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| Registros recuperados : 4 | |
| 1. |  | 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. Are remote sensing evapotranspiration models reliable across South American ecoregions? Water Resources Research, v. 57, n. 11, e2020WR028752, 2021.| Biblioteca(s): Embrapa Meio Ambiente; Embrapa Semiárido. |
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| 2. | | MELO, D. C. D.; ANACHE, J. A. A.; BORGES, V. P.; MIRALLES, D. G.; MARTENS, B.; FISHER, J. B.; NÓBREGA, R. L. B.; MORENO, A.; CABRAL, O. M. R.; RODRIGUES, T. R.; BEZERRA, B.; SILVA, C. M. S.; MEIRA NETO, A. A.; MOURA, M. S. B. de; MARQUES, T. V.; CAMPOS, S.; NOGUEIRA, J. S.; ROSOLEM, R.; SOUZA, R. M. S.; ANTONINO, A. C. D.; HOLL, D.; GALLEGUILLOS, M.; PEREZ-QUEZADA, J. F.; VERHOEF, A.; KUTZBACH, L.; LIMA, J. R. S.; SOUZA, E. S.; GASSMAN, M. I.; PEREZ, C. F.; TONTI, N.; POSSE, G.; RAINS, D.; OLIVEIRA, P. T. S.; WENDLAND, E. Are remote sensing evapotranspiration models reliable across South American ecoregions? Water Resources Research, v. 57, n. 11, e2020WR028752, 2021.| Biblioteca(s): Embrapa Meio Ambiente; Embrapa Semiárido. |
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| 3. | | LIEBIG, M. A.; FRANZLUEBBERS, A. J.; ALVAREZ, C.; CHIESA, T. D.; LEWCZUK, N.; PIÑEIRO, G.; POSSE, G.; YAHDJIAN, L.; GRACE, P.; CABRAL, O. M. R.; MARTIN NETO, L.; RODRIGUES, R. de A. R.; AMIRO, B.; ANGERS, D.; HAO, X.; OELBERMANN, M.; TENUTA, M.; MUNKHOLM, L. J.; REGINA, K.; CELLIER, P.; EHRHARDT, F.; RICHARD, G.; DECHOW, R.; AGUS, F.; WIDIARTA, N.; SPINK, J.; BERTI, A.; GRIGNANI, C.; MAZZONCINI, M.; ORSINI, R.; ROGGERO, P. P.; SEDDAIU, G.; TEI, F.; VENTRELLA, D.; VITALI, G.; KISHIMOTO-MO, A.; SHIRATO, Y.; SUDO, S.; SHIN, J.; SCHIPPER, L.; SAVÉ, R.; LEIFELD, J.; SPADAVECCHIA, L.; YELURIPATI, J.; DEL GROSSO, S.; RICE, C.; SAWCHIK, J. MAGGnet: an international network to foster mitigation of agricultural greenhouse gases. Carbon Management, v. 7, N. 3-4, P. 243-248, 2016.| Biblioteca(s): Embrapa Meio Ambiente; Embrapa Solos. |
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| 4. | | PASTORELLO, G.; TROTTA, C.; CANFORA, E.; CHU, H.; CHRISTIANSON, D.; CHEAH, Y.; POINDEXTER, C.; CHEN, J.; ELBASHANDY, A.; HUMPHREY, M.; ISAAC, P.; POLIDORI, D.; RIBECA, A.; VAN INGEN, C.; ZHANG, L.; AMIRO, B.; AMMANN, C.; ARAIN, M. A.; ARDÖ, J.; ARKEBAUER, T.; ARNDT, S. K.; ARRIGA, N.; AUBINET, M.; AURELA, M.; BALDOCCHI, D.; BARR, A.; BEAMESDERFER, E.; BELELLI, L. M.; BERGERON, O.; BERINGER, J.; BERNHOFER, C.; BERVEILLER, D.; BILLESBACH, D.; BLACK, T. A.; BLANKEN, P. D.; BOHRER, G.; BOIKE, J.; BOLSTAD, P. V.; BONAL, D.; BONNEFOND, J.; BOWLING, D. R.; ROSVEL, B.; BRODEUR, J.; BRÜMMER, C.; BUCHMANN, N.; BURBAN, B.; BURNS, S. P.; BUYSSE, P.; CALE, P.; CAVAGNA, M.; CELLIER, P.; CHEN, S.; CHINI, I.; CHRISTENSEN, T. R.; CLEVERLY, J.; COLLALTI, A.; CONSALVO, C.; COOK, B. D.; COOK, D.; COURSOLLE, C.; CREMONESE, E.; CURTIS, P. S.; D'ANDREA, E.; ROCHA, H. da; DAI, X.; DAVIS, K. J.; DE