FLORES - Mechanistically tracking forest photosynthesis through multiscale chlorophyll fluorescence signals

Project outcome

Expected scientific results

FLORES is expected to deliver a refined mechanistic model for forest photosynthesis and transpiration that improves on existing empirical or big-leaf approaches. The project will generate a unique multiscale dataset linking leaf physiology, canopy structure, proximal sensing, and satellite observations at two forest sites. It will also produce new methods for scaling tower SIF measurements to stand-level signals and for using FLEX, SMAP, and related EO products to estimate regional forest carbon uptake and water fluxes. In scientific terms, the project should clarify how forest structure, water stress, and environmental variability shape the relationship between SIF, photosynthesis, and transpiration.

Expected products and services

The main products include multiscale forest datasets of physiological parameters, a validated mechanistic modelling framework, stand-scale SIF products, and regional-scale GPP and TR estimates derived from satellite data. The project will also deliver benchmarking against existing products such as FLUXCOM and against ISBA simulations, along with uncertainty and performance assessments. In addition, FLORES will generate scientific publications, presentations, and open data/code outputs, and it will provide a flexible framework that can be applied to current and future SIF missions such as FLEX and Sentinel-4.