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Registro Completo |
Biblioteca(s): |
Embrapa Rondônia. |
Data corrente: |
03/07/1997 |
Data da última atualização: |
03/07/1997 |
Autoria: |
MORAES, L. D. de; TRINDADE, A. G. da; LEONIDAS, F. das C.; COSTA, M. C.; PEREIRA, R. G. de; MAGALHAES, J. A. |
Afiliação: |
UNIR e Embrapa-CPAF-Rondonia (C.Postal 406, CEP 78900-000 - Porto Velho, RO). |
Título: |
Ocorrencia de chuvas acidas em Porto Velho. |
Ano de publicação: |
1994 |
Fonte/Imprenta: |
In: CONGRESSO DE ECOLOGIA DO BRASIL, 2., 1994, Londrina. Programa e resumos. Londrina: UEL / Sociedade de Ecologia do Brasil, 1994. v.2. p.597. |
Volume: |
v.2 |
Páginas: |
p.597. |
Idioma: |
Português |
Palavras-Chave: |
Brasil; Porto Velho; Rondonia. |
Thesagro: |
Acidez; Água; Chuva Acida; Ph. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00779naa a2200277 a 4500 001 1698925 005 1997-07-03 008 1994 bl uuuu u00u1 u #d 100 1 $aMORAES, L. D. de 245 $aOcorrencia de chuvas acidas em Porto Velho. 260 $c1994 300 $ap.597. v.2 490 $vv.2 650 $aAcidez 650 $aÁgua 650 $aChuva Acida 650 $aPh 653 $aBrasil 653 $aPorto Velho 653 $aRondonia 700 1 $aTRINDADE, A. G. da 700 1 $aLEONIDAS, F. das C. 700 1 $aCOSTA, M. C. 700 1 $aPEREIRA, R. G. de 700 1 $aMAGALHAES, J. A. 773 $tIn: CONGRESSO DE ECOLOGIA DO BRASIL, 2., 1994, Londrina. Programa e resumos. Londrina: UEL / Sociedade de Ecologia do Brasil, 1994.$gv.2. p.597.
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Embrapa Rondônia (CPAF-RO) |
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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. |
Registro Completo
Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
05/05/2020 |
Data da última atualização: |
06/05/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 5 |
Autoria: |
AGUIAR, J. T. de; LOBO JUNIOR, M. |
Afiliação: |
JORDENE TEIXEIRA DE AGUIAR, UFG; MURILLO LOBO JUNIOR, CNPAF. |
Título: |
Reliability and discrepancies of rainfall and temperatures from remote sensing and Brazilian ground weather stations. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Remote Sensing Applications: Society and Environment, v. 18, 100301, Apr. 2020. |
ISSN: |
2352-9385 |
DOI: |
https://doi.org/10.1016/j.rsase.2020.100301 |
Idioma: |
Inglês |
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 |
Thesagro: |
Climatologia; Modelo de Simulação; Sensoriamento Remoto. |
Thesaurus NAL: |
Agriculture; Climatology; Decision support systems; Remote sensing. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 02613naa a2200241 a 4500 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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Embrapa Arroz e Feijão (CNPAF) |
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