Agri-12: Screencast-04 LMU - Validate your retrieval results with in situ data
Channel: HYPERedu
Date: 2022-12-12

The following video is “hands on”, meaning you can actively participate in the exercises! The software and data can be downloaded for free and we will guide you step by step. Please refer to the course below for details on data download and ensure that you are following the screencasts in the correct order (this is the fourth and last).
Check out our other tutorials on Youtube and get to know the **free** EnMAP-Box software:
Basic-22: Hands-on training: EnMAP-Box Installation: https://www.youtube.com/watch?v=_SNbLmB8aCQ
Basic-23: Hands-on training: EnMAP-Box Intro: https://www.youtube.com/watch?v=31FQ5zXl2Rw

**** FREE ONLINE COURSE ****
'Beyond the Visible – Imaging Spectroscopy for Agricultural Applications' is an advanced short course on Hyperspectral Remote Sensing particularly for agricultural applications and is presented by HYPERedu and EO-College. 

OPEN FROM 12 DECEMBER 2022, 08:00 GMT 

Register now: https://eo-college.org/courses/beyond-the-visible-imaging-spectroscopy-for-agricultural-applications/

Building on the fundamentals covered in the Basic MOOC ‚Beyond the Visible – Introduction to Hyperspectral Remote Sensing’, this course teaches the application of imaging spectroscopy in an agricultural context. Participants are introduced to the physicochemical basics of leaf and canopy reflectance, biophysical and biochemical variables known as 'vegetation traits', various data sources, specific methods commonly used in agricultural applications and open-source software to process imaging spectroscopy data, all while comprising several hands-on training exercises. 
The course was developed by GFZ Potsdam and LMU Munich as part of the HYPERedu initiative. 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.

Note: You might find that your results differ from Tobis. Don’t worry, that’s normal and we can explain why: On the one hand, this is due to the fact that while the training database (the LUT file) is compiled according to the same statistical criteria, the dice are rolled anew each time you run the tool and the resulting training database can be very different, despite identical parameterization. Another deviation can happen when training the model, because the ANN does not necessarily pick the same data out of the training dataset each time. And finally, numerical uncertainties can occur when inverting the ANN (though they should be rather small in comparison). 

**** For FURTHER INFORMATION we recommend ****
Introduction to the EnMAP-Box
• General information on the EnMAP-Box, a free and open source python plugin for QGIS: https://www.enmap.org/data_tools/enmapbox/
• Download, documentation and tutorials of and with the EnMAP-Box:  https://enmap-box.readthedocs.io/
EnMAP-Box Agricultural Apps
• General information on the “Agricultural Apps” toolbox and guidance on how to apply the individual tool: https://enmap-box-lmu-vegetation-apps.readthedocs.io/en/latest/