A coarse pixel scale ground "truth" dataset based on the global in situ site measurements to support validation and bias correction of satellite surface albedo products

Abstract. In situ measurements from sparsely distributed networks worldwide are a valuable source of the reference data for validating or correcting the bias of satellite products. However, the significant differences in spatial scale between in situ and satellite measurements make them incomparable except for that the underlying surface of in situ sites is absolutely homogeneous. Instead, in site measurements need to be upscaled to be matched with satellite pixel. Based on the upscaling model we proposed as well as the consideration that in-situ observation generally lacks spatial representativeness due to the widely distributed spatial heterogeneity, we have developed a coarse pixel-scale ground "truth" dataset based on ground measurements of 368 in situ sites from the sparsely distributed observation networks. Furthermore, the effectiveness of the dataset was carefully evaluated over the sites with different degrees of spatial representativeness. The results demonstrate that using this dataset in validation outperforms the direct comparison between satellite and in situ site measurements. The accuracy of the reference data employed for validation or bias correction can be enhanced by 3.5 % overall with this dataset. But the performance of the dataset show dependence on the degree of spatial heterogeneity. Specifically, the improvement of accuracy was the most significant over the regions with strong spatial heterogeneity, with the accuracy of reference data enhanced by 7.3 %. To the best of our knowledge, this dataset is unique in providing coarse pixel scale ground truth with the widest spatial distribution and longest time series. Its ability to capture both spatial and temporal variations of surface albedo at coarse spatial scales makes it an invaluable resource for validating and correcting global surface albedo products.