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Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
07/07/2015 |
Data da última atualização: |
19/05/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
GRUNWALD, S.; VASQUES, G. M.; RIVERO, R. G. |
Afiliação: |
SABINE GRUNWALD, UNIVERSITY OF FLORIDA; GUSTAVO DE MATTOS VASQUES, CNPS; ROSANNA G. RIVERO, UNIVERSITY OF GEORGIA. |
Título: |
Fusion of soil and remote sensing data to model soil properties. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
Advances in Agronomy, v. 131, p. 1-109, 2015. |
DOI: |
10.1016/bs.agron.2014.12.004 |
Idioma: |
Inglês |
Conteúdo: |
Grand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contrast the benefits and constraints of proximal and remote sensing, fusion of soil-environmental data, and integration pathways to mash data and methods into complex soil assessments. MenosGrand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contr... Mostrar Tudo |
Palavras-Chave: |
Covariáveis ambientais; Detecção proximal; Fusão de dados; Mapeamento digital do solo; Modelagem digital do solo; Modelo meta do solo; Pedometria; Propriedades do solo; Sensores; Vias de integração. |
Thesagro: |
Mapa; Sensoriamento remoto. |
Thesaurus Nal: |
Remote sensing; Soil surveys. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 02618naa a2200325 a 4500 001 2019476 005 2020-05-19 008 2015 bl uuuu u00u1 u #d 024 7 $a10.1016/bs.agron.2014.12.004$2DOI 100 1 $aGRUNWALD, S. 245 $aFusion of soil and remote sensing data to model soil properties.$h[electronic resource] 260 $c2015 520 $aGrand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contrast the benefits and constraints of proximal and remote sensing, fusion of soil-environmental data, and integration pathways to mash data and methods into complex soil assessments. 650 $aRemote sensing 650 $aSoil surveys 650 $aMapa 650 $aSensoriamento remoto 653 $aCovariáveis ambientais 653 $aDetecção proximal 653 $aFusão de dados 653 $aMapeamento digital do solo 653 $aModelagem digital do solo 653 $aModelo meta do solo 653 $aPedometria 653 $aPropriedades do solo 653 $aSensores 653 $aVias de integração 700 1 $aVASQUES, G. M. 700 1 $aRIVERO, R. G. 773 $tAdvances in Agronomy$gv. 131, p. 1-109, 2015.
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Embrapa Solos (CNPS) |
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Biblioteca(s): |
Embrapa Agropecuária Oeste. |
Data corrente: |
02/06/2014 |
Data da última atualização: |
02/06/2014 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 2 |
Autoria: |
SANTOS, L. O.; PINTO, J. J. O.; PIVETA, L. B.; NOLDIN, J. A.; GALON, L.; CONCENCO, G. |
Afiliação: |
L. O. SANTOS, DOCENTE - UFPEL; J. J. O. PINTO, UFPEL; L. B. PIVETA, UFPEL; J. A. NOLDIN, EPAGRI; LEANDRO GALON, UFFS; GERMANI CONCENCO, CPAO. |
Título: |
Carroyover effect of imidazolinone herbicides for crops following rice. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
American Journal of Plant Sciences, v. 5, n. 8, p. 1049-1058, Mar. 2014. |
DOI: |
10.4236/ajps.2014.58117 |
Idioma: |
Português |
Thesagro: |
Arroz. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/102992/1/AJPS-2014032614335904.pdf
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Marc: |
LEADER 00564naa a2200193 a 4500 001 1987458 005 2014-06-02 008 2014 bl uuuu u00u1 u #d 024 7 $a10.4236/ajps.2014.58117$2DOI 100 1 $aSANTOS, L. O. 245 $aCarroyover effect of imidazolinone herbicides for crops following rice. 260 $c2014 650 $aArroz 700 1 $aPINTO, J. J. O. 700 1 $aPIVETA, L. B. 700 1 $aNOLDIN, J. A. 700 1 $aGALON, L. 700 1 $aCONCENCO, G. 773 $tAmerican Journal of Plant Sciences$gv. 5, n. 8, p. 1049-1058, Mar. 2014.
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