The Future Spaceborne Hyperspectral Imager EnMAP: Its Calibration, Validation, and Processing Chain

The Applied Remote Sensing Cluster of the German Aerospace Center (DLR) is responsible for the establishment of the payload ground segment for the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program), which is planned to be launched in 2012. EnMAP covers the spectrum from 420 nm to 2450 nm with a spectral resolution of at least 10 nm and a spatial resolution of 30 m x 30 m with a swath width of 30 km. The primary goal of EnMAP is to quantify and analyze diagnostic parameters describing key processes on the Earth’s surface. To achieve high-quality and consistent data with respect to the same and other missions, extensive calibration and validation activities are foreseen during the five years of mission operations. The calibration results will be integrated in the processing chain to obtain standardized products, which include radiometric, geometric, and atmospheric correction. Here we focus on the following three aspects of the EnMAP mission: (a) analysis of data of the various calibration sources, (b) geometric processing with precise orbit and attitude data as well as atmospheric correction, and (c) supporting ground, airborne, and spaceborne campaigns to assess the quality of the output data delivered by the processing chain.

[1]  U. Benz,et al.  The EnMAP hyperspectral imager—An advanced optical payload for future applications in Earth observation programmes , 2006 .

[2]  Timo Stuffler,et al.  EnMAP A Hyperspectral Sensor for Environmental Mapping and Analysis , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[3]  Andreas Neumann,et al.  Resume of seven-year MOS in-orbit calibration: events, effects, and explanations , 2003, SPIE Optics + Photonics.

[4]  Kurtis J. Thome,et al.  A generalized approach to the vicarious calibration of multiple Earth observation sensors using hyperspectral data , 2001 .

[5]  Andreas Neumann,et al.  Experience and results of long-term in-orbit sun calibration of the Modular Optoelectronic Scanner (MOS) on the Indian IRS-P3 mission , 1998, Remote Sensing.

[6]  R. Richter,et al.  Correction of satellite imagery over mountainous terrain. , 1998, Applied optics.

[7]  J. Huisman The Netherlands , 1996, The Lancet.

[8]  R. Richter A spatially adaptive fast atmospheric correction algorithm , 1996 .

[9]  Martin Bachmann,et al.  Including Quality Measures in an Automated Processing Chain for Airborne Hyperspectral Data , 2007 .

[10]  Peter Reinartz,et al.  AUTOMATIC PRODUCTION OF A EUROPEAN ORTHOIMAGE COVERAGE WITHIN THE GMES LAND FAST TRACK SERVICE USING SPOT 4/5 AND IRS-P6 LISS III DATA , 2007 .

[11]  Peter Reinartz,et al.  EVALUATION OF SPACEBORNE AND AIRBORNE LINE SCANNER IMAGES USING A GENERIC ORTHO IMAGE PROCESSOR , 2005 .

[12]  R. Richter,et al.  Implementation of the Automatic Processing Chain for ARES , 2005 .

[13]  Thomas Heege,et al.  Mapping Aquatic Systems with a Physically Based Process Chain , 2004 .

[14]  S. M. de Jong,et al.  Imaging spectrometry : basic principles and prospective applications , 2001 .

[15]  P. Reinartz,et al.  Intercalibration of Optical Satellites , 2000 .

[16]  M. Lehner,et al.  Semi-Automatic Derivation of Digital Elevation Models from Stereoscopic 3-Line Scanner Data , 1992 .