Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis
Institute for Resources Environment and Sustainability, University of British Columbia, Vancouver, BC, Canada; Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC, Canada; Department of Geography, University of British Columbia, Vancouver, BC, Canada; Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC, Canada; Environmental Sciences Graduate Program, University of Cuiabá, Cuiabá, Brazil; National Center for AgroMeteorology, Seoul, South Korea; Department of Landscape Architecture & Rural Systems Engineering, Seoul National University, Seoul, South Korea; Interdisciplinary Program in Agricultural & Forest Meteorology, Seoul National University, Seoul, South Korea; Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, United States
Recommended Citation:
Kim Y.,Johnson M.S.,Knox S.H.,et al. Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis[J]. Global Change Biology,2020-01-01,26(3)