## 4 Summary and Conclusions  
The presented review of research works published within the last ten years indicates a gradual increase in the number of studies on peatlands that employ remote sensing data. RS was applied for monitoring present-day peatland transformations (including their individual components) and the analysis of the historical development of peatlands associated with temporal changes in environmental factors (both natural and anthropogenic).  
The data are predominantly used in studies involving land classification/identification of peatlands or changes in peatland water conditions, as well as for monitoring soil moisture in peatlands. The most frequently employed methods include supervised classification and land cover classification predicated in machine learning, as well as vegetation indices. Because remote sensing data have become readily available and free of charge, the number of studies on peatlands featuring such data has increased. Research in this field was also positively impacted by the rapid development of open-source applications.  
Remote sensing data combined with field research information can improve the accuracy of results obtained in peatland studies. This, in turn, may support the preservation of peatlands and their environmental functions. The development of indices related to several features of peatlands, calculated based on available free-of-charge remote data, provides an opportunity to conduct studies on peatlands at a local and regional level. Therefore, these methods may be useful for studying, monitoring and supporting the management of peatlands both as stand-alone areas and as an element of the hydrological and environmental network. Bearing in mind that peatland studies call for, and benefit from, a multiproxy approach, remote sensing may prove to be a highly efficient method of choice, as it not only enables detection of peatlands, but facilitates the monitoring of their function in the environment.  
Observed limitations related to the application of RS methods in studies on peatlands are connected with the spatial resolution of satellite imagery; cloud interference in the case of passive remote sensing data; restrictions to the surface layer of the peatlands; the considerable impact of high vegetation (trees, shrubs) on the results of analyses; differences in the interpretation of data results; and a lack of standardised data.  
Accurate information regarding the distribution and extent of peatlands is deemed essential in the context of the sustainable management of these areas. Therefore, we recommend the further development of RS methods and their implementation with peatland analyses in mind, especially in comprehensive and multiproxy studies. Radar data (provided by, for example, the Sentinel-1 system) seem particularly noteworthy due to their availability (open access), high repeatability over time and independence from weather conditions.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/land11010024/s1](https://www.mdpi.com/article/10.3390/land11010024/s1)]([https://www.mdpi.com/article/10.3390/land11010024/s1](https://www.mdpi.com/article/10.3390/land11010024/s1)), Table S1: Characteristics of selected remote sensing data developed in the years 2000-2021; Figure S1: Multispectral range of Landsat 7-8 and Sentinel-2 satellite systems; Table S2: Characteristics of reviewed studies  
Conceptualization, S.C. and D.S.; methodology, S.C. and D.S.; formal analysis, S.C.; resources, S.C.; writing--original draft preparation, S.C.; writing--review and editing, S.C. and D.S.; visualization S.C. and D.S.; supervision, D.S.; project administration, S.C. and D.S.; funding acquisition, S.C. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Project Supporting Maintenance of Research Potential at Kazimierz Wielki University.  
Not applicable.  
The authors declare no conflict of interest.