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Abstract

To meet the sustainable development goals and enable sustainable management and protection of peatlands, there is a strong need for improving the mapping of peatlands. Here we present a novel approach to identify peat soils based on a high-resolution digital soil moisture map that was produced by combining airborne laser scanning-derived terrain indices and machine learning to model soil moisture at 2 m spatial resolution across the Swedish landscape. As soil moisture is a key factor in peat formation, we fitted an empirical relationship between the thickness of the organic layer (measured at 5479 soil plots across the country) and the continuous SLU (Swedish University of Agricultural Science) soil moisture map (R2= 0.66, p 

Published in

Soil
2022, volume: 8, number: 2, pages: 733-749

SLU Authors

UKÄ Subject classification

Soil Science
Agricultural Science

Publication identifier

  • DOI: https://doi.org/10.5194/soil-8-733-2022

Permanent link to this page (URI)

https://res.slu.se/id/publ/120026