Ongoing projects

ChaKe

The main objective of the "Use of satellite imagery for monitoring changes in kelp forests" (ChaKe) project is to explore the potential of using satellite imagery as a tool for mapping and monitoring the spatial and temporal distribution of kelp forests. Sentinel-2 MSI observations and in-situ kelp observations will be used to calibrate machine learning models for mapping kelp coverage in Norwegian coastal waters at 10 m spatial resolution.  

Roadmap CIMR CRISTAL

The project «National roadmap for CIMR and CRISTAL» aims to develop a roadmap for how Norway can best utilise data from the CIMR and CRISTAL satellites. It will support Norwegian research, industry, and management, ensure access to key climate data, and strengthen Norway’s role in European space collaboration, particularly in monitoring the ocean, ice, and climate in polar regions.

C3S2_375

The «Development of C3S Decadal Prediction Service» project will deliver high-quality decadal forecasts from five leading climate systems, develop software for data standards and post-processing, and co-create tailored products with users. Researchers will also explore how such forecasts can be used in the event of major and unexpected climate disruptions, such as volcanic eruptions.

S1-POP

The aim is to develop advanced "Sentinel-1 processor for ocean products" (S1-POP), improving accuracy and adding new variables for operational and scientific users. The project focuses on enhancing sea state measurements, quantifying Surface Current Radial Velocity, harmonizing wind and wave retrievals, and introducing new SAR-derived parameters like rain rate and sea-ice concentration to optimize ocean state estimation.

ORBITAS

The main objective of the project "Orbit impact on sea-level trend and acceleration stability uncertainty" is to evaluate how a new reference orbit for Sentinel-6 Next Generation will impact the current mean sea level uncertainty.

FRACCEO

The project "Flood Risk Assessment in a Changing Climate for Cities using EO" focuses on the development of a methodology that combines Earth Observation data, specifically the Essential Climate Variable datasets from the ESA Climate Change Initiative programmes, with hydrodynamic models to improve the prediction of extreme water levels at the coast and subsequently the assessment of flood risk in coastal cities under climate change.

VIBRIO

In the project “Occurrence and drivers of Vibrio bacteria”, researchers will generate the data to understand how remote sensing can identify predictors for Vibrio blooming, a rapid increase in Vibrio-bacteria in seawater. The abundance of Vibrio and bacterial population structures will be described through cultivation methods from samples coming from both seafood (bivalves) and seawater (main routes for human infections). The observed bacterial data will be mapped against remote sensing observations (e.g., sea surface temperature) with a 1-2 week lag before sampling to analyse whether remote sensing can indicate potential Vibrio blooming.

DRAGON6

"DRAGON 6 - Lofoten eddies on Atlantic Water" is a collaborative program between ESA and MOST (Ministry of Science and Technology) in China with a focus on using measurements from satellites (Copernicus Sentinels, ESA, Chinese) in geoscience and monitoring. There are Chinese and European collaborative teams participating that address different topics within Earth system research, including oceanography, meteorology, geology, ecosystem, environment and climate.

COASTALDYN

The project addresses key scientific and observational challenges in understanding coastal ocean dynamics. The project "Coastal Dynamics Land-Ocean" aims combine data from the growing European Earth Observation satellite fleet, particularly Sentinel missions, combined with in-situ observations and advanced algorithms and IA techniques, to develop high-resolution, multi-mission products describing coastal dynamics, e.g., sea level, currents, wind, AND waves.

SAGE

The "Sea Ice Age and Drift" project will deliver the first global, long-term Climate Data Records for sea-ice age and drift, spanning the satellite era since the late 1970s. It will combine Lagrangian tracking with satellite-based classification to generate a hybrid sea-ice-age algorithm, extend drift records before 1991, and integrate multi-sensor data from passive microwave and scatterometer missions. Machine learning will enhance performance under challenging conditions such as melt–refreeze cycles and pancake ice regions. Products will be validated with in different data, include uncertainty estimates, and be applied in case studies to advance sea-ice research and climate model evaluation.