Terrestrial Gross Primary Production （GPP） is the total amount of organic carbon fixed by plant photosynthesis, and it is also the start of terrestrial carbon cycles. The remote sensing data-driven GPP models can accurately monitor the spatio-temporal pattern of GPP at the regional scale. The remote sensing data-driven GPP products support the studies of terrestrial carbon cycles, ecosystem responses to climate change and ecosystem service. However, large discrepancies in absolute magnitude, spatial distribution, and interannual variability in remote sensing data-driven GPP models lead to a large uncertainty in the estimation of global GPP.The uncertainty mainly stems from the fact that different researchers have adopted different modeling frame-works and assumptions, different remote sensing data sources, and different research periods, and thus researchers have come up with diverse conclusions. For this reason, it is necessary to summarize the results of existing representative studies to form a knowledge base and understanding of the remote sensing data-driven GPP models and their applications at the macro-scale level. Meanwhile, this study also discusses the common problems in current remote sensing data-driven GPP models and provides an outlook for future model developments.