Plant phenology refers to the emergence of an annual cycle of natural phenomena in plants affected by climate and other environmental factors. Information of crop phenology can directly reflect the effect of temperature and precipitation on the crop, which is essential for evaluating crop growth, productivity and crop management. Remote sensing technique is an important method to detect vegetation phenology with high spatial-temporal scales. Vegetation index generated by infrared and near-infrared band based on satellite remotely sensed data can reflect the status of vegetation growth and coverage more accurately. The Northeast China includes Liaoning, Jilin and Heilongjiang provinces, which rice area accounts for about 10% of total rice area and its rice yield accounts for 11% of total rice yield in China. Rice planting in Northeast China plays an important role in China's food safety. In this study, we developed a method for detecting phenological stages of rice in Northeast China based on the EOS-MODIS multi-temporal remote sensing data, including MOD09A1 and MCD12Q1 in 2008. The rice crop phenological stages were detected by using EOS-MODIS Enhanced Vegetation Index (EVI) data, and compared with the observed rice phenological stages in 24 selected agro-meteorological sites in Northeast China. The method consists of four procedures: (1) calculating the EVI value and extracting time profile from the location where 24 selected sites through MODIS-EVI data; (2) filtering the noise in EVI time profile by wavelet transform with twenty six types of wavelets; (3) identifying the rice planting date, heading date and ripening date by the variation characteristics in the smoothed EVI time profile; (4) comparing the result calculated by this method with the observed data from the 24 agro-meteorological sites in study area in 2008 and calculating the root mean square error (RMSE), then choosing the best type of wavelets. Due to the temporal resolution of the MODIS/Terra is 8 days, there will be missing data in the EVI time profile. The cubic spline interpolated (CSI) method (more smoother and stabler) was applied to repair the missing data, which can reflect the missing data more authentic. The twenty six types of wavelet: Daubechies(7-20), Coiflet(3-5) and Symlet(7-15) were used when filtering EVI time profile. The results showed that, the case using Symlet11 shows a remarkably good result in determining phenological stages, which is compared with the observed data. Most of the RMSE in planting date were less than 16 days. Most of the RMSE in heading date and ripening date were less than 8 days. It was shown that the Symlet11 filtering is the best method, which can be used to detect rice phenology in Northeast China. Furthermore, the Symlet11 filtering can be used to analyze variations and distribution of rice phenology with high-spatial scale in the whole Northeast China.