Basic-02: Expert interview: application field "soils and geology"
Channel: HYPERedu
Date: 25/11/2021

What is the actual benefit of hyperspectral data? Hear what Prof. Dr. Sabine Chabrillat (professor for “digital soil mapping” at Leibniz University Hanover, Institute of Soil Sciences, German Research Centre for Geosciences – GFZ in Potsdam and Principal Investigator of the EnMAP satellite mission) thinks about the advantages of hyperspectral imagery and current challenges for imaging spectroscopy in the context of soils and geology.

**** FREE ONLINE COURSE ****

‘Beyond the Visible – Introduction to Hyperspectral Remote Sensing‘ is the first Massive Open Online Course (MOOC) on Hyperspectral Remote Sensing presented by HYPERedu and EO-College
 
OPEN FROM 24 NOVEMBER 2021, 08:00 am GMT 

Register now: https://eo-college.org/courses/beyond-the-visible/

The course teaches the principles of imaging spectroscopy, sensor technologies and data acquisition techniques, hyperspectral data processing approaches and data sources and provides expert-guided hands-on training. 

The course was developed by GFZ Potsdam as part of the HYPERedu initiative with support from experts at various partner institutions. The development was funded within the EnMAP science program under the German Space Agency at DLR with resources from the German Federal Ministry of Economic Affairs and Energy.

**** For FURTHER INFORMATION we recommend ****

A selection of own publications including the ones that were mentioned in the interview:
• Chabrillat S., Ben-Dor E., Cierniewski J., Gomez C., Schmid T., van Wesemael B. (2019): Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics. 40, 361–399. https://doi.org/10.1007/s10712-019-09524-0 
• Chabrillat S., Guillaso S., Rabe A., Foerster S., Guanter L. (2016): From HYSOMA to ENSOMAP—a new open source tool for quantitative soil properties mapping based on hyperspectral imagery from airborne to spaceborne applications. General Assembly European Geosciences Union, Vienna, Austria, 2016, Geophysical Research Abstracts, Vol 18, EGU2016-14697
• Chabrillat S., Eisele A., Guillaso S., Rogaß C., Ben-Dor E., Kaufmann H. (2011): HYSOMA: an easy-to-use software interface for soil mapping applications of hyperspectral imagery. In: Proceedings 7th EARSeL SIG imaging spectroscopy workshop, Edinburgh, Scotland
• Guanter L., Kaufmann H., Segl K., Foerster S., Rogass C., Chabrillat S., Kuester T., Hollstein A., Rossner G., Chlebek C., Straif C., Fischer S., Schrader S., Storch T., Heiden U., Mueller A., Bachmann M., Mühle H., Müller R., Habermeyer M., Ohndorf A., Hill J., Buddenbaum H., Hostert P., Van der Linden S., Leitão P.J., Rabe A., Doerffer R., Krasemann H., Xi H., Mauser W., Hank T., Locherer M., Rast M., Staenz K., Sang B. (2015): The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sens. 2015, 7(7), 8830-8857. https://doi.org/10.3390/rs70708830 
• Mielke C., Rogaß C., Bösche N., Segl K., Altenberger U. (2016): EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission. Remote Sensing, 8, 2, 127. https://doi.org/10.3390/rs8020127 
• Milewski, R. (2013): Analyses of hyperspectral and LiDAR data toward the morphological assessment of erosion stages of soils in semi-arid Spain. Master Thesis. Freie Universität Berlin, Berlin.
• Steinberg A., Chabrillat S., Stevens A., Segl K., Foerster S. (2016): Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution. – Remote Sensing, 8, 7, 613. https://doi.org/10.3390/rs8070613 
• Ward K.J., Chabrillat S., Brell M., Castaldi F., Spengler D., Foerster S. (2020): Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR. Remote Sensing. 12(20), 3451. https://doi.org/10.3390/rs12203451