Researchers compare lidar reflectance data with imaging spectroscopy to improve snow grain size measurement. This study evaluates lidar’s effectiveness in challenging terrains, offering potential benefits for environmental monitoring by enhancing the accuracy of snow property assessments in mountainous regions.
This article was contributed by Lars Langhorst of The Engineering Times.
Understanding the Importance of Snow Albedo
Snow albedo, the measure of a snow surface’s reflectivity, significantly influences net solar radiation, impacting snowmelt timing and magnitude. This is crucial for watershed ecosystems and communities dependent on snowmelt for water. Snow grain size, a key factor affecting albedo, has traditionally been measured using imaging spectroscopy. However, this method faces challenges in mountainous regions due to variable illumination and mixed pixels. Lidar technology offers a promising alternative.
Lidar, or Light Detection and Ranging, uses light to measure distances and is increasingly used for high-resolution topographic mapping and monitoring snow volume and glacier mass balance. The 1064 nm wavelength lidar is effective for snow altimetry due to shallow light penetration and high reflectance. Yet, converting lidar intensity to reflectance for accurate snow grain size retrievals is complex, with existing methods lacking comprehensive accuracy comparisons.
This research addresses a significant gap by evaluating lidar-derived reflectance against imaging spectroscopy retrievals. The study focuses on the Place Glacier in British Columbia, Canada, using data from the Airborne Coastal Observatory to assess differences among various lidar reflectance products and their implications for snow property retrievals in mountainous terrains.
Innovative Methodology and Techniques
The research utilized data from May 15, 2021, collected by the Airborne Coastal Observatory, equipped with a Riegl Q780 lidar and a Specim Fenix imaging spectrometer. Three methods for deriving reflectance from the 1064 nm lidar were evaluated:
- An independent range and incidence angle correction (LRIC ).
- A vendor-provided reflectance product accounting for range (LRriegl).
- A vendor-provided product with an additional incidence angle correction (LRrieglθ).
The methodology involved correcting lidar intensity for range and incidence angle using filters and calculations. The corrected intensity was converted to reflectance using calibration factors derived from imaging spectroscopy reflectance at 1064 nm in selected regions of interest. This approach enabled the retrieval of snow grain size and associated clean snow broadband albedo, which were then compared to imaging spectroscopy results.
Key Findings and Insights
The study found that the reflectance magnitudes of LRIC and LRrieglθ were similar to those from the spectrometer, albeit with greater variability. Grain sizes were underestimated by approximately 136 μm, resulting in a median relative error in albedo of 2%. Conversely, LRriegl showed a positive bias, with a greater underestimation of grain size (455 μm) and a higher albedo error of 6%.
These findings highlight the importance of incorporating incidence angle corrections when using lidar for surface reflectance retrievals in mountainous regions. The research demonstrates the potential of lidar intensity for high-resolution snow grain size estimation, providing a valuable complement to imaging spectroscopy, especially in environments where passive observations are constrained by shadows and mixed pixels.
Future Directions and Impact
This research underscores the promising role of lidar technology in advancing snow property assessments, particularly in challenging mountainous terrains. By improving the accuracy of snow grain size measurements, lidar can enhance our understanding of snowmelt dynamics and their impact on ecosystems and water resources. The study encourages further exploration of lidar’s capabilities, potentially leading to more comprehensive environmental monitoring and management strategies.
Reference: Chelsea Ackroyd, Christopher P. Donahue, Brian Menounos, S. McKenzie Skiles. Spatial assessment of snow grain size from airborne lidar reflectance against coincident imaging spectroscopy retrievals. Remote Sensing of Environment 338 (2026) 115366. DOI: https://doi.org/10.1016/j.rse.2026.115366
