK over ordinary kriging were lower than 10%, indicating that further analyses are necessary to connect soil proxies (vegetation and relief types) with soil attributes |
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Palavras-Chave: |
Geoestatística; Krigagem; Mapeamento digital do solo. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/155043/1/2016-153.pdf
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Marc: |
LEADER 02090naa a2200205 a 4500
001 2063163
005 2021-11-10
008 2017 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs9020124$2DOI
100 1 $aCEDDIA, M. B.
245 $aSoil carbon stock and particle size fractions in the Central Amazon predicted from remotely sensed relief, multispectral and radar data.$h[electronic resource]
260 $c2017
520 $aSoils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps. The objectives of this study were to: (a) evaluate the addition of relief, and vegetation covariates derived from multispectral images with distinct spatial and spectral resolutions (Landsat 8 and RapidEye) and L-band radar (ALOS PALSAR) for the prediction of soil organic carbon stock (CS) and particle size fractions; and (b) evaluate the performance of four geostatistical methods to map these soil properties. Overall, the results show that, even under forest coverage, the Normalized Difference Vegetation Index (NDVI) and ALOS PALSAR backscattering coefficient improved the accuracy of CS and subsurface clay content predictions. The NDVI derived from RapidEye sensor improved the prediction of CS using isotopic cokriging, while the NDVI derived from Landsat 8 and backscattering coefficient were selected to predict clay content at the subsurface using regression kriging (RK). The relative improvement of applying cokriging and RK over ordinary kriging were lower than 10%, indicating that further analyses are necessary to connect soil proxies (vegetation and relief types) with soil attributes
653 $aGeoestatística
653 $aKrigagem
653 $aMapeamento digital do solo
700 1 $aGOMES, A. S.
700 1 $aVASQUES, G. M.
700 1 $aPINHEIRO, E. F. M.
773 $tRemote Sensing$gv. 9, n. 2, Feb. 2017.
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