Temporal Variability of Surface Reflectance Supersedes Spatial Resolution in Defining Greenland’s Bare-Ice Albedo

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-01, Vol.14 (1), p.62
• Main Authors: Irvine-Fynn, Tristram D. L., Bunting, Pete, Cook, Joseph M., Hubbard, Alun, Barrand, Nicholas E., Hanna, Edward, Hardy, Andy J., Hodson, Andrew J., Holt, Tom O., Huss, Matthias, McQuaid, James B., Nilsson, Johan, Naegeli, Kathrin, Roberts, Osian, Ryan, Jonathan C., Tedstone, Andrew J., Tranter, Martyn, Williamson, Christopher J.
• Format: Article
• Language: English
• Subjects: Ablation; Aerosols; Albedo; Algae; Datasets; Drone aircraft; Greenland ice sheet; Heterogeneity; Ice; Ice sheets; Imagery; Imaging spectrometers; Impurities; Mass balance models; MODIS; Optical properties; Radiation; Reflectance; Remote sensing; Runoff; Seasonal variations; Seasons; Sentinel-2; Spatial discrimination; Spatial distribution; Spatial resolution; Spectrometry; Spectrum analysis; uncrewed aerial vehicle; Variability
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14010062

Ice surface albedo is a primary modulator of melt and runoff, yet our understanding of how reflectance varies over time across the Greenland Ice Sheet remains poor. This is due to a disconnect between point or transect scale albedo sampling and the coarser spatial, spectral and/or temporal resolutions of available satellite products. Here, we present time-series of bare-ice surface reflectance data that span a range of length scales, from the 500 m for Moderate Resolution Imaging Spectrometer’s MOD10A1 product, to 10 m for Sentinel-2 imagery, 0.1 m spot measurements from ground-based field spectrometry, and 2.5 cm from uncrewed aerial drone imagery. Our results reveal broad similarities in seasonal patterns in bare-ice reflectance, but further analysis identifies short-term dynamics in reflectance distribution that are unique to each dataset. Using these distributions, we demonstrate that areal mean reflectance is the primary control on local ablation rates, and that the spatial distribution of specific ice types and impurities is secondary. Given the rapid changes in mean reflectance observed in the datasets presented, we propose that albedo parameterizations can be improved by (i) quantitative assessment of the representativeness of time-averaged reflectance data products, and, (ii) using temporally-resolved functions to describe the variability in impurity distribution at daily time-scales. We conclude that the regional melt model performance may not be optimally improved by increased spatial resolution and the incorporation of sub-pixel heterogeneity, but instead, should focus on the temporal dynamics of bare-ice albedo.