Coherent Northern peatlands retrieval at 90 m using machine learning based on satellite observations and a priori information

Peatlands cover just 3% 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, peatland spatial extent remains highly uncertain, particularly at fine spatial scales and in data-sparse regions. Existing global peatland datasets rely on heterogeneous inventories and regional products, leading to large inconsistencies in both total peat area and spatial distribution. These limitations hinder accurate assessments of peatland-climate feedbacks, carbon budgets, national policy development, and restoration efforts. We propose a machine learning framework that combines a priori information from existing peat databases (PEATMAP, Global Peatland Database, and CORINE Land Cover) with satellite observations in the visible, together with topographic and hydrological information. Our methodology employs a neural network trained with 17 input variables including Landsat-8 surface reflectance, topographic attributes from the MERIT database (elevation, slope, distance to drainage, height above drainage), and water table depth data. The model first generates a continuous Peatland Index (PI) at 3 arc-second (~90m) resolution, that can be thresholded to obtain a binary peat classification. In regions with reliable coarse resolution peat information, the PI can be used to downscale it and obtain a coherent  high resolution peat classification. The obtained pan-boreal/Northern Hemisphere peatland map at 90m 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 68.4% and an F1-score of 0.80. Regional assessments show 69.2% overall accuracy (F1=0.81) in Eurasia and 63.8% (F1=0.74) in North America. Qualitative spatial evaluation across multiple case-study regions reveals that the proposed 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. The product exhibits improved spatial coherency with high-resolution imagery while remaining consistent with large-scale patterns from current peat databases. 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. Future improvements will incorporate SAR data, additional environmental drivers, and deep learning-based feature extraction to further enhance classification accuracy, spatial details, time-evolution, and peat information.