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| Registros recuperados : 11 | |
| 3. |  | RIZZO, R.; GARCIA, A. S.; VILELA, V. M. de F. N.; BALLESTER, M. V. R.; NEILL, C.; VICTORIA, D. de C.; ROCHA, H. R. da; COE, M. T. Land use changes in Southeastern Amazon and trends in rainfall and water yield of the Xingu River during 1976-2015. Climatic Change, v. 162, n. 3, p. 1419-1436, Oct. 2020.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 4. | | VASQUES, G. M.; DEMATTÊ, J. A. M.; VISCARRA ROSSEL, R. A.; RAMÍREZ LÓPEZ, S.; TERRA, F. S.; RIZZO, R.; SOUZA FILHO, C. R. de. Integrating geospatial and multi-depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil. European Journal of Soil Science, v. 66, n. 4, p. 767-779, Jul. 2015.| Biblioteca(s): Embrapa Solos. |
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| 6. | | 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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| 7. | | 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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| 8. | | STEINFELD, J. P.; BIANCHI, F. J. J. A.; LOCATELLI, J. L.; RIZZO, R.; RESENDE, M. E. B. DE; BALLESTER, M. V. R.; CERRI, C. E. P.; BERNARDI, A. C. de C.; CREAMER, R. E. Increasing complexity of agroforestry systems benefits nutrient cycling and mineral-associated organic carbon storage, in south-eastern Brazil. Geoderma, v. 440, dec. 2023, 116726. 12 p.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 9. | | DEMATTÊ, J. A. M; DOTTO, A. C.; SILVEIRA, A. F. D. da; SATO, M. V.; DALMOLIN, R. S. D.; ARAÚJO, M. do S. B. de; SILVA, E. B. da; NANNI, M. R.; NORONHA, N. C.; LACERDA, M. P. C.; ARAUJO FILHO, J. C. de; RIZZO, R. The Brazilian Soil Spectral Library (BSSL): a general overview. In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2019. v. 2, p. 538. WCSS 2018.| Biblioteca(s): Embrapa Solos. |
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| 10. | | DEMATTÊ, J. A. M.; SILVEIRA, A. F. D. da; DOTTO, A. C.; SATO, M. V.; DALMOLIN, R. S. D.; ARAÚJO, M. do S. B. de; SILVA, E. B. da; NANNI, M. R.; NORONHA, N. C.; LACERDA, M. P. C.; ARAUJO FILHO, J. C. de; RIZZO, R. The geographic and environmental characterization of the Brazilian Soil Spectral Library (BSSL). In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2019. v. 2, p. 538-539. WCSS 2018.| Biblioteca(s): Embrapa Solos. |
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| 11. | | DEMATTÊ, J. A. M.; DOTTO, A. C.; PAIVA, A. F. S.; SATO, M. V.; DALMOLIN, R. S. D.; ARAÚJO, M. do S. B. de; SILVA, E. B. da; NANNI, M. R.; CATEN, A. ten; NORONHA, N. C.; LACERDA, M. P. C.; ARAUJO FILHO, J. C. de; RIZZO, R.; BELLINASO, H.; FRANCELINO, M. R.; SCHAEFER, C. E. G. R.; VICENTE, L. E.; SANTOS, U. J. dos; SAMPAIO, E. V. de S. B.; MENEZES, R. S. C.; SOUZA, J. J. L. L. de; ABRAHÃO, W. A. P.; COELHO, R. M.; GREGO, C. R.; LANI, J. L.; FERNANDES, A. R.; GONÇALVES, D. A. M.; SILVA, S. H. G.; MENEZES, M. D. de; CURI, N.; COUTO, E. G.; ANJOS, L. H. C. dos; CEDDIA, M. B.; PINHEIRO, E. F. M.; GRUNWALD, S.; VASQUES, G. de M.; MARQUES JÚNIOR, J.; SILVA, A. J. da; BARRETO, M. C. de V.; NÓBREGA, G. N.; SILVA, M. Z. da; SOUZA, S. F. de; VALLADARES, G. S.; VIANA, J. H. M.; TERRA, F. da S.; HORÁK-TERRA, I.; FIORIO, P. R.; SILVA, R. C. da; FRADE JÚNIOR, E. F.; LIMA, R. H. C.; FILIPPINI ALBA, J. M.; SOUZA JUNIOR, V. S. de; BREFIN, M. de L. M. S.; RUIVO, M. de L. P.; FERREIRA, T. O.; BRAIT, M. A.; CAETANO, N. R.; BRINGHENTI, I.; MENDES, W. de S.; SAFANELLI, J. L.; GUIMARÃES, C. C. B.; POPPIEL, R. R.; SOUZA, A. B. e; QUESADA, C. A.; COUTO, H. T. Z. do. The Brazilian Soil Spectral Library (BSSL): a general view, application and challenges. Geoderma, v. 354, 113793, 2019. Na publicação: Gustavo M. Vasques.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Clima Temperado; Embrapa Cocais; Embrapa Meio Ambiente; Embrapa Milho e Sorgo; Embrapa Solos. |
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| Registros recuperados : 11 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
19/11/2019 |
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Data da última atualização: |
19/11/2019 |
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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: |
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. |
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Afiliação: |
CAIO TROULA FONGARO, ESALQ-USP; JOSE ALEXANDRE MELO DEMATTE, ESALQ-USP; RODNEI RIZZO, CENA-USP; JOSE LUCAS SAFANELLI, ESALQ-USP; WANDERSON DE SOUSA MENDES, ESALQ-USP; ANDRE CARNIELETTO DOTTO, ESALQ-USP; LUIZ EDUARDO VICENTE, CNPMA; MARSTON HERACLES DOMINGUES FRANCESCHINI, Wageningen University; SUSAN L USTIN, University of California-Davis. |
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Título: |
Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Remote Sensing, v. 10, n. 10, p. 1-21, 2018. Article 1555. |
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DOI: |
https://doi.org/10.3390/rs10101555 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0?20 cm depth, 919 points) from an area of 14,614 km 2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R 2 = 0.83; RMSE = 65.0 g kg − 1 ) and sand (R 2 = 0.86; RMSE = 79.9 g kg − 1 ). Multispectral satellite images were more stable for the identification of soil properties than relief parameters. |
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Palavras-Chave: |
Imagem de satélite; Mapeamento do solo. |
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Thesagro: |
Satélite; Sensoriamento Remoto; Solo Arenoso; Solo Argiloso. |
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Thesaurus NAL: |
Clay soils; Multispectral imagery; Precision agriculture; Reflectance spectroscopy; Remote sensing; Sandy soils; Satellites; Soil degradation; Soil map. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/204955/1/Vicente-Clay-Sand-AP-2019.pdf
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Marc: |
LEADER 02461naa a2200409 a 4500
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008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs10101555$2DOI
100 1 $aFONGARO, C. T.
245 $aImprovement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images.$h[electronic resource]
260 $c2018
520 $aAbstract: Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0?20 cm depth, 919 points) from an area of 14,614 km 2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R 2 = 0.83; RMSE = 65.0 g kg − 1 ) and sand (R 2 = 0.86; RMSE = 79.9 g kg − 1 ). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.
650 $aClay soils
650 $aMultispectral imagery
650 $aPrecision agriculture
650 $aReflectance spectroscopy
650 $aRemote sensing
650 $aSandy soils
650 $aSatellites
650 $aSoil degradation
650 $aSoil map
650 $aSatélite
650 $aSensoriamento Remoto
650 $aSolo Arenoso
650 $aSolo Argiloso
653 $aImagem de satélite
653 $aMapeamento do solo
700 1 $aDEMATTÊ, J. A. M.
700 1 $aRIZZO, R.
700 1 $aSAFANELLI, J. L.
700 1 $aMENDES, W. de S.
700 1 $aDOTTO, A. C.
700 1 $aVICENTE, L. E.
700 1 $aFRANCESCHINI, M. H. D.
700 1 $aUSTIN, S. L.
773 $tRemote Sensing$gv. 10, n. 10, p. 1-21, 2018. Article 1555.
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Embrapa Meio Ambiente (CNPMA) |
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