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Peat soil degradation in rural areas contributes to 3% of the annual GHG emissions in the Netherlands. In the 2019 climate agreement, the Dutch government set a goal to cut these emissions by 25% by 2030 and initiated a research consortium to achieve this, the Dutch National Research Programme on Greenhouse Gases in Peatlands (NOBV). The NOBV established a GHG monitoring network to map emissions based on the diversity of peat, edaphic conditions, grassland management, and water table management.Eddy-Covariance plays an essential role in this monitoring network. More than 20 sites are part of it, including permanent and mobile EC towers, while an intensive airborne measurement campaign co-occurs above the studied areas. The first offers a high-temporal resolution monitoring of flux, covering a limited spatial landscape diversity; the latter provides a comparative map embracing the whole heterogeneity of the landscape during short timeslots.A data-driven bottom-up model will be implemented. This model will focus on considering site-specific factors and characterizing the heterogeneities of the surroundings through footprint analysis. This approach is a progression from previous efforts that compared annual carbon budgets across various locations based on external data sources, which combined remote sensing and hydrological models and implemented a machine-learning framework to gap-fill time series.Validation of the model includes a comparison with the airborne datasets. Beyond evaluating the quality of the model, the objective is to assess the strengths and limitations of this bottom-up approach when compared to real, independent datasets describing accurately spatial fluctuation of fluxes in heterogeneous landscapes.The insights are essential for future developments, including models that leverage ground network and airborne measurements in the training process for more robust and accurate CO2 Fluxes modelling.
Bataille et al. (Fri,) studied this question.