Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements

• Published in: The Cryosphere
• Main Authors: Donahue, Christopher, Skiles, S. McKenzie, Hammonds, Kevin
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-16-43-2022; https://noa.gwlb.de/receive/cop_mods_00059812; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059461/tc-16-43-2022.pdf; https://tc.copernicus.org/articles/16/43/2022/tc-16-43-2022.pdf

It is well understood that the distribution and quantity of liquid water in snow is relevant for snow hydrology and avalanche forecasting, yet detecting and quantifying liquid water in snow remains a challenge from the micro- to the macro-scale. Using near-infrared (NIR) spectral reflectance measurements, previous case studies have demonstrated the capability to retrieve surface liquid water content (LWC) of wet snow by leveraging shifts in the complex refractive index between ice and water. However, different models to represent mixed-phase optical properties have been proposed, including (1) internally mixed ice and water spheres, (2) internally mixed water-coated ice spheres, and (3) externally mixed interstitial ice and water spheres. Here, from within a controlled laboratory environment, we determined the optimal mixed-phase optical property model for simulating wet snow reflectance using a combination of NIR hyperspectral imaging, radiative transfer simulations (Discrete Ordinate Radiative Transfer model, DISORT), and an independent dielectric LWC measurement (SLF Snow Sensor). Maps of LWC were produced by finding the lowest residual between measured reflectance and simulated reflectance in spectral libraries, generated for each model with varying LWC and grain size, and assessed against the in situ LWC sensor. Our results show that the externally mixed model performed the best, retrieving LWC with an uncertainty of ∼1 %, while the simultaneously retrieved grain size better represented wet snow relative to the established scaled band area method. Furthermore, the LWC retrieval method was demonstrated in the field by imaging a snowpit sidewall during melt conditions and mapping LWC distribution in unprecedented detail, allowing for visualization of pooling water and flow features.