High spatial resolution surface albedo datasets are of critical importance for weather forecast and global climate change studies. The Chinese Huanjing-1 satellites (HJ-1 A/B) can provide wide swath, short revisit time, and high spatial resolution (30 m) remote sensing observations, and hence can be considered as a perfect input data source for generating high spatial resolution surface albedo datasets. In this study, the authors compared and evaluated two methods for estimating surface albedo from HJ-1 A/B CCD data: the direct estimation algorithm from surface reflectance (DEA-SUR) and the method based on MODIS kernel coefficients (MKC). The visual interpretation and clarity index methods were employed for evaluating the fineness of the images. The results show that the clarity and fineness of imagery were greatly improved by the DEA-SUR and MKC methods, compared with the MODIS surface albedo products. It has been demonstrated that the DEA-SUR method is much better than MKC method in avoiding the mosaic effects. Four sites (US-MMS, CN-Cng, Yingke, and Namco) were used for validating and comparing the DEA-SUR and MKC methods. The results show that the DEA-SUR method and the MKC method have similar estimation accuracies during the snow-free period (root mean squared error (RMSE) is 0.015~0.041). In contrast, the estimation error is much larger during the snow-covered period.