Deriving LAI from remote sensed images for data assimilation into a crop model

Crop science

Leaf area index (LAI) is a main indicator of plant growth and development and its also a state variable used in simulation modeling. Remote sensed images from unnamed aerial vehicles (UAV) vehicles can be used to derive spatiotemporal dynamics of LAI growth that can be assimilated into

The main goal of this study will be to derive LAI values from UAV images and assimilate the into a crop model and to compare model performance when assimilating LAI from remote sensed images into a crop model in SIMPLACE.

Multispectral images already collected from a wheat field experiment in Klein-Altendorf site will be used to derive LAI dynamics, the values will then be assimilated into the crop model.

Comparison of performance of a crop model with and without data assimilation which could finally issue into a peer-reviewed paper

6 to 8 Month

English

Interest in remote sensed data assimilation into crop models, good quantitative and analytical skills (eg good marks in math and statistics classes), preferable basic knowledge in the use of R and/or XML scripting

Thomas Gaiser and Ixchel Hernandez Ochoa
(Universität Bonn)

tgaiser@uni-bonn.de, ihernandez@uni-bonn.de