Spatial assessment of snow grain size from airborne lidar reflectance against coincident imaging spectroscopy retrievals

Snow albedo modulates net solar radiation, influencing snowmelt timing and magnitude, which is critical to watershed ecosystems and downstream communities. Snow grain size, a primary control on albedo, can be derived from near infrared lidar reflectance, a potentially valuable supplement to altimetry data. However, converting raw lidar intensity to reflectance is complex, with several existing methods but no comprehensive accuracy comparison to date. To address this gap, we leverage data from the Airborne Coastal Observatory (ACO), a paired lidar and imaging spectrometer platform, collected on May 15, 2021, over the snow-covered Place Glacier in the Coast Mountains of British Columbia, Canada. Three methods used to derive reflectance from the Riegl 1064 nm lidar were assessed, including: (1) an independent range and incidence angle correction (LR IC ); (2) the vendor product that accounts for range (LR riegl ); and (3) the vendor product with an additional incidence angle correction (LR rieglθ ). Radiative transfer modeling and lidar reflectance were used to derive grain size and the associated theoretical clean snow albedo. Reflectance and property retrievals were assessed against those derived from the imaging spectrometer (Specim Fenix). Reflectance magnitude of LR IC and LR rieglθ were similar in reflectance magnitude to the spectrometer but displayed greater variability. Grain sizes were underestimated (∼136 μm) producing a median relative error in albedo of 2%. LR riegl was positively biased, with greater underestimation of grain size (455 μm) and higher albedo error (6%). Results highlight the necessity of incorporating incidence angle corrections when using lidar for surface reflectance retrievals in mountainous terrain and further demonstrate the potential of lidar intensity for high-resolution snow grain size estimation. • We compare snow surface properties from lidar intensity to imaging spectroscopy. • Spatial comparison over a mountain glacier included variable snow conditions. • Range and incidence angle corrections produced comparable reflectance. • Manufacturer-provided reflectance (range corrected) compared poorly. • Relatively small errors in reflectance produced larger errors in grain size.

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Publication Details

Journal
Remote Sensing of Environment
Published
2026-03-13
DOI
https://doi.org/10.1016/j.rse.2026.115366
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
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article

Spatial assessment of snow grain size from airborne lidar reflectance against coincident imaging spectroscopy retrievals

Brian Menounos, Christopher Donahue, C. Ackroyd, S. McKenzie Skiles
Remote Sensing of Environment
Atmospheric aerosols and clouds
article

Spatial assessment of snow grain size from airborne lidar reflectance against coincident imaging spectroscopy retrievals

Brian Menounos, Christopher Donahue, C. Ackroyd, S. McKenzie Skiles
article en

Abstract

Snow albedo modulates net solar radiation, influencing snowmelt timing and magnitude, which is critical to watershed ecosystems and downstream communities. Snow grain size, a primary control on albedo, can be derived from near infrared lidar reflectance, a potentially valuable supplement to altimetry data. However, converting raw lidar intensity to reflectance is complex, with several existing methods but no comprehensive accuracy comparison to date. To address this gap, we leverage data from the Airborne Coastal Observatory (ACO), a paired lidar and imaging spectrometer platform, collected on May 15, 2021, over the snow-covered Place Glacier in the Coast Mountains of British Columbia, Canada. Three methods used to derive reflectance from the Riegl 1064 nm lidar were assessed, including: (1) an independent range and incidence angle correction (LR IC ); (2) the vendor product that accounts for range (LR riegl ); and (3) the vendor product with an additional incidence angle correction (LR rieglθ ). Radiative transfer modeling and lidar reflectance were used to derive grain size and the associated theoretical clean snow albedo. Reflectance and property retrievals were assessed against those derived from the imaging spectrometer (Specim Fenix). Reflectance magnitude of LR IC and LR rieglθ were similar in reflectance magnitude to the spectrometer but displayed greater variability. Grain sizes were underestimated (∼136 μm) producing a median relative error in albedo of 2%. LR riegl was positively biased, with greater underestimation of grain size (455 μm) and higher albedo error (6%). Results highlight the necessity of incorporating incidence angle corrections when using lidar for surface reflectance retrievals in mountainous terrain and further demonstrate the potential of lidar intensity for high-resolution snow grain size estimation. • We compare snow surface properties from lidar intensity to imaging spectroscopy. • Spatial comparison over a mountain glacier included variable snow conditions. • Range and incidence angle corrections produced comparable reflectance. • Manufacturer-provided reflectance (range corrected) compared poorly. • Relatively small errors in reflectance produced larger errors in grain size.

Remote Sensing of EnvironmentVol. 338
Natural Resources Canada (CA), Geological Survey of Canada (CA), University of Northern British Columbia (CA), University of Utah (US), Tula Foundation (CA), Bridger Photonics (United States) (US)
Climate action
Openalex Percentile: Top 36%
Atmospheric aerosols and clouds
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