Land surface albedo is a key parameter to describe the surface energy budget. An increasing need for fine-scale albedo products is promoted in regional applications of radiative forcing and coarse-scale albedo product validation. However, the long-term fine-scale albedo products over mountainous areas are currently unavailable. The topographic slope, aspect, and land cover types make the sloping surface more heterogeneous than the flat surface. Existing fine-scale albedo estimation algorithms may carry the uncertainties due to the complex topography. Moreover, the fine-scale albedo observations are often unavailable due to cloud contamination, making it difficult to obtain time series albedo estimations.To overcome these problems, we adopt the improved Angular Bin algorithm and Ensemble Kalman Filter Algorithm in this study to estimate a time-series fine-scale satellite-based albedo over a rugged terrain. The preliminary approach of the new built albedo estimation over mountainous areas was carried out in the Heihe River Basin by using the Chinese GF-4 satellite data.Validation results against ground measurements over various land cover types and topographic slopes show that our algorithm is effective for the selected land surfaces and can achieve root mean square errors of not more than 0.03. When compared with the referenced albedo product retrieved by direct retrieval algorithm, the GF-4 albedo products show a good performance with the RMSE smaller than 0.02.The retrieved long time series GF-4 albedo can improve the understanding of scale effects among different spatial resolution albedo products and help upscale in ground-based albedo measurements to coarse-scale during the multi-scale validation workflow. This algorithm also provides an example for other satellite-based remote sensing product retrieval over a rugged terrain.

land surface albedo;rugged terrain;GF-4 satellite;EnKF;long time series