| Summary: | Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.
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