Snow and ice broadband albedo from Sentinel-3 OLCI measurements and empirical regression

This dataset contains: snow and ice broadband albedo mosaics over Greenland for years 2018 and 2019 computed from a simple empirical approach (Wehrlé et al, 2021). This approach consists of a fit between 4729 hourly PROMICE albedo measurements and the nearest in time and space OLCI Top of Atmosphere (TOA) reflectances spanning 3 years (2017–2019). We then defined the broadband albedo from a fit to the average of four OLCI TOA reflectances: A = α (R400 nm+ R560 nm+ R865 nm + R1020 nm) / 4+ β where α corresponds to the slope of the linear regression between OLCI TOA and PROMICE albedo measurements (1.003), and β is its intercept (0.058). Clouds were detected and thereafter masked in Sentinel-3 imagery using the Simple Cloud Detection Algorithm (SCDA) version 2.0 (Metsämäki et al. 2015; Wehrlé & Box 2021). This algorithm consists of up to six tests on Sea and Land Surface Temperature Radiometer (SLSTR) TOA reflectances (550 and 1600 nm) and brightness temperatures (3.7, 11 and 12 μm). Processing scripts: https://github.com/AdrienWehrle/SICE_tools Related studies: - Wehrlé A.; Box J.E.; Niwano M.; Anesio A.M.; Fausto R.S., Greenland bare ice albedo from PROMICE automatic weather station measurements and Sentinel-3 satellite observations, GEUS bulletin, in press 2021 - Wehrlé, A. & Box, J., SICE implementation of the Simple Cloud Detection Algorithm (SCDA) v2.0, GEUS Dataverse, 2021 https://doi.org/10.22008/FK2/N0XWSJ - Metsämäki, S.; Pulliainen, J.; Salminen, M.; Luojus, K.; Wiesmann, A.; Solberg, R.; Böttcher, K.; Hiltunen, M.;Ripper E, Introduction to globSnow snow extent products with considerations for accuracy assessment. Remote Sensing of Environment, 2015