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Registro Completo |
Biblioteca(s): |
Embrapa Territorial. |
Data corrente: |
14/08/2017 |
Data da última atualização: |
14/08/2017 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
TEIXEIRA, A. H. de C.; LEIVAS, J. F.; HERNANDEZ, F. B. T.; FRANCO, R. A. M. |
Afiliação: |
ANTONIO HERIBERTO DE C TEIXEIRA, CPATSA; JANICE FREITAS LEIVAS, CNPM; FERNANDO BRAZ TANGERINO HERNANDEZ, UNESP; RENATO ALBERTO MOMESSO FRANCO, UNESP. |
Título: |
Large-scale radiation and energy balances with Landsat 8 images and agrometeorological data in the Brazilian semiarid region. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Journal of Applied Remote Sensing, v. 11, n. 1, jan./mar. 2017. |
Páginas: |
p. 016030-1-016030-15. |
DOI: |
10.1117/1.JRS.11.016030. |
Idioma: |
Inglês |
Conteúdo: |
Aiming to subsidize the rational water resources management, four Landsat 8 (L8) images along different conditions of the year 2014 were used for modeling the radiation and energy balances in the mixed agroecosystems inside a Brazilian reference semiarid area. The SAFER algorithm was applied to calculate the latent heat flux (λE); net radiation (Rn) was acquired by the Slob equation; ground heat flux (G) was considered a fraction of Rn; and the sensible heat flux (H) was retrieved by residue in the energy balance equation. For classifying the vegetation, the surface resistance algorithm (SUREAL) was used to estimate the surface resistance to the water fluxes (rs) with threshold values for rs. Clearly, one could see higher λE values from irrigated crops (ICs) than those for natural vegetation (NV) with some situations of heat horizontal advection. The respective λE, H, and G average ratios to Rn for the ICs ecosystem were 64% to 79%, 18% to 28%, and 3%, respectively. For the NV ecosystem, the corresponding fractions were 4% to 37%, 60% to 94%, and 4%, respectively. The algorithms proved to have strong sensibility to quantifying the large-scale energy and mass exchanges by applying L8 images in mixed agroecosystems of semiarid environments. |
Palavras-Chave: |
Energy partition; Latent heat flux; Net radiation; Sensible heat flux; Soil heat flux. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/162671/1/4838.pdf
|
Marc: |
LEADER 02062naa a2200241 a 4500 001 2073988 005 2017-08-14 008 2017 bl uuuu u00u1 u #d 024 7 $a10.1117/1.JRS.11.016030.$2DOI 100 1 $aTEIXEIRA, A. H. de C. 245 $aLarge-scale radiation and energy balances with Landsat 8 images and agrometeorological data in the Brazilian semiarid region.$h[electronic resource] 260 $c2017 300 $ap. 016030-1-016030-15. 520 $aAiming to subsidize the rational water resources management, four Landsat 8 (L8) images along different conditions of the year 2014 were used for modeling the radiation and energy balances in the mixed agroecosystems inside a Brazilian reference semiarid area. The SAFER algorithm was applied to calculate the latent heat flux (λE); net radiation (Rn) was acquired by the Slob equation; ground heat flux (G) was considered a fraction of Rn; and the sensible heat flux (H) was retrieved by residue in the energy balance equation. For classifying the vegetation, the surface resistance algorithm (SUREAL) was used to estimate the surface resistance to the water fluxes (rs) with threshold values for rs. Clearly, one could see higher λE values from irrigated crops (ICs) than those for natural vegetation (NV) with some situations of heat horizontal advection. The respective λE, H, and G average ratios to Rn for the ICs ecosystem were 64% to 79%, 18% to 28%, and 3%, respectively. For the NV ecosystem, the corresponding fractions were 4% to 37%, 60% to 94%, and 4%, respectively. The algorithms proved to have strong sensibility to quantifying the large-scale energy and mass exchanges by applying L8 images in mixed agroecosystems of semiarid environments. 653 $aEnergy partition 653 $aLatent heat flux 653 $aNet radiation 653 $aSensible heat flux 653 $aSoil heat flux 700 1 $aLEIVAS, J. F. 700 1 $aHERNANDEZ, F. B. T. 700 1 $aFRANCO, R. A. M. 773 $tJournal of Applied Remote Sensing$gv. 11, n. 1, jan./mar. 2017.
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