A Brightness-Temperature-Variance-Based Passive Microwave Algorithm for Monitoring Soil Freeze/Thaw State on the Tibetan Plateau

Thursday, 18 December 2014
Menglei Han1,2, Kun Yang1, Jun Qin1, Rui Jin3, Yaoming Ma1, Jun Wen3, Yingying Chen1, Long Zhao1,2, Zhu La1,2 and Wenjun Tang1, (1)ITP Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China, (2)University of Chinese Academy of Sciences, Beijing, China, (3)CAREERI/CAS Cold and Arid Regions Environmental and Engineering Research Institute, Lanzhou, China
The land surface on the Tibetan Plateau experiences typical diurnal and seasonal freeze/thaw processes that play important roles in the regional water and energy exchanges, and recent passive microwave satellites provide opportunities to detect the soil state for the unique region. With the support of three soil moisture and temperature networks in the Tibetan Plateau, a dual-index microwave algorithm with AMSR-E (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 all AMSR-E frequencies. 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, which is linearly correlated with ground temperature and thus is utilized to detect it. The threshold values of the two indices (SDI and the brightness temperature at 36.5 GHz vertically-polarized) are determined based on a part of in situ data from the network located in a semi-arid climate, and the algorithm was validated against other in situ data from this network. Further validations were conducted based on the other two networks located in different climates (semi-humid and arid, respectively). Results show that this algorithm has accuracy of more than 90% for the semi-humid and semi-arid regions, and misclassifications mainly occur at the transition period between unfrozen and frozen seasons. Nevertheless, the microwave signals have limited capability in identifying the soil surface freeze/thaw state in the arid region, because they can penetrate deep dry soils and thus embody the bulk information beneath the surface.