The use of OLCI Sentinel-3 data for assessing the effect of climate change in coastal and inland waters

The main hypothesis for the PhD project is that climate change leads to an increase in precipitation in Northern Europe, and generally to more frequent storm events.

The latter cause increased erosion and run-off of coloured dissolved organic matter (CDOM) and suspended particulate matter (SPM) into lakes and coastal waters.

Some of the level 2 products from ocean colour data (such as Chlorophyll-a and reflectance) have already been adopted as essential climate change variables by the World Metrological Organization (WMO).

Besides this, turbidity and humic substances can also act as important indicators of climate change events (indicating e.g. increased frequency of torrential rainfall followed by flooding events).

Combined with MERIS, OLCI data can be used for long-term monitoring purposes, providing unprecedented temporal and spatial coverage.

By applying the same processors to MERIS and OLCI data, one can go back in time and use trend analysis to e.g. assess the impact of climate change on e.g. coastal turbidity.Another important part of the project is to improve the use of satellite data for societal needs.

In order to deliver reliable products to the end-user community we need to have good validation systems in place which allow to both validate and tune the algorithms and processors for this specific region (e.g. via improved description of the inherent optical properties).

We will support the Ocean Colour Thematic Assembly Centre of the Copernicus Marine Environment Monitoring Service (CMEMS) with validation data for the OLCI Neural Network Swarm (ONNS) as well as assessing products from the Swedish Space Data Lab.Our Group is member of EUMETSAT’s Sentinal-3 Validation team with focus on optically-complex waters (with a high content of CDOM).

We validate state of the art processors (adapted for coastal and inland waters) and products, and assess their uncertainties and report the results to ESA/EUMETSAT for the required improvements over absorbing waters. Our group has also expertise in developing regional algorithms based on optical in situ measurements.

These algorithms can be applied to OLCI Sentinel-3 and MERIS data in order to do long-term assessment of water quality, providing crucial information on the ecological status of water bodies and the effects of climate change.

We will use some of our own regional algorithms developed in previous SNSA projects as well as other published algorithms and products from the Ocean Colour Climate Change Initiative portal (Ocean Colour – CCI) to evaluate long-term trends in ocean colour from 1998 onwards in the Baltic proper, the Bothnian Sea and Lake Vänern.

These waters are challenging from a remote sensing point of view, because of the very high CDOM absorption, leading to a low remote sensing reflectance, and thus a low signal-to-noise ratio, especially in the blue.This PhD project is an important component of the ongoing research project ‘The use of Copernicus Sentinel-3 data for research and societal needs’ (Dnr. 2021‐00064) which unfortunately was only partially funded by the SNSA.

This complementary project will allow to fulfil the aims and objectives of the project.

Marine remote sensing is a very interdisciplinary field which requires a large range of expertise, spanning from bio-optics to ocean colour theory, as well as image analysis.

The PhD position will fill the current gap in the group regarding remote sensing and image analysis.The planned PhD project addresses the UN´s global environmental Goal 13 "Climate action" as well as Goal 14 "Life below water".

The project also addresses several of the Swedish environmental goals, e.g. "Reduced climate impact", "A balanced sea" and "No eutrophication".

We have already set up collaborations with end-users, i.e. the Swedish Agency for Water and Coastal Management, as well as the County Boards in Stockholm and Norrbotten. We also collected validation data within the Swedish monitoring program during 2019-20