Satellite maps reveal peatlands with new precision, opening carbon accounting possibilities
Scientists combined radar and optical satellite data to accurately map peatland types across the Arctic, achieving classification accuracy rates that suggest commercial viability for monitoring carbon stocks. The approach matters to carbon credit markets, environmental regulators, and companies building climate commitments—peatlands hold vast carbon reserves, but measuring them has been difficult.
Originaltitel: Multi‐Source Mapping of Peatland Types Using Sentinel‐1, Sentinel‐2, and Terrain Derivatives—A Comparison Between Five High‐Latitude Landscapes
<p>Mapping wetland types in northern-latitude regions with Earth Observation (EO) data is important for several practical and scientific applications, but at the same time challenging due to the variability and dynamic nature in wetland features introduced by differences in geophysical conditions. The objective of this study was to better understand the ability of Sentinel-1 radar data, Sentinel-2 optical data and terrain derivatives derived from Copernicus digital elevation model to distinguish three main peatland types, two upland classes, and surface water, in five contrasting landscapes located in the northern parts of Alaska, Canada and Scandinavia. The study also investigated the potential benefits for classification accuracy of using regional classification models constructed from region-specific training data compared to a global classification model based on pooled reference data from all five sites. Overall, the results show high promise for classifying peatland types and the three other land cover classes using the fusion approach that combined all three EO data sources (Sentinel-1, Sentinel-2 and terrain derivatives). Overall accuracy for the individual sites ranged between 79.7% and 90.3%. Class specific accuracies for the peatland types were also high overall but differed between the five sites as well as between the three classes bog, fen and swamp. A key finding is that regional classification models consistently outperformed the global classification model by producing significantly higher classification accuracies for all five sites. This suggests for progress in identifying effective approaches for continental scale peatland mapping to improve scaling of for example, hydrological- and greenhouse gas-related processes in Earth system models.</p>