Análise das componentes da equação do balanço de radiação utilizando o modelo Sebal no sul do Brasil

• Ano de defesa: 2019
• Autor(a) principal: Fagundes, Lucas Augusto
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Meteorologia; UFSM; Programa de Pós-Graduação em Meteorologia; Centro de Ciências Naturais e Exatas
• Palavras-chave em Português: MODIS; SEBAL; Arroz; Saldo de Radiação; Rice; Net radiation; CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/22350

Recently, the use of geotechnologies allows the identification in real time of the changes that occur in the terrestrial surface, resulting from the natural phenomena and several anthropic processes. Many changes of this level can be detected from the monitoring and determination of radiative changes occurring on the surface. In this sense, the objective of the present study is to estimate the components of the surface radiation balance using the Surface Energy Balance Algorithms for Land (SEBAL) model with images generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor in an area of flooded rice in the municipality of Cachoeira do Sul, Brazil. The study was carried out for the years 2013 to 2017, in which the results of the SEBAL model were validated with measurements made at a micrometeorological tower installed in the area of study. The fraction of cloudiness (FN) in 11 classes were classified in order to evaluate the impact of the data obtained in the days with different FN on the estimation of the Rn components by the SEBAL model. We identified the best values of the statistical indices for the estimated radiation balance during the lowest fraction of cloudiness. However, no significant differences were found in the statistical indices using the different FN classes. For the L _ component the estimation was proposed from the equation of Idso and Jackson (1969), whose presented better estimates and consequently better statistical indices for Rn. The input variables of the SEBAL model were analyzed together with the outputs and experimental measures using the Pearson correlation coefficient, showing high correlation between the input and output variables of the SEBAL model. In general, using the equation of Idso and Jackson (1969), the SEBAL model accurately estimates, through remote data from the MODIS sensor, the components of the surface energy balance for the experimental area in flooded rice for the different FN. In general, the SEBAL model accurately estimates the surface energy balance components using the equation of Idso and Jackson (1969) and remote data of the MODIS sensor for a flooded rice experimental area for with different FN classes.