CINTI, B.; DE GRANDCOURT, A.; DE LIGNE, A.; OLIVEIRA JUNIOR, R. C. de; DELPIERRE, N.; DESAI, A. R.; DI BELLA, C. M.; DI TOMMASI, P.; DOLMAN, H.; DOMINGO, F.; DONG, G.; DORE, S.; DUCE, P.; DUFRÊNE, E.; DUNN, A.; DUSEK, J.; EAMUS, D.; EICHELMANN, U.; ELKHIDIR, H. A. M.; EUGSTER, W.; EWENZ, C. M.; EWERS, B.; FAMULARI, D.; FARES, S.; FEIGENWINTER, I.; FEITZ, A.; FENSHOLT, R.; FILIPPA, G.; FISCHER, M.; FRANK, J.; GALVAGNO, M.; GHARUN, M.; GIANELLE, D.; GIELEN, B.; GIOLI, B.; GITELSON, A.; GODED, I. B.; GOECKEDE, M.; GOLDSTEIN, A. H.; GOUGH, C. M.; GOULDEN, M. L.; GRAF, A.; GRIEBEL, A.; GRUENING, C.; GRÜNWALD, T.; HAMMERLE, A.; HAN, S.; HAN, X.; HANSEN, B. U.; HANSON, C.; HATAKKA, J.; HE, Y.; HEHN, M.; HEINESCH, B.; HINKO-NAJERA, N.; HÖRTNAGL, L.; HUTLEY, L.; IBROM, A.; IKAWA, H.; JACKOWICZ-KORCZYNSKI, M.; JANOUS, D.; JANS, W.; JASSAL, R.; JIANG, S.; KATO, T.; KHOMIK, M.; KLATT, J.; KNOHL, A.; KNOX, S.; KOBAYASHI, H.; KOERBER, G.; KOLLE, O.; KOSUGI, Y.; KOTANI, A.; KOWALSKI, A.; KRUIJT, B.; KURBATOVA, J.; KUTSCH, W. L.; KWON, H.; LAUNIAINEN, S.; LAURILA, T.; LAW, B.; LEUNING, R.; LI, Y.; LIDDELL, M.; LIMOUSIN, J.; LION, M.; LISKA, A.; LOHILA, A.; LÓPEZ-BALLESTEROS, A.; LÓPEZ-BLANCO, E.; LOUBET, B.; LOUSTAU, D.; LUCAS-MOFFAT, A.; LÜERS, J.; MA, S.; MACFARLANE, C.; MAGLIULO, V.; MAIER, R.; MAMMARELLA, I.; MANCA, G.; MARCOLLA, B.; MARGOLIS, H.; MARRAS, S.; MASSMAN, W.; MASTEPANOV, M.; MATAMALA, R.; MATTHES, J. H.; MAZZENGA, F.; MCCAUGHEY, H.; MCHUGH, I.; MCMILLAN, A. M. S.; MERBOLD, L.; MEYER, W.; MEYERS, T.; MILLER, S. D.; MINERBI, S.; MODEROW, U.; MONSON, R. K.; MONTAGNANI, L.; MOORE, C. E.; MOORS, E.; MOREAUX, V.; MOUREAUX, C.; MUNGER, J. W.; NAKAI, T.; NEIRYNCK, J.; NESIC, Z.; NICOLINI, G.; NOORMETS, A.; NORTHWOOD, M.; NOSETTO, M.; NOUVELLON, Y.; NOVICK, K.; WALTER, O.; OLESEN, J. E.; OURCIVAL, J.; PAPUGA, S. A.; PARMENTIER, F.; PAUL-LIMOGES, E.; PAVELKA, M.; PEICHL, M.; PENDALL, E.; PHILLIPS, R. P.; PILEGAARD, K.; PIRK, N.; POSSE, G.; POWELL, T.; PRASSE, H.; PROBER, S. M.; RAMBAL, S.; RANNIK, U.; RAZ-YASEEF, N.; REED, D.; RESCO DE DIOS, V.; RESTREPO-COUPE, N.; REVERTER, B. R.; ROLAND, M.; SABBATINI, S.; SACHS, T.; SALESKA, S. R.; SÁNCHEZ-CAÑETE, E. P.; SANCHEZ-MEJIA, Z. M.; SCHMID, H. P.; SCHMIDT, M.; SCHNEIDER, K.; SCHRADER, F.; SCHRODER, I.; SCOTT, R. L.; SEDLÁK, P.; SERRANO-ORTÍZ, P.; SHAO, C.; SHI, P.; SHIRONYA, I.; SIEBICKE, L.; SIGUT, L.; SILBERSTEIN, R.; SIRCA, C.; SPANO, D.; STEINBRECHER, R.; STEVENS, R. M.; STURTEVANT, C.; SUYKER, A.; TAGESSON, T.; TAKANASHI, S.; TANG, Y.; TAPPER, N.; THOM, J.; TIEDEMANN, F.; TOMASSUCCI, M.; TUOVINEN, J.; URBANSKI, S.; VALENTINI, R.; VAN DER MOLEN, M.; VAN GORSEL, E.; VAN HUISSTEDEN, K.; VARLAGIN, A.; VERFAILLIE, J.; VESALA, T.; VINCKE, C.; VITALE, D.; VYGODSKAYA, N.; WALKER, J. P.; WALTER-SHEA, E.; WANG, H.; WEBER, R.; WESTERMANN, S.; WILLE, C.; WOFSY, S.; WOHLFAHRT, G.; WOLF, S.; WOODGATE, W.; YUELIN, L.; ZAMPEDRI, R.; ZHANG, J.; ZHOU, G.; ZONA, D.; AGARWAL, D.; BIRAUD, S.; TORN, M.; PAPALE, D. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data, v. 7, Article number 225, 2020. 27 p.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| Registros recuperados : 4 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Semiárido. |
