In this video, we will focus on quantitative approaches in the soil context and take a closer look at how the algorithms work. We will have a look at the two methodological categories spectral feature analysis and chemometrics, which include algorithms such as multivariate statistics, artificial intelligence, machine learning, and most recently deep learning. In the end, we will present an overall workflow for the retrieval of soil properties using imaging spectroscopy data.
**** FREE ONLINE COURSE ****
This MOOC ‘Beyond the Visible – Imaging Spectroscopy for Soil Applications’ is an advanced short course on the application of hyperspectral imaging spectroscopy for soil applications. The course is designed for advanced users and is offered by HYPERedu and EO-College.
OPEN FROM 12th June 2024, 12:00 GMT
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This course teaches the principles of imaging spectroscopy for soil applications and techniques for the hyperspectral retrieval of soil properties.
This course was created by the 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 Climate Action.
**** For FURTHER INFORMATION we recommend ****
Chabrillat, S., Eisele, A., Guillaso, S., Rogaß, C., Ben-Dor, E. and Kaufmann, H. (2011), HYSOMA: An easy-to-use software interface for soil mapping applications of hyperspectral imagery, Proceedings of the 7th EARSeL SIG Imaging Spectroscopy Workshop, Edinburgh, Scotland, UK, 11-13 April 2011, On CD-Rom, 7pp.
