Abstract Peatlands are natural terrestrial carbon stores that play a key role in climate change mitigation. Accurate mapping of these ecosystems remains challenging due to their complex surface characteristics and the limitations of traditional optical‐based methods. This paper presents a method for the detection of peatland‐like displacement dynamics using open‐source advanced differential interferometric synthetic aperture radar data from the European Ground Motion Service. Vertical displacement time series are analysed using a two‐step unsupervised framework that integrates principal component analysis and k ‐means clustering in Great Britain to identify areas exhibiting peat‐like displacement behaviour. This provides a process‐based complement to traditional optical peatland mapping approaches. The results reveal distinct subsidence trends and seasonal deformation patterns typical of peat soils, even in regions not classified as peatlands in existing land cover databases. Cross‐correlation analysis with climatic variables further highlights the sensitivity of peatlands to moisture and temperature variations. Three case studies are shown to validate the approach and demonstrate its potential for refining peatland inventories: (i) Hatfield Moors, (ii) New Forest and (iii) an area north of Tilshead not classified as peatland in the land cover database. The proposed solution exploits the potential of Sentinel‐1 data to enhance peatland identification, characterise their response to environmental parameters and support the updating of existing land cover maps. A more precise identification of peatland ecosystems is relevant for climate change analysis and resilience. This methodology provides a robust, cost‐effective tool for identifying peat‐like behaviour areas and for significantly enhancing conservation and restoration efforts.
Fibbi et al. (Mon,) studied this question.