Inference of Soil Freezing Front Depth during the Freezing Period from the L-Band Passive Microwave Brightness Temperature

The freezing front depth ( zff ) of annual freeze- thaw cycles is critical for monitoring the dynamics cryosphere under climate change because a sensitive indicator heat balance over atmosphere-cryosphere interface. Meanwhile, although it very promising acquiring global soil moisture distribution, L-band microwave remote sensing products seasonal frozen grounds and permafrost much less than in wet soil. This study develops an algorithm, i.e., Brightness Temperature inferred Freezing Front (BT-FF) model, retrieving interannual with diurnal amplitude variation (DAV) L band brightness temperature xmlns:xlink="http://www.w3.org/1999/xlink">ΔTB during period. new algorithm assumes 1) daily-scale solar radiation heating/cooling effect causes daily surface thawing xmlns:xlink="http://www.w3.org/1999/xlink">ztf variation, which leads further to ; 2) Δ xmlns:xlink="http://www.w3.org/1999/xlink">TB can be captured by radiometer; 3) are negatively linear correlated their relation quantified using Stefan Equation. In this study, modeled profiles from land model (STEMMUS-FT, Simultaneous Transfer Energy, Mass, Momentum Unsaturated Soil Freeze Thaw) observations tower- based radiometer (ELBARA-III) at Maqu used validate BT-FF model. It shows that: precisely estimated daytime; decreasing linearly related increase Equation; accuracy retrieved about 5-25 cm; 4) proposed applicable expected extend application -band data cryosphere/meteorology construct set future.