Uniform upscaling techniques for eddy covariance FLUXes (UFLUX)

References

• Ai, J., G. Jia, H. E. Epstein, H. Wang, A. Zhang, and Y. Hu. 2018. “MODIS-Based Estimates of Global Terrestrial Ecosystem Respiration.” Journal of Geophysical Research: Biogeosciences 123 (2): 326–352. https://doi.org/10.1002/2017JG004107.
   Web of Science ®Google Scholar
• Altmann, A., L. Toloşi, O. Sander, and T. Lengauer. 2010. “Permutation importance: a corrected feature importance measure.” Bioinformatics 26 (10): 1340–1347. https://doi.org/10.1093/bioinformatics/btq134.
   PubMed Web of Science ®Google Scholar
• Aubinet, M., T. Vesala, and D. Papale. 2012. Eddy Covariance: A Practical Guide to Measurement and Data Analysis. Salmon Tower Building New York City USA: Springer Science & Business Media.
   Google Scholar
• Awad, M., and R. Khanna. 2015. “Support Vector Regression.” In Efficient Learning Machines, 67–80. Salmon Tower Building New York City USA: Springer.
   Google Scholar
• Badgley, G., L. D. L. Anderegg, J. A. Berry, and C. B. Field. 2019. “Terrestrial Gross Primary Production: Using NIRV to Scale from Site to Globe.” Global Change Biology 25 (11): 3731–3740. https://doi.org/10.1111/gcb.14729.
   PubMed Web of Science ®Google Scholar
• Baldocchi, D. D. 2020. “How Eddy Covariance Flux Measurements Have Contributed to Our Understanding of Global Change Biology.” Global Change Biology 26 (1): 242–260. https://doi.org/10.1111/gcb.14807.
   PubMed Web of Science ®Google Scholar
• Baldocchi, D., E. Falge, L. Gu, R. Olson, D. Hollinger, S. Running, P. Anthoni, et al. 2001. “FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities.” Bulletin of the American Meteorological Society 82 (11): 2415–2434. https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2.
   Web of Science ®Google Scholar
• Beer, C., M. Reichstein, E. Tomelleri, P. Ciais, M. Jung, N. Carvalhais, C. Rödenbeck, et al. 2010. “Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate.” Science 329 (5993): 834–838. https://doi.org/10.1126/science.1184984.
   PubMed Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Chapin, F. S., III, P. A. Matson, and P. Vitousek. 2011. Principles of Terrestrial Ecosystem Ecology. Salmon Tower Building New York City USA: Springer Science & Business Media.
   Google Scholar
• Chen, T., and C. Guestrin 2016. Xgboost: A Scalable Tree Boosting System. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, San Francisco California USA. pp 785–794.
   Google Scholar
• Dong, J., L. Li, Y. Li, and Q. Yu. 2022. “Inter-Comparisons of Mean, Trend and Interannual Variability of Global Terrestrial Gross Primary Production Retrieved from Remote Sensing Approach.” Science of the Total Environment 822:153343. https://doi.org/10.1016/j.scitotenv.2022.153343.
   PubMed Web of Science ®Google Scholar
• Duveiller, G., F. Filipponi, S. Walther, P. Köhler, C. Frankenberg, L. Guanter, A. Cescatti, et al. 2020. “A Spatially Downscaled Sun-Induced Fluorescence Global Product for Enhanced Monitoring of Vegetation Productivity.” Earth System Science Data 12 (2): 1101–1116. https://doi.org/10.5194/essd-12-1101-2020.
   Web of Science ®Google Scholar
• Fisher, R. A., and C. D. Koven. 2020. “Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems.” Journal of Advances in Modeling Earth Systems 12 (4): e2018MS001453. https://doi.org/10.1029/2018MS001453.
   Web of Science ®Google Scholar
• Fu, Z., P. Ciais, I. C. Prentice, P. Gentine, D. Makowski, A. Bastos, X. Luo, et al. 2022. “Atmospheric Dryness Reduces Photosynthesis Along a Large Range of Soil Water Deficits.” Nature Communications 13 (1): 1–10. https://doi.org/10.1038/s41467-022-28652-7.
   PubMed Web of Science ®Google Scholar
• Guanter, L., C. Bacour, A. Schneider, I. Aben, T. A. van Kempen, F. Maignan, C. Retscher, et al. 2021. “The TROPOSIF Global Sun-Induced Fluorescence Dataset from the Sentinel-5P TROPOMI Mission.” Earth System Science Data 13 (11): 5423–5440. https://doi.org/10.5194/essd-13-5423-2021.
   Web of Science ®Google Scholar
• Guanter, L., C. Frankenberg, A. Dudhia, P. E. Lewis, J. Gómez-Dans, A. Kuze, H. Suto, et al. 2012. “Retrieval and Global Assessment of Terrestrial Chlorophyll Fluorescence from GOSAT Space Measurements.” Remote Sensing of Environment 121:236–251. https://doi.org/10.1016/j.rse.2012.02.006.
