Surface reflectance anisotropy and insufficient multi-angular observations are the main challenges in albedo estimation from satellite observations. Numerous studies have been developed for albedo retrieval from a single directional reflectance by associating the anisotropy information extracted from coarse-resolution bidirectional-reflectance distribution function (BRDF) data. The contribution of land-cover type (LCT) and the Normalized Difference Vegetation Index (NDVI) in distinguishing reflectance anisotropy in these methods remains controversial. This study first proposed an approach to extracting a priori BRDF (F) from the MODIS BRDF/albedo product by considering the distribution characteristics of the model parameters. LCT- and NDVI-based F were also extracted from the corresponding subset. Then, the F-based albedo was derived from simulated or satellite directional reflectance and the anisotropic information of F. Finally, the directional reflectance and F-based albedo were compared with the MODIS albedo or ground measurement, in order to show the ability of F to compensate for the effect of reflectance anisotropy in the albedo retrieval process. The method was fully validated by the global and time-series MODIS BRDF data. The results showed that reflectance anisotropy has an aggregated distribution pattern, and F can represent the reflectance anisotropy of most pixels within a tile. The improvement of LCT and NDVI only occurs when the tile contains a large area of vegetated and barren ground. With the exception of the hotspot and large viewing-zenith-angle area in the forward hemisphere, the F-based shortwave albedo has high consistency with the MODIS albedo product. A comparison with the ground measurements and MODIS albedo showed that the F-based albedo from a single directional reflectance generally achieves an absolute accuracy requirement, with a root-mean-square error (RMSE) of 0.027 and 0.036.

The land surface albedo reflects the ability of the surface to reflect solar radiation and is a critical physical variable in the study of the Earth’s energy budget and global climate change. Algorithms for the retrieval of albedo usually require multi-angle measurements due to surface anisotropy. However, most of the satellites cannot currently provide sufficient and well-distributed observations; therefore, the accuracy of remotely sensed albedo is constrained. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) and albedo product (MCD43A1), this study proposed a method to further subdivide reflectance anisotropy and build an updated database of BRDF archetype, using both the Anisotropic Flat Index (AFX) and Perpendicular Anisotropic Flat Index (PAFX). The BRDF archetypes were used to fit the corresponding MODIS BRDF, and the optimal number of BRDF archetype categories was determined according to the tendency of fitting error. The effect of surface anisotropy and observation noise on albedo retrieval were explored based on simulated MODIS reflectance. Finally, the BRDF archetype A2P2 was taken as prior knowledge to retrieve albedo from a different number of MODIS observations, and the result was validated by the high-quality MODIS albedo product. The results show that the fitting error between BRDF archetypes and MODIS BRDF shows a rapid decline when introducing the PAFX in the classification process. A 3-by-3 matrix of BRDF archetypes, which occupy 73.44% and 70.13% of the total decline in the red and NIR band, can be used to represent the characteristics of reflectance anisotropy. The archetype A2P2 may be used as prior knowledge to improve the albedo retrieval from insufficient observations. The validation results based on MODIS observations show that the archetype A2P2-based albedo can reach root-mean-square errors (RMSEs) of no more than 0.03.