Agricultural and Forest Meteorology, 308-309:108528, 1-22. Elsevier Agricultural and Forest Meteorology Irvin, J, Zhou, S, Mcnicol, G, Lu, F, Liu, V, Fluet-chouinard, E, Ouyang, Z, Knox, S H, Lucas-moffat, A, Trotta, C, Papale, D, Vitale, D, Mammarella, I, Alekseychik, P, Aurela, M, Avati, A, Baldocchi, D, Bansal, S, Bohrer, G, Campbell, D I, Chen, J, Chu, H, Dalmagro, H J, Delwiche, K B, Desai, A R, Euskirchen, E, Feron, S, Goeckede, M, Heimann, M, Helbig, M, Helfter, C, Hemes, K S, Hirano, T, Iwata, H, Jurasinski, G, Kalhori, A, Kondrich, A, Lai, D Y, Lohila, A, Malhotra, A, Merbold, L, Mitra, B, Ng, A, Nilsson, M B, Noormets, A, Peichl, M, Rey-sanchez, A C, Richardson, A D, Runkle, B R, Schäfer, K V, Sonnentag, O, Stuart-haëntjens, E, Sturtevant, C, Ueyama, M, Valach, A C, Vargas, R, Vourlitis, G L, Ward, E J, Wong, G X, Zona, D, Alberto, M C R, Billesbach, D P, Celis, G, Dolman, H, Friborg, T, Fuchs, K, Gogo, S, Gondwe, M J, Goodrich, J P, Gottschalk, P, Hörtnagl, L, Jacotot, A, Koebsch, F, Kasak, K, Maier, R, Morin, T H, Nemitz, E, Oechel, W C, Oikawa, P Y, Ono, K, Sachs, T, Sakabe, A, Schuur, E A, Shortt, R, Sullivan, R C, Szutu, D J, Tuittila, E, Varlagin, A, Verfaillie, J G, Wille, C, Windham-myers, L, Poulter, B & Jackson, R B 2021, ' Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands ', Agricultural and Forest Meteorology, vol. 308-309, 108528 . https://doi.org/10.1016/j.agrformet.2021.108528 Agricultural and Forest Meteorology, 2021, 308-309, pp.108528. ⟨10.1016/j.agrformet.2021.108528⟩ Agricultural and Forest Meteorology, 308:108528. ELSEVIER SCIENCE BV Irvin, J, Zhou, S, McNicol, G, Lu, F, Liu, V, Fluet-Chouinard, E, Ouyang, Z, Knox, S H, Lucas-Moffat, A, Trotta, C, Papale, D, Vitale, D, Mammarella, I, Alekseychik, P, Aurela, M, Avati, A, Baldocchi, D, Bansal, S, Bohrer, G, Campbell, D I, Chen, J, Chu, H, Dalmagro, H J, Delwiche, K B, Desai, A R, Euskirchen, E, Feron, S, Goeckede, M, Heimann, M, Helbig, M, Helfter, C, Hemes, K S, Hirano, T, Iwata, H, Jurasinski, G, Kalhori, A, Kondrich, A, Lai, D Y, Lohila, A, Malhotra, A, Merbold, L, Mitra, B, Ng, A, Nilsson, M B, Noormets, A, Peichl, M, Rey-Sanchez, A C, Richardson, A D, Runkle, B R, Schäfer, K V, Sonnentag, O, Stuart-Haëntjens, E, Sturtevant, C, Ueyama, M, Valach, A C, Vargas, R, Vourlitis, G L, Ward, E J, Wong, G X, Zona, D, Alberto, M C R, Billesbach, D P, Celis, G, Dolman, H, Friborg, T, Fuchs, K, Gogo, S, Gondwe, M J, Goodrich, J P, Gottschalk, P, Hörtnagl, L, Jacotot, A, Koebsch, F, Kasak, K, Maier, R, Morin, T H, Nemitz, E, Oechel, W C, Oikawa, P Y, Ono, K, Sachs, T, Sakabe, A, Schuur, E A, Shortt, R, Sullivan, R C, Szutu, D J, Tuittila, E-S, Varlagin, A, Verfaillie, J G, Wille, C, Windham-Myers, L, Poulter, B & Jackson, R B 2021, ' Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands ', Agricultural and Forest Meteorology, vol. 308-309, 108528, pp. 1-22 . https://doi.org/10.1016/j.agrformet.2021.108528 Agricultural and Forest Meteorology, 308-309