In addition to investigate the rainfall over the Tibetan Plateau, Microwave Imager observations on board of the satellite Tropical Rainfall Measurement Mission (TRMM) have been also used to retrieve land surface parameters, such as sur-face temperature (Te), vegetation water content (Wc), and volumetric soil surface moisture (Mv). A three dimensional Look-up Table (LUT) scheme, by using one band brightness temperature, a 'Polarization Index' (PI), and an 'Index for Soil Wetness' (ISW), was developed for this purpose, which can retrieve the three basic parameters Te, Wc, and Mv simultaneously. Considered that there are still clouds as well as heavy rainfall disturbance in deriving surface parameters over the Tibetan Plateau, 10-day composite TMI images were used. For the five months from May to September 1998, the distribution of surface parameters of each ten days on the mesoscale intensive experimental region of GAME-Tibet was evaluated. The results were compared with field observations; particularly, for the most concerned surface soil moisture, the results are quite acceptable. 3-D LUT is an easy and effective method to be used in the passive microwave remote sensing.
The estimation of snow parameters such as snow extent, snow depth and snow water equivalent are very important. They are parameters in land surface schemes and are very useful in snow disaster assessment. Passive microwave remote sensing has advantages in retrieving these parameters, especially snow depth. However, this technique has not been applied to monitor snow in Tibetan Plateau so far. So since last winter we tried to operationally monitor snow in this area by using SSM/I data, providing daily snow depth maps to the concerning sections of local government. In the meantime, the in-situ measurements of snow depth data in the Tibetan Plateau were collected to validate the retrieval algorithm employed in this study. In the paper, SSM/I images before and after a heavy snowfall were analyzed and compared with MODIS images. The results showed that the snow extent from SSM/I data is consistent with that from MODIS data, and snow depths from SSM/I are helpful for the assessment of snow disaster. However, compared with in-situ observations SSM/I derived snow depths are significantly overestimated. Since passive microwave remote sensing is almost transparently to atmosphere and cloud, it will play an important role in monitoring snow in the Tibetan Plateau, wih the retreival algorithm being improved. This will be more dominant when AMSR data are available.