Agri-07: Imaging Spectroscopy for Agricultural Applications - Hybrid Models
Channel: HYPERedu
Date: 2022-12-12

In our free online course, we focus on the application of hybrid methods as they combine the flexibility of parametric approaches with the physics encoded in machine learning algorithms. Therefore, in the following video, Charly will be a bit more specific on the definition of “hybrid methods”, what they include and which models are frequently used.
Check out our hands-on tutorials on YouTube and learn how to apply hybrid retrieval techniques yourself!

**** 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 ****
• Baret, F., Buis, S., 2008. Estimating canopy characteristics from remote sensing observations: Review of methods and associated problems, Advances in land remote Sensing. Springer, Dordrecht, Netherlands, pp. 173-201.
• Berger, K., Atzberger, C., Danner, M., D’Urso, G., Mauser, W., Vuolo, F., Hank, T., 2018. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sens. 10, 85.
• Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C., Ustin, S.L., 2009. PROSPECT + SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 113, Supplement 1, S56-S66. 
• Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., Benediktsson, J., 2019. Deep Learning for Hyperspectral Image Classification: An Overview. IEEE Trans. Geosci. Remote Sens. PP, 1-20.
• Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A., 2019. Deep learning in remote sensing applications: A meta-analysis and review. Isprs J. Photogramm. 152, 166-177.
• Verrelst, J., Camps-Valls, G., Muñoz-Marí, J., Rivera, J.P., Veroustraete, F., Clevers, J.G.P.W., Moreno, J., 2015. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review. Isprs J. Photogramm. 108, 273-290.
• Verrelst, J., Malenovský, Z., Van der Tol, C. et al. (2019): Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. Surveys in Geophysics. 40, 589–629. https://doi.org/10.1007/s10712-018-9478-y
• Weiss, M., Jacob, F., Duveiller, G., 2020. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ. 236, 111402.
• Xue, J., Su, B., 2017. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors 2017, 1353691.

**** You can find almost all FIGURES OF THIS VIDEO in the HYPERedu slide collection ****

K. Berger, J. Verrelst, C. Atzberger, M. Weiss, S. Steinhauser, M. Wocher, S. Foerster, T. Kuester (2020). Retrieval approaches of vegetation traits from imaging spectroscopy data, HYPERedu, EnMAP education initiative, September 2020, LMU Munich.

Available in the EO-College hyperspectral resources section: https://eo-college.org/resource/retrieval-approaches-of-vegetation-traits-from-imaging-spectroscopy-data/