Wetlands play a complex role as both sources of greenhouse gases (GHGs) and carbon sinks, making it essential to understand their dynamics and effects on biodiversity. The increasing pressures from climate change and human activities can disrupt the natural balance of these ecosystems, resulting in elevated GHG emissions. Therefore, enhancing our understanding of the factors influencing GHG production in wetlands is urgent for improving large-scale modelling of these processes. This study focused on N2O, which is primarily produced through the microbial process of denitrification, and for which a satisfactory large-scale model formulation is lacking. Our objective was to quantify and characterize these emissions in situ and to identify unifying abiotic factors (humidity, carbon, temperature, and nitrate). To achieve this, in the framework of the European project Alfawetlands, we selected contrasting study sites that varied by wetland type (floodplains, peatlands, drained forests…) and climate zone, thereby gathering extensive data essential for our modelling efforts. A total of 21 sites were selected across Belgium, Estonia, Finland, France, and Spain. For each location, three treatments were applied directly in soil: water, water with nitrate, and water with nitrate and acetate. N2O emissions, soil moisture, and soil temperature were regularly measured during one or two days after treatment. The results indicated that N2O emissions varied significantly based on wetland type and latitude. Drained forests located in north-boreal vegetation regions in Finland demonstrated the lowest N2O fluxes, while floodplains and mountain peatlands respectively in Belgium and Spain showed the highest fluxes. As the lowest fluxes were observed in the highest latitudes, soil temperature could be an important factor to control denitrification and N2O emissions. Ongoing data analysis and measurements are focused on further elucidating the spatial and temporal heterogeneities of denitrification processes, with the goal of effectively incorporating these factors into our modelling efforts.