Summary Soil is a major source of nitrogen trace gases (NTGs). Microbial denitrification has long been identified as a source of NTGs under reducing conditions, whereas the production of NTGs during nitrification is far from being completely understood. This review updates information about the role of abiotic processes in the formation of gaseous N products in soil and brings attention to the potential interplay of microbial and chemical soil processes that tend to be neglected in research on NTG emissions. Several reactions that involve the nitrification intermediates, nitrite (NO2−) and hydroxylamine (NH2OH), are known to produce the NTGs nitric oxide (NO) and nitrous oxide (N2O). These abiotic reactions are: the self‐decomposition of NO2−, reactions of NO2− with reduced metal cations, nitrosation of soil organic matter (SOM) by NO2−, the reaction between NO2− and NH2OH, and the oxidation of NH2OH by Fe3+ or MnO2. These reactions can occur over a broad range of soil characteristics, but they are disregarded in most current research on NTG studies in favour of biological processes only. Relatively few studies have tried to quantify the contribution of abiotic processes to total NTG emissions, which results in uncertainty in emission models and mitigation strategies. It is difficult to discriminate between biological and abiotic sources because both processes can proceed at the same time in the same soil layer. The potential of stable isotope techniques to disentangle the different processes in soil and to constrain budgets of atmospheric NTGs better are highlighted. Recent advances in stable isotope technologies, such as infrared real‐time laser spectroscopy, provide considerable potential for both natural abundance and tracer studies in this field.

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The fine-scale mapping of soil organic matter (SOM) in croplands is vital for the sustainable management of soil. Traditionally, SOM mapping relies on laboratory methods that are labor-intensive and costly. Recent advances in unmanned aerial vehicles (UAVs) afford new opportunities for rapid and low-cost SOM mapping at the field scale. However, the conversion from UAV measurements to SOM maps requires specific transfer models that still rely on local sampling. This study aimed to develop a method for predicting topsoil SOM at a high resolution on the field scale based on soil color information gained from low-altitude UAV imagery and machine learning. For this, we performed a UAV survey in cropland within the German loess belt. We used two fields, one for training and one for validation of the model, to test the model transferability. We analyzed 91 soil samples for SOM in the laboratory for the model calibration and 8 additional samples for external model validation. A random forest model (RF) showed good performance for the prediction of SOM based on UAV-derived color information with an RMSE of 0.13% and with an RPIQ of 2.42. The RF model was used to predict SOM at a point-support of 1 × 1 m. The SOM map revealed spatial patterns within the fields with a uniform spread of the prediction uncertainty. The validation of the model performed similarly to the calibration with an RMSE of 0.12% and an RPIQ of 2.05, albeit with a slight bias of 0.05%. This validation using external data showed that prediction models are transferable to neighboring fields, thus permitting the prediction on larger scale farms or enabling carbon monitoring over time.