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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
23/12/2021 |
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Data da última atualização: |
15/03/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MELO, D. de C. D.; ANACHE, J. A. A.; BORGES, V. P.; GONZALEZ MIRALLES, D.; MARTENS, B.; FISHER, J. B.; NÓBREGA, R. L. B.; MORENO, Á.; CABRAL, O. M. R.; RODRIGUES, T. R.; BEZERRA, B.; SILVA, C. M. S. e; MEIRA NETO, A. A.; MOURA, M. S. B. de; MARQUES, T. V.; CAMPOS, S.; NOGUEIRA, J. de S.; ROSOLEM, R.; SOUZA, R. M. S.; ANTONINO, A. C. D.; HOLL, D.; GALLEGUILLOS, M.; PEREZ-QUEZADA, J.; VERHOEF, A.; KUTZBACH, L.; LIMA, J. R. de S.; SOUZA, E. S. de; GASSMANN, M. I.; PÉREZ, C. F.; TONTI, N. E.; POSSE, G.; RAINS, D.; OLIVEIRA, P. T.; WENDLAND, E. |
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Afiliação: |
DAVI DE CARVALHO DINIZ MELO, UFPB; JAMIL ALEXANDRE AYACH ANACHE, UFMS; VALÉRIA PEIXOTO BORGES, UFPB; DIEGO GONZALEZ MIRALLES, Ghent University; BRECHT MARTENS, Ghent University; JOSHUA B FISHER, Chapman University; RODOLFO LUIZ BEZERRA NÓBREGA, Imperial Coolege London; ÁLVARO MORENO MARTÍNEZ, University of Montana; OSVALDO MACHADO RODRIGUES CABRAL, CNPMA; THIAGO RANGEL RODRIGUES, UFMS; BERGSON BEZERRA, UFRN; CLÁUDIO MOISÉS SANTOS E SILVA, UFRN; ANTONIO ALVES MEIRA NETO, University of Arizona; MAGNA SOELMA BESERRA DE MOURA, CPATSA; THIAGO VALENTIM MARQUES, UFRN; SUANY CAMPOS, UFRN; JOSE DE SOUZA NOGUEIRA, UFMS; RAFAEL ROSOLEM, University of Bristol; RODOLFO M S SOUZA, Texas A&M University; ANTONIO CELSO DANTAS ANTONINO, UFPE; DAVID HOLL, Universität Hamburg; MAURICIO GALLEGUILLOS, Universidad de Chile; JORGE PEREZ-QUEZADA, Universidad de Chile; ANNE VERHOEF, University of Reading; LARS KUTZBACH, Universität Hamburg; JOSÉ ROMUALDO DE SOUSA LIMA, UFAPE; EDUARDO SOARES DE SOUZA, UFRPE; MARÍA ISABEL GASSMANN, Universidad de Buenos Aires; CLAUDIO F PÉREZ, CONICET; NATALIA EDITH TONTI, Universisidad de Buenos Aires; GABRIELA POSSE, INTA; DOMINIK RAINS, Ghent University; PAULO TARSO OLIVEIRA, UFMS; EDSON WENDLAND, EESC-USP. |
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Título: |
Are remote sensing evapotranspiration models reliable across South American ecoregions? |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Water Resources Research, v. 57, n. 11, e2020WR028752, 2021. |
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ISSN: |
0043-1397 |
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DOI: |
https://doi.org/10.1029/2020WR028752 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman-Monteith Mu model (PM-MOD), and Penman-Monteith Nagler model (PM-VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias (%). As for PM-VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land-use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America. MenosAbstract: Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman-Monteith Mu model (PM-MOD), and Penman-Monteith Nagler model (PM-VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias (%). As for PM-VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land-use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristi... Mostrar Tudo |
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Thesagro: |
Evapotranspiração; Modelo de Simulação; Sensoriamento Remoto. |
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Thesaurus Nal: |
Climate models; Evapotranspiration; Mathematical models; Remote sensing. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
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100 1 $aMELO, D. de C. D.
245 $aAre remote sensing evapotranspiration models reliable across South American ecoregions?$h[electronic resource]
260 $c2021
520 $aAbstract: Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman-Monteith Mu model (PM-MOD), and Penman-Monteith Nagler model (PM-VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias (%). As for PM-VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land-use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.
650 $aClimate models
650 $aEvapotranspiration
650 $aMathematical models
650 $aRemote sensing
650 $aEvapotranspiração
650 $aModelo de Simulação
650 $aSensoriamento Remoto
700 1 $aANACHE, J. A. A.
700 1 $aBORGES, V. P.
700 1 $aGONZALEZ MIRALLES, D.
700 1 $aMARTENS, B.
700 1 $aFISHER, J. B.
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700 1 $aCABRAL, O. M. R.
700 1 $aRODRIGUES, T. R.
700 1 $aBEZERRA, B.
700 1 $aSILVA, C. M. S. e
700 1 $aMEIRA NETO, A. A.
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700 1 $aMARQUES, T. V.
700 1 $aCAMPOS, S.
700 1 $aNOGUEIRA, J. de S.
700 1 $aROSOLEM, R.
700 1 $aSOUZA, R. M. S.
700 1 $aANTONINO, A. C. D.
700 1 $aHOLL, D.
700 1 $aGALLEGUILLOS, M.
700 1 $aPEREZ-QUEZADA, J.
700 1 $aVERHOEF, A.
700 1 $aKUTZBACH, L.
700 1 $aLIMA, J. R. de S.
700 1 $aSOUZA, E. S. de
700 1 $aGASSMANN, M. I.
700 1 $aPÉREZ, C. F.
700 1 $aTONTI, N. E.
700 1 $aPOSSE, G.
700 1 $aRAINS, D.
700 1 $aOLIVEIRA, P. T.
700 1 $aWENDLAND, E.
773 $tWater Resources Research$gv. 57, n. 11, e2020WR028752, 2021.
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