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| 7. |  | BRANDAO, Z. N.; ZONTA, J. H.; FERREIRA, G. B. Agricultura de precisão na cultura do algodão. In: BERNARDI, A. C. DE C.; NAIME, J. DE M.; RESENDE, A. V. DE; BASSOI, L. H.; INAMASU, R. Y. (Ed.). Agricultura de precisão: resultados de um novo olhar. Brasília, DF : Embrapa, 2014. p. 295-305| Biblioteca(s): Embrapa Algodão. |
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| 18. | | GONDIM, T. M. de S.; ARAÚJO, J. B. de; BRANDÃO, Z. N.; MEDEIROS, E. P. de. Prospecção de genótipos de agave para obtenção de suco para bioinseticida. In: SILVA-MATOS, R. R. S. da; MORAES, L. F.; SILVA, F. L. de S. (org.). Desenvolvimento da pesquisa científica, tecnologia e inovação na agronomia 3. Ponta Grossa: Atena, 2022. cap. 12, p. 129-137. 234 p.| Biblioteca(s): Embrapa Algodão. |
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| Registros recuperados : 117 | |
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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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