# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 4 Discussion  
The production of high-quality surface albedo products at higher spatial resolution allows for more nuanced understanding of broad landscape changes. The use of concurrent MODIS data for the BRDF attribution enables the capture of rapid and ephemeral changes in the landscape which can have large impacts on the overall albedo of a system. As surface albedo plays a critical role in the climatic, hydrological, biophysical, and biogeochemical processes through its regulation of surface energy budgets, the improved spatial and temporal resolution and quality of albedo products serves broad interests in advancing the understanding, monitoring, and modeling of these interrelated processes under fragmented land cover and land use changes in the circumpolar domain. The ability of this Landsat derived albedo product to capture land surface albedo more accurately in highly heterogeneous landscapes enables further investigation into the land-atmosphere interactions that drive surface warming in high latitude systems. The improved accuracy and specificity, especially in the presence of snow is especially applicable in the higher latitudes where changing land cover and land use have a significant impact on the accumulation and melt patterns of snow in vegetated areas. The expansion and increased stature of shrubs in the northern latitudes, for example, changes the energy partitioning as darker biomass protrudes above the snow lowering the overall albedo and helps snow accumulation as a shrub canopy can trap and shade snow under its canopy insulating the ground [59; 61]. The unique characteristics of these landscapes, the rapidity at which they are changing and their role in global forcing dynamics means the effects of changes in snow cover and extent on albedo, surface energy budget and surface shortwave radiative forcing will likely become  
Figure 6: Comparison of Landsat (**a**) and MODIS (**b**) albedo results of varying landcovers on the northern edge of the Seward Peninsula, Alaska USA. Imagery from mid-summer (22 July 2020—middle row) and the early spring (03 May 2020—bottom row) show the landscape in the presence and absence of snow. The 18 km subsets highlight the heterogeneity of the region as a whole as captured by the Landsat-scale albedo and landcover results. The 2 km subsets and coincident density estimations of albedo show the increase in information available using higher resolution imagery. The range in albedo values is greater in both higher resolution plots and clear separation in albedo signature can be seen in the landcovers presented here. more pronounced. This Landsat albedo products and like products from Sentinel-2 [30] and Landsat-9 will be important in capturing and quantifying these changes. One of the greatest advantages of this higher resolution albedo product is the ability to capture ephemeral (e.g., snow, fire, flooding) events at the landscape scale. The results presented here show that this albedo product performs well under a wide range of conditions and landscapes, however, most of the scatter on the validation data plots occurred when snow was present. Heterogeneously snowy conditions are difficult to capture due to the difference in the bright snow signal as compared to the darker vegetation signals. A good deal of data from the tower-sites presented here was not used as the surface reflectance values fell outside of the acceptable/valid range of 0 to 1. There is a continuing effort to optimize snow surface reflectance values and both data accuracy and data availability are likely to improve. The recent launch of Landsat-9, its improved radiometric resolution to 14-bit quantization [62], and the increase in imaging frequency, the high-quality data available in these regions will continue to enable a more comprehensive understanding of these systems [9; 24; 25; 26]. The validation of an operational 30 m blue-sky albedo product allows accurate characterization of smaller-scale landscape drivers and potentially the earlier identification of landscape changes.