ENGEOMAP — A GEOLOGICAL MAPPING TOOL APPLIED TO THE ENMAP MISSION

  • Christian Rogaβ Helmholtz Centre Potsdam–GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany
  • Karl Segl Helmholtz Centre Potsdam–GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany
  • Christian Mielke Helmholtz Centre Potsdam–GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany
  • Yvonne Fuchs Helmholtz Centre Potsdam–GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany
  • Hermann Kaufmann Helmholtz Centre Potsdam–GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany

Abstract

Hyperspectral imaging spectroscopy offers a broad range of spatial applications that are primarily based on the foregoing identification of surface cover materials. In this context, the future hyperspectral sensor EnMAP will provide a new standard of highly qualitative imaging spectroscopy data from space that enables spatiotemporal monitoring of surface materials. The high SNR of EnMAP offers the possibility to differentiate and to identify minerals that are showing characteristic absorption features as a 30 m × 30 m spatial mixture in the visible, the near infrared and the short wave infrared range (0.4–2.5 μm). For this purpose, spectral mixture analysis (SMA) approaches are traditionally used. However, these approaches lack in transferability, repeatability and inclusion of sensor characteristics. Additionally, they rely on imagebased and randomly detected endmembers as well as on in situ or laboratory spectra that are not spatially stable in case of an imagebased extraction and are assumed to be spectrally pure. In this work, a new framework is proposed that addresses these limitations considering the EnMAP sensor characteristics. It is named EnMAP. Geological Mapper — EnGeoMAP. It consists of several new and adapted approaches to identify spectrally homogeneous regions. In parallel, minerals are identified and semiquantified by a sensor related and knowledgebased fitting approach. Supplementary outputs are abundance, classification, homogeneity and uncertainty maps. First results show that the proposed approach offers 100% repeatability and gains an identification error for minerals of about 2% on average for different studies. In this work, an approach is proposed that aims on spectroscopic mineral modelling by image synthesis that might be applied for geological mapping.
Section
Fundamentals of remote sensing