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Agricultural greenhouse gas (GHG) release is the most dominant anthropogenic emission sector of methane (CH4) and nitrous oxide (N2O), therefore contribute significantly to global warming. However, there are large uncertainties in both, top-down and bottom-up emission estimates especially on the regional scale. Process models have difficulties to properly reproduce the complexity of the underlying GHG formation processes. In addition, the complicated measurement conditions, such as large areas, strong temporal variability and the spatial heterogeneity caused by the variety of agricultural emitters, makes measurements challenging. Hence the data situation is sparse. With regard to effective mitigation guidelines, observations with innovative measurement techniques are urgently needed in order to improve process-based models and therefore leading to a better understanding of agricultural GHG emissions. Here we present first results of the in-situ aircraft campaign GHG Monitoring (GHGMon), which took place in June 2023 in the Netherlands, the worlds second largest exporter of agricultural products and thus one of the most prominent hotspots of associated N2O and CH4 emissions. The main objective of the campaign was to investigate agricultural emissions in this important source region and to provide a basis for the evaluation of bottom-up estimates. To this end, we setup a new eddy-covariance measurement system, based on a Quantum Cascade Laser Spectrometer and suitable for the direct and continuous airborne measurement of N2O and CH4 fluxes - to our knowledge, a novelty for N2O. A total of 14 scientific research flights (45 hours) were conducted with the DLR research aircraft Cessna C208-B Grand Caravan to investigate the GHG fluxes of a variety of agricultural emitters under different meteorological conditions. The flight patterns were optimized for the eddy covariance measurement principle and to evaluate and optimize the new measurement system and to quantify this important source region. The gathered dataset will enable unique insights into the agricultural emissions of the Netherlands and will enable the evaluation of bottom-up emission estimates including process-based models. We show that derived fluxes were consistent for repeated legs over the same target areas and during similar meteorological conditions, even on different days, indicating that our measurement approach is robust and reliable. In contrast, under different meteorological conditions, we observed different fluxes, e.g. N2O fluxes after rainfall following on a drought period were multiple times larger than during the drought. There were also large differences measured between the emissions of diverse agricultural subsectors (cattle vs. crops vs. swine). Preliminary turbulent fluxes of 0 0.3 gm-2d-1(CH4) and of 0 0.05 gm-2d-1(N2O) where found during GHGMon, which in a next step will be compared to available inventories, such as the Netherlands national inventory. Our measurements demonstrate the usefulness of the airborne eddy covariance method to study agricultural N2O and CH4 emissions on a regional scale and to evaluate process models.
Waldmann et al. (Mon,) studied this question.