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| 16. |  | DANTAS, B. F.; MOURA, M. S. B. de; ANGELOTTI, F. Germinação de sementes de espécies nativas de Caatinga em cenários futuros de mudanças climáticas. Informativo ABRATES, Londrina, v. 27, n. 2, p. 40, ago. 2017. Número especial. Edição dos Resumos do XX Congresso Brasileiro de Sementes, Foz do Iguaçu, 2017.| Biblioteca(s): Embrapa Semiárido. |
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| Registros recuperados : 443 | |
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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 Semiárido. |
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Data corrente: |
05/07/2023 |
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Data da última atualização: |
23/01/2024 |
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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: |
BEZERRA, U. A.; CUNHA, J.; VALENTE, F.; NÓBREGA, R. L. B.; ANDRADEM J. M.; MOURA, M. S. B. de; VERHOEF, A.; PEREZ-MARIN, A. M.; GALVÃO, C. O. |
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Afiliação: |
ULISSES A. BEZERRA, UFCG; JOHN CUNHA, UFCG; FERNANDA VALENTE, Forest Research Centre, University of Lisboa, Lisboa, Portugal; RODOLFO L. B. NÓBREGA, University of Bristol, School of Geographical Sciences, United Kingdom; JOÃO M. ANDRADE, UFPE; MAGNA SOELMA BESERRA DE MOURA, CPATSA; ANNE VERHOEF, The University of Reading, Department of Geography and Environmental Science, United Kingdom; ALDRIN M. PEREZ-MARIN, INSA; CARLOS O. GALVÃO, UFCG. |
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Título: |
STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Agricultural and Forest Meteorology, v. 333, 109408, 2023. |
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ISSN: |
0168-1923 |
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DOI: |
https://doi.org/10.1016/j.agrformet.2023.109408 |
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Idioma: |
Inglês |
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Conteúdo: |
Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF. MenosImprovement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additiona... Mostrar Tudo |
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Palavras-Chave: |
Balanço de energia de superfície; Google Earth Engine; Resistência aerodinâmica para transferência de calor. |
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Thesagro: |
Balanço de Energia; Caatinga; Evapotranspiração; Floresta Tropical; Sensoriamento Remoto. |
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Thesaurus NAL: |
Energy balance; Evapotranspiration; Remote sensing; Tropical forests. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03237naa a2200385 a 4500
001 2154801
005 2024-01-23
008 2023 bl uuuu u00u1 u #d
022 $a0168-1923
024 7 $ahttps://doi.org/10.1016/j.agrformet.2023.109408$2DOI
100 1 $aBEZERRA, U. A.
245 $aSTEEP$ba remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests.$h[electronic resource]
260 $c2023
520 $aImprovement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.
650 $aEnergy balance
650 $aEvapotranspiration
650 $aRemote sensing
650 $aTropical forests
650 $aBalanço de Energia
650 $aCaatinga
650 $aEvapotranspiração
650 $aFloresta Tropical
650 $aSensoriamento Remoto
653 $aBalanço de energia de superfície
653 $aGoogle Earth Engine
653 $aResistência aerodinâmica para transferência de calor
700 1 $aCUNHA, J.
700 1 $aVALENTE, F.
700 1 $aNÓBREGA, R. L. B.
700 1 $aANDRADEM J. M.
700 1 $aMOURA, M. S. B. de
700 1 $aVERHOEF, A.
700 1 $aPEREZ-MARIN, A. M.
700 1 $aGALVÃO, C. O.
773 $tAgricultural and Forest Meteorology$gv. 333, 109408, 2023.
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