Sentinel data processing and applications
During this 2 day hands-on training session, participants will access a Virtual Machine from their own laptop to exploit the open source toolboxes available in the RUS environment to download and process Sentinel data.
Day 1 - Morning
- Introductory session
Day 1 - Afternoon
- RUS hands-on excercise (Agriculture) - In this tutorial, we aim to introduce Optical (Sentinel-2) and SAR (Sentinel-1) data feature level fusion for crop monitoring. We will use a time series of optical and SAR GRD data to demonstrate how multi-temporal and multi-sensor approach can increase the accuracy of our classification. Optical data provide accurate land cover/crop classifications if the cloud free acquisitions time series is large enough and if the acquisition dates are taken in the suitable period of the year. However, in study areas with extensive cloud cover, the number of available optical images could be insufficient for accurate classification. SAR time series from Sentinel-1 can be used combined with the optical Sentinel-2 data in these cases to improve the results.
Day 2 - Morning
- RUS hands-on excercise (Ship Detection) - In this session we will employ the ESA SNAP Sentinel-1 Toolbox to demonstrate an example of maritime surveillance using Sentinel-1 satellite-borne Synthetic Aperture Radar (SAR). Depending on the resolution of the SAR data used, earth observation satellites may detect vessels not carrying tracking systems on board, for example small fishing ships. Moreover, SAR is not reliant on solar illumination and is rather independent of weather conditions, therefore enabling frequent monitoring. In addition, what distinguishes Sentinel-1 as a valuable tool for marine surveillance, compared to other SAR satellites, is a routine collection of a large amount of data (as opposed to acquisitions on demand) that are made freely available.
Day 2 - Afternoon
- RUS hands-on excercise (Ocean Colour) - In this session we will employ the ESA SNAP Sentinel-3 Toolbox to demonstrate the methodology for the detection and mapping of phytoplankton blooms. Algal blooms are indicators of marine ecosystem health; thus, their monitoring is a key component of effective management of coastal and oceanic resources. Sentinel-3 Ocean and Land Colour Instrument (OLCI) imagery provides continuous, high frequency water quality monitoring of coastal waters.
More info and registration here.
Attendees must bring their own laptop and will be provided with access to the RUS platform Virtual Machine.
WeWork - 2 Dublin Landings
N Wall Quay, North Dock, Dublin D1
Cost & Participation
Attendance to this RUS training course is subject to the Space Industry Skillnet fee to cover expenses such as the training venue, catering and logistics and do not constitute an income for RUS organizers or ESA.
Thursday 28th of March 2019 - Friday 29th of March 2019
Application opening - closing
Friday 1st of March 2019 - Saturday 9th of March 2019
Application is closed
LocationNorth Wall Quay
N Wall Quay, North Dock, Dublin, Ireland