Gap-filling greenhouse gas fluxes for the closed chamber method at paddy fields using machine learning techniques
Author:
Affiliation:
1. Graduate School of Agriculture, Meiji University
2. School of Science and Technology, Meiji University
3. School of Agriculture, Meiji University
Publisher
Society of Agricultural Meteorology of Japan
Subject
Atmospheric Science,Agronomy and Crop Science
Link
https://www.jstage.jst.go.jp/article/agrmet/79/4/79_D-22-00024/_pdf
Reference30 articles.
1. Alberto MCR, Wassmann R, Buresh RJ et al., 2014: Measuring methane flux from irrigated rice fields by eddy covariance method using open-path gas analyzer. Field Crops Research 160, 12-21. https://doi.org/10.1016/j.fcr.2014.02.008
2. Bouslihim Y, Rochdi A, El Amrani Paaza N, 2021: Machine learning approaches for the prediction of soil aggregate stability. Heliyon 7. https://doi.org/10.1016/j.heliyon.2021.e06480
3. Chaichana N, Bellingrath-Kimura SD, Komiya S et al., 2018: Comparison of closed chamber and eddy covariance methods to improve the understanding of methane fluxes from rice paddy fields in Japan. Atmosphere 9. https://doi.org/10.3390/atmos9090356
4. Cicerone RJ, Shetter JD, Delwiche CC, 1983: Seasonal variation of methane flux from a California rice paddy. Journal Of Geophysical Research-Oceans 88, 1022-1024. https://doi.org/10.1029/JC088iC15p11022
5. de Mello WZ, Hines ME, 1994: Application of static and dynamic enclosures for determining dimethyl sulfide and carbonyl sulfide exchange in Sphagnum peatlands: implications for the magnitude and direction of flux. Journal of Geophysical Research 99, 14601-14607. https://doi.org/10.1029/94jd01025
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