During the last decades, land surface models have proven their usefulness for a number of purposes, including water management, climatology, meteorology, etc. A critical point in the application of these models is their parameterization, more specifically, the determination of spatially distributed data sets describing the topography, land use, and soil texture of the study area. Because accurate estimates of these data sets can be very difficult to obtain, combined with the inherent simplification of the physical reality by the models and uncertainties in the meteorological forcings, land surface model results are always prone to errors. This session focuses on the use of remote sensing data to minimize these model errors.
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.