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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
05/05/2020 |
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Data da última atualização: |
06/05/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
AGUIAR, J. T. de; LOBO JUNIOR, M. |
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Afiliação: |
JORDENE TEIXEIRA DE AGUIAR, UFG; MURILLO LOBO JUNIOR, CNPAF. |
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Título: |
Reliability and discrepancies of rainfall and temperatures from remote sensing and Brazilian ground weather stations. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing Applications: Society and Environment, v. 18, 100301, Apr. 2020. |
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ISSN: |
2352-9385 |
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DOI: |
https://doi.org/10.1016/j.rsase.2020.100301 |
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Idioma: |
Inglês |
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Conteúdo: |
Insufficient ground meteorological stations limit agricultural research in wide geographic areas, but high-quality data from remote sensing may decrease information gaps, when surface stations are scarce. This study compared meteorological datasets, estimated from satellite and ground meteorological stations in latitudes from 0 to 33 oS, to support agricultural research in Brazil. The dataset comprised 3600 records of monthly temperatures and rainfall from 01 Jan 2004 to 31 Dec 2014 in 30 Brazilian municipalities distributed in six regions, labeled according to their precipitation homogeneity. Climatic records from NASA?s Prediction of Worldwide Energy Resource (POWER) online database were compared with data from Brazilian surface stations managed by National Institute of Meteorology (INMET). Monthly rainfall data showed satisfactory correlation coefficients for almost all locations, between 0.75 and 0.95 (p < 0.01), and simple linear models were fit for estimated (satellite) and observed (ground) rainfall relationship (p < 0.001). Complimentary accuracy and precision tests endorsed rainfall satellite-estimated data according to the root mean square error (RMSE) and the modified index of agreement. Maximum and minimum temperatures estimated by remote sensing in the Brazilian South Region were also statistically supported, but unsuitable results were found especially in lower latitudes, based on higher RMSE. The Pearson?s correlation coefficient for temperatures increased proportionally with latitude, while rainfall did not show this correlation. These results showed satellite-data quality varies regionally and is affected by seasonal variation. Remote sensors may not detect extreme climatic events such as heavy rainfall or draught and, therefore, need to be appraised carefully. MenosInsufficient ground meteorological stations limit agricultural research in wide geographic areas, but high-quality data from remote sensing may decrease information gaps, when surface stations are scarce. This study compared meteorological datasets, estimated from satellite and ground meteorological stations in latitudes from 0 to 33 oS, to support agricultural research in Brazil. The dataset comprised 3600 records of monthly temperatures and rainfall from 01 Jan 2004 to 31 Dec 2014 in 30 Brazilian municipalities distributed in six regions, labeled according to their precipitation homogeneity. Climatic records from NASA?s Prediction of Worldwide Energy Resource (POWER) online database were compared with data from Brazilian surface stations managed by National Institute of Meteorology (INMET). Monthly rainfall data showed satisfactory correlation coefficients for almost all locations, between 0.75 and 0.95 (p < 0.01), and simple linear models were fit for estimated (satellite) and observed (ground) rainfall relationship (p < 0.001). Complimentary accuracy and precision tests endorsed rainfall satellite-estimated data according to the root mean square error (RMSE) and the modified index of agreement. Maximum and minimum temperatures estimated by remote sensing in the Brazilian South Region were also statistically supported, but unsuitable results were found especially in lower latitudes, based on higher RMSE. The Pearson?s correlation coefficient for temperatures increased pro... Mostrar Tudo |
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Thesagro: |
Climatologia; Modelo de Simulação; Sensoriamento Remoto. |
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Thesaurus Nal: |
Agriculture; Climatology; Decision support systems; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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001 2122081
005 2020-05-06
008 2020 bl uuuu u00u1 u #d
022 $a2352-9385
024 7 $ahttps://doi.org/10.1016/j.rsase.2020.100301$2DOI
100 1 $aAGUIAR, J. T. de
245 $aReliability and discrepancies of rainfall and temperatures from remote sensing and Brazilian ground weather stations.$h[electronic resource]
260 $c2020
520 $aInsufficient ground meteorological stations limit agricultural research in wide geographic areas, but high-quality data from remote sensing may decrease information gaps, when surface stations are scarce. This study compared meteorological datasets, estimated from satellite and ground meteorological stations in latitudes from 0 to 33 oS, to support agricultural research in Brazil. The dataset comprised 3600 records of monthly temperatures and rainfall from 01 Jan 2004 to 31 Dec 2014 in 30 Brazilian municipalities distributed in six regions, labeled according to their precipitation homogeneity. Climatic records from NASA?s Prediction of Worldwide Energy Resource (POWER) online database were compared with data from Brazilian surface stations managed by National Institute of Meteorology (INMET). Monthly rainfall data showed satisfactory correlation coefficients for almost all locations, between 0.75 and 0.95 (p < 0.01), and simple linear models were fit for estimated (satellite) and observed (ground) rainfall relationship (p < 0.001). Complimentary accuracy and precision tests endorsed rainfall satellite-estimated data according to the root mean square error (RMSE) and the modified index of agreement. Maximum and minimum temperatures estimated by remote sensing in the Brazilian South Region were also statistically supported, but unsuitable results were found especially in lower latitudes, based on higher RMSE. The Pearson?s correlation coefficient for temperatures increased proportionally with latitude, while rainfall did not show this correlation. These results showed satellite-data quality varies regionally and is affected by seasonal variation. Remote sensors may not detect extreme climatic events such as heavy rainfall or draught and, therefore, need to be appraised carefully.
