Regional and spatial continuous land surface temperature (LST) can be retrieved from satellite remote sensing data, and has an important significance in such fields as global change, ecology, environment, and agricultural production. However, the LST retrieved by remote sensing usually has missing data in time and space due to the influence of clouds, aerosols, satellite viewing angle and solar illumination angle, which limits the application of LST products. In this paper, the authors reconstructed FY-2F daily LST data of 2013 in the Yangtze River delta region using Savitzky-Golay (S-G) filter based on the characteristics of long time-series LST. The results show that S-G filter can fill the missing values effectively and ensure the spatial distribution consistency of the LST after reconstruction. The average time-series loss rate of the original FY-2F LST product is 19.43%, and then decreases to 1.69% after S-G filtering. In order to verify the reconstruction accuracy of S-G filter, the authors randomly selected some regions that are not deficient, and then made comparison with the results after S-G filtering. It is proved that S-G filter reconstructing method has obtained high accuracy, with the mean absolute error 1.35 K and the fitting accuracy 0.95. Higher quality and long time-series FY-2F LST which is reconstructed based on S-G filter offers a good foundation to the study of temporal and spatial distribution of further thermal environment.