Simulation of Cereal Canopy Reflectance

4D plant models and canopy reflectance calculation

Build up of 4D-plant models

The 4D plant models are developed with the scientific plant modelling system AMAPsim (Barczi et al. 2008), a scientific software based on botanical theory.


Fig. 4D model of winter rye at different growth stages (high resolution image)



3D Geometry

Each plant is described by facets (leaves) and cylinder objects (stems, ears). The underlying soil surface is modelled by in-situ measured height profiles. The plant positions are determined by the 3D soil surface entering the number of plants per meter and row.

The canopy consists of several 3D plants cloned and superposed on the 3D soil surface. Each clone is randomly twisted around itself and placed on the calculated position.
 
Data acquisition

The simulation process requires information about optical material properties in terms of reflectance and transmittance of each plant organ (stem, leaf, and ear). The data are measured with an ASD FieldSpec Pro in the spectral range of 350 nm – 2500 nm.

The development of the plant models is based on a detailed study of the geometrical appearance and growth stages. Therefore, numerous features describing the plant's shape were collected in the field.


Fig. Model composition


Ray tracing

The canopy reflectance is calculated by by the aDvanced Radiometric rAy Tracer (drat), an efficient monte-carlo ray tracing renderer developed by P. Lewis (1999).

The resulting canopy reflectance data is determined by the acquisition and illumination geometry as well as the geometry and spectral information of the objects on the surface.


Fig. Ray tracing




Literature

J.-F. Barczi, H. Rey, Y. Caraglio, P. de Reffye, D. Barthélémy, Q. X. Dong and T. Fourcaud (2008). AmapSim: A structural whole-plant simulator based on botanical knowledge and designed to host external functional models. Annals of Botany, 101, 1125-1138.

P. Lewis (1999). Three-dimensional plant modelling for remote sensing simulation studies using the botanical plant modelling system. Agronomie, 19, 185-210.


Publications

Peisker, T.; Spengler, D.; Segl, K.; Itzerott, S.; Kaufmann, H. (2008). Simulation of Hyperspectral Reflectance Data Using Artificial 3D Crop Fields. In: Digital Earth Summit on Geoinformatics 2008: Tools for Global Change Research. Ed.: Ehlers, M.; Behnke, K.; Gerstengarbe, F.-W.; Hillen, F.; Koppers, L.; Stroink, L.; Wächter, J. 2008. 39-44 p.

Spengler, D.; Peisker, T.; Bochow, M.; Segl, K.; Kaufmann, H. (2009). Determination of cereal type and growth stage using simulated reflectance data. In: Imaging Spectroscopy: Innovative tool for scientific and commercial environmental applications. 6th EARSeL SIG IS Workshop (Ramat Aviv, Tel Aviv, Israel 2009)

Peisker, T.; Spengler, D.; Segl, K.; Kaufmann, H. (2009). On the spectral resolution requirements for the derivation of leaf area index from hyperspectral remote sensing data In: Imaging spectroscopy: Innovative tool for scientific and commercial environmental applications 6th EARSeL SIG IS Workshop (Ramat Aviv, Tel Aviv, Israel 2009)

Peisker, T.; Spengler, D.; Segl, K.; Hostert, P.; Kaufmann, H. (2010). Simulation of EnMAP measured cereal canopy spectra - Challenges posed by varying observation geometry and plant phenology. In: Hyperspectral Workshop 2010: From CHRIS/Proba to PRISMA & EnMAP and beyond (ESA ESRIN, Frascati (Rome), Italy (Will be published Eend of June 2010)


Contacts

Dr. Karl Segl
Dr. Theres Küster (née Peisker)
Daniel Spengler