Assimilating remote sensing data into a land-surface process model
Abstract:A PROcess-oriented Modular Environment and Vegetation model (PROMET-V) was developed to calculate plant growth, water and nitrogen fluxes, their spatial patterns and interactions. A central goal of PROMET-V was to develop interfaces to use remote sensing data for spatially distributed modelling. Remote sensing measurements are used by PROMET-V to initialize the model, to adjust and update model parameters, and to validate model results. Model results show that management related parameters, such as the cutting date of meadows or the sowing density of crops, are largely responsible for spatial heterogeneities. These impacts cannot be modelled accurately. They must be observed instead. The same is true for pests, diseases or catastrophic events, e.g. flood, fire, storm damage. Thematic Mapper (TM) images were used to update model parameters and to estimate management related parameters. The use of remote sensing data not only leads to improved model results, it also allows the detection of spatial heterogeneities which otherwise cannot be accounted for. Remote sensing data were also used to validate model results. The calculated surface soil moisture compared favourably to soil moisture patterns derived from European Remote Sensing Synthetic Aperture Radar (ERS SAR) data.
Document Type: Research Article
Affiliations: Department of Geography, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany;, Email: firstname.lastname@example.org
Publication date: 2003-07-01