Generating a gap-free shortwave land surface albedo dataset for the Heilong-Amur River Basin from 2000 to 2020

Land surface albedo (LSA) plays a critical role in modulating global energy and water cycles. However, the spatiotemporal continuity and data quality of existing LSA products pose significant challenges to their applications and analyses over seasonal snow-covered areas. To address this issue, we developed a gap-free shortwave LSA dataset with daily temporal resolution for the Heilong-Amur River Basin (HARB) from 2000 to 2020. This dataset, termed HARB-LSA, was generated by integrating the multi-source remote sensing data, direct-estimation algorithm, and statistical-based temporal filter. The performance of the HARB-LSA dataset was assessed against various in situ measured data. The results reveal that the HARB-LSA dataset exhibits comparable accuracy to currently used satellite-derived LSA datasets and offers significant advantages in terms of extensive temporal coverage, higher spatial and temporal resolution (500 m and daily), and enhanced spatiotemporal continuity. Specifically, the HARB-LSA dataset demonstrates good agreement with ground measurements, with a correlation coefficient (R) of 0.8487, a root-mean-squared error (RMSE) of 0.0664, and a bias of 0.0344. Importantly, the spatiotemporal continuity of the newly generated dataset has been significantly improved, resulting in a gap-free LSA product. These findings suggest that the HARB-LSA dataset holds substantial potential for applications in global climate change studies.