数字农科院2.0

Machine learning-driven method for in-situ high-frequency CH4 measurement in paddy fields based on water-soil-air factors: A case study of the Yangtze River Basin

文献类型： 外文期刊

作者： Qinjing Zhang;Weijia Wen;Yanhua Zhuang;Liang Zhang;Limei Zhai;Sisi Li;Hongbin Liu;Yun Du

作者机构：

关键词： In-situ monitoring;Machine learning;Methane (CH4);Paddy fields;Water-soil-air environment factors

期刊名称： Journal of Environmental Management

ISSN： 0301-4797

年卷期： 2025 年 393 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： 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 (Hpw), soil electrical conductivity (EC), and soil temperature (Ts) 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 Ts 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.).

分类号：

  • 相关文献

[1]Water Regime-Nitrogen Fertilizer Incorporation Interaction: F.ield Study On Methane A nd Nitrous Oxide Emissions From A Rice Agroecosystem In Harbin, China. Dong, WJ, Guo, J, Xu, LJ, Song, ZF, Zhang, J, Tang, A, Zhang, XJ, Leng, CX, Liu, YH, Wang, LM, Wang, LZ, Yu, Y, Yang, ZL, Yu, YL, Meng, Y, Lai, YC. 2018

[2]Soil physical stresses beyond the dynamic least limiting water range determine crop yields in wheat-maize system in a Vertisol. Yuekai Wang,Zhongbin Zhang,Zichun Guo,Yue Zhang,Ping Zhang,Xun Xiao,Fengmin Li,Xinhua Peng. 2024

[3]Treated domestic sewage irrigation significantly decreased the CH4, N2O and NH3 emissions from paddy fields with straw incorporation. Wang, Shaohua,Xu, Shanshan,Hou, Pengfu,Xue, Lihong,Yang, Linzhang.

[4]Comprehensive assessment of integrated rice-crayfish farming system as a new paradigm to air-water-food nexus sustainability. Qiaoyu Sun,Benyamin Khoshnevisan,Jianqiang Zhu,Weiwei Wang,Yilin Liu,Junting Pan,Xianpeng Fan,Dingyue Zhang,Maoqian Wu,Hongbin Liu. 2022

[5]Potential of optimizing irrigation and fertilization management for sustainable rice production in China. Lianhua Liu,Zhongmin Lian,Wei Ouyang,Lingling Yan,Hongbin Liu,Fanghua Hao. 2023

[6]Distribution, uptake, and daily exposure of per- and polyfluoroalkyl substances in a paddy field: A growth cycle study. Bowen Li,Hongping Wang,Peiwen Yu,Pan Zou,Dongfei Tan,Fen Jin. 2025

[7]Evolutionarily Informed Deep Learning Methods F.or Predicting Relative Transcript A bundance From Dna Sequence. Washburn, Jacob D.,Wang, Hai,Wang, Hai,Wang, Hai,Valluru, Ravi,Ramstein, Guillaume,Mejia-Guerra, Maria Katherine,Kremling, Karl A.,Wang, Hai,Buckler, Edward S.,Buckler, Edward S.. 2019

[8]Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China. Zheng, Yinger,Zheng, Yinger,Zheng, Haixia,Zheng, Haixia,Ye, Xinyue. 2016

[9]Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie.

[10]A comparative study for least angle regression on NIR spectra analysis to determine internal qualities of navel oranges. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie. 2015

[11]Using evolutionary machine learning to characterize and optimize co-pyrolysis of biomass feedstocks and polymeric wastes. Shahbeik H.,Shafizadeh A.,Nadian M.H.,Jeddi D.,Mirjalili S.,Yang Y.,Lam S.S.,Pan J.,Tabatabaei M.,Aghbashlo M.. 2023

[12]Virtual screening strategy for anti-DPP-IV natural flavonoid derivatives based on machine learning. Lu, Gen,Pan, Fei,Li, Xiaotong,Zhu, Zehui,Zhao, Lei,Wu, Ya,Tian, Wenli,Peng, Wenjun,Liu, Jinling. 2023

[13]Effects of different floral periods and environmental factors on royal jelly identification by stable isotopes and machine learning analyses during non-migratory beekeeping. Zhaolong Liu,Xin Yin,Hongxia Li,Dong Qiao,Lanzhen Chen. 2023

[14]Several models combined with ultrasound techniques to predict breast muscle weight in broilers. Zhengda Li,Jumei Zheng,Bingxing An,Xiaochun Ma,Fan Ying,Fuli Kong,Jie Wen,Guiping Zhao. 2023

[15]Ecosystem responses dominate the trends of annual gross primary productivity over terrestrial ecosystems of China during 2000–2020. Xian Jin Zhu,Gui Rui Yu,Zhi Chen,Wei Kang Zhang,Lang Han,Qiu Feng Wang,Hua Qi,Meng Yang,Zhao Gang Liu,Xiao Jun Dou,Le Xin Ma,Shi Ping Chen,Shao Min Liu,Hui Min Wang,Jun Hua Yan,Jun Lei Tan,Fa Wei Zhang,Feng Hua Zhao,Ying Nian Li,Yi Ping Zhang,Pei Li Shi,Jiao Jun Zhu,Jia Bing Wu,Zhong Hui Zhao,Yan Bin Hao,Li Qing Sha,Yu Cui Zhang,Shi Cheng Jiang,Feng Xue Gu,Zhi Xiang Wu,Yang Jian Zhang,Li Zhou,Ya Kun Tang,Bing Rui Jia,Yu Qiang Li,Qing Hai Song,Gang Dong,Yan Hong Gao,Zheng De Jiang,Dan Sun,Jian Lin Wang,Qi Hua He,Xin Hu Li,Fei Wang,Wen Xue Wei,Zheng Miao Deng,Xiang Xiang Hao,Xiao Li Liu,Xi Feng Zhang,Zhi Lin Zhu. 2023

[16]Advances in the Study of Biochemical, Morphological and Physiological Traits of Wheat and Sorghum Crops in Australia Using Hyperspectral Data and Machine Learning. A. B. Potgieter,C. Camino,T. Poblete,X. Zhi,S. Reynolds-Massey-Reed,Y. Zhao,A. Belwalkar,J. Ruizhu,B. George-Jaeggli,S. Chapman,D. Jordan,A. Wu,G. L. Hammer,P. J. Zarco-Tejada. 2023

[17]Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Yi Ming Cao,Yan Zhang,Shuang Ting Yu,Kai Kuo Wang,Ying Jie Chen,Zi Ming Xu,Zi Yao Ma,Hong Lu Chen,Qi Wang,Ran Zhao,Xiao Qing Sun,Jiong Tang Li. 2023

[18]Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization. Liang, Mang,An, Bingxing,Li, Keanning,Du, Lili,Deng, Tianyu,Cao, Sheng,Du, Yueying,Xu, Lingyang,Gao, Xue,Zhang, Lupei,Li, Junya,Gao, Huijiang. 2022

[19]Enhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography. Shuaipeng Fei,Shunfu Xiao,Qing Li,Meiyan Shu,Weiguang Zhai,Yonggui Xiao,Zhen Chen,Helong Yu,Yuntao Ma. 2023

[20]Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries. Yadong Yang,Hossein Shahbeik,Alireza Shafizadeh,Nima Masoudnia,Shahin Rafiee,Yijia Zhang,Junting Pan,Meisam Tabatabaei,Mortaza Aghbashlo. 2022

作者其他论文 更多>>