## 1 Introduction  
Peatlands cover around 3% of terrestrial area but contain around 30% of terrestrial carbon [1]; therefore, the peatlands have the potential for strong feedback on the global climate system, but their response to future climatic conditions is highly uncertain [2, 3, 4]. The dynamics of peatlands are considered to be nonlinear, which may consist of long periods of stasis punctuated by occasional abrupt shifts to totally different regimes [5]. Thus, the peatlands, which at present act as a carbon sink, may act as a source of carbon with the future changes in climatic conditions. Therefore, monitoring peatland vegetation is needed to have an idea about this ecosystem in a rapidly changing environment. The observation of peatland vegetation directly by personal surveys and measurements is tedious and time-consuming. Therefore, remote sensing, especially satellite remote sensing is an appreciated alternative approach for vegetation assessment. [6]. However, for a better understanding of remote-sensing signals obtained through satellites, there is a need to perform studies at the ground level. In recent times, several studies have been performedon peatland using remote-sensing techniques reviewed in [PERSON] et al. [7]. Different reflectance-based vegetation indices (VIs) are commonly used to monitor vegetation at satellite or ground scales. Some of the most commonly used VIs are Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Photochemical Reflectance Index (PRI), and Enhanced Vegetation Index (EVI) [8; 9]. Many more indices/signals have been developed over decades, and recently indices/signals such as Near-Infrared Reflectance of vegetation (NIRv) and Sun Induced Fluorescence (SIF) aim to assess the photosynthetic activity of plants [10; 11; 12]. The reflectance-based indices are connected to photosynthetic activity only indirectly, and the changes in the physiological status of plants are apparent after some time when structural changes appear in plants. The Fluorescence Correction Vegetation Index (FCFI) was developed to consider the structural changes for a more precise measurement of photosynthetic activity in combination with SIF [13]. In the last decade, due to the availability of a high number of spectral bands and resolutions, hyperspectral remote sensing has been used to understand the vegetation properties of peatlands [14; 15; 16; 17]. In peatland, the vegetation properties and structure are changing rapidly. A period of warm weather without rain causes mosses to dry, whereas vascular plants continue to grow due to high groundwater levels. This changes rapidly after the rain when the mosses start to become green. The dynamic phenomena occurring in peatland lead to remote sensing signals that need to be explained differently compared to croplands or ecosystems made of similar vegetation groups. To understand the dynamic behavior of peatland, a dedicated field experiment was designed in Poland [3; 18]. The vegetation coverage and its structural properties play an important role in the understanding of light interaction; therefore, along with vegetation indices, the canopy-leaf area index (LAI) is often used for the estimation of plant productivity and biomass. Even if the LAI is an important factor for ecosystem understanding, its measurement/estimation is not easy due to spatial and temporal heterogeneity. Several methods to measure or estimate LAI are available [19]. Direct methods are the most accurate but tedious and time-consuming and can be destructive, whereas ground-based methods of LAI measurements are based on radiation transmittance, and remote-sensing methods are based on reflectance. For an ecosystem like peatland, the exact measurement of transmittance is impossible due to the presence of mosses attached to the ground; therefore, the ground-based method can only be used to estimate the vascular plants' LAI and are of limited use.