Abstract: 【Objective】NDVI time-series can simulate plant growth and reflect its growing status. Several models have been fitted in the past to smooth time-series vegetation index data from different satellite sensors. However, differences between the models and fine tuning of model parameters lead to uncertainty and bias between the results amongst users. The current research assessed three techniques: Whittaker smoother, HANTS, and Savitzky-Golay filter for smoothing multi-temporal satellite sensor observations with the ultimate purpose of estimating phenological parameters and deriving the annual multiple cropping index (MCI) reliably. 【Method】The research used MODIS MOD13Q1 data over the year 2000 to 2012 composed at sixteen day intervals covering the Shaanxi Province, and three models were fitted to reconstruct NDVI time-series. First, samples time-series of different vegetation types in three climate zones of research area were picked, and the fitting effect was contrasted by vision. Second, three evaluation indexes (RMSE, the correlation coefficient, and SNR) were used to compare the quality of each model, and then the differences between different types of vegetation were discussed. Third, three phenological parameters (Start of Season, End of Season, and Length of Season) were estimated by the dynamic threshold method, while the means and standard deviations of phenological parameters of different vegetation types estimated from three models were compared. Finally, the MCIs of each year were derived by quadratic difference algorithm and rules using the NDVI time-series of each model, and then were compared with the MCIs derived from statistics.【Result】Savitzky-Golay performed better than others on RMSE and the correlation coefficient, but has a bigger error in extracting phenological parameters. HANTS performed well in extracting phenological parameters, but which needs more complex parameter settings and has a poor accuracy. Whittaker can effectively reduce the signal to noise ratio of the original image with only one parameter, which also has a good performance in getting phenological parameters and the annual multiple cropping index. RMSE has a negative correlation with the correlation coefficient, but the correlation coefficient is more sensitive.【Conclusion】It was concluded that Whittaker smoother is the best approach in the test, which has outperformance in extracting phenological parameters, but remains to be further studied in terms of extracting multiple cropping index.