Peatlands are among the most important terrestrial ecosystems for their large role in the carbon cycle. Assessment of the peatland vegetation ecophysiology and quantifying the ecophysiological role of plant functional groups (PFGs) found in the peatlands is an important task requiring interdisciplinary effort. Remote sensing, and particularly sun-induced fluorescence, has the potential to facilitate such an effort. However, mechanistic or causal links between top-of-canopy remote sensing signals and the plant physiology of major PFGs found in peatlands have to be established before the full potential of remote sensing can be realised. Therefore, my work aims to link the information gained by passive remote sensing, active chlorophyll fluorescence and gas exchange measurements done over peatland PFGs monoculture canopies and mixed peatland vegetation by means of machine learning and mechanistic modelling to significantly contribute to peatland ecophysiology assessment.