The solar radiation data plays an important role in land surface energy balance assessment such as sensible heat， latent heat， solar energy assessment etc. However， Radiation data is missing from observed meteorological data by meteorological bureau. Hence， it is imperative to predict solar radiation in a large area with accessible data sources. This research uses the widely accessible data such as （Reanalysis data MERRA 2， remote sensing data MODIS and extra-terrestrial radiation） to drive the commonly used machine learning models for estimating daily solar radiation. The results show that the reanalysis data can replace ground variables and achieve the similar level of precision prediction（difference value 0.14 MJ/m²（MAE）、0.22 MJ/m²（RMSE）、1.13 %（NRMSE））. Particularly， the machine learning models reach the best prediction accuracy（MAE 3.42 MJ/m²，RMSE 4.86 MJ/m²，NRMSE 26.87 %） when driven by re-analysis， remote sensing data and extra-terrestrial radiation together. Meanwhile we also noticed the ensemble of the multiple machine learning model also have a better performance for using any single model. This study highlights that a satisfied radiation data can be generated with widely accessible multi-resource data as well as a couple of the machine learning models.