Vapor Pressure Deficit (VPD) is an important climatic variable widely used in many ecosystem models to simulate fluxes and states of water and carbon; it plays an important role in fire warning and epidemic disease early warning systems. Accurate estimation of spatio-temporally distributed VPD is critical for ecosystem and climate modeling efforts. In this paper, the available remote sensing datasets for satellitebased VPD estimation are analyzed, the precision and spatial resolution are two important factors for selecting remote sensing data. Then, the principle and advantages of different estimation algorithms are analyzed, which include the regression method and analytic method. The regression method is simple, but requires mass sample data and can not be used in other region before calibration. The analytic method is more complex, but can be used anywhere once established. The near surface air temperature and humidity are two key parameters for estimating VPD, which are usually estimated from the satellite retrieved land surface temperature and total precipitable water vapor. The errors in estimated VPD cloud are further eliminated by improving the accuracy of input remote sensing data and improving estimation algorithms of near surface air temperature and humidity. Finally, the existing problems and the VPD estimation research prospect are discussed. Most research work is limited in clear sky days until now, and VPD estimation under cloudy days is a challenging work, but it is important for many applications. A full VPD map could be achieved by combining several satellite data from different instruments, especially by taking the advantages of optical and microwave remote sensing. The prospects of the satellitebased VPD estimation technologies are presented.