Inference of Soil Freezing Front Depth During the Freezing Period From the L-Band Passive Microwave Brightness Temperature

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, Δ
T_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.