An Algorithm Based on the Standard Deviation of Passive Microwave Brightness Temperatures for Monitoring Soil Surface Freeze/Thaw State on the Tibetan Plateau

The land surface on the Tibetan Plateau (TP) experiences diurnal and seasonal freeze/thaw processes that play important roles in the regional water and energy exchanges, and passive microwave satellites provide opportunities to detect the soil state for this region. With the support of three soil moisture and temperature networks on the TP, a dual-index microwave algorithm with Advanced Microwave Scanning Radiometer-Earth Observing System data is developed for the detection of soil surface freeze/thaw state. One index is the standard deviation index (SDI) of brightness temperature (TB), which is defined as the standard deviation of horizontally polarized brightness temperatures at 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz. It is the major index and is used to reflect the reduction of liquid water content after soils get frozen. The other index is the 36.5-GHz vertically polarized brightness temperature
$(\hbox{TB}_{36.5}^{\rm V})$
, which is linearly correlated with ground temperature. The threshold values of the two indices (SDI and
$\hbox{TB}_{36.5}^{\rm V}$
) are determined with one grid from the network located in a semiarid climate, and the algorithm is validated with other grids from the same network. Further validations are conducted based on the other two networks located in different climates (semihumid and arid, respectively). Results show that the classification accuracy using this algorithm is more than 90% for the semihumid and semiarid regions, and misclassifications mainly occur at the transition period between unfrozen and frozen seasons. Nevertheless, the algorithm has limited capability in identifying the soil surface freeze/thaw state in the arid region because the microwave signals can penetrate deep dry soils and thus embody the bulk information beyond the surface layer.