Statistics – Applications
Scientific paper
Jan 2002
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002esasp.475..195b&link_type=abstract
In: Proceedings of the Third International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Appl
Statistics
Applications
Process Modelling
Scientific paper
Land surface process models describe the energy-, water-, carbon- and nutrient-fluxes at the land surface on a regional scale by combining a given set of environmental parameters and variables (e.g. water balance model, plant physiology model, atmospheric boundary layer model, erosion model). They need spatially distributed input parameters, which can be delivered from remote sensing analyses using both optical and microwave sensors. Thus, land surface process models are the main drivers for four dimensional data assimilation (4DDA) which is based on the synergistic data utilization of remote sensing and ancillary data both in space and time. To ensure the constant flow of the necessary input parameters and variables, the development of adequate data-assimilation and data-fusion techniques is mandatory. Parameter models operate at the centre of this data fusion process to convert remote sensing measurements into a set of model input parameters and variables. Different strategies to use remote sensing derived parameters in models are demonstrated. They span from the simple delivery of static input-parameters, over the provision of dynamic model parameters, model forcing and recalibration of internal model variables, to inversion and validation of land surface process models. Examples will illustrate these different data assimilation strategies using SAR and optical data sources. The integration of land surface parameters derived from remote sensing (e.g. land use, digital terrain model, surface soil moisture) in flood forecast is a rather straight forward task. For water balance modelling, soil moisture and snow cover assessment will be illustrated. This task is already more complex, since a continuous process must be simulated and the data assimilation must avoid inconsistencies in model performance. The application of remote sensing data assimilation methods for crop growth and agricultural production models further requires complex feedback mechanisms. Examples will be presented from the ESA-study GeoBIRD (back et al. 2001).
Bach Heike
Mauser Wolfram
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