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

In situ measurements from sparsely distributed networks worldwide are a critical source of reference data for validating or correcting biases in satellite products. However, due to the substantial difference in spatial scales between in situ and satellite measurements, the two cannot be compared except for the fact that the underlying surface of in situ sites is absolutely homogeneous. Instead, the in situ measurements needed to be upscaled to be matched with the satellite pixels. Based on the upscaling model, we also proposed the consideration that in situ observation generally lacks spatial representativeness due to the widely distributed spatial heterogeneity, and we have developed a coarse pixel-scale ground "truth" dataset based on ground measurements of 416 in situ sites from the sparsely distributed observation networks. Furthermore, we thoroughly assessed the effectiveness of the dataset at 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 over heterogeneous surfaces when in situ measurement footprints are less than satellite pixel size. The accuracy of the reference data employed for validation or bias correction can be boosted by 17.09 % over the regions with strong spatial heterogeneity. However, the degree of improvement with this dataset displays a decreasing trend with the reduction in spatial heterogeneity. At a global scale, the pixel-scale ground "truth" dataset enhances the accuracy of pixel-scale reference data in general, with the overall relative root-mean-square error (RRMSE) decreasing by 6.04 % compared to in situ single-site measurements. Our results suggest that in situ single-site measurements are limited in their ability to capture surface spatial variability information at a coarse pixel scale (i.e., the kilometer scale). The dataset we provided, which merges temporal information from ground-based observations and spatial information from high-resolution data, represents a valuable resource for validating and correcting worldwide surface albedo products over heterogeneous surfaces. To the best of our knowledge, this dataset is unique in providing a coarse pixel-scale ground "truth" with the widest spatial distribution and longest time series.