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Data corrente: |
04/07/2022 |
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Data da última atualização: |
04/07/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MELO, D. C. D.; ANACHE, J. A. A.; BORGES, V. P.; MIRALLES, D. G.; MARTENS, B.; FISHER, J. B.; NÓBREGA, R. L. B.; MORENO, A.; CABRAL, O. M. R.; RODRIGUES, T. R.; BEZERRA, B.; SILVA, C. M. S.; MEIRA NETO, A. A.; MOURA, M. S. B. de; MARQUES, T. V.; CAMPOS, S.; NOGUEIRA, J. S.; ROSOLEM, R.; SOUZA, R. M. S.; ANTONINO, A. C. D.; HOLL, D.; GALLEGUILLOS, M.; PEREZ-QUEZADA, J. F.; VERHOEF, A.; KUTZBACH, L.; LIMA, J. R. S.; SOUZA, E. S.; GASSMAN, M. I.; PEREZ, C. F.; TONTI, N.; POSSE, G.; RAINS, D.; OLIVEIRA, P. T. S.; WENDLAND, E. |
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Afiliação: |
D. C. D. MELO, UFPB; J. A. A. ANACHE, UFMS; V. P. BORGES, UFPB; D. G. MIRALLES, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium; B. MARTENS, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium; J. B. FISHER, Schmid College of Science and Technology, Chapman University, Orange, CA; R. L. B. NÓBREGA, Department of Life Sciences, Imperial College London; A. MORENO, Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT; OSVALDO MACHADO RODRIGUES CABRAL, CNPMA; T. R. RODRIGUES, UFMS; B. BEZERRA, UFRN; C. M. S. SILVA, UFRN; A. A. MEIRA NETO, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ; MAGNA SOELMA BESERRA DE MOURA, CPATSA; T. V. MARQUES, UFRN; S. CAMPOS, UFRN; J. S. NOGUEIRA, UFMT; R. ROSOLEM, University of Bristol, Bristol, UK; R. M. S. SOUZA, Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX; A. C. D. ANTONINO, UFPE; D. HOLL, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany; M. GALLEGUILLOS, Department of Environmental Science and Renewable Natural Resources, University of Chile, Santiago, Chile; J. F. PEREZ-QUEZADA, Department of Environmental Science and Renewable Natural Resources, University of Chile, Santiago, Chile; A. VERHOEF, Department of Geography and Environmental Science, The University of Reading, Reading, UK; L. KUTZBACH, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany; J. R. S. LIMA, Federal University of the Agreste of Pernambuco, Garanhuns, PE; E. S. SOUZA, UFRPE, Garanhuns, PE; M. I. GASSMAN, Department of Atmospheric and Ocean Sciences, FCEN — UBA, Buenos Aires, Argentina; C. F. PEREZ, Department of Atmospheric and Ocean Sciences, FCEN ? UBA, Buenos Aires, Argentina; N. TONTI, Department of Atmospheric and Ocean Sciences, FCEN — UBA, Buenos Aires, Argentina; G. POSSE, INTA; D. RAINS, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium; P. T. S. OLIVEIRA, UFMS; E. WENDLAND, Department of Hydraulics and Sanitary Engineering, University of São Paulo, São Carlos, SP. |