   Web of Science ®Google Scholar
• Hatfield, J. L., and C. Dold. 2019. “Water-Use Efficiency: Advances and Challenges in a Changing Climate.” Frontiers in Plant Science 10:103. https://doi.org/10.3389/fpls.2019.00103.
   PubMed Web of Science ®Google Scholar
• Hill, T., M. Chocholek, and R. Clement. 2017. “The Case for Increasing the Statistical Power of Eddy Covariance Ecosystem Studies: Why, Where and How?” Global Change Biology 23 (6): 2154–2165. https://doi.org/10.1111/gcb.13547.
   PubMed Web of Science ®Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Huete, A., H. Liu, K. Batchily, and W. Van Leeuwen. 1997. “A Comparison of Vegetation Indices Over a Global Set of TM Images for EOS-MODIS.” Remote Sensing of Environment 59 (3): 440–451. https://doi.org/10.1016/S0034-4257(96)00112-5.
   Web of Science ®Google Scholar
• Ichii, K., M. Ueyama, M. Kondo, N. Saigusa, J. Kim, M. C. Alberto, J. Ardö, et al. 2017. “New Data-Driven Estimation of Terrestrial CO2 Fluxes in Asia Using a Standardized Database of Eddy Covariance Measurements, Remote Sensing Data, and Support Vector Regression.” Journal of Geophysical Research: Biogeosciences 122 (4): 767–795. https://doi.org/10.1002/2016JG003640.
   Web of Science ®Google Scholar
• Jiang, F., W. Ju, W. He, M. Wu, H. Wang, J. Wang, M. Jia, et al. 2022. “A 10-Year Global Monthly Averaged Terrestrial Net Ecosystem Exchange Dataset Inferred from the ACOS GOSAT V9 XCO 2 Retrievals (GCAS2021).” Earth System Science Data 14 (7): 3013–3037. https://doi.org/10.5194/essd-14-3013-2022.
   Web of Science ®Google Scholar
• Joiner, J., and Y. Yoshida. 2020. “Satellite-Based Reflectances Capture Large Fraction of Variability in Global Gross Primary Production (GPP) at Weekly Time Scales.” Agricultural and Forest Meteorology 291:108092. https://doi.org/10.1016/j.agrformet.2020.108092.
   Web of Science ®Google Scholar
• Joiner, J., and Y. Yoshida. 2021. Global MODIS and FLUXNET-Derived Daily Gross Primary Production, V2. Oak Ridge, Tennessee, USA: ORNL DAAC.
   Google Scholar
• Jung, M., S. Koirala, U. Weber, K. Ichii, F. Gans, G. Camps-Valls, D. Papale, et al. 2019. “The FLUXCOM Ensemble of Global Land-Atmosphere Energy Fluxes.” Scientific Data 6 (1): 74. https://doi.org/10.1038/s41597-019-0076-8.
   PubMedGoogle Scholar
• Jung, M., M. Reichstein, C. R. Schwalm, C. Huntingford, S. Sitch, A. Ahlström, A. Arneth, et al. 2017. “Compensatory Water Effects Link Yearly Global Land CO 2 Sink Changes to Temperature.” Nature 541 (7638): 516–520. https://doi.org/10.1038/nature20780.
   PubMed Web of Science ®Google Scholar
• Jung, M., C. Schwalm, M. Migliavacca, S. Walther, G. Camps-Valls, S. Koirala, P. Anthoni, et al. 2020. “Scaling Carbon Fluxes from Eddy Covariance Sites to Globe: Synthesis and Evaluation of the FLUXCOM Approach.” Biogeosciences 17 (5): 1343–1365. https://doi.org/10.5194/bg-17-1343-2020.
   Web of Science ®Google Scholar
• Keenan, T., and C. Williams. 2018. “The Terrestrial Carbon Sink.” Annual Review of Environment and Resources 43 (1): 219–243. https://doi.org/10.1146/annurev-environ-102017-030204.
   Google Scholar
• Liu, X., L. Liu, J. Hu, J. Guo, and S. Du. 2020. “Improving the Potential of Red SIF for Estimating GPP by Downscaling from the Canopy Level to the Photosystem Level.” Agricultural and Forest Meteorology 281:107846. https://doi.org/10.1016/j.agrformet.2019.107846.
   Web of Science ®Google Scholar
• Lloyd, J., and J. Taylor. 1994. “On the Temperature Dependence of Soil Respiration.” Functional Ecology 8 (3): 315–323. https://doi.org/10.2307/2389824.
   Web of Science ®Google Scholar
• Marchetti, F. 2021. “The Extension of Rippa’s Algorithm Beyond LOOCV.” Applