Research on the Improvement of Passive Microwave Freezing and Thawing Discriminant Algorithms for Complicated Surface Conditions

Soil freezing and thawing processes play important roles in water and energy exchanges, weather and climatology. Passive microwave remote sensing tends to be one of the most effective ways of monitoring global surface state of freezing and thawing. However, Due to the complexity and variability of surface environmental factors, the thresholds in many algorithms are not universally suitable and the selection of thresholds mainly depends on the existing ground data. In addition, there is still a lack of comprehensive consideration of the complexity of the real surface in the modeling process. In order to solve these problems, firstly, a comprehensive database which contains complex surface conditions was built based on the data simulated from Cold Area Microwave Radiation Model and observed from satellite and ground sites. In this database, the effect of soil organic matter on microwave radiation was considered, the effective range of forest stock was redefined based on the biomass data, and the long-range satellite observations of brightness temperatures and nationwide ground-based meteorological site data were integrated. Then, the discrimination indexes ($\mathrm{Tb}_{36.5\mathrm{v}}$ and Qe, Tb36.5v and SDI) of “DFT algorithm” and “standard deviation algorithm” were respectively selected from the comprehensive database and used to establish new discrimination formulas based on the Fisher discrimination method. Through validated with the ground data, the F/T discrimination results based on the new formulas showed better performance than those based on the original algorithms, which demonstrated a better applicability for complicated surface conditions.