## 1 Introduction  
Peatlands contain around one-third of the carbon stored in terrestrial ecosystems and play a crucial role in the global carbon cycle. Therefore, changes in the carbon balance of peatlands may directly impact atmospheric greenhouse gas (GHG) concentrations [1; 2; 3]. Peatlands provide a vital contribution to ecosystem services, not only by their massive carbon storage but also by their biodiversity and distinctive plant species composition. This determines why peatlands are globally recognized as an important ecosystem [4; 5; 6; 7]. Further, peatlands represent a wide array of wetlands composed of a diverse collection of plant species [8]. The diversity and complexity of peatland vegetation are based on their composition and interaction among vegetation types [7; 9]. Due to their many environmental functions (e.g., biodiversity hotspots, sink/source of GHG fluxes), large heterogeneity, and internal diversity, it is often difficult to understand the functionality of peatlands and the physiology and behavior of peatland vegetation at local to global scales [10]. Remote sensing (RS) offers unique possibilities to monitor the status of vegetation, its phenology, and its functioning [7]. Remote and proximal sensing systems can provide a multitude of information, with the main focus to map, monitor, and model the vegetation traits. However, recent advancements in multispectral and hyperspectral remote sensing have not been exploited yet for peatland monitoring [3; 11]. Historically, only aerial photography has been employed for the mapping of peatlands. Understanding of ecosystem functioning was not possible due to the limited spectral information contained in the images [12]. Nowadays, a variety of multispectral and hyperspectral sensors are available to map and monitor peatland vegetation [8; 10]. Substantial spatial and spectral variations were found in peatland vegetation groups, due to the large heterogeneity and biodiversity of peatlands. This determines these ecosystems as a very complex target for RS approaches compared to other terrestrial ecosystems [8; 10; 13]. For example, it is difficult to separate the RS signal contribution of peatland vegetation from the underlying peat soils and surface water [14]. The complexity of peatland ecosystems and the large heterogeneity of their surface yields several challenges to understand vegetation functioning and properties through RS-based approaches across scales. Narrow-band hyperspectral imaging spectrometers are rapidly evolving and provide an opportunity for mapping and modeling of terrestrial vegetation and its functioning that has not yet been exploited for peatlands. Particularly, the measurement of emitted Sun-Induced Fluorescence (SIF) is an emerging research field with growing interest. SIF is considered the most direct measurement of photosynthesis and facilitates the assessment of vegetation functioning [15; 16]. Substantial progress has been made to assess the information content of SIF and its relation to plant photosynthesis across scales. Studies by [PERSON] et al. [17], [PERSON] et al. [18; 19], [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] et al. [22] has indicate that the measurement of SIF provides new avenues to infer plant photosynthetic activity and advance estimates of gross primary production (GPP) of natural and managed ecosystems. [PERSON] et al. [17] did a comparative analysis for continuous and long-term measurements of SIF and reflectance signals from sugar beet, grassland, and lawn carpet canopies using automated field spectroscopy systems. The study focused on continuous and seasonal monitoring of plant growth and activity of agricultural crop, with diverse and specific daily course patterns of different types of canopies. [PERSON] et al. (2019) revealed the seasonal cycle of savannahs, evergreen broadleaf, cropland, and mixed forest photosynthesis based on SIF. [PERSON] et al.