## 1 Introduction
(2019) analyzed red and far-red SIF and their ratio for different plant species, including crops, broadleaf species, and needle leaf species. The study suggested that different plant species can be associated with different fluorescence magnitudes, where highest fluorescence emissions were observed in crops, followed by broadleaf and needle leaf species. [PERSON] et al. (2019) investigated ecosystem functioning based on SIF-GPP relationships over the forest, scrubland, grassland, and cropland. [PERSON] et al. (2019) assessed multi-year SIF-GPP relationships over a temperate mixed forest, grassland, and a rotating crop ecosystem, and concluded on the need to apply hyperbolic and ecosystem-specific relationships. [PERSON] et al. (2019) found that species composition affects SIF via the canopy structure and leaf orientations in grassland. Apart from the studies mentioned above, other studies on SIF were mainly focused on broad- to local-scale analysis of forest, cropland, and grassland ecosystems ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2021; [PERSON] et al., 2020; [PERSON] et al., 2021; [PERSON] et al., 2020; [PERSON] et al., 2021). Despite this progress in SIF research, studies mainly focused on grassland, cropland, and forest ecosystems--but no studies have employed SIF to understand the functioning of peatland ecosystems. In recent years, few studies have used classical hyperspectral data analysis to assess the diversity of peatland vegetation. For example, [PERSON] et al. (2018) classified the peatland vegetation based on manual hyperspectral measurements. The study used several vegetation indices (VIs) to understand the biophysical activity of peatland vegetation. [PERSON] et al. (2018) observed phenological changes using narrowband VIs over peatlands. [PERSON] et al. (2019) did isometric feature and floristic gradient mapping to describe the community structure of peatland vegetation using hyperspectral imagery. [PERSON] et al. (2019) demonstrated the potential of hyperspectral reflectance that provides wide knowledge into spatial ecological patterns of peatland vegetation. [PERSON] et al. (2019); [PERSON] et al. (2019) showed the impact of environmental manipulation on peatland surfaces using simple hyperspectral indices. Considering the above research, no studies have considered SIF and VIs and their relationships over the peatland surface. We hypothesize that a combination of novel SIF measurements and classical VIs will provide new avenues to assess the large heterogeneity of peatland vegetation and its functioning. Since airborne-based approaches offer interesting opportunities to assess vegetation functioning at ecologically relevant scales ([PERSON] et al., 2019), we employ data acquired with the airborne HyPlant sensor ([PERSON] et al., 2019). HyPlant is a high-performance airborne imaging spectrometer for vegetation monitoring, developed by the Forschungszentrum Julich (Germany) in cooperation with SPECIM Spectral Imaging Ltd. (Finland). HyPlant is the airborne demonstrator of ESA FLEX mission. Data acquisition took place during the SWAMP (Spectrometry of a Wetland and Modelling of Photosynthesis) airborne campaign held on 11 July 2015 in Poland (the first HyPlant campaign taken over heterogeneous peatlands and surrounding ecosystems organized under the FLuorescence Explorer (FLEX) project supported by the European Space Agency (ESA)). Previous studies using HyPlant data have already revealed highly interesting insight into the information content of SIF and the functioning of ecosystems, mainly homogenous ones like croplands, grasslands, and forest areas ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2021; [PERSON] et al., 2021). [PERSON] et al. (2021) measured the red and far-red SIF values with HyPlant over grass carpets treated with herbicides to estimate the rate of photosynthesis (P) and relationships between SIF and P. [PERSON] et al. (2019) used the HyPlant-based SIF retrievals over sugar beet fields in the Rur river catchment to estimate GPP. [PERSON] et al.