## 2 Remote Sensing in Peatland Research
### RS Analytical Methods Used in Peatland Research  
When analysing remote sensing research methods in peatland studies, the most common approaches include analyses related to area classification and complex methods of terrain cover analysis employing machine learning (Figure 3A). However, many studies tend to use popular vegetation indices and soil moisture indices. The use of these remote sensing analysis methods is correlated with major research topics, such as land classification/identification of peatlands, changes in water conditions, the monitoring of soil moisture in peatlands and peatland vegetation mapping (Figure 3B). Figure 2: (**A**)–Type of remote sensing data: 1—Multispectral/hyperspectral, 2—Radar, 3—Both; (**B**)—Remote sensing platform: 1—Landsat (5–8)/Sentinel-2, 2—SPOT/MODIS/ASTER, 3—Other Multispectral/hyperspectral platforms, 4—Sentinel-1 and other radars, 5—Airborne and UAV data; (prepared based on works provided in Supplementary Material, Table S2). The analysis of the surface vegetation state in peatlands uses multispectral images (spectral resolution 400-2400 nm Supplementary Material, Figure S1). A key element of multispectral analysis is image acquisition time (or revisit capability), which proves to be of utmost importance in the context of employing remote sensing in the analysis of vegetation ecosystems [79; 80]. This procedure allows for the selection of appropriate satellite systems and bands for detailed analyses. A commonly used indicator based on multispectral imaging is the Normalised Difference Vegetation Index (NDVI) for a normalised (values from \(-1\) to \(1\)) evaluation of vegetation state [22; 81; 82]. NDVI is calculated based on red and near-infrared spectrum ranges (RED-NIR 620-875 nm (Supplementary Material, Figure S1)). For recurring time intervals, NDVI allows for the monitoring of plant growth in the vegetation period and their degradation caused by disasters, such as fires [83]. Changes in vegetation enable the intermediate evaluation of changes in hydrological conditions, as well as microclimate and CO\({}_{2}\) fluxes in a peatland and its surroundings [83]. A significant limitation regarding access to data from multispectral satellite sensors is connected to cloud cover, since electromagnetic radiation emitted by the sun does not penetrate through clouds within the spectrum range used for calculating the index. Another indicator used  
Figure 3: (**A**)—Used research methods: 1—Vegetation indices, 2—Soil moisture indices and Ground-water table, 3—Water indices and Water table depth, 4—Supervised classification and Machine learning land cover classification, 5—Gross Primary Productivity, 6—Ground deformation; (**B**)—The main focus of the research: 1—Monitoring of peatlands state, 2—Changes in water conditions in the peatland and monitoring soil moisture in peatlands, 3—Estimating balance of greenhouse gases, 4—Land classification/identification of peatlands, 5—Estimating carbon resources in peatlands, 6—Peatland vegetation mapping, 7—Monitoring of peatland state, based on Table 1.  
widely in the analyses of peatland vegetation is the chlorophyll index (CI) [80], designed to detect changes in chlorophyll in peatlands. The indicator implies general disappearance of chlorophyll as the peatland vegetation begins to dry out. Remote sensing analyses concerning vegetation conditions are also primarily based on measuring the amount of water in plants. Therefore, another indicator frequently applied in studies on peatland vegetation is the Moisture Stress Index (MSI) [80; 84]. MSI is primarily used to study the effects of water stress on plant health, following the assumption that a plant is under stress when its surface temperature is higher than the air temperature [85]. One of the basic indicators analysing the amount of water in plants is the Water Index (WI). Water Index (WI) is used for the estimation of plant water concentration (PWC) in ground-based reflectance measurements [86].