We present the Airborne Prism Experiment (APEX), its calibration and subsequent radiometric measurements as well as Earth science applications derived from this data. APEX is a dispersive pushbroom imaging spectrometer covering the solar reflected wavelength range between 372 and 2540nm with nominal 312 (max. 532) spectral bands. APEX is calibrated using a combination of laboratory, in-flight and vicarious calibration approaches. These are complemented by using a forward and inverse radiative transfer modeling approach, suitable to further validate APEX data. We establish traceability of APEX radiances to a primary calibration standard, including uncertainty analysis. We also discuss the instrument simulation process ranging from initial specifications to performance validation. In a second part, we present Earth science applications using APEX. They include geometric and atmospheric compensated as well as reflectance anisotropy minimized Level 2 data. Further, we discuss retrieval of aerosol optical depth as well as vertical column density of NOx, a radiance data-based coupled canopy–atmosphere model, and finally measuring sun-induced chlorophyll fluorescence (Fs) and infer plant pigment content. The results report on all APEX specifications including validation. APEX radiances are traceable to a primary standard with 625 for all spectral bands. Radiance based vicarious calibration is traceable to a secondary standard with ≤6.5% uncertainty. Except for inferring plant pigment content, all applications are validated using in-situ measurement approaches and modeling. Even relatively broad APEX bands (FWHM of 6nm at 760nm) can assess Fs with modeling agreements as high as R2=0.87 (relative RMSE=27.76%). We conclude on the use of high resolution imaging spectrometers and suggest further development of imaging spectrometers supporting science grade spectroscopy measurements

Dry grassland sites are amongst the most species-rich habitats of central Europe and it is necessary to design effective management schemes for monitoring of their biomass production. This study explored the potential of hyperspectral remote sensing for mapping aboveground biomass in grassland habitats along a dry-mesic gradient, independent of a specific type or phenological period. Statistical models were developed between biomass samples and spectral reflectance collected with a field spectroradiometer, and it was further investigated to what degree the calibrated biomass models could be scaled to Hyperion data. Furthermore, biomass prediction was used as a surrogate for productivity for grassland habitats and the relationship between biomass and plant species richness was explored. Grassland samples were collected at four time steps during the growing season to capture normally occurring variation due to canopy growth stage and management factors. The relationships were investigated between biomass and (1) existing broad- and narrowband vegetation indices, (2) narrowband normalized difference vegetation index (NDVI) type indices, and (3) multiple linear regression (MLR) with individual spectral bands. Best models were obtained from the MLR and narrowband NDVI-type indices. Spectral regions related to plant water content were identified as the best estimators of biomass. Models calibrated with narrowband NDVI indices were best for up-scaling the field-developed models to the Hyperion scene. Furthermore, promising results were obtained from linking biomass estimations from the Hyperion scene with plant species richness of grassland habitats. Overall, it is concluded that ratio-based NDVI-type indices are less prone to scaling errors and thus offer higher potential for mapping grassland biomass using hyperspectral data from space-borne sensors.