Description
This presentation summarises recent activities to observe grassland features using remote sensing and uses this data to feed mechanistic simulation models for temperate grassland vegetation in Central Europe, in order to assess underlying processes that are difficult to observe. Public interest has recently focused on grassland ecosystem services, such as carbon stocks, nitrate retention and greenhouse gas emissions; variables that in principle can be simulated using models. However, current grassland models suffer from the fact that species dynamics in grasslands are very active, and may change in response to water supply and management. As different species come with different traits, a uniform representation of grassland in models is often not wise. This contribution highlights the potential of remote sensing in this problem space and gives an overview of where we stand now when aiming at predicting grass biomass yields and quality features in heterogeneous landscapes of temperate climate.
DOI
https://doi.org/10.13023/5c9y-6t25
Citation
Nendel, C., "Observing and Simulating Temperate Grasslands in Central Europe" (2024). IGC Proceedings (1993-2023). 58.
https://uknowledge.uky.edu/igc/XXV_IGC_2023/Sustainability/58
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Plant Biology Commons, Plant Pathology Commons, Soil Science Commons, Weed Science Commons
Observing and Simulating Temperate Grasslands in Central Europe
This presentation summarises recent activities to observe grassland features using remote sensing and uses this data to feed mechanistic simulation models for temperate grassland vegetation in Central Europe, in order to assess underlying processes that are difficult to observe. Public interest has recently focused on grassland ecosystem services, such as carbon stocks, nitrate retention and greenhouse gas emissions; variables that in principle can be simulated using models. However, current grassland models suffer from the fact that species dynamics in grasslands are very active, and may change in response to water supply and management. As different species come with different traits, a uniform representation of grassland in models is often not wise. This contribution highlights the potential of remote sensing in this problem space and gives an overview of where we stand now when aiming at predicting grass biomass yields and quality features in heterogeneous landscapes of temperate climate.
