APEX is a dispersive pushbroom imaging spectrometer operating in the spectral range between 380 - 2500 nm. The spectral resolution will be better than 10 nm in the SWIR and < 5 nm in the VNIR range of the solar reflected range of the spectrum. The total FOV will be ± 14 deg, recording 1000 pixels across track with about 300 spectral bands simultaneously. A large variety of characterization measurements will be performed in the scope of the APEX project, e.g., on-board characterization, frequent laboratory characterization, and vicarious calibration. The retrieved calibration parameters will allow a data calibration in the APEX Processing and Archiving Facility (PAF). The data calibration includes the calculation of the required, time-dependent calibration coefficients from the calibration parameters and, subsequently, the radiometric, spectral and geometric calibration of the raw data. Because of the heterogeneity of the characterization measurements, the optimal calibration for each data set is achieved using a special assimilation algorithm. In the paper the different facilities allowing characterization measurements, the PAF and the new data assimilation scheme are outlined.
Spectro-directional surface measurements can either be performed in the field or within a laboratory setup. Laboratory measurements have the advantage of constant illumination and neglectable atmospheric disturbances. On the other hand, artificial light sources are usually less parallel and less homogeneous than the clear sky solar illumination. To account for these differences and for determining for which targets a replacement of field by laboratory experiments is indeed feasible, a quantitative comparison is a prerequisite. Currently, there exists no systematic comparison of field and laboratory measurements using the same targets.
In this study we concentrate on the difference in spectro-directional field and laboratory data of the same target due to diffuse illumination. The field data were corrected for diffuse illumination following the proposed procedure by Martonchik . Spectro-directional data were obtained with a GER3700 spectroradiometer. In the field, a MFR sun photometer directly observed the total incoming diffuse irradiance. In the laboratory, a 1000W brightness-stabilized quartz tungsten halogen lamp was used. For the first direct comparison of field and laboratory measurements, we used an artificial and inert target with high angular anisotropy. Analysis shows that the diffuse illumination in the field is leading to a higher total reflectance and less pronounced angular anisotropy.
Recently, a joint Swiss/Belgian initiative started a project to build a new generation airborne imaging spectrometer, namely APEX (Airborne Prism Experiment) under the ESA funding scheme named PRODEX. APEX is a dispersive pushbroom imaging spectrometer operating in the spectral range between 380 - 2500 nm. The spectral resolution will be better then 10 nm in the SWIR and < 5 nm in the VNIR range of the solar reflected range of the spectrum. The total FOV will be ± 14 deg, recording 1000 pixels across track with max. 300 spectral bands simultaneously. APEX is subdivided into an industrial team responsible for the optical instrument, the calibration homebase, and the detectors, and a science and operational team, responsible for the processing and archiving of the imaging spectrometer data, as well as for its operation. APEX is in its design phase and the instrument will be operationally available to the user community in the year 2006.
The handling of satellite or airborne earth observation data for scientific applications minimally requires pre-processing to convert
raw digital numbers into scientific units. However depending on sensor characteristics and architecture, additional work may be
needed to achieve spatial and/or spectral uniformity. Standard
higher level processing also typically involves providing orthorectification and atmospheric correction. Fortunately some of the computational tasks required to perform radiometric and geometric calibration can be decomposed into highly independent
subtasks making this processing highly parallelizable. Such
"embarrassingly parallel" problems provide the luxury of being
able to choose between cluster or grid based solutions to perform
these functions. Perhaps the most convenient solutions are grid-based, since most research groups making these kinds of measurements are likely to have access to a LAN whose spare computing resources could be non-obtrusively employed in a grid. However, since many higher level scientific applications of earth observation data might be composed of more highly interdependent subtasks, the parallel
computing resources allocated for these tasks might also be made
available for low level pre-processing as well. We look at two
modules developed for our prototype data calibration processor for
APEX, an airborne imaging spectrometer, which have been implemented
on both a cluster and a grid leading us to be able to make observations and comparisons of the two approaches.
The underlying algorithmic architecture of the level 0 to 1 processing
of the APEX spectrometer is presented. This processing step calculates
the observed radiances in physical units from the recorded raw digital
numbers. APEX will operate airborne and record radiance in the solar reflected wavelength range. The system is optimized for land applciations including limnology, snow, soil, amongst others. The instrument will be calibrated with a flexible setup in a laboratory as well as on-board. A concept for the dynamic update of the radiance calibration coefficients for the APEX spectrometer is presented. The time evolution of the coefficients is calculated from the heterogeneous calibration measurements with a data assimilation technique. We propose a Kalman filter for the initial version of the processor. Additionally, the structure of the instrument model suitable for the analysis of APEX data is developed. We show that this model can be used for the processing of observations as well as for the calculation of calibration coefficients. Both processes can be understood as inverse problems with the same forward model, i.e. the instrument model.
Over the past few years, a joint Swiss/Belgium ESA initiative resulted in a project to build a precursor mission of future spaceborne imaging spectrometers, namely APEX (Airborne Prism Experiment). APEX is designed to be an airborne dispersive pushbroom imaging spectrometer operating in the solar reflected wavelength range between 4000 and 2500 nm. The system is optimized for land applications including limnology, snow, and soil, amongst others. The instrument is optimized with various steps taken to allow for absolute calibrated radiance measurements. This includes the use of a pre- and post-data acquisition internal calibration facility as well as a laboratory calibration and a performance model serving as a stable reference. The instrument is currently in its breadboarding phase, including some new results with respect to detector development and design optimization for imaging spectrometers. In the same APEX framework, a complete processing and archiving facility (PAF) is developed. The PAF not only includes imaging spectrometer data processing up to physical units, but also geometric and atmospheric correction for each scene, as well as calibration data input. The PAF software includes an Internet based web-server and provides interfaces to data users as well as instrument operators and programmers. The software design, the tools and its life cycle are discussed as well.
The high resolution airborne imaging spectrometer APEX (Airborne Prism Experiment) is currently being built. In parallel, its data processing calibration chain is being designed. The complex design of this high resolution pushbroom instrument bears the risk of optical aberrations in the registered spatio-spectral frames. Such aberrations consist of so-called frown and smile effects, as well as ghost image, smear, and stray light contributions. A concept is presented which shall operationally improve image calibration by inversion of the sensor model.
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