## 1 Introduction  
Surface shortwave albedo, defined as the ratio of the reflected to the incident solar shortwave radiation at the Earth's surface, is one of the key parameters in surface energy budget studies [1, 2, 3]. With advancement in the remote sensing techniques over the past several decades, land surface albedo algorithms have been developed for many remote sensing platforms [4, 5]. Coarse resolution (\(>\)300 m) satellite albedo products have been widely used for climate modeling and weather forecasting purposes. However, their uncertainties remain a critical issue, preventing a refined understanding of anthropogenic effects on global climate change, particularly over middle- to high-latitude winters [4]. Due to the scale differences between satellite pixels and footprints of ground measurements, data at finer spatial resolutions are needed to bridge the gap over heterogeneous landscapes for a better understanding of uncertainties of coarse-resolution products [6, 7]. Moreover, there is an increasing need for the availability of medium-resolution surface albedo for applications at medium-to-fine scales (\(<\)100 m), including forest management [8, 9, 10], agriculture monitoring [11], and urban environment monitoring [12, 13]. China launched two HuanJing (HJ) satellites in September 2008, carrying 30-m resolution multispectral optical sensors similar to Landsat but with swaths that were nearly two times larger, with a revisiting frequency of four days, bringing an opportunity of monitoring surface conditions as a supplementary source to the existing satellite products. The radiometric calibration for HJ data [14, 15, 16] allows for many quantitative remote sensing applications monitoring vegetation, atmosphere, and water bodies [17, 18, 19, 20]. Thanks to their wide swath, HJ sensors observe the Earth's surface with view zenith angles up to 35\({}^{\circ}\), making it possible to obtain some angular information for surface Bidirectional Reflectance Distribution Function (BRDF) retrievals, which is not available from the nadir-viewing Landsat sensors. With high spatial and temporal resolutions, HJ data can serve as a key supplementary source for monitoring the surface radiation budget and calibrating coarse resolution data. Effects of surface BRDF on the satellite observations are prominent and have been extensively studied [11, 21]. Failure to correct these effects could lead to errors up to 60%, which could be deleterious for applications that require accurate and consistent satellite-derived information [11]. To satisfy the need for medium-resolution albedo products, efforts have been made to derive surface albedo utilizing space-borne and airborne sensors [6, 22, 23, 24]. Limited by the low revisiting frequency of medium-resolution data, the temporal composite method developed for coarse-resolution data (e.g., [25]) cannot be applied without enough angular samples of surface BRDF. To overcome this limitation, a few algorithms have been developed through using ancillary information on surface BRDF to normalize the surface reflectance after atmospheric correction to obtain surface albedo, which relies on concurrent high-quality coarse-resolution data and is particularly effective for dense vegetation [23, 26]. Particularly for HJ data, [PERSON] _et al._[27] refined the algorithm originally proposed by [PERSON] _et al._[22] using the surface BRDF information from coarse-resolution products and land cover maps to convert surface reflectance to albedo. However, the availability of concurrent high-quality clear-sky coarse-resolution BRDF data significantly limited the application of these methods, particularly over rapidly changing surfaces.