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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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DOI: |
https://doi.org/10.1029/2020WR028752 |
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Idioma: |
Inglês |
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Conteúdo: |
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). E ET was predicted satisfactorily by all four models, with correlations consistently higher (?20.6ER) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias (?? ?1010EPBIAS%). 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. MenosMany 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). E ET was predicted satisfactorily by all four models, with correlations consistently higher (?20.6ER) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias (?? ?1010EPBIAS%). 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 int... Mostrar Tudo |
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Palavras-Chave: |
MODIS; Penman-Monteith; Priestley-Taylor. |
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Thesagro: |
Evapotranspiração; Sensoriamento Remoto; Vegetação. |
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Thesaurus NAL: |
Remote sensing. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03247naa a2200613 a 4500
001 2144450
005 2022-07-04
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1029/2020WR028752$2DOI
100 1 $aMELO, D. C. D.
245 $aAre remote sensing evapotranspiration models reliable across South American ecoregions?$h[electronic resource]
260 $c2021
520 $aMany 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). E ET was predicted satisfactorily by all four models, with correlations consistently higher (?20.6ER) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias (?? ?1010EPBIAS%). 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 $aRemote sensing
650 $aEvapotranspiração
650 $aSensoriamento Remoto
650 $aVegetação
653 $aMODIS
653 $aPenman-Monteith
653 $aPriestley-Taylor
700 1 $aANACHE, J. A. A.
700 1 $aBORGES, V. P.
700 1 $aMIRALLES, D. G.
700 1 $aMARTENS, B.
700 1 $aFISHER, J. B.
700 1 $aNÓBREGA, R. L. B.
700 1 $aMORENO, A.
700 1 $aCABRAL, O. M. R.
700 1 $aRODRIGUES, T. R.
700 1 $aBEZERRA, B.
700 1 $aSILVA, C. M. S.
700 1 $aMEIRA NETO, A. A.
700 1 $aMOURA, M. S. B. de
700 1 $aMARQUES, T. V.
700 1 $aCAMPOS, S.
700 1 $aNOGUEIRA, J. 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. F.
700 1 $aVERHOEF, A.
700 1 $aKUTZBACH, L.
700 1 $aLIMA, J. R. S.
700 1 $aSOUZA, E. S.
700 1 $aGASSMAN, M. I.
700 1 $aPEREZ, C. F.
700 1 $aTONTI, N.
700 1 $aPOSSE, G.
700 1 $aRAINS, D.
700 1 $aOLIVEIRA, P. T. S.
700 1 $aWENDLAND, E.
773 $tWater Resources Research$gv. 57, n. 11, e2020WR028752, 2021.
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Embrapa Meio Ambiente (CNPMA) |
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