Land-surface albedo plays a critical role in the Earth's radiant energy budget studies. Satellite remote sensing is an effective approach to acquire regional and global albedo observations. However, owing to cloud coverage, seasonal snow and sensor malfunctions, spatially-temporally continuous albedo datasets are often inaccessible. GLASS preliminary albedo datasets (GLASS02A2<i>x</i>, <i>x</i> = 1, 2, 3 and 4) are newly developed global daily land-surface albedo products. Like other products, GLASS02A2<i>x</i> albedo surfers from large areas of missing data. Beside this, sharp fluctuations exist in GLASS02A2<i>x</i> time series due to data noise and algorithm uncertainties. In this study, a statistics-based temporal filterer (STF) is proposed to fill the data gaps and smooth the fluctuations in GLASS02A2<i>x</i> albedo time series. The result of STF algorithm is the GLASS final albedo product (GLASS02A06). Results show that the STF method has greatly improved the integrity and smoothness of the GLASS final albedo product. Seasonal trends in albedo are well depicted by the GLASS final albedo product. Compared with MODIS product, the final GLASS albedo product is much more competent in capturing the surface albedo variations. Although the STF algorithm is designed for GLASS albedo product, it is able to incorporate other albedo products. The STF method may also be applied to other parameters, such as the LAI and soil moisture