Abstract: A method to monitoring winter wheat phenology based on reconstructing Leaf area index (LAI) by assimilating crop model and remotely sensed LAI product is introduced in this paper. WOFOST is a crop model developed by the Center for World Food Studies in cooperation with the Wageningen Agricultural University to simulate and estimate the growth of winter wheat. LAI is one of the important input and output parameters in the model, and the next day's LAI output is derived from the previous day's LAI input. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) LAI time series has a good time resolution and can reflect the growth trend of crops, but the LAI values is generally lower than actual value for the impact from mixed pixels and cloud noises. Resampling particle filter (SIP) is a sequential Monte Carlo state estimation method for Non-Linear and Non-Gaussian system, and is introduced to reconstruct the winter wheat LAI time series by assimilating the WOFOST model and MODIS LAI. In the SIP assimilating algorithm, the WOFOST model is imported as the state transition equation of LAI for its ability to simulating the winter wheat growth process, and the MODIS LAI time series is treated as the observations from the winter wheat. To get the trend as MODIS LAI, the weights of particles are calculated from the first derivative of the MODIS LAI time series. The particles that have the similar trend as the MODIS LAI time series can achieve a higher weight. The algorithm was applied on the winter wheat in Hebei province, China. First, a WOFOST model was calibrated to get applicable to local region, and the actual meteorological data were preprocessed to meet the format requirement, then the algorithm was run and the reconstructed LAI time series are achieved. The results show that the reconstructed LAI curve has good temporal continuity. The reconstruct algorithm can decrease the LAI change scope and avoid the problem of low value of MODIS LAI to a certain degree, and the day corresponding to the reconstructed LAI curve peak is basically the same as that of the MODIS LAI curve. At the regional scale, the reconstructed LAI images have higher LAI values and good spatial continuity, and reduce the impact from the low-value and missing data of MODIS LAI. Based on the reconstructed LAI time series, the key stages of the winter wheat are monitored including its green-returning stage, heading stage, and ripen stage. The reconstructed LAI curve is a substantially horizontal line in over-wintering stage, and has an initial value of approximately 0.4, which is higher than the actual value in the early green-returning stage. Therefore the start day of the green-returning stage is identified by the upslope point of the reconstructed LAI curve. In the heading stage, the winter wheat LAI reached the maximum, so the day corresponding to the curve peak was identified as the start day of the heading stage. The identification of the ripen stage is made by the threshold method. The threshold is set to 20% of the spacing between maximum and minimum from the minimum of the right side of the LAI curve. The day corresponding to the threshold is identified as the start day of the ripen stage. Comparing the monitoring results with the actual situation, basically both have the same spatial distribution, and may be the bias for the high initial value in the early green-returning stage and the uncertainty of the minimum in the ripen stage of the LAI curve.