## 1 Introduction
Satellites orbit the earth from a distance of several hundred kilometres and are equipped with sensors that detect different wavelengths of electromagnetic radiation, such as visible light, infrared, and radar [34; 35; 36]. They offer several advantages for peatland applications, including global coverage, repeatability, and relatively high spatial and temporal resolution [37]. Studies such as [PERSON] et al. (2017) have delved into the impact of land-use intensity on vegetation indices, photosynthesis, and biomass within intensively and extensively managed grassland fens [38]. The analysis of satellite imagery has allowed for the assessment of vegetation dynamics and ecosystem exchange, as [PERSON] et al. (2018) demonstrated in their comparison between satellite and airborne imagery for estimating peatland water table depth and net ecosystem exchange [18]. However, satellites also have limitations, such as the need for cloud-free conditions and limited ability to penetrate vegetation canopies [39; 40]. Meanwhile, airborne remote sensing platforms, such as aircraft-mounted sensors, offer several advantages for peatland applications, including high spatial and spectral resolution, the ability to acquire data under a wide range of atmospheric conditions, and the ability to cover large areas quickly [25; 41; 42]. These advancements in technology have facilitated the mapping of peatland attributes, as exemplified by [PERSON] et al. (2018) in their implementation of an airborne-hyperspectral system for the evaluation of maximum gross photosynthesis and uptake efficiency of CO\({}_{2}\) in ombrotrophic peatland [43]. However, airborne remote sensing can be costly, time-consuming, and requires specialised aircraft and trained personnel [44; 45]. UAV-based remote sensing involves sensors on small, unmanned aircraft (drones). It has emerged as a versatile tool for peatland research, enabling ultra-high-resolution mapping and monitoring [46]. UAVs offer several benefits for peatland applications, including low cost, ultra-high spatial and temporal resolution, and access to difficult-to-reach areas. [PERSON] et al. (2017) introduced a new method to map groundwater tables in peatlands using UAVs, highlighting the potential of this platform in hydrological investigations [47]. A study by [PERSON] et al. (2018) showcased the utilisation of UAVsto uncover the effects of low-impact seismic lines on surface morphology, hydrology, and methane release within a boreal treed bog [32]. UAVs have also been employed in mapping peatland vegetation and quantifying carbon stocks, as [PERSON] and his team demonstrated in their 2018 examination of bog-healthland mosaics employing exceptionally detailed imagery [48]. However, UAVs also have limitations, requiring skilled operators and having limited payload capacity [23]. In situ-based remote sensing complements remote sensing data by providing ground truth measurements and detailed characterisation of peatlands. [PERSON] et al. (2020) utilised spectral indices for the estimation of gross primary productivity (GPP) and water content, mainly in Sphagnum moss and other plant species in peatland, bridging the gap between field measurements and remote sensing [49]. [PERSON] et al. (2019) used in situ observations to examine how boreal peatland community composition and Normalized Difference Vegetation Index (NDVI) responded to alterations in hydrology, warming, and elevated carbon dioxide levels. However, in situ- and ground-based remote sensing can be labour-intensive, time-consuming, and is typically limited to small areas [50]. Furthermore, integrating multiple remote sensing platforms, such as satellites and UAVs, has shown promise in enhancing peatland monitoring, such as combining high spatial resolution with global coverage or accessing difficult-to-reach areas with high temporal resolution [51]. [PERSON] et al.