Soil surface temperature () is an important indicator of global temperature change and a key input parameter for retrieving land surface variables using remote sensing techniques. Due to the masking in the thermal infrared band and the scattering in the microwave band of snow, the temperature of soil surfaces covered by snow is difficult to infer from remote sensing data. We attempted to estimate under snow cover using brightness temperature data from the special sensor microwave/imager. under snow cover was underestimated due to the strong scattering effect of snow on upward soil microwave emissions at 37 GHz. The underestimated portion of is related to snow properties, such as depth, grain size, and moisture. Based on the microwave emission model of layered snowpacks, the simulated results revealed a linear relationship between the underestimated and the brightness temperature difference (TBD) at 19 and 37 GHz. When TBDs at 19 and 37 GHz were introduced to the estimation method, accuracy improved, i.e., the root mean square error and bias of the estimated decreased greatly, especially for dry snow. This improvement allows estimation of snow-covered surfaces from 37 GHz microwave brightness temperature.