MOD16: Desafios e limitações para a estimativa global de evapotranspiração

• Autor(a) principal: Ruhoff, Anderson Luis
• Data de Publicação: 2011
• Outros Autores: Aragão, Luiz Eduardo Oliveira e Cruz de, Collischonn, Walter, Rocha, Humberto Ribeiro da, Qiaozhen, Mu, Running, Steven
• Tipo de documento: Artigo de conferência
• Idioma: por
• Título da fonte: Repositório Institucional da FURG (RI FURG)
• Texto Completo: http://repositorio.furg.br/handle/1/1256
• topic: MODIS; MOD16; Evapotranspiration; Tropical biomes; Energy fluxes; Evapotranspiração; Fluxos de energia; Biomas tropicais

This article present preliminary results from the NASA’s EOS MOD16 Project, which aims to estimate global evapotranspiration (ET) using remote sensing and meteorological data. The MOD16 algorithm considers both the surface energy partitioning process and environmental constraints on ET to provide critical information on the regional and global water cycle. The objective of this research is to evaluate the version 1 of the global remote sensing evapotranspiration algorithm (MOD16). We analyzed the accuracy of the algorithm using ET observations at two eddy covariance (EC) flux tower sites in different land uses and land covers (tropical rainforest (K34) and seasonal forest (RJA)), from the Large Scale Biosphere-Atmosphere in Amazonia Project (LBA). The result shows that 8-days average, monthly ET and yearly ET are in consistent with observations of eddy covariance flux tower sites when the land cover classification is correct. However misclassification of the land cover leads to the selection of wrong parameters for vapor pressure deficit (VPD) and minimum air temperature for stomatal conductance constraints, resulting in less accurate ET estimates. The existing biases between MOD16 ET and EC observations and hydrological models may be influenced by algorithm input data, such as LAI and land cover classification. Developing a robust algorithm to estimate global ET is a significant challenge because traditionally ET models require explicit characterization of numerous surface and atmospheric parameters which are difficult to determine globally.