# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 1 Introduction
In tall forests, the presence of snow beneath an evergreen canopy can have a varying and complex influence on surface albedo. A study in northeastern U.S. forests, found that snow under the canopy only increased albedo by +0.08 at most, indicating the strong masking effect of standing biomass [22] and resulting in positive climate forcing (i.e., warming). However, forests at higher latitudes tend to have less dense canopies with lower values of total leaf area per unit ground surface area (Leaf Area Index--LAI), but more pronounced surface anisotropy. In these systems, the masking effect of standing biomass on snow is lower and the SAF effects expected to be higher. In one study in southern Finland, snow cover in a boreal Scots pine (_Pinus sylvestris_) was shown to increase albedo by 0.2, but the results varied greatly between and within canopy highlighting the need for higher resolution snow cover and stand information on larger scales [23]. Additionally, both surface albedo and large-scale vegetation dynamics are affected by wetland area and water budgets. Changes in wetland areas and lakes in the Arctic have been shown to be quite variable. In Siberia, lake area is increasing in northern continuous permafrost regions and decreasing further south [24]. In North America, the opposite trend has been observed with small gains in the south offset by larger losses further north [25]. These observed changes in surface water cover will have a strong impact on the energy budget both locally and regionally. A radiative transfer modeling study found the fraction of standing water is a critical driver of landscape albedo [26]. Using MODIS imagery, a study found changes in surface water extent were a key driver in the spatial variation of albedo change between 2000 and 2019, especially in the summer months [9]. These changes are often driven by permafrost degradation where localized thawing of ground ice can cause subsidence and the accumulation of surface water. This surface water lowers albedo, increases surface temperatures, and can perpetuate further permafrost degradation. Again, higher resolution albedo products will be key in understanding these relationships as many of the waterbodies in the northern latitudes are not effectively captured at the MODIS scale. Monitoring these systems using higher resolution albedo products such as the Landsat albedo products evaluated here, allows for a more nuanced understanding of how the spatial patterns of inland water, as well as vegetation dynamics, contribute to large variability at the local scale and aid in resolving unexplained drivers in identified trends. In this paper, a pan-arctic 30 m albedo algorithm for snow and snow-free periods from Landsat-8 Operational Land Imager is presented and evaluated. The development and availability of an operational white-sky, black-sky and blue-sky albedo product suite will help facilitate analysis of albedo dynamics across the tundra and boreal biomes. The target area for this production and validation is between 40\({}^{\circ}\)-84\({}^{\circ}\)N, as 84\({}^{\circ}\)N represents the upper range of the Landsat acquisition paths [27] and the extent to 40\({}^{\circ}\)N at the southern edge allows the capture of much of the boreal and temperate forest biomes across northern North America and Eurasia, all of which are heavily impacted by changing climate and disturbance patterns. The Landsat albedo product is evaluated both with internal quality metrics and against ground tower measurements across a diversity of biomes and solar geometries. Previous preliminary iterations of the Landsat albedo product have been validated, including the initial snow-free product [28], and the North American Albedo Product which included snow retrievals [29]. Further development of Sentinel-2 albedo products [30] and implementation of the algorithm on the Harmonized Landsat Sentinel-2 (HLS) surface reflectance data is ongoing.