Faculty of Mathematics and Natural Sciences - Earth Observation Lab


The forthcoming Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission which will provide a means for frequent global sampling of high quality imaging spectroscopy data. The Earth Observation Lab contributes to the scientific preparation of the EnMAP mission through our participation in the EnMAP Science Advisory Group (EnSAG), the EnMAP Core Science Team (ECST), and the EnMAP-Box project.
person contours and gear wheels associating group workEnMAP Science Advisory Group

It is the objective of the EnSAG to support the communication with the wider science community of EnMAP and to ensure scientific state of the art data exploitation. In this context the EnSAG shall be dedicated to the strategic planning and management of scientific algorithm and application development outside the ground processing. More...

Joshua treeEnMAP Core Science Team Project

With our ECST project “Mapping Vegetation under Global Change”, the Earth Observation Lab is at the forefront of research into hyperspectral image analysis and realizing its potential in helping us monitor and assess natural and semi-natural ecosystems in the context of environmental change and disturbance. More...

letters EnMap Box with spectral color radiance from the A downwardsEnMAP-Box Project

The EnMAP Box is a freely available, platform-independent software designed to visualize and process hyperspectral remote sensing data, and particularly developed to handle data from the EnMAP sensor. It is programmed in Python and provided as a plug-in for QGIS as free and open source software (FOSS). More...
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Selected Publications

van der Linden S, Okujeni A, Canters F, Degerickx J, Heiden U, Hostert P, Priem F, Somers B, Thiel F (2018). Imaging Spectroscopy of Urban Environments. Surveys in Geophysics, pages pending. https://doi.org/10.1007/s10712-018-9486-y.

Okujeni A, Canters F, Cooper SD, Degerickx J, Heiden U, Hostert P, Priem F, Roberts DA,  Somers B, van der Linden S (2018). Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Remote Sensing of Environment, 216, 482-496.  https://doi.org/10.1016/j.rse.2018.07.011

Guanter, L., et al. (2015). The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sens. 2015, 7(7), 8830-8857; doi: 10.3390/rs70708830

Leitão, P., Schwieder, M., Suess, S., Okujeni, A., Galvão, L., Linden, S., Hostert, P. (2015). Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP. Remote Sens. 2015, 7(10), 13098-13119; doi: 10.3390/rs71013098

Okujeni, A.; van der Linden, S.; Hostert, P. (2015). Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning. Remote Sensing of Environment, 158, 69-80.

Suess, S., van der Linden, S., Okujeni, A., Leitão, P., Schwieder, M., Hostert, P. (2015). Using Class Probabilities to Map Gradual Transitions in Shrub Vegetation from Simulated EnMAP Data. Remote Sens. 2015, 7(8), 10668-10688; doi: 10.3390/rs70810668

van der Linden, S., Rabe, A., Held, M., Jakimow, B., Leitão, P., Okujeni, A., Schwieder, M., Suess, S., & Hostert, P. (2015). The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing. Remote Sensing, 7, 11249