ABSTRACT. Snow grain-size characterization, its vertical and temporal evolution is a key parameter for the improvement and validation of snow and radiative transfer models (optical and microwave) as well as for remote-sensing retrieval methods. We describe two optical methods, one active and one passive shortwave infrared, for field determination of the specific surface area (SSA) of snow grains. We present a new shortwave infrared (SWIR) camera approach. This new method is compared with a SWIR laserbased system measuring snow albedo with an integrating sphere (InfraRed Integrating Sphere (IRIS)). Good accuracy (10%) and reproducibility in SSA measurements are obtained using the IRIS system on snow samples having densities greater than 200 kg m -3 , validated against X-ray microtomography measurements. The SWIRcam approach shows improved sensitivity to snow SSA when compared to a near-infrared camera, giving a better contrast of the snow stratigraphy in a snow pit.
[1] Satellite-passive microwave remote sensing has been extensively used to estimate snow water equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered snow model with a passive microwave emission model. Results show that the snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the snow and microwave emission models with successive iterations to correct the influence of snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the snow model.
Abstract. This study aims to better understand and quantify the uncertainties in microwave snow emission models using the Dense Media Radiative Theory Multi-Layer model (DMRT-ML) with in situ measurements of snow properties. We use surface-based radiometric measurements at 10
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