Small inland waters are important greenhouse gas (GHG) sources; however, most field campaigns rely on daytime-only measurements collected at multi-week intervals, potentially biasing flux estimates. We present the Buoyant Emission Recorder for Temporal High-Frequency Analyses (BERTHA), a mid-cost automated floating chamber system for high-frequency measurements of CO₂ and CH₄ fluxes on multiple spatial scales. We describe the system’s design and test how empirically derived pressure and water vapour correction factors for the mid-cost CO₂ and CH₄ sensors increase the accuracy of reported concentrations and derived flux estimates. Here, we found a 17–37 % (CO₂) and 4–9 % (CH₄) higher mean flux estimate with pressure compensation during the 97-day measuring period. Compensation for water vapour resulted in much smaller differences (~1–2 % increase). Additionally, we calculated the mean CO₂ and CH₄ emission using high-frequency measurements during 97 days and simulated alternative measurement scenarios based on the full dataset and evaluated how well these ‘reference means’ could be approached by 1) single, daytime-only chamber flux measurements at daily to monthly intervals, and 2) short-term (2–3 days) automated chamber deployments every two–three weeks combined with machine learning-based gap-filling. Results of the simulation analysis showed that daytime-only measurements consistently and strongly underestimated the reference CO₂ mean. CH₄ emissions matched the reference mean well for daily, but not for monthly measurements (>90 % and ~23 % of simulations deviated <20 % of the reference mean, respectively). Short-term automated chamber campaigns, gap-filled using machine learning closely reproduced the CO₂ reference mean, with 89 % and 48 % of means being within ±20 % of the reference mean, for campaign intervals of two and three weeks, respectively. We conclude that high-frequency chamber measurements using mid-cost GHG analysers combined with empirical pressure corrections provide accurate emission estimates, a robust benchmark for evaluating reduced measurements strategies and gap-filling approaches, and potential for improved process-based understanding of GHG fluxes when combined with ancillary high-frequency measurements.