EVAPOTRANSPIRAÇÃO NO ESTADO DO MATO GROSSO DE SUL POR SENSORIAMENTO REMOTO

• Ano de defesa: 2020
• Autor(a) principal: CARLOS ROBERTO WASSOLOWSKI
• Orientador(a): Ricardo Gava
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Fundação Universidade Federal de Mato Grosso do Sul
• País: Brasil
• Palavras-chave em Português: Análise da evapotranspiração no Estado de Mato Grosso do Sul através do produto MODIS
• Link de acesso: https://repositorio.ufms.br/handle/123456789/3655

The state of Mato Grosso do Sul is among the largest food producers, its production has grown in recent decades and along with this there is a change in coverage and land use. Knowledge of evapotranspiration (ET) in a region remains an important variable for research in hydrology, water resources and agriculture. It is a relevant factor for the production of climatological models that allow visualizing future scenarios to achieve sustainability of production systems and food security. This work sought to evaluate the spatial and temporal evapotranspiration in the state of Mato Grosso do Sul using remote sensing. For this, products from the MODIS sensor of the TERRA satellite were used through the GEE (Google Earth Engine) web platform. Average altimetry and current evapotranspiration images for January, March, May, July, September and November, between 2002 and 2019 were obtained. The data were subjected to statistical tests to assess the temporal and spatial variation. Evapotranspiration in July is around 30% less than in January. The current average ET is slightly higher in the region of the Paraná River watershed compared to the Paraguay River watershed. Evapotranspiration levels decrease as the altitude decreases. It is concluded that ETa differs between otto 2 level hydrographic basins in the state of Mato Grosso do Sul. Evapotranspiration is higher in hydrographic basins with higher average altitude. There is a tendency to increase evapotranspiration in the State from 2008 onwards for the studied time series.