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.