The Kobresia pygmaea alpine meadow is a main vegetation type in the Qinghai-Tibetan Plateau. An accurate detection of the green-up dates for K. pygmaea is important to simulate and predict vegetation phenology shifts under the influence of climate change in the Qinghai-Tibetan Plateau. Green-up date estimation methods from remote sensing data generally include two processes: reconstruction of high-quality vegetation index time-series data through noise removal and calculation of green-up dates from the reconstructed vegetation index time series. The reconstruction methods for vegetation index time-series data can be divided into two categories: filter fitting and curve fitting methods. The green-up date retrieval methods include the threshold, maximum slope, curvature, and moving average methods. The green-up date identification method is a combination of the reconstruction methods for vegetation index time-series data and the retrieval methods for green-up dates under different study conditions. The accuracy of the green-up date identification methods is usually affected by many factors, such as specific geographic location, prior experience, parameterization, and initial parameters. In this study, we adopted a simulated annealing algorithm to optimize the reconstruction process and thus avoid the problems of low efficiency and local optimum caused by traditional optimal methods. We first used the double-Gaussian, double-Logistic, and polynomial functions to reconstruct the Normalized Difference Vegetation Index (NDVI) time series. After evaluations with visual inspections and root mean square error, we identified the most feasible reconstruction method. We then used the maximum slope, threshold, curvature, and dynamic threshold methods to derive the green-up dates from the best reconstructed NDVI time series. The performance of these three methods for green-up date identification were tested using the green-up data from 34 ground observation samples and their corresponding National Oceanic and Atmospheric Administration NDVI time-series data at 8-kilometer resolution. We selected additional 153 samples, which were evenly distributed in the K. pygmaea alpine meadow in the Qinghai-Tibetan Plateau, to test the identified optimal green-up estimation method and to investigate the changes in green-up dates in the study area. The reconstructed NDVI time series with the double-Gaussian function had the smallest deviation from the original NDVI time series, and the noises can be reduced effectively through the double-Gaussian fitting process. Therefore, the aforementioned method was the most suitable for describing the intra-annual growth cycle of the K. pygmaea alpine meadow. The reconstructed NDVI time series with the double-Gaussian function method indicated that the green-up dates identified with the maximum slope threshold method agreed with the observed ground phenology data. The correlation coefficients between the identified green-up dates and the observed dates were 0.823 (P<0.001) and 0.646 (P <0.01) at the Haiyan and Gande stations, respectively. The average green-up dates for the K. pygmaea alpine meadow in the Qinghai-Tibetan Plateau were mainly located between DOY (Day of Year) 120 (i.e., 30 April) and 140 (i.e., 20 May). The green-up onset date advanced by an average of 7 days from 1982 to 2011 in the study region.