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| 9. |  | HENN, R.; KALL, P.; KONIG, H.; KORTE, B.; KRAFFT, O.; NEUMANN, K.; OETTLI, W.; RITTER, K.; ROSENMULLER, J.; SCHMITZ, N.; SCHUBERT, H.; VOGEL, W. (ed.). Methods of operations research. Konigstein: Athenaum:Hain; Cambridge: Oelgeschlager, Gunn & Hain, 1981. 206 p. (Methods of Operations Research, 39).| Biblioteca(s): Embrapa Agricultura Digital. |
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| 10. | | HENN, R.; KALL, P.; KONIG, H.; KORTE, B.; KRAFFT, O.; NEUMANN, K.; OETTLI, W.; RITTER, K.; ROSENMULLER, J.; SCHMITZ, N.; SCHUBERT, H.; VOGEL, W. (ed.). Methods of operations research. Konigsteim: Athenaum: Hain: Hanstein, 1983. 122 p. (Methods of Operations Research, 47).| Biblioteca(s): Embrapa Agricultura Digital. |
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| 11. | | HENN, R.; KALL, P.; KONIG, H.; KORTE, B.; KRAFFT, O.; NEUMANN, K.; OETTLI, W.; RITTER, K.; ROSENMULLER, J.; SCHUBERT, H.; VOGEL, W. (ed.). Methods of operations research. Konigstein: Verlag Anton Hain, 1985. 410 p. (Methods of Operations Research, 55).| Biblioteca(s): Embrapa Agricultura Digital. |
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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
001 2114592
005 2019-11-19
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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