Talks and Poster Presentations (with Proceedings-Entry):

W. Dorigo, H. Gerighausen:
"Automatic retrieval of crop characteristics: an example for hyperspectral AHS data from the AgriSAR campaign";
Talk: AGRISAR and EAGLE Campaigns Final Workshop, Noorddwijk; 2007-10-15 - 2007-10-16; in: "Proc. on AGRISAR and EAGLE Campaigns Final Workshop,", (2007), 9 pages.

English abstract:
This paper presents the results of automated extraction of crop characteristics from hyperspectral earth observation data. The data was acquired with an airborne AHS imaging spectrometer in the framework of the joint European AgriSAR 2006 campaign. The AgriSAR campaign was directed by the ESA and took place at the DEMMIN test site in northeast Germany, an agricultural area dominated by large monocultures. An important objective of this campaign was to establish to what degree novel radar and optical technologies are able to provide accurate agro-meteorological parameters for precision farming purposes.
Parameter retrieval in this study was performed with the CRASh approach, a software module based on the inversion of radiative transfer models. CRASh was developed at DLR as part of an automated operative processing chain for future hyperspectral missions. Validation of the model inversion results was performed with field measurements of leaf area index and leaf chlorophyll content which were carried out for winter wheat, winter barley, winter rape, maize, and sugar beet at two time steps during the 2006 growing season. Although spatial patterns of the model results generally coincide with the trends observed in the field, absolute accuracy of the fully automatically extracted variables appeared insufficient for precision agriculture purposes. The unsatisfying results are ascribed to a combination of causes, including angular anisotropy across the swath-width of the flight lines, the configuration of the applied bands, and the large number of model inversion solutions inherent to an automated environment in which little additional information on the observed canopy is present. Employing the airborne version of CRASh and incorporating a priori information on land cover and variable distributions is expected to drastically increase the retrieval performance.

hyperspectral, AHS, imaging spectroscopy, radiative transfer model inversion, CRASh, PROSPECT,

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