This half-day tutorial will be held on Friday September 13th, on the occasion of the AGSE 2019 Conference – Applied Geoinformatics for Society and Environment – Digital Landscapes : Chances for Development Conference, in Stuttgart (11 – 14 Sept. 2019).
It is available only for the registered participants to the AGSE 2019 Conference.

It will demonstrate the usage of Open Tools (ESA SNAP; QGIS; R) available within the RUS environment to run a supervised classification over an agricultural area using Sentinel-1 GRD and Sentinel-2 products. You will be able to choose between two of the most relevant machine learning algorithms used for this purpose: Random Forest and Support Vector Machine (SVM).
A multi-temporal and multi-sensor pixel-based data fusion approach will be used in various scenarios to analyse the influence of different input data in the final classification accuracy. The data fusion will be implemented at feature level (fusion of images is done before applying the core processing task, e.g. classification).
You will also get familiar with SAR and optical data pre-processing using batch processing in SNAP and its command-line implementation (GPT) as well as using existing R scripts implemented in a Graphical User Interface (GUI) within QGIS.
For more information, visit our Training website :

Agricultural monitoring with Sentinel-1 and Sentinel-2 data

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