FLORES
Mechanistically Tracking Forest Photosynthesis Through Multiscale Chlorophyll Fluorescence Signals
Forests represent one of the most effective natural carbon sinks, sequestering significant amounts of atmospheric carbon dioxide while regulating directly regional climate through evapotranspiration of water. The advent of advanced remote sensing technologies has revolutionized our ability to monitor ecosystem functions, with sun-induced chlorophyll fluorescence (SIF) and microwave vegetation optical depth (VOD) emerging as powerful indicators of photosynthetic activity and vegetation water status, respectively. Current approaches for estimating gross primary production (GPP) and transpiration at large scales predominantly rely on empirical relationships and machine learning algorithms. While valuable, these stochastic methods often fail to accurately represent carbon and water fluxes under extreme environmental conditions or across complex landscapes.
FLORES is a research project funded by the Belgian Science Policy Office (BELSPO) through the STEREO IV Research Programme for Earth Observation (Support To Exploitation and Research in Earth Observation). Led by the University of Liège, and carried out in collaboration with the University of Antwerp, the Royal Meteorological Institute of Belgium (RMI), and the French National Research Institute for Agriculture, Food and Environment (INRAE), the project aims to enhance our capacity to monitor forest carbon and water dynamics under increasingly frequent climate extremes—an essential step toward understanding forest resilience and vulnerability. FLORES addresses this objective by developing an innovative mechanistic approach for tracking GPP and transpiration of European forests using multiscale SIF observations. This approach relies on a process-based framework that mechanistically links SIF to GPP and transpiration, offering the advantage of detecting early signs of vegetation stress before changes become apparent in conventional vegetation indices. A groundbreaking aspect of FLORES is its multiscale approach that deploys unmanned aerial vehicle (UAV)-mounted SIF sensors over forests, effectively bridging the critical observational gap between fine scale leaf-level measurements and broader scale observations from tower-mounted instruments and satellite platforms.
The project will generate a unique dataset by complementing CO₂ and H₂O flux measurements from eddy covariance with proximal remote sensing data capturing SIF, thermal, hyperspectral, and LiDAR signals from UAVs, alongside ground-based VOD measurements using global navigation satellite system transmissometry (GNSS-T) and SIF from tower-mounted spectrometers. This integrated dataset, complemented by detailed leaf-level physiological measurements, will facilitate the development of a process-based modelling framework.
When applied to TROPOMI and SMAP satellite observations across temperate European forests, this mechanistic approach will generate regional-scale estimates of gross primary productivity and transpiration, providing process-based assessments of forest carbon sequestration and resilience to extreme events, which will support European forest policy. The innovative framework for interpreting SIF data can also be applied to future geostationary or sunsynchronous orbit satellite missions (e.g., FLEX, Sentinel-4), enhancing our ability to detect early signs of forest stress, and improving the scientific basis for sustainable forest management strategies in a changing climate.
