Retrieval of Snow Density Based on Space-Borne L-Band Passive Microwave Observations

Snow density is the key parameter in converting snow depth to snow mass. A two-parameter retrieval algorithm has been developed to estimate snow density and soil permittivity simultaneously in ground-based experiments. This study tested the two-parameter retrieval algorithm applied for the L-band multiple-angle SMOS (Soil Moisture Ocean Salinity) and single-angle SMAP (Soil Moisture Active Passive) missions, respectively, at 46 sites in Quebec, Canada. To alleviate the ill-posed problem, we also developed a one-parameter retrieval algorithm, where only the snow density was retrieved using a soil permittivity calculated from the GLDAS-Noah soil simulations. Results showed that, the two-parameter retrieval algorithm achieved an ubRMSE of 40 and 60 kg⁄m
³
for SMOS and SMAP, respectively, but the correlation is low (0.23-0.24), because the sensitivity of observations to snow density is weaker than soil parameters, and the estimates from coarse-resolution satellite observations were validated against point-scale measurements. On the contrary, if soil permittivity is determined despite a small bias, the one-parameter retrieval algorithm based on SMOS can increase the correlation coefficient to 0.65 in the October to June period. It indicates the importance of a soil permittivity prior or a stable snow condition (for example, frozen soil condition) to achieve high accuracy for satellite-based snow density estimation.