THE ADVANCES AND DEVELOPMENTOF REMOTE SENSING APPLICATIONS TO LAND SURFACE PROCESS PARAMETERIZATION

Recent years, many meteorologists and climatologists pay more attention to land surface condition, which effects on the changes of heat, water and matter between land surface vegetation and climate in the condition of global climate change. On the one hand, it is widely recognized that there is a close relationship between and terrestrial ecosystem in global scale; the climate variations have significant impact on the vegetation distributional pattern and its growing rate and development. On the other hand, the global vegetation has a prominent feedback on climate through surface roughness length, albedo and evapotranspiration. The development of AGCM and Regional Climate Model (RCM) plays an important role for us to understand the relationship between climate change and terrestrial ecosystems change. In these models, many key biophysical parameters are required to act as input variables for each model runs. However, over past few years' studies suggest that the spatial distribution patterns for some input biophysical parameters (e. g. LAI, canopy conductance of water vapor and CO2 etc.) and relationship with bio-climate are obscure. These conditions affect the simulating precision to a great extent. Hence, it is essential to carry out the studies of parameters derived from spatial satellite data. At present, the development of remote sensing technology has provided a useful tool to study the land surface eco-process. In the current research, the important remote sensing information data used to study land surface process are analyzed. Then, the main applications of remote sensing information to land surface process parameterization are reviewed. Finally, the main problem and development of remote sensing information used in land surface process parameterization are discussed.