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| Registros recuperados : 180 | |
| 23. |  | 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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| 25. | | CASTRO, V. H. M. e de; PARREIRAS, T. C.; BOLFE, E. L.; VICENTE, L. E. Mapeamento da citricultura imagens Sentinel-2 e Random Forest: o exemplo de Casa Branca. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 2229-2232. p. 2229-2232 Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Meio Ambiente. |
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| 33. | | VICENTE, L. E.; LOEBMANN, D. G. dos S. W.; VICTORIA, D. de C.; KOGA-VICENTE, A. Monitoring of agricultural land use in São Paulo State - Brazil using geotechnology approach. In: INTERNATIONAL CONSORTIUM ON APPLIED BIOECONOMY RESEARCH, 16.; SEMINAR EAAE, 128., 2012, Ravello, Italy. Anais... Ravello: CABR, 2012. 14 p.| Biblioteca(s): Embrapa Territorial. |
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| 40. | | NOGUEIRA, S. F.; SILVA, G. B. S. da; ANDRADE, R. G.; VICENTE, L. E. Geotecnologias para o monitoramento dos níveis de degradação das pastagens no Brasil. MundoGEO, Curitiba, PR, v. 15, n. 72, p. 60-64, 2013 p. 60-64.| Biblioteca(s): Embrapa Territorial. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
13/03/2020 |
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Data da última atualização: |
13/03/2020 |
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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: |
URBINA SALAZAR, D. F.; DEMATTÊ, J. A. M.; VICENTE, L. E.; GUIMARAES, C. C. B.; SAYÃO, V. M.; CERRI, C. E. P.; PADILHA, M. C. de C.; MENDES, W. de S. |
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Afiliação: |
DIEGO FERNANDO URBINA SALAZAR, ESALQ-USP; JOSÉ ALEXANDRE MELO DEMATTÊ, ESALQ-USP; LUIZ EDUARDO VICENTE, CNPMA; CLECIA CRISTINA GUIMARÃES, ESALQ-USP; VERIDIANA MARIA SAYÃO, ESALQ-USP; CARLOS EDUARDO PELLEGRINO CERRI, ESALQ-USP; MANUELA CORREA DE CASTRO PADILHA, ESALQ-USP; WANDERSON DE SOUSA MENDES, ESALQ-USP. |
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Título: |
Emissivity of agricultural soil attributes in southeastern Brazil via terrestrial and satellite sensors. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Geoderma, v. 361, article 114038, 2020. |
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DOI: |
https://doi.org/10.1016/j.geoderma.2019.114038 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Abstract: Soil texture and organic carbon (OC) content influence the spectral response. These attributes are relevant for the preservation and proper management of land use in the pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a powerful tool for studying so is, but their analysis using these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The aim of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range (ASTER, Landsat satellites). The study performed qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil, where collected 150 soil samples (0?20 cm depth). Soil texture was determined by the pipette method and OC via dry combustion. Reflectance and emissivity (?) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image ?ASTER_05? from July 15, 2017 was acquired with values of ?. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, TIR, TIR Correlation Index (TIR_CID), and MIR Correlation Index (MIR_CID)), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR region, the models for total sand content and for fine and coarse fractions were good. Models created with ASTER sensor data were not as promising as those with laboratory ones. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors. MenosAbstract: Soil texture and organic carbon (OC) content influence the spectral response. These attributes are relevant for the preservation and proper management of land use in the pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a powerful tool for studying so is, but their analysis using these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The aim of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range (ASTER, Landsat satellites). The study performed qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil, where collected 150 soil samples (0?20 cm depth). Soil texture was determined by the pipette method and OC via dry combustion. Reflectance and emissivity (?) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image ?ASTER_05? from July 15, 2017 was acquired with values of ?. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor ba... Mostrar Tudo |
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Palavras-Chave: |
Grain size; Satellite image analyses; Soil attributes; Soil emissivity; Spectral behavior; Thermal infrared (TIR). |
|
Thesagro: |
Satélite; Sensoriamento Remoto. |
|
Thesaurus NAL: |
Satellites; Soil properties. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 03618naa a2200337 a 4500
001 2121231
005 2020-03-13
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.geoderma.2019.114038$2DOI
100 1 $aURBINA SALAZAR, D. F.
245 $aEmissivity of agricultural soil attributes in southeastern Brazil via terrestrial and satellite sensors.$h[electronic resource]
260 $c2020
520 $aAbstract: Soil texture and organic carbon (OC) content influence the spectral response. These attributes are relevant for the preservation and proper management of land use in the pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a powerful tool for studying so is, but their analysis using these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The aim of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range (ASTER, Landsat satellites). The study performed qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil, where collected 150 soil samples (0?20 cm depth). Soil texture was determined by the pipette method and OC via dry combustion. Reflectance and emissivity (?) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image ?ASTER_05? from July 15, 2017 was acquired with values of ?. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, TIR, TIR Correlation Index (TIR_CID), and MIR Correlation Index (MIR_CID)), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR region, the models for total sand content and for fine and coarse fractions were good. Models created with ASTER sensor data were not as promising as those with laboratory ones. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors.
650 $aSatellites
650 $aSoil properties
650 $aSatélite
650 $aSensoriamento Remoto
653 $aGrain size
653 $aSatellite image analyses
653 $aSoil attributes
653 $aSoil emissivity
653 $aSpectral behavior
653 $aThermal infrared (TIR)
700 1 $aDEMATTÊ, J. A. M.
700 1 $aVICENTE, L. E.
700 1 $aGUIMARAES, C. C. B.
700 1 $aSAYÃO, V. M.
700 1 $aCERRI, C. E. P.
700 1 $aPADILHA, M. C. de C.
700 1 $aMENDES, W. de S.
773 $tGeoderma$gv. 361, article 114038, 2020.
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