The Atmosphere-Land Exchange Inverse (ALEXI) a two-source energy balance model was developed to estimate ET. The Visible Infrared Imaging Radiometer Suite (VIIRS) a polar satellite used in this research to provide 375-m resolution compared to other geostationary satellite data which have more than 1 km resolution. VIIRS acquires images of the Globe on daily basis; day/night images. The ALEXI model takes advantage of day/night thermal infrared imaging to produce daily regional ET estimates using a LST differential to retrieve energy balance components between midmorning after sunrise and before noon local time. Daily Evapotranspiration maps were produced with 15o X 15o grid size (Tile). We ran ALEXI for Tile 153, over Brazil for the years 2013-2018. We created a website called Global Daily Evapo-Transpiration (GloDET) where we publish these maps at (https://glodet.nebraska.edu). The ALEXI estimated ET values were compared with ground data from eddy covariance flux towers. ALEXI ET results were extracted at the towers locations for 2013-2016, to serve as comparison for each tower with energy balance closure. The linear correlation was excellent for all sites with R2 between 0.78 - 0.88, for different types of vegetation.
Evapotranspiration (ET) quantification improves the comprehension of the water, heat, and carbon interactions and the feedback to the climate, which is essential for global change research. We aimed to model ET using artificial neural networks (ANNs) based on Landsat-8 and reanalysis data from the National Centers for Environmental Prediction over the grasslands of the Pampa biome. The output variable was the ET trained by eddy covariance (EC) measurements acquired from a flux tower located in Santa Maria, Brazil. ANN was performed using the backpropagation algorithm with four remote sensing input variables (albedo, normalized difference vegetation index, land surface temperature, and surface net radiation). In addition, four meteorological variables from the Environmental Prediction Climate Forecast System Version 2 hourly product were included in the model (air temperature, atmospheric pressure, relative humid, and wind speed). We analyzed 67 clear-sky scenes between 2014 and 2019. Results produced very robust daily ET estimates. ANN exhibited a correlation of 0.88 relative to in situ EC data, demonstrating a good linear relationship between ET estimated and measured and producing a root-mean-square error (mean absolute error) of 0.75 (0.58) mm/day. The ANN model was also compared with the widely known simplified surface energy balance index (S-SEBI) model. S-SEBI exhibited lower correlation with the ET in situ compared to the ANN model. Furthermore, the ANN model had a superior performance in summer and winter seasons in which S-SEBI was found to outperform the ET in situ. The model developed in our research is an alternative to approaches that need a great number of input variables or in situ data since it is only dependent on freely available data. Therefore, it should support future integrated strategies of water resources allocation over the natural grasslands of the Brazilian Pampa.
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