A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach

•Machine learning provides a feasible framework for estimating actual evapotranspiration at weather stations without flux observations.•Seasonal ET simulations performed better than that of daily, 8-day and monthly using pooled data from all flux towers.•The predictors with high contribution to the model are Tmax, Tmin, Tmean, Prcp, SWI, Rn, NDVI, LAI, FPAR, SWC and Tsoil.•Changes in the importance ranking and value of predictor variables partly explained the variations of model performance.