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2019
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Seamless terrestrial evapotranspiration estimation by machine learning models across the Contiguous United States
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The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation
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Overview of the radiometric and biophysical performance of the MODIS vegetation indices
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Satellite-based near-real-time global daily terrestrial evapotranspiration estimates
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