MOTrainer
Measurement Operator Trainer for data assimilations purposes.
Global vegetation water dynamics using radar satellite data
During this project, we developed an AI-based workflow to improve the understanding of how vegetation regulates land-atmosphere exchanges of water, carbon, and energy. The study utilized data from the Advanced Scatterometer on the MetOp satellite series, which enabled monitoring of radar backscatter as a function of incidence angle, reflecting soil moisture, vegetation structure, and the moisture content of vegetation constituents. For the first time, this information was used to study global vegetation water dynamics.
The study has contributed to the fields of hydrology, water management, and climate science. The satellite radar observations provided unique insights into how water is transferred from the soil to the atmosphere through vegetation and how this process is regulated by vegetation’s physiological response to environmental conditions.
MIcrowaves for a New Era of Remote sensing of Vegetation for Agricultural monitoring
Delivering a pangenome approach that drastically improves the analytical power on plant data
Chasing shadows to investigate glacier change worldwide
Using remote sensing to develop damage indicators across all Antarctic ice shelves
Demonstrating the potential of European Sentinel satellite data
Advanced data science to assist the design of cleaner, safer and smarter ships
eScience infrastructure for ecological applications of LiDAR point clouds
Arctic impact on weather and climate
Metrics and Access to Global Indices for Climate Projections
Translating weather extremes into the future – a case for Norway
The current decline of global biodiversity
The country below sea level
Processing large datasets on consumer-grade computers
Environmental re-analysis of urban areas: quantifying high-resolution energy and water budgets of...
Virtual laboratories for inspiration and discovery in ecology
Measurement Operator Trainer for data assimilations purposes.