Machine learning-driven method for in-situ high-frequency CH₄ measurement in paddy fields based on water-soil-air...

Accurate and high-frequency monitoring of methane (CH₄) from rice paddies is crucial for effective carbon emission control but remains challenging due to fluctuant emissions and complex field environments. This study proposed a new in-situ high-frequency CH4 measurement method based on machine learning and sensor-measurable water-soil-air environment factors. The results show that: (1) soil and paddy water serve as critical media influencing CH₄ production and transportation, with paddy water depth (H_pw), soil electrical conductivity (EC), and soil temperature (T_s) being significantly positively correlated with CH₄ emission flux, while soil redox potential (Eh) had a negative effect (p < 0.05). (2) The decision tree (DTR) showed the best accuracy for CH₄ inversion, with soil factors being the optimal input group (R² = 0.84), which was superior to water-soil (0.83), water-soil-air (0.55), and air-soil (0.45) groups; Eh, EC, soil pH, and T_s are the essential input variables (R²>0.80). (3) Combining the immediacy of multi-sensor detection and the accuracy of machine learning, the new method demonstrates notable advantages in high frequency, high accuracy, synchronous multiparameter monitoring, and low cost. This method enables the real-time monitoring and control of CH₄ emission from paddy fields, thereby offering new perspectives for CH₄ monitoring in small water bodies (such as ditches, ponds, lakes, etc.).