Assimilation of hyperspectral and laser scanning data: Extension and transfer of a regional crop growth model to Northeast China

Coordinators: Prof. Georg Bareth, Prof. Karl Schneider

in cooperation with:

Xinping Chen, Prof. Dr., University professor
Yuxin Miao, PhD, Associate professor

College of Resources and Environmental Sciences, Department of Plant Nutrition
No.2 Yuanmingyuan West Road, Beijing, 100193, China

funded by DFG

Regional modelling of agro-ecosystems offers insight into the dynamics of nitrogen, carbon and water fluxes in a landscape and allows for predicting yield and detecting crop vigour. The model results are of pronounced importance not only for scientists but also for politicians since these data can be used to develop effective strategies for a sustainable management of resources. Vegetation characteristics (e.g. biomass) are derived from analysing the relation between crop data and spectral reflectances or laser scanning data, all measured on the field scale. The results are transferred to remote sensing (RS) data. During a model run on the regional scale, remotely observed crop data are assimilated to improve the model’s accuracy. The data assimilation method includes the comparison of modelled and remotely observed vegetation features and aims to minimize differences by re-initialising the model run with optimized values of input parameters. The central objective is to analyze the potential of hyperspectral optical and laser scanning RS data to improve the process-oriented modelling of agro-ecosystems on a regional scale. This frames two key objectives: (i) to model nitrogen, carbon and water fluxes in the intensively used agricultural region of Northeast China and - within this context - (ii) to improve the methods of regional agro-ecosystem modelling by integrating RS data of hyperspectral satellite sensors and laser scanners. The linkage of these RS data with a state-of-the-art crop growth model provides a large innovative potential for regional agro-ecosystem modelling.