MODIS time series surface reflectance data is widely used in the dynamic monitoring of land surface， but the influence of factors such as cloud cover causes spatial and temporal gaps in the data， which affects the data availability. In this paper， we propose a time-domain reconstruction method based on L1 regularization， which can effectively repair the gaps in MODIS surface reflectance data and realize the reconstruction of long time-series data with high accuracy. The proposed method firstly identifies the noise generated by natural and systematic factors in the time-series data， and then pre-fills the missing information region inter-annually based on noise detection. On this basis， we introduce a L1 regularization model that is more robust to abrupt noise， and construct a variational model combining the noise masks to restore the time series trend of land surface. The experimental results show that compared with SG filtering， HP filtering， L1 filtering and HANTS， the method in this paper achieves the highest reconstruction accuracy at different percentages of missing pixels of 10%， 25%， 50% and 75%， and also achieves better reconstruction results under different ground surface scenes. Therefore， this method has more advantages in both time series curves reconstruction and spatial details restoration， which shows a high practical value.