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Proceedings Paper

Wheat growth modelling by a combination of a biophysical model approach and hyperspectral remote sensing data
Author(s): Natascha M. Oppelt
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Paper Abstract

The study presented here investigates the potential of improvement for a physically based model approach, when the static input data is enhanced by dynamic remote sensing information. The land surface model PROMET (Processes of Radiation, Mass and Energy Transfer) was generally applied, while the remote sensing input data was derived from hyperspectral data of the CHRIS (Compact High Resolution Imaging Spectrometer) sensor, which is operated by ESA (European Space Agency). The PROMET model, whose vegetation routine basically applies the Farquhar et al. photosynthesis approach, was set up to a field scale model run (10 x 10m) for a test acre tilled with wheat (Triticum aestivum L.) mapping the crop development of the season 2005. During the model run, information on the absorptive capacity of the leaves for two canopy layers (top, sunlit layer and bottom, shaded layer) was updated from remote sensing measurements, where angular CHRIS images were available. Control data were acquired through an intensive field campaign, which monitored the development of the stand throughout the vegetation period of the year 2005, also accompanying the satellite overflights. While the model without additional dynamic input data was able to reasonably reproduce the average development of the crop and yield, the spatial heterogeneity was severely underestimated. The combination of remote sensing information with the vegetation model led to a significant improvement of both the spatial heterogeneity of the crop development in the model and yield, which again entailed an overall improvement of the model results in comparison to measured reference data.

Paper Details

Date Published: 18 September 2009
PDF: 11 pages
Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 74721B (18 September 2009); doi: 10.1117/12.830322
Show Author Affiliations
Natascha M. Oppelt, Christian-Albrechts-Univ. Kiel (Germany)

Published in SPIE Proceedings Vol. 7472:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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