Overcoming barriers in long-term, continuous monitoring of soil CO2 flux: a low-cost sensor system

Abstract

Abstract. Soil CO2 flux (Fs) is a carbon cycling metric crucial for assessing ecosystem carbon budgets and global warming. However, global Fs datasets often suffer from low temporal-spatial resolution, as well as from spatial bias. Fs observations are severely deficient in tundra and dryland ecosystems due to financial and logistical constraints of current methods for Fs quantification. In this study, we introduce a novel, low-cost sensor system (LC-SS) for long-term, continuous monitoring of soil CO2 concentration and flux. The LC-SS, built from affordable, open-source hardware and software, offers a cost-effective solution (∼ USD 700 and ∼ 50 h for assembling and troubleshooting), accessible to low-budget users, and opens the scope for research with a large number of sensor system replications. The LC-SS was tested over ∼ 6 months in arid soil conditions, where fluxes are small, and accuracy is critical. CO2 concentration and soil temperature were measured at 10 min intervals at depths of 5 and 10 cm. The LC-SS demonstrated high stability during the tested period. Both diurnal and seasonal soil CO2 concentration variabilities were observed, highlighting the system's capability of continuous, long-term, in-situ monitoring of soil CO2 concentration. In addition, Fs was calculated using the measured CO2 concentration via the gradient method and validated with Fs measured by the flux chamber method using the well-accepted LI-COR gas analyzer system. Gradient method Fs was in good agreement with flux chamber Fs (RMSE = 0.15 µmol m−2 s−1), highlighting the potential for alternative or concurrent use of the LC-SS with current methods for Fs estimation – particularly in environments characterized by consistently low soil water content, such as drylands. Leveraging the accuracy and cost-effectiveness of the LC-SS (below 10 % of automated gas analyzer system cost), strategic implementation of LC-SSs could be a promising means to effectively increase the number of measurements, spatially and temporally, ultimately aiding in bridging the gap between global Fs uncertainties and current measurement limitations.