<p>Surface albedo changes driven by changes in land cover—such as cropland expansion, urbanization, and forestry—are a known driver of climate change, yet these impacts are rarely quantified alongside impacts of greenhouse gas emissions in environmental impact assessments. One barrier to their inclusion is a lack of surface albedo data fit for this purpose, that capture spatial and temporal variations in surface albedo values, represent realistic atmospheric conditions, and, importantly, that can quantify surface albedo <i>changes</i> caused by changes in land cover. This study develops a methodology to estimate daily-mean blue-sky albedo values for multiple land cover types at the same location, enabling direct calculation of surface albedo changes. Applying this methodology, we produced a global climatological dataset of spatially- and temporally-specific surface albedo values for 22 different land cover types, with each land cover type found to be statistically distinct. We compared this novel dataset with values derived from existing simplified methods that do not account for diurnal fluctuations or represent realistic atmospheric conditions, finding that the simplified values underestimate surface albedo values by 2.7–5.8% on average, with errors reaching more than ± 50% in some cases. The dataset presented here provides a meaningful step towards the inclusion of the impacts of surface albedo changes into environmental impact assessment frameworks such as life cycle assessment, national and corporate carbon accounting and climate policy evaluations, enabling more complete evaluations of the climate impacts of land use and land cover changes.</p>