The Bidirectional Reflectance Distribution Function（ BRDF） qualifies variations in reflectance patterns as a function of illumination and viewing geometry. Statistically,directional reflectance in a given direction is considered as the mean. The variation in that direction can then be characterized by its variance. The BRVF is a useful method for characterizing reflectance anisotropy.In this study,we first explored the variance in bidirectional reflectance through the use of not only a BRVF technique based on the MODIS BRDF model but also an error propagation method. Then,we performed a method validation by using the MODIS BRDF / Albedo product from selected EOS Land Validation Core sites（ LVCS） with underlying typical IGBP land cover types and NDVI threshold. Finally,as an initial application of the BRVF,we applied the aforementioned theoretical results in the simulation result derived from the collected 69 field measurements.Major results demonstrate that the spatial distribution patterns of the BRVF are linear combinations of the first-degree term and the second-degree term of geometric optical kernels（ i. e.,K geo） and volumetric scattering kernels（ i. e.,K vol）. Validation results based on the MODIS BRDF / Albedo product are consistent with the theoretical derivations. In general,the BRVF spatial distribution pattern reaches its peak value at the hotspot direction. Beyond the large viewing angle（ VZA > 60°）,the BRVF tends to increase along with the increase in VZA. The BRVF value in the near-infrared band is greater than that in the red band,thereby indicating that for the MODIS sensor,the BRVF is a band-dependent variable. The initial application of the BRVF to the simulation result derived from the collected 69 field measurements shows that at a large VZA where observations are not available,the modelextrapolated BRVF value increases prominently,thereby affecting the accuracy of surface albedo retrievals. The maximum relative error for the white sky albedo retrieval in the red band is about 38. 26%.This study is very helpful in understanding the uncertainty of surface albedo retrieved by a narrow VZA sensor. The results are also necessary in understanding a priori knowledge applications for the retrieval of surface albedo and other biophysical parameters.

BRVF;BRDF;MODIS;land cover types;EOS land validation core sites;error propagation;albedo;kernel-driven BRDF model