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

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