Soil and vegetation patterns are important parameters in hydrological and geomorphological processes. These patterns vary across spatial scales, with significant implications on the process interactions, and on the response to global change. Land surface models have been proven to be very useful tools in the analysis of these issues. However, a critical point in the application of these models is their parameterization, especially at large spatial scales. More specifically, significant research efforts are dedicated to the determination of spatially distributed data sets describing the topography, land use, and soil properties at large spatial scales.

Remote sensing is the primary source of information for this purpose, more specifically, for the acquisition of information regarding spatial distributions of physical, chemical and biological surface properties.

The proposed session focuses on studies dealing with the survey and analysis of surface properties using remote sensing and image processing techniques at large spatial scales, as well as the development of methodologies to optimally use these data in land surface models. Contributions can include advances in the application of data assimilation, model parameter estimation, disaggregation of remotely sensed data using models, data fusion techniques, use of remote sensing for validation studies, etc.