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Abstract Non‐steady‐state chambers are widely employed for quantifying soil emissions of CO 2 , CH 4 , and N 2 O. Automated non‐steady‐state (a‐NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high‐frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post‐measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high‐resolution data obtained from an a‐NSS sampling system. Applied to a 38‐day dataset, the algorithm captured concurrent field measurements of CO 2 , CH 4 , and N 2 O fluxes. The automated sampling system enables the acquisition of high‐frequency data, allowing the detection of episodic gas flux events. By using shape‐constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO 2 and N 2 O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO 2 , CH 4 , and N 2 O fluxes, providing both instantaneous and diel values directly from raw, high‐temporal‐resolution data. These advancements significantly contribute to the field of GHG flux measurement, enhancing both the efficiency and accuracy of calculations for a‐NSS soil chambers and deepening our understanding of GHG emissions and their temporal dynamics.
Themistokleous et al. (Sun,) studied this question.