Surface Albedo and Temperature Models for Surface Energy Balance Fluxes and Evapotranspiration Using SEBAL and Landsat 8 over Cerrado-Pantanal, Brazil

The determination of the surface energy balance fluxes (SEBFs) and evapotranspiration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantic...

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Autores principales: Lucas Peres Angelini, Marcelo Sacardi Biudes, Nadja Gomes Machado, Hatim M. E. Geli, George Louis Vourlitis, Anderson Ruhoff, José de Souza Nogueira
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
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Acceso en línea:https://doaj.org/article/3c61010b3e884a439710f148da4b6e38
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Sumario:The determination of the surface energy balance fluxes (SEBFs) and evapotranspiration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula>) is fundamental in environmental studies involving the effects of land use change on the water requirement of crops. SEBFs and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> have been estimated by remote sensing techniques, but with the operation of new sensors, some variables need to be parameterized to improve their accuracy. Thus, the objective of this study is to evaluate the performance of algorithms used to calculate surface albedo and surface temperature on the estimation of SEBFs and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> in the Cerrado-Pantanal transition region of Mato Grosso, Brazil. Surface reflectance images of the Operational Land Imager (OLI) and brightness temperature (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>b</mi></msub></mrow></semantics></math></inline-formula>) of the Thermal Infrared Sensor (TIRS) of the Landsat 8, and surface reflectance images of the MODIS MOD09A1 product from 2013 to 2016 were combined to estimate SEBF and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> by the surface energy balance algorithm for land (SEBAL), which were validated with measurements from two flux towers. The surface temperature (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula>) was recovered by different models from the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>b</mi></msub></mrow></semantics></math></inline-formula> and by parameters calculated in the atmospheric correction parameter calculator (ATMCORR). A model of surface albedo (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>s</mi><mi>u</mi><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula>) with surface reflectance OLI Landsat 8 developed in this study performed better than the conventional model (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>c</mi><mi>o</mi><mi>n</mi></mrow></msub></mrow></semantics></math></inline-formula>) SEBFs and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> in the Cerrado-Pantanal transition region estimated with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>s</mi><mi>u</mi><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> combined with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>b</mi></msub></mrow></semantics></math></inline-formula> performed better than estimates with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>c</mi><mi>o</mi><mi>n</mi></mrow></msub></mrow></semantics></math></inline-formula>. Among all the evaluated combinations, SEBAL performed better when combining <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>s</mi><mi>u</mi><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> with the model developed in this study and the surface temperature recovered by the Barsi model (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mrow><msub><mi>s</mi><mrow><mi>b</mi><mi>a</mi><mi>r</mi><mi>s</mi><mi>i</mi></mrow></msub></mrow></msub></mrow></semantics></math></inline-formula>). This demonstrates the importance of an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>s</mi><mi>u</mi><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> model based on surface reflectance and atmospheric surface temperature correction in estimating SEBFs and <i>ET</i> by SEBAL.