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Abstract. The Greenland and Antarctic ice sheets are covered in a layer of porous firn. Knowledge of firn structure improves our understanding of ice sheet mass balance, supra- and englacial hydrology, and ice core paleoclimate records. While macroscale firn properties, such as firn density, are relatively easy to measure in the field or lab, more intensive measurements of microstructural properties are necessary to reduce uncertainty in remote sensing observations of mass balance, model meltwater infiltration, and constrain ice age – gas age differences in ice cores. Additionally, as the duration and extent of surface melting increases, refreezing meltwater will greatly alter firn structure. Field observations of firn grain size and ice layer stratigraphy are required to test and validate physical models that simulate the ice-sheet-wide evolution of the firn layer. However, visually measuring grain size and ice layer distributions is tedious, is time-consuming, and can be subjective depending on the method. Here we demonstrate a method to systematically map firn core grain size and ice layer stratigraphy using a near-infrared hyperspectral imager (NIR-HSI; 900–1700 nm). We scanned 14 firn cores spanning ∼ 1000 km across western Greenland’s percolation zone with the NIR-HSI mounted on a linear translation stage in a cold laboratory. We leverage the relationship between effective grain size, a measure of NIR light absorption by firn grains, and NIR reflectance to produce high-resolution (0.4 mm) maps of effective grain size and ice layer stratigraphy. We show the NIR-HSI reproduces visually identified ice layer stratigraphy and infiltration ice content across all cores. Effective grain sizes change synchronously with traditionally measured grain radii with depth, although effective grains in each core are 1.5× larger on average, which is largely related to the differences in measurement techniques. To demonstrate the utility of the firn stratigraphic maps produced by the NIR-HSI, we track the 2012 melt event across the transect and assess its impact on deep firn structure by quantifying changes to infiltration ice content and grain size. These results indicate that NIR-HSI firn core analysis is a robust technique that can document deep and long-lasting changes to the firn column from meltwater percolation while quickly and accurately providing detailed firn stratigraphy datasets necessary for firn research applications. 

Snow albedo, a measure of the amount of solar radiation that is reflected at the snow surface, plays a critical role in Earth’s climate and in regional hydrology because it is a primary driver of snowmelt timing. Satellite multi-spectral remote sensing provides a multi-decade record of land surface reflectance, from which snow albedo can be retrieved. However, this observational record is challenging to assess because discretein situobservations are not well suited for validation of snow properties at the spatial resolution of satellites (tens to hundreds of meters). For example, snow grain size, a primary driver of snow albedo, can vary at the sub-meter scale driven by changes in aspect, elevation, and vegetation. Here, we present a new uncrewed aerial vehicle hyperspectral imaging (UAV-HSI) method for mapping snow surface properties at high resolution (20 cm). A Resonon near-infrared HSI was flown on a DJI Matrice 600 Pro over the meadow encompassing Swamp Angel Study Plot in Senator Beck Basin, Colorado. Using a radiative transfer forward modeling approach, effective snow grain size and albedo maps were produced from measured surface reflectance. Coincident ground observations were used for validation; relative to retrievals from a field spectrometer the mean grain size difference was 2 μm, with an RMSE of 12 μm, and the mean broadband albedo was within 1% of that measured near the center of the flight area. Even though the snow surface was visually homogenous, the maps showed spatial variability and coherent patterns in the freshly fallen snow. To demonstrate the potential for UAV-HSI to be used to improve validation of satellite retrievals, the high-resolution maps were used to assess grain size and albedo retrievals, and subpixel variability, across 17 Landsat 9 OLI pixels from a satellite overpass with similar conditions two days following the flight. Although Landsat 9 did not capture the same range of values and spatial variability as the UAV-HSI, on average the comparison showed good agreement, with a mean grain size difference of 9 μm and the same broadband albedo (86%).