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| Registros recuperados : 84 | |
| 4. |  | COELHO, M. R.; LUMBRERAS, J. F.; VASQUES, G. de M. Atributos químicos e granulometria dos solos da XI Reunião Brasileira de Classificação e Correlação de Solos (RCC de Roraima). In: BATISTA, K. D.; LUMBRERAS, J. F.; COELHO, M. R.; OLIVEIRA, V. A. de; VALE JÚNIOR, J. F. do (Ed.). Guia de campo da XI Reunião Brasileira de Classificação e Correlação de Solos: RCC de Roraima. Brasília, DF: Embrapa, 2018. E-book. cap. 4.| Biblioteca(s): Embrapa Solos. |
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| 14. | | COELHO, M. R.; LUMBRERAS, J. F.; VASQUES, G. de M.; OLIVEIRA, V. A. de. Atributos químicos e granulometria dos solos da XII Reunião Brasileira de Classificação de Solos (RCC de Rondônia): um enfoque pedológico. In: LUMBRERAS, J. F.; SILVA, L. M. da; ANJOS, L. H. C. dos; OLIVEIRA, V. A. de; WADT, P. G. S.; PEREIRA, M. G.; DELARMELINDA-HONORÉ, E. A.; BURITY, K. T. L. (Ed.). Guia de campo da XII Reunião Brasileira de Classificação e Correlação de Solos: RCC de Rondônia. Brasília, DF: Embrapa, 2019. E-book. cap. 10.| Biblioteca(s): Embrapa Solos. |
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| 15. | | DEMATTÊ, J. A. M.; VASQUES, G. de M.; CORREA, E. A.; ARRUDA, G. P. de. Fotopedologia, espectroscopia e sistema de informação geográfica na caracterização de solos desenvolvidos do Grupo Barreiras no Amapá. Bragantia, Campinas, v. 71, n. 3, p. 438-446, 2012.| Biblioteca(s): Embrapa Solos. |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
06/03/2018 |
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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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Circulação/Nível: |
A - 1 |
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Autoria: |
PINHEIRO, E. F. M.; CEDDIA, M. B.; CLINGENSMITH, C. M.; GRUNWALD, S.; VASQUES, G. de M. |
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Afiliação: |
ÉRIKA F. M. PINHEIRO, UFRRJ; MARCOS BACIS CEDDIA, UFRRJ; CHRISTOPHER M. CLINGENSMITH, UNIVERSITY OF FLORIDA; SABINE GRUNWALD, UNIVERSITY OF FLORIDA; GUSTAVO DE MATTOS VASQUES, CNPS. |
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Título: |
Prediction of soil physical and chemical properties by visible and near-infrared diffuse reflectance spectroscopy in the Central Amazon. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Remote Sensing, v. 9, n. 4, Apr. 2017. |
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DOI: |
https://doi.org/10.3390/rs9040293 |
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Idioma: |
Inglês |
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Conteúdo: |
Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350-2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization. MenosVisible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350-2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predic... Mostrar Tudo |
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Palavras-Chave: |
Carbono orgânico do solo; Pré-processamento espectral; Quimiometria; Solos tropicais. |
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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/173558/1/2018-003.pdf
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Marc: |
LEADER 02749naa a2200229 a 4500
001 2088619
005 2021-11-10
008 2017 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs9040293$2DOI
100 1 $aPINHEIRO, E. F. M.
245 $aPrediction of soil physical and chemical properties by visible and near-infrared diffuse reflectance spectroscopy in the Central Amazon.$h[electronic resource]
260 $c2017
520 $aVisible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350-2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization.
653 $aCarbono orgânico do solo
653 $aPré-processamento espectral
653 $aQuimiometria
653 $aSolos tropicais
700 1 $aCEDDIA, M. B.
700 1 $aCLINGENSMITH, C. M.
700 1 $aGRUNWALD, S.
700 1 $aVASQUES, G. de M.
773 $tRemote Sensing$gv. 9, n. 4, Apr. 2017.
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