Mapping of peatland in Mongolia using machine learning
Researchers combine field surveys, remote sensing and machine learning to create a detailed national peatland map for Mongolia. The study estimates peatlands cover 47,050 km2, or 2.9% of the country, concentrated in permafrost mountain regions, providing new data to support carbon assessment, conservation planning and climate mitigation efforts.
Subject Tags
- Forests
- Carbon storage
- Conservation Planning
Highlights
- A peatland map of Mongolia at 250 m resolution is produced.
- Peatlands cover 47,050 km2 or 2.9% of Mongolia's total territory.
- Mongolian peatlands are mainly distributed in permafrost regions.
- Vegetation and soil moisture indices are key controlling factors for peatlands.
- SOCC with field validation and uncertainty analysis created the first detailed national peatland map.
Abstract
Mongolia ranks among the top 10 countries by peatland area and represents an important global carbon sink. However, the distribution of its peatlands remains largely unconfirmed. Here, we combine field survey and remote sensing data to create a detailed map of peatland distribution by modeling and mapping soil organic carbon content (SOCC). The dataset comprises 5,400 soil samples from 1,242 sites collected between 2000 and 2023, spanning Mongolia's major natural zones. Peat soils were defined using a threshold of ≥120 g kg−1 SOCC for the upper 0–30 cm of the soil profile. To predict SOCC, we evaluated thirty-five environmental covariates, including climate data, optical and thermal Landsat-8 imagery, vegetation indices, terrain factors, and soil properties. We implemented a random forest model within a spatial prediction framework, training it on the field soil observations. Cross-validation showed that the SOCC model achieved high accuracy (R² = 0.89), with the most important predictors being temperature, silt content, vegetation productivity, and moisture availability. We estimated total peatland extent at 47,050 km2, equivalent to 2.9% of Mongolia's territory, concentrated in permafrost high-mountain, taiga, and forest-steppe zones of the major northern and western mountain ranges. Unlike previous estimates, which relied on coarse extrapolation or unvalidated inventories, our approach integrates an extensive field dataset with a machine learning method to produce the first detailed, field-validated national peatland map for Mongolia at 250 m resolution. This studyresolves fine-scale spatial patterns of carbon storage while quantifying predictor importance, providing a critical dataset and ecological indicator for evaluating carbon sinks and sources and for conservation planning in this climate-sensitive region.
Citation
Saruulzaya, A., Maralmaa, A., Purevdulam, Y., Fesenmyer, K., Leavitt, S., Erdenechimeg, A., ... & Dolgor, B. (2026). Mapping of peatland in Mongolia using machine learning. Ecological Indicators, 115550.
TNC Authors
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Kurt Fesenmyer
Senior NCS Spatial Data Scientist
The Nature Conservancy
Email: kurt.fesenmyer@tnc.org -
Sara Leavitt
MEL Lead, Global Climate
The Nature Conservancy
Email: sara.leavitt@tnc.org -
Avidsuren Erdenechimeg
Climate Project Lead
The Nature Conservancy
Email: e.avidsuren@tnc.org -
Byambadorj Dolgor
Climate Project Officer
The Nature Conservancy
Email: b.dolgor@tnc.org