Low-Frequency Microwave Emission Signatures of Wet Snow-Covered Polar Ice Sheet

Surface melting of polar ice sheets alters their energy-mass balance, leading to sea level rise and exacerbating climate feedback effects. This study examines P-to-L-band emissions signatures to variations in surface wet snow profiles at the DYE-3 site, Greenland, using a partially coherent model developed by the authors. L-band model predictions, combined with Soil Moisture and Ocean Salinity (SMOS) observations, are used to estimate coupled parameters such as liquid water content (LWC) and snow depth ( $d_{\mathrm {ws}}$ ). Initially, short-scale density fluctuation parameters are calibrated using both partially coherent and incoherent models against the SMOS satellite brightness temperatures for DYE-3 in dry seasons by minimizing root mean square error (RMSE). The partially coherent model, which showed better accuracy, is then parameterized across realistic ranges of LWC in snow and its depth from the surface to predict average Tb. The DYE-3 melt days are identified from annual SMOS Tb data using the Leduc-Leballeur et al. (2020) algorithm, and RMSE analysis with the simulated responses indicates that multiple wet snow parameter sets are associated with single melting events. Additionally, ultrawideband emissions from 0.5 to 2 GHz are analyzed to clarify the potential ambiguity of deriving wet snow parameters from Tb observations, revealing that wet snow profiles with higher moisture exhibit consistent frequency responses. The study also compares the frequency spectra from 0.5 to 2 GHz of the partially coherent model with a radiative transfer (RT) theory-based incoherent model, highlighting stronger coherent wave effects for moist-shallow (3%, 0.5 m) surface snow compared to the damp-deep (1%, 8 m) conditions. This research extends the use of the partially coherent model to examine how low-frequency microwave emissions are affected by thermal and density changes at ice sheet depth during surface melting. The findings indicate that these variations in ice sheet properties distinctly influence microwave emissions signatures during surface melt events. This work develops a rigorous model to support the retrieval of ice sheets and near-surface wet snow parameters using low-frequency passive microwave observations.