Remote sensing-supported vegetation parameters for regional climate models: a brief review

Land surface plays a key role in a climate system. Thus, the land surface description will become increasingly important for climate modelling by its feedbacks on the climate. Various forms of active/passive remotely sensed data are nowadays being used to provide continuous and up-to-date information on the earth's surface on both global and regional scales. This information is useful to be included in climate models. This review summarizes how LAI and albedo, two of the most important land surface parameters, could be derived from remote sensing. Whereas the high acquisition frequency, accessibility, and spatial continuality are referred to potential advantages, the scaling is still a drawback which may cause further problems such as incompatibility of different remote sensing data sources for a specific climate model. Moreover, issues like shadow and atmospheric effects are often problematic, especially when optical remote sensing is applied. Here, suggestions for improvement are made and open questions are pointed out.