Digital mapping of peatlands – A critical review

Budiman Minasny*, Örjan Berglund, John Connolly, Carolyn Hedley, Folkert de Vries, Alessandro Gimona, Bas Kempen, Darren Kidd, Harry Lilja, Brendan Malone, Alex McBratney, Pierre Roudier, Sharon O'Rourke, Rudiyanto, José Padarian, Laura Poggio, Alexandre ten Caten, Daniel Thompson, Clint Tuve, Wirastuti Widyatmanti

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)


Peatlands offer a series of ecosystem services including carbon storage, biomass production, and climate regulation. Climate change and rapid land use change are degrading peatlands, liberating their stored carbon (C) into the atmosphere. To conserve peatlands and help in realising the Paris Agreement, we need to understand their extent, status, and C stocks. However, current peatland knowledge is vague—estimates of global peatland extent ranges from 1 to 4.6 million km2, and C stock estimates vary between 113 and 612 Pg (or billion tonne C). This uncertainty mostly stems from the coarse spatial scale of global soil maps. In addition, most global peatland estimates are based on rough country inventories and reports that use outdated data. This review shows that digital mapping using field observations combined with remotely-sensed images and statistical models is an avenue to more accurately map peatlands and decrease this knowledge gap. We describe peat mapping experiences from 12 countries or regions and review 90 recent studies on peatland mapping. We found that interest in mapping peat information derived from satellite imageries and other digital mapping technologies is growing. Many studies have delineated peat extent using land cover from remote sensing, ecology, and environmental field studies, but rarely perform validation, and calculating the uncertainty of prediction is rare. This paper then reviews various proximal and remote sensing techniques that can be used to map peatlands. These include geophysical measurements (electromagnetic induction, resistivity measurement, and gamma radiometrics), radar sensing (SRTM, SAR), and optical images (Visible and Infrared). Peatland is better mapped when using more than one covariate, such as optical and radar products using nonlinear machine learning algorithms. The proliferation of satellite data available in an open-access format, availability of machine learning algorithms in an open-source computing environment, and high-performance computing facilities could enhance the way peatlands are mapped. Digital soil mapping allows us to map peat in a cost-effective, objective, and accurate manner. Securing peatlands for the future, and abating their contribution to atmospheric C levels, means digitally mapping them now.

Original languageEnglish
Article number102870
JournalEarth-Science Reviews
Publication statusPublished - 1 Sep 2019

Fingerprint Dive into the research topics of 'Digital mapping of peatlands – A critical review'. Together they form a unique fingerprint.

  • Cite this

    Minasny, B., Berglund, Ö., Connolly, J., Hedley, C., de Vries, F., Gimona, A., Kempen, B., Kidd, D., Lilja, H., Malone, B., McBratney, A., Roudier, P., O'Rourke, S., Rudiyanto, Padarian, J., Poggio, L., ten Caten, A., Thompson, D., Tuve, C., & Widyatmanti, W. (2019). Digital mapping of peatlands – A critical review. Earth-Science Reviews, 196, [102870].