Peatlands constitute one of the largest terrestrial carbon reservoirs and are highly sensitive to climatic forcing, yet climate change and anthropogenic disturbances can shift them from long-term carbon sinks to substantial sources of atmospheric CO₂. Across Europe, decades of drainage, agricultural cultivation, and peat extraction have disrupted peatland functioning and amplified greenhouse-gas emissions (Hopple et al., 2020; van Giersbergen et al., 2025).

In Denmark, extensive agricultural drainage, together with heterogeneity in topography, hydrology, land-use intensity, and management history, is associated with pronounced spatial variability in peatland properties and hydro-biogeochemical conditions (Beucher et al., 2020; Beucher et al., 2023). However, uncertainty in characterizing peat-soil properties and functions persists, limiting the accuracy of national greenhouse-gas inventories and the targeting of sustainable peatland preservation and management (Adhikari et al., 2014; Beucher et al., 2020; Beucher et al., 2023).

This postdoctoral project will apply advanced digital soil mapping to improve predictions of key peat-soil properties and functions across multiple spatial scales in Denmark (Gomes et al., 2023), and assess spatiotemporal variability across Danish peatlands. It will integrate field observations and laboratory analyses with environmental covariates and airborne- and satellite-based remote-sensing products, and use machine learning/deep learning with explainable AI to identify dominant drivers of spatial and temporal variability (Minasny et al., 2024). The project will deliver reproducible, updatable maps with uncertainty estimates and interpretable model outputs to support evidence-based restoration prioritization and sustainable land management.