650 $aAgriculture
650 $aClimatology
650 $aDecision support systems
650 $aRemote sensing
650 $aClimatologia
650 $aModelo de Simulação
650 $aSensoriamento Remoto
700 1 $aLOBO JUNIOR, M.
773 $tRemote Sensing Applications: Society and Environment$gv. 18, 100301, Apr. 2020.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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| Registros recuperados : 6 | |
| 1. |  | KLEINERT, A. M. P.; RAMALHO, M.; CORTOPASSI-LAURINO, M.; RIBEIRO, M. de F.; IMPERATRIZ-FONSECA, V. L. Abelhas sociais (Bombini, Apini, Meliponini). In: PANIZZI, A. R.; PARRA, J. R. P. (Ed.). Bioecologia e nutrição de insetos: base para o manejo integrado de pragas. Brasília, DF: Embrapa Informação Tecnológica; Londrina: Embrapa Soja, 2009. cap. 10, p. 371-424.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Semiárido. |
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| 2. | | KOFFLER, S.; MENEZES, C.; MENEZES, P. R.; KLEINERT, A. de M. P.; IMPERATRIZ-FONSECA, V. L.; POPE, N.; JAFFÉ, R. Efeitos do clima e do manejo sobre a produção de mel pela jandaíra: o que podemos aprender com a meliponicultura de Mossoró, Rio Grande do Norte? In: IMPERATRIZ-FONSECA, V. L.; KOEDAM, D.; HRNCIR, M. (Ed.). A abelha jandaíra no passado, no presente e no futuro. Mossoró, RN: EDUFERSA, 2017. Cap. 17, p. 175-182.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 3. | | KLEINERT, A. M. P.; RAMALHO, M.; CORTOPASSI-LAURINO, M.; RIBEIRO, M. de F.; IMPERATRIZ-FONSECA, V. L. Social bees (Bombini, Apini, Meliponini). In: PANIZZI, A. R.; PARRA, J. R. P. (Ed.). Insect bioecology and nutrition for integrated pest management. Boca Raton: CRC Press; Brasília, DF: Embrapa, 2012. cap. 11, p. 237-271.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Semiárido. |
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| 4. | | KOFFLER, S.; MENEZES, C.; MENEZES, P.; KLEINERT, A. de M. P.; IMPERATRIZ-FONSECA, V. L.; POPE, N.; JAFFÉ, R. Stingless beekeeping development in Northeastern Brazil may be impaired my extreme weather conditions. In: ENCONTRO SOBRE ABELHAS, 11., 2015, Ribeirão Preto. Anais... Ribeirão Preto: USP: FCLRP, 2015. p. 107.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 5. | | KOFFLER, S.; MENEZES, C.; MENEZES, P. R.; KLEINERT, A. de M. P.; IMPERATRIZ-FONSECA, V. L.; POPE, N.; JAFFE, R. Temporal Variation in Honey Production by the Stingless Bee Melipona subnitida (Hymenoptera: Apidae): Long-Term Management Reveals its Potential as a Commercial Species in Northeastern Brazil. Journal of Economic Entomology, v. 108, n. 3, p. 858-867, June 2015.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 6. | | SILVA, C.; FREITAS, B.; BAUERMANN, S.; KLEINERT, A.; IMPERATRIZ-FONSECA, V.; MAUES, M. M.; ALBUQUERQUE, P.; NOVAIS, J.; TOLEDO, V.; BLOCHTEIN, B.; SARMENTO, E.; KRUG, C.; CURE, J.; ESTEVES, V.; MENDONÇA, C.; VIANA, B.; SANTOS, F.; SANTOS, I.; SARAIVA, A. Bee flora and pollen grains study for the Online Pollen Catalogue Network: basis for plant-pollinator interactions research. In: ANNUAL MEETING OF THE ASSOCIATION FOR TROPICAL BIOLOGY AND CONSERVATION, 53., 2016, Montpellier. Tropical ecology and society: reconciling conservation and sustainable use of biodiversity: program & abstracts. [S.l.]: ATBC, 2016. p. 409.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Amazônia Ocidental; Embrapa Amazônia Oriental. |
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| Registros recuperados : 6 | |
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