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| Registros recuperados : 33 | |
| 21. |  | LEMES, M. W. A.; VASQUES, G. de M.; MACHADO, A. S.; RODRIGUES, H. M.; LOPES, R. T.; ROSAS, R. O. X-ray computed microtomography against retention curve for assessing porosity in Latossolos (Ferralsols) with contrasting vegetation cover. 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. 19-20. WCSS 2018.| Biblioteca(s): Embrapa Solos. |
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| 22. | | ESQUENAZI, D.; WIGG, M. D.; MIRANDA, M. M. F. S.; RODRIGUES, H. M.; TOSTES, J. B. F.; ROZENTAL, S.; SILVA, A. J. R. da; ALVIANO, C. S. Antimicrobial and antiviral activities of polyphenolics from Cocos nucifera Linn. (Palmae) husk fiber extract. Research in Microbiology, v.153, p.647-652, 2002.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 23. | | VASQUES, G. de M.; RODRIGUES, H. M.; COELHO, M. R.; BACA, J. F. M.; DART, R. de O.; OLIVEIRA, R. P. de; TEIXEIRA, W. G.; CEDDIA, M. B. Field proximal soil sensor fusion for improving high-resolution soil property maps. Soil Systems, v. 4, n. 3, 52, 2020.| Biblioteca(s): Embrapa Solos. |
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| 24. | | RODRIGUES, H. M.; VASQUES, G. de M.; OLIVEIRA, R. P. de; TAVARES, S. R. de L.; CEDDIA, M. B.; HERNANI, L. C. Finding suitable transect spacing and sampling designs for accurate soil ECa mapping from EM38-MK2. Soil Systems, v. 4, n. 3, 56, 2020.| Biblioteca(s): Embrapa Solos. |
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| 26. | | NASCIMENTO, C. W. R. do; RODRIGUES, H. M.; CEDDIA, M. B.; VASQUES, G. de M.; DURÃO, S. M. de O.; FIGUEIRA, H. F. V. Identificação de limites entre duas classes de solo utilizando radar de penetração no solo com profundidades ajustadas por barras de ferro e validação com trado holandês. In: SIMPÓSIO BRASILEIRO DE GEOGRAFIA FÍSICA APLICADA, 18., 2019, Fortaleza. Geografia física e as mudanças globais. Fortaleza: Editora UFC, 2019.| Biblioteca(s): Embrapa Solos. |
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| 27. | | NASCIMENTO, C. W. R. do; RODRIGUES, H. M.; CEDDIA, M. B.; VASQUES, G. de M.; DURÃO, S. M. de O.; SANTOS, W. de M.; FREIRE, M. de O. Identificação em profundidade de barras de ferro utilizando radar de penetração do solo (GPR) com antena de 450 MHz em três classes de solo. In: SIMPÓSIO BRASILEIRO DE GEOGRAFIA FÍSICA APLICADA, 18., 2019, Fortaleza. Geografia física e as mudanças globais. Fortaleza: Editora UFC, 2019.| Biblioteca(s): Embrapa Solos. |
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| 29. | | VASQUES, G. de M.; RODRIGUES, H. M.; HUBER, E.; TAVARES, S. R. de L.; MARQUES, F. A.; SILVA, M. S. L. da. Ground penetrating radar non-invasively positions an underground dam and estimates its water reservoir shape and volume. In: PEDOMETRICS BRAZIL, 2., 2021, Rio de Janeiro. Annals [...]. Rio de Janeiro: Embrapa Solos, 2022. Não paginado. Evento online.| Biblioteca(s): Embrapa Solos. |
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| 30. | | OLIVEIRA, R. P. de; RODRIGUES, H. M.; VASQUES, G. de M.; TAVARES, S. R. de L.; HERNANI, L. C.; BACA, J. F. M.; COELHO, M. R. Proximal soil sensing platform for effective mapping of soil attributes in Brazil. In: GLOBAL WORKSHOP ON PROXIMAL SOIL SENSING, 5., 2019, Columbia, MO. Program and proceedings. Columbia, MO: University of Missouri, 2019. p. 273-278.| Biblioteca(s): Embrapa Solos. |
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| 31. | | RODRIGUES, H. M.; NASCIMENTO, C. W. R. do; CEDDIA, M. B.; VASQUES, G. de M.; NUNES, J. F.; SANTOS, F. B. dos. Uso de barras de ferro para diferenciação entre horizontes de três classes de solo utilizando radar de penetração do solo (GPR) com antena monoestática de 750 MHz. In: SIMPÓSIO BRASILEIRO DE GEOGRAFIA FÍSICA APLICADA, 18., 2019, Fortaleza. Geografia física e as mudanças globais. Fortaleza: Editora UFC, 2019.| Biblioteca(s): Embrapa Solos. |
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| 32. | | RODRIGUES, H. M.; CEDDIA, M. B.; VASQUES, G. M.; MULDER, V. L.; HEUVELINK, G. B. M.; OLIVEIRA, R. P. de; BRANDAO, Z. N.; MORAIS, J. P. S.; NEVES, M. L.; TAVARES, S. R. de L. Remote sensing and kriging with external drift to improve sparse proximal soil sensing data and define management zones in precision agriculture. AgriEngineering, v. 5, n. 4, p. 2326-2348, 2023.| Biblioteca(s): Embrapa Algodão; Embrapa Solos; Embrapa Solos / UEP-Recife. |
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| 33. | | NASCIMENTO, C. W. R. do; RODRIGUES, H. M.; CEDDIA, M. B.; VASQUES, G. de M.; DURÃO, S. M. de O.; GONÇALVES, J. F. da S.; FREIRE, M. de O. Utilização integrada do radar de penetração do solo (GPR) e gamaespectrômetro como subsídio na identificação da transição entre um Planossolo Háplico e um Argissolo Vermelho. In: SIMPÓSIO BRASILEIRO DE GEOGRAFIA FÍSICA APLICADA, 18., 2019, Fortaleza. Geografia física e as mudanças globais. Fortaleza: Editora UFC, 2019.| Biblioteca(s): Embrapa Solos. |
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| Registros recuperados : 33 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Algodão; Embrapa Solos; Embrapa Solos / UEP-Recife. |
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Data corrente: |
07/12/2023 |
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Data da última atualização: |
07/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
C - 0 |
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Autoria: |
RODRIGUES, H. M.; CEDDIA, M. B.; VASQUES, G. M.; MULDER, V. L.; HEUVELINK, G. B. M.; OLIVEIRA, R. P. de; BRANDAO, Z. N.; MORAIS, J. P. S.; NEVES, M. L.; TAVARES, S. R. de L. |
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Afiliação: |
HUGO RODRIGUES, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; MARCOS BACIS CEDDIA, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; GUSTAVO DE MATTOS VASQUES, CNPS; VERA LEATITIA MULDER, WAGENINGEN UNIVERSITY; GERARD B. M. HEUVELINK, WAGENINGEN UNIVERSITY; RONALDO PEREIRA DE OLIVEIRA, CNPS; ZIANY NEIVA BRANDAO, CNPA; JOAO PAULO SARAIVA MORAIS, CNPA; MATHEUS LEAL NEVES, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; SILVIO ROBERTO DE LUCENA TAVARES, CNPS. |
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Título: |
Remote sensing and kriging with external drift to improve sparse proximal soil sensing data and define management zones in precision agriculture. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
AgriEngineering, v. 5, n. 4, p. 2326-2348, 2023. |
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DOI: |
https://doi.org/10.3390/agriengineering5040143 |
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Idioma: |
Inglês |
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Conteúdo: |
The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide. MenosThe precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clu... Mostrar Tudo |
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Palavras-Chave: |
Geoestatística; Management zones; Proximal soil sensing. |
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Thesagro: |
Agricultura de Precisão; Sensoriamento Remoto. |
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Thesaurus NAL: |
Geostatistics; Precision agriculture; Remote sensing. |
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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/doc/1159261/1/Remote-sensing-and-kriging-with-external-drift-2023.pdf
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Marc: |
LEADER 03816naa a2200337 a 4500
001 2159261
005 2023-12-07
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/agriengineering5040143$2DOI
100 1 $aRODRIGUES, H. M.
245 $aRemote sensing and kriging with external drift to improve sparse proximal soil sensing data and define management zones in precision agriculture.$h[electronic resource]
260 $c2023
520 $aThe precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide.
650 $aGeostatistics
650 $aPrecision agriculture
650 $aRemote sensing
650 $aAgricultura de Precisão
650 $aSensoriamento Remoto
653 $aGeoestatística
653 $aManagement zones
653 $aProximal soil sensing
700 1 $aCEDDIA, M. B.
700 1 $aVASQUES, G. M.
700 1 $aMULDER, V. L.
700 1 $aHEUVELINK, G. B. M.
700 1 $aOLIVEIRA, R. P. de
700 1 $aBRANDAO, Z. N.
700 1 $aMORAIS, J. P. S.
700 1 $aNEVES, M. L.
700 1 $aTAVARES, S. R. de L.
773 $tAgriEngineering$gv. 5, n. 4, p. 2326-2348, 2023.
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