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The Ocean and Land Colour Instrument (OLCI) provides spectral information on the colour of the oceans. This data can be used to monitor global ocean primary production by phytoplankton, the basis of nearly all life in our seas.
Ocean colour data is also vital to understand climate change — ocean colour is one of the Essential Climate Variables listed by the World Meteorological Organization to detect biological activity in the ocean’s surface layer. Phytoplankton take up carbon dioxide (CO2) during photosynthesis, making them important carbon sinks. Ocean colour data can be used to monitor the annual global uptake of CO2 by phytoplankton on a global scale. Using this data we can study the wider Earth system, for instance the El Niño/La Niña phenomena and how these impacts the ocean ecosystem.
Beyond climate, ocean colour data is also useful to look at more sporadic events. OLCI data can be used track sediment transport, monitor coastal water quality and track and forecast harmful algal blooms that are a danger to humans, marine/freshwater life and aquaculture.
The global picture of ocean ecosystems provided by ocean colour data can guide sustainable marine resource management and support reporting obligations of the European Union's legislation within Marine Strategy Framework Directive and Water Framework Directive, the goal of which is to achieve or maintain Good Environmental Status of the seas by the year 2020
Further information on the sensor and its data can be found in the Sentinel-3 section of our User Portal.
The learn-olci module consists of a collection of Python-based Jupyter notebooks designed to demonstrate the capability of the Ocean and Land Colour Instrument (OLCI), carried by the Sentinel-3 satellites, and to help users begin to work with its data at level-1B and level-2. The module will introduce you to:
- the specifics of the OLCI sensor
- the ways in which you can access OLCI data
- the parameters that OLCI provides at level-1b and level-2
- approaches to working with OLCI data in common use cases
This module is based around a series of Jupyter Notebooks. These support high-level interactive learning by allowing us to combine code, text descriptions and data visualisations. If you have not worked with Jupyter Notebooks before, please look at the
Introduction to Python and Project Jupyter module to get a short introduction to their usage and benefits. Refer to this module's README for further information on how to set up Jupyter.
This module is split in to two parts, designed for:
- introductory users, who have basic knowledge of Python and are new to using OLCI data
- advanced users, who may be looking into more advanced use cases for the data.
There is a suggested order in which to access the notebooks, and pre-requisites where applicable are listed at the top of each notebook. However, not all notebooks may be relevant for every user, so we have tried to make each one as self contained as possible.
From this module, you can expect to learn:
- How to access OLCI data from both the EUMETSAT data services, and WEkEO
- The general file structure of OLCI products, and how to read them using Python
- How OLCI captures the spectral reflectance of the ocean, and how this is used to derive geophysical products
- How to visualise OLCI data products, applying appropriate flags
- The limitations and uncertainties associated with OLCI products
- How to apply a SNAP based processor to OLCI data, using Python.