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Proceedings Paper

Preparatory analyses and development of algorithms for agricultural applications in the context of the EnMAP hyperspectral mission
Author(s): K. Richter; T. Hank; W. Mauser
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Paper Abstract

With the launch of the German hyperspectral satellite mission 'Environmental Mapping and Analysis Program' (EnMAP), anticipated in 2014, unprecedented opportunities will open up for a wide range of applications. Along with different areas of application, the agricultural sector will particularly benefit from the availability of such observation capability. Information about state and dynamics of the (non-)vegetated land surface, expressed by biophysical variables, is required for instance in irrigation water determination, stress detection or in advanced crop production modeling. In the context of the mission, a toolbox will be provided to determine these variables from hyperspectral imagery. Algorithms to be implemented will range from empirical methods, such as hyperspectral vegetation indices, to physically based approaches, involving the inversion of canopy reflectance models. In this study, potential techniques for the EnMAP toolbox are selected and tested using data from two field campaigns conducted in two different geographic regions. One of the campaigns was carried out in summer 2009 at the German agricultural 'Landau test site' as a first step towards the scientific preparation of the EnMAP mission. During the campaign, data of the airborne hyperspectral scanner HyMap were acquired concurrently with ground measurements of canopy water content and other variables. The second campaign was conducted in the Cuga river basin in Sardinia (Italy) during summer 2007. First results of data analyses will be presented and discussed, emphasizing in particular the benefits of multi-temporal and multi-seasonal hyperspectral data availability over current operational systems.

Paper Details

Date Published: 22 October 2010
PDF: 11 pages
Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 782407 (22 October 2010); doi: 10.1117/12.864217
Show Author Affiliations
K. Richter, Ludwig-Maximilians-Univ. München (Germany)
T. Hank, Ludwig-Maximilians-Univ. München (Germany)
W. Mauser, Ludwig-Maximilians-Univ. München (Germany)


Published in SPIE Proceedings Vol. 7824:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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