Agricultural monitoring with Sentinel-1 and Sentinel-2 data

This half-day tutorial, which will be held on the occasion of  the Applied Geoinformatics for Society and Environment (AGSE) 2019: Digital Landscapes: Chances for Development conference, 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.

Background

A wide range of different and complementary data (RADAR, optical, IR) from Sentinel-1,2,3 and now also from Sentinel-5P are nowadays available with an open and free policy. For some applications, such as agriculture, the synergy between these data has been already shown. For several applications, there has been an increasing interest in jointly using both RADAR and optical data to compensate for the limitations of using single data products alone. The combination of the weather and illumination independence and the sensitivity to the size, density, orientation and dielectric properties of SAR sensors together with the multi-spectral information related to the leaf structure, pigmentation and moisture captured by optical sensors can provide greater insight and context in many areas of application.

Audience and selection

Attendance to the training session is free of charge. However, the application is open only to participants registered to the Applied Geoinformatics for Society and Environment (AGSE) 2019: Digital Landscapes: Chances for Development conference.

Seats will be available on a first-come first-served basis registration.

Please note that the RUS Copernicus Virtual Machines used for this training course can only be provided to citizens/residents of the Copernicus programme member countries (EU plus Iceland and Norway).

Equipment

Attendees will be required to bring their own laptop and will be provided with access to the RUS Copernicus Virtual Machine.

Max no. of participants

25 

Training time 

14:00 - 18:00 

More details about the schedule available soon.

Training date

Friday 13th of September 2019