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| Registros recuperados : 8 | |
| 1. |  | LONG, R. M.; GREGO, C. R.; VICENTE, L. E.; FRANCESCHINI, M. H. D.; SATO, M. V. Análise geoestatística da granulometria do solo como suporte na montagem de biblioteca espectral em área de pastagem. In: CONGRESSO INTERINSTITUCIONAL DE INICIAÇÃO CIENTÍFICA, 7., 2013, Campinas, SP. Anais... Campinas: IAC, 2013. 8 p.| Biblioteca(s): Embrapa Territorial. |
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| 3. | | FRANCESCHINI, M. H. D.; DEMATTÊ, J. A. M.; SATO, M. V.; VICENTE, L. E.; GREGO, C. R. Abordagem semiquantitativa e quantitativa na avaliação da textura do solo por espectroscopia de reflectância bidirecional no VIS-NIR-SWIR. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 48, n. 12, p. 1569-1582, dez. 2013. Título em inglês: Semiquantitative and quantitative approaches for soil texture evaluation through VIS?NIR?SWIR bidirectional reflectance spectroscopy.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 6. | | FRANCESCHINI, M. H. D.; DEMATTÊ, J. A. M.; TERRA, F. DA S.; ARAÚJO, S. R.; SOUZA FILHO, C. R. DE; VICENTE, L. E. Qualificação d atributos fisico-químicos do solo através de dados espectrais (Vis-NIR-SWIR) obtidos em laboratório e por imagem aérea hiperespectral. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 16., 2013, Foz do Iguaçú. Anais... São José dos Campos: INPE, 2013. p.0530-0538 1 CD-ROM| Biblioteca(s): Embrapa Territorial. |
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| 7. | | DEMATTÊ, J. A. M.; MORGAN, C. L. S.; CHABRILLAT, S.; RIZZO, R.; FRANCESCHINI, M. H. D.; TERRA, F. da S.; VASQUES, G. M.; WETTERLIND, J. Spectral sensing from ground to space in soil science: state of the art, applications, potential, and perspectives. In: THENKABAIL, P. S. (Ed.). Remote sensing handbook. Boca Raton: CRC Press, 2015. v. 2, cap. 24, p. 661-732.| Biblioteca(s): Embrapa Solos. |
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| 8. | | FONGARO, C. T.; DEMATTÊ, J. A. M.; RIZZO, R.; SAFANELLI, J. L.; MENDES, W. de S.; DOTTO, A. C.; VICENTE, L. E.; FRANCESCHINI, M. H. D.; USTIN, S. L. Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images. Remote Sensing, v. 10, n. 10, p. 1-21, 2018. Article 1555.| Biblioteca(s): Embrapa Meio Ambiente. |
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| Registros recuperados : 8 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Territorial. Para informações adicionais entre em contato com cnpm.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
26/11/2015 |
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Data da última atualização: |
26/11/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
FRANCESCHINI, M. H. D.; DEMATÊ, J. A. M.; VICENTE, L. E.; BARTHOLOMEUS, H.; SOUZA FILHO, C. R. DE. |
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Afiliação: |
M. H. D. FRANCESCHINI, USP; J. A. M. DEMATÊ, USP; LUIZ EDUARDO VICENTE, CNPM; H. BARTHOLOMEUS, WAGENING UNIVERSITY; C. R. DE SOUZA FILHO, UNICAMP. |
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Título: |
Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
International Journal of Applied Earth Observation and Geoinformation, v. 38, p. 358-370, 2015. |
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DOI: |
http://dx.doi.org/10.1016/j.jag.2015.01.019 |
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Idioma: |
Inglês |
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Conteúdo: |
Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS ? Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy. MenosSpectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS ? Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for exam... Mostrar Tudo |
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Palavras-Chave: |
Hyperspectral; Pedometrics; Unmixing analysis. |
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Thesaurus NAL: |
Reflectance spectroscopy; Soil properties. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02646naa a2200241 a 4500
001 2029643
005 2015-11-26
008 2015 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1016/j.jag.2015.01.019$2DOI
100 1 $aFRANCESCHINI, M. H. D.
245 $aPrediction of soil properties using imaging spectroscopy$bConsidering fractional vegetation cover to improve accuracy.$h[electronic resource]
260 $c2015
520 $aSpectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS ? Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.
650 $aReflectance spectroscopy
650 $aSoil properties
653 $aHyperspectral
653 $aPedometrics
653 $aUnmixing analysis
700 1 $aDEMATÊ, J. A. M.
700 1 $aVICENTE, L. E.
700 1 $aBARTHOLOMEUS, H.
700 1 $aSOUZA FILHO, C. R. DE
773 $tInternational Journal of Applied Earth Observation and Geoinformation$gv. 38, p. 358-370, 2015.
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