Abstract
State-of-the-art crop growth models are powerful tools to assess the response of crops to altered environmental conditions and cultivation practices. In order to develop effective strategies for a sustainable management of resources and for adapting to environmental conditions, these models are applied not only on the field scale but also on the regional scale. So far the regional DANUBIA crop growth model has been successfully validated and applied in Germany. A new challenge is the extension and transfer of this process-based and object-oriented model to an intensively used agricultural region in Northeast China. This study area is part of the Sanjiang Plain in the Heilongjiang (Amur) river region, where a few decades ago, wetland ecosystems were converted to mainly large-scale irrigated rice fields. In this contribution, the generic DANUBIA crop growth model will be presented. Included are (i) the necessary steps for extending the model for simulating rice growth as well as (ii) a concept for integrating optical remote sensing and laser scanning data into the model. This method aims at improving the model results by analyzing and updating observed vegetation characteristics during a model run. Assimilating these data into a state-of-the-art crop growth model provides a large innovative potential for regional agro-ecosystem modelling.
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