## 4 Remote Sensing Platforms Used in Peatland Research
### Multiple Platforms
#### 4.5.1 Vegetation Dynamics and Phenology  
Several studies have explored using combined remote sensing platforms, data or measuring techniques to assess vegetation dynamics and phenology in peatland ecosystems [141; 38; 142]. [PERSON] et al. (2021) present an innovative approach to monitoring wetlands, specifically emphasising ombrotrophic peatlands [143]. Their methodology combines machine learning algorithms, satellite data analysis and drone-RGB image processing to generate seasonality maps of vegetation communities across wetland areas [143]. Notably, the authors successfully demonstrate how a limited number of drone images can enhance satellite imagery, improving classification accuracy [143]. This proposed approach stands out as a robust, efficient, and cost-effective method for mapping wetland habitats [143]. However, it is worth noting that while the study offers valuable insights into wetland monitoring, its primary focus is on a particular type of peatland, potentially not accounting for the full range of peatland conditions [143].  
Building upon the work of [PERSON] et al. (2021) in [143], [PERSON] et al. (2019, 2020) emphasised the importance of high-resolution imagery in mapping peatland vegetation [30; 144]. They compared multiple optical remote-sensing data from satellite imagery, airborne and UAV, for mapping and detecting spatial patterns of vegetation properties in three northern peatland areas. The article highlights the rarity of a universal approach suitable for grouping and mapping peatland vegetation, as the diversity within peatland ecosystems often necessitates tailored methodologies. Therefore, the authors recommended some guidelines for mapping vegetation patterns in peatlands. They suggest that different methods and data sources should be tried and compared to find the best fit for each case and purpose. They also advise that various maps should show different vegetation features and their interactions. They emphasise that each map's strengths, weaknesses and uncertainties should be clearly stated [30; 51].  
Moreover, [PERSON] et al. (2018) explored the potential of Sentinel-2A imagery for monitoring phenospectral dynamics, providing valuable insights into the phenological changes of peatland vegetation [141]. However, validation approaches using higher spatial and spectral resolution data, such as airborne hyperspectral imagery (HSI) and in situ measurements, are necessary to improve the accuracy of these satellite-based land products. That is what was concluded by the researchers who utilised three datasets: Sentinel-2A imagery, airborne hyperspectral imagery (HSI), and field spectra [141].  
[PERSON] and [PERSON] (2018) and [PERSON] et al. (2020) also focus on vegetation phenology and biomass estimation in peatlands. These studies demonstrate the use of remote sensing data, including hyperspectral drone data and satellite imagery, to estimate vegetation indices, leaf-area index (LAI), and biomass. They emphasise the importance of considering vegetation heterogeneity and microtopographic features when modelling and upscaling peatland characteristics [51; 77].