6 May 2025

Applications of the CRREL–-Geometric Optics Snow Radiative Transfer (GOSRT) Model: Incorporating Diffraction and Simulating Detection of Buried Targets

Abstract: Radiative transfer through a snow surface within the visible and near infrared (NIR) spectra is complicated by the shape, size, and configuration of the snow grains that comprise the snow surface. Ray-tracing and photon-tracking techniques combined with 3D renderings of snow resolved at the microscale have shown promise as a means to directly simulate radiative transfer through snow with no restrictions on the snow grain configuration. This report describes and evaluates the US Army Cold Regions Research and Engineering Laboratory (CRREL) Geometric Optics Snow Radiative Transfer (GOSRT) model. In particular, we describe the incorporation of the diffraction process into the photon-tracking framework and evaluate how accurately the model simulates the spectral albedo of targets buried within the snow. We find that the model simulated spectral albedo is little affected by the incorporation of diffraction for most applications. However, there are nonnegligible impacts on simulated albedo for small grains in the NIR due to a reduction in forward scattering. We conclude by recommending that diffraction is neglected in CRREL–GOSRT for most cases, as including it substantially increases the computational expense with minimal impacts on the result. Finally, we show that buried targets are only distinguishable for very shallow snowpacks.

2 Jun 2023

A Generalized Photon-Tracking Approach to Simulate Spectral Snow Albedo and Transmittance Using X-ray Microtomography and Geometric Optics

Abstract: A majority of snow radiative transfer models (RTMs) treat snow as a collection of idealized grains rather than an organized ice–air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light reflectance and transmittance of snow based on X-ray micro- tomography images, treating snow as a coherent 3D structure rather than a collection of grains. The model uses a blended approach to expand ray-tracing techniques applied to sub-1 cm3 snow samples to snowpacks of arbitrary depths. While this framework has many potential applications, this study’s effort is focused on simulating reflectance and transmittance in the visible and near infrared (NIR) through thin snow- packs as this is relevant for surface energy balance and remote sensing applications. We demonstrate that this framework fits well within the context of previous work and capably reproduces many known optical properties of a snow surface, including the dependence of spectral reflectance on the snow specific surface area and incident zenith angle as well as the surface bidirectional reflectance distribution function (BRDF). To evaluate the model, we compare it against reflectance data collected with a spectroradiometer at a field site in east-central Vermont. In this experiment, painted panels were inserted at various depths beneath the snow to emulate thin snow. The model compares remarkably well against the reflectance measured with a spectroradiometer, with an average RMSE of 0.03 in the 400–1600 nm range. Sensitivity simulations using this model indicate that snow transmittance is greatest in the visible wavelengths, limiting light penetration to the top 6 cm of the snowpack for fine-grain snow but increasing to 12 cm for coarse-grain snow. These results suggest that the 5% transmission depth in snow can vary by over 6 cm according to the snow type.