LAUSR

The freezing front depth (z_ff) of annual freeze–thaw cycles is critical for monitoring the dynamics of the cryosphere under climate change because z_ff is a sensitive indicator of the heat balance over the atmosphere-cryosphere interface. Meanwhile, although it is very promising for acquiring global soil moisture distribution, the L-band microwave remote sensing products over seasonally frozen grounds and permafrost is much less than in wet soil. This study develops an algorithm, i.e., the brightness temperature inferred freezing front (BT-FF) model, for retrieving the interannual z_ff with the diurnal amplitude variation of L-band brightness temperature (ΔT_B) during the freezing period. The new algorithm assumes first, the daily-scale solar radiation heating/cooling effect causes the daily surface thawing depth (z_tf) variation, which leads further to ΔT_B; second, ΔT_B can be captured by an L-band radiometer; third, z_tf and z_ff are negatively linear correlated and their relation can be quantified using the Stefan equation. In this study, the modeled soil temperature profiles from the land surface model (STEMMUS-FT, i.e., simultaneous transfer of energy, mass, and momentum in unsaturated soil with freeze and thaw) and T_B observations from a tower-based L-band radiometer (ELBARA-III) at Maqu are used to validate the BT-FF model. It shows that, first, ΔT_B can be precisely estimated from z_tf during the daytime; second, the decreasing of z_tf is linearly related to the increase of z_ff with the Stefan equation; third, the accuracy of retrieved z_ff is about 5–25 cm; fourth, the proposed model is applicable during the freezing period. The study is expected to extend the application of L-band T_B data in cryosphere/meteorology and construct global freezing depth dataset in the future.