| Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes AM Moffat, D Papale, M Reichstein, DY Hollinger, AD Richardson, ... Agricultural and Forest Meteorology 147 (3-4), 209-232, 2007 | 944 | 2007 |
| The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data G Pastorello, C Trotta, E Canfora, H Chu, D Christianson, YW Cheah, ... Scientific data 7 (1), 225, 2020 | 869 | 2020 |
| Basic and extensible post-processing of eddy covariance flux data with REddyProc T Wutzler, A Lucas-Moffat, M Migliavacca, J Knauer, K Sickel, L Šigut, ... Biogeosciences 15 (16), 5015-5030, 2018 | 659 | 2018 |
| Cross-site evaluation of eddy covariance GPP and RE decomposition techniques AR Desai, AD Richardson, AM Moffat, J Kattge, DY Hollinger, A Barr, ... agricultural and forest meteorology 148 (6-7), 821-838, 2008 | 309 | 2008 |
| Using model‐data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling TF Keenan, E Davidson, AM Moffat, W Munger, AD Richardson Global Change Biology 18 (8), 2555-2569, 2012 | 190 | 2012 |
| Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals AD Richardson, MD Mahecha, E Falge, J Kattge, AM Moffat, D Papale, ... agricultural and forest meteorology 148 (1), 38-50, 2008 | 162 | 2008 |
| Characterization of ecosystem responses to climatic controls using artificial neural networks AM Moffat, C Beckstein, G Churkina, M Mund, M Heimann Global change biology 16 (10), 2737-2749, 2010 | 96 | 2010 |
| Management effects on European cropland respiration W Eugster, AM Moffat, E Ceschia, M Aubinet, C Ammann, B Osborne, ... Agriculture, Ecosystems & Environment 139 (3), 346-362, 2010 | 75 | 2010 |
| Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands J Irvin, S Zhou, G McNicol, F Lu, V Liu, E Fluet-Chouinard, Z Ouyang, ... Agricultural and Forest Meteorology 308, 108528, 2021 | 48 | 2021 |
| Strong radiative effect induced by clouds and smoke on forest net ecosystem productivity in central Siberia SB Park, A Knohl, AM Lucas-Moffat, M Migliavacca, C Gerbig, T Vesala, ... Agricultural and Forest Meteorology 250, 376-387, 2018 | 46 | 2018 |
| REddyProc: Data processing and plotting utilities of (half-) hourly eddy-covariance measurements M Reichstein, AM Moffat, T Wutzler, K Sickel R package version 0.6–0/r9 755, 2014 | 46 | 2014 |
| Towards pairing plot and field scale measurements in managed ecosystems: Using eddy covariance to cross-validate CO2 fluxes modeled from manual chamber campaigns AM Lucas-Moffat, V Huth, J Augustin, C Brümmer, M Herbst, WL Kutsch Agricultural and Forest Meteorology 256, 362-378, 2018 | 38 | 2018 |
| A new methodology to interpret high resolution measurements of net carbon fluxes between terrestrial ecosystems and the atmosphere AM Moffat | 35 | 2012 |
| Two linear beetle-type scanning tunneling microscopes JM MacLeod, A Moffat, JA Miwa, AG Mark, GK Mullins, RHJ Dumont, ... Review of scientific instruments 74 (4), 2429-2437, 2003 | 34 | 2003 |
| Gas chromatography vs. quantum cascade laser-based N2O flux measurements using a novel chamber design C Brümmer, B Lyshede, D Lempio, JP Delorme, JJ Rüffer, R Fuß, ... Biogeosciences 14 (6), 1365-1381, 2017 | 24 | 2017 |
| REddyProc: Post processing of (half-) hourly eddy-covariance measurements T Wutzler, M Reichstein, AM Moffat, M Migliavacca R package version 1 (2), 2020 | 21 | 2020 |
| Random errors in carbon and water vapor fluxes assessed with Gaussian Processes O Menzer, AM Moffat, W Meiring, G Lasslop, EG Schukat-Talamazzini, ... Agricultural and forest meteorology 178, 161-172, 2013 | 20 | 2013 |
| Beyond distance-invariant survival in inverse recruitment modeling: A case study in Siberian Pinus sylvestris forests S Tautenhahn, H Heilmeier, M Jung, A Kahl, J Kattge, A Moffat, C Wirth Ecological Modelling 233, 90-103, 2012 | 14 | 2012 |
| Is the biosphere-atmosphere exchange of total reactive nitrogen above forest driven by the same factors as carbon dioxide? An analysis using artificial neural networks U Zöll, AM Lucas-Moffat, P Wintjen, F Schrader, B Beudert, C Brümmer Atmospheric environment 206, 108-118, 2019 | 12 | 2019 |
| Rattling modes and the intrinsic vibrational spectrum of beetle-type scanning tunneling microscopes JA Miwa, JM MacLeod, A Moffat, AB McLean Ultramicroscopy 98 (1), 43-49, 2003 | 11 | 2003 |
