PeatCover: Towards a peat mapping at 90 m using satellite remote sensing and a priori inventories

Abstract. Peatlands cover just 3–4 % of Earth's land surface, yet store an estimated 600–700 Pg carbon (PgC), approximately one-third of Earth's soil carbon, making them critical regulators of the global carbon cycle. However, their spatial extent remains highly uncertain, particularly at fine spatial scales and in data-sparse regions. Existing global peatland datasets rely on inventories and regional products of heterogeneous quality, leading to inconsistencies and uncertainties in both estimated peat area and its spatial distribution. These limitations hinder accurate assessments of peatland-climate feedbacks, carbon budgets, policy development and restoration efforts. Here we propose a machine learning framework to map Northern peatland (boreal and subarctic) at a spatial resolution of 90 m that combines a priori information from existing peat databases (PEATMAP, Global Peatland Database, and CORINE Land Cover) with satellite observations (Landsat optical indices, topographic and hydrological data). The model first generates a continuous Peatland Index (PI) at 3 arc-second (~90 m) resolution, that can be combined with user-defined thresholds to obtain a binary peat classification. In regions with reliable coarse resolution peat cover information, the PI is used to downscale this peat fraction and obtain a high resolution classification. Our peatland map at 90 m was evaluated through both quantitative and qualitative approaches. Fully independent validation using the Peat-DBase field dataset (over 180,000 peat and non-peat observations) demonstrates an overall accuracy of 69.0 % and an F1-score of 0.81. Regional comparisons with published maps shows an overall accuracy of 69.9 % (F1=0.82) in Eurasia and of 63.9 % (F1=0.74) in North America. Qualitative spatial evaluation against Google Earth map across multiple case-study regions reveals that our map successfully captures fine-scale spatial details absent in existing inventories, including explicit delineation of open water bodies, river networks, and topographic constraints on peatland distribution. This work provides a spatially coherent, high-resolution peatland dataset spanning the Northern Hemisphere, offering improved capabilities for carbon stock estimation, hydrological modeling, and monitoring peatland degradation. The PeatCover dataset is available at https://doi.org/10.5281/zenodo.21031869 (Chen et al., 2026).