Vegetation greenup is closely related to seasonal dynamics of the lower atmosphere and is therefore an important variable in influences and feedback mechanisms of global climate variation to terrestrial ecosystem. Detecting vegetation greenup using remotely sensed data at regional or global scales has become an advanced topic in geography and ecology. However, current remote sensing-based vegetation greenup detection methods are short of valid comparison and do not have a consistent understanding. So, This paper utilizes computer simulation technique to study four remote sensing-based detecting methods (Moving average method, NDVI ratio threshold method, the greatest change slope method and Logistic function fitting method) after summarizing advanced developments of traditional measuring methods and remote sensing-based detecting methods of vegetation greenup. Simulation results indicate that:①simulated results of greenup from the four methods make a difference of 20 days, which strongly reflect all method's differences in building principle. Moving average method defines vegetation greenup as the period beginning at the point where the smoothed time-series data suddenly increases. NDVI Ratio Threshold method defines vegetation greenup as the period beginning at the point of the greatest slope. The greatest change slope method defines vegetation greenup as the period beginning at the point of the maximum change in the NDVI slope angle. Logistic Function Fitting method defines vegetation greenup as the period beginning at the point where the rate of change of curvature exhibits a local maximum in the fitting curve. In addition, the four methods have a same character that the NDVI time-series data should maintains the greatest sustained increase after vegetation greenup begins. ②Computer simulating technique is helpful to understand the great potential of Remote sensing-based vegetation greenup detection methods in the future. ③Validation of the results is very important in the course of using remote sensing-based detecting methods, which this paper is short of because of lacking in practical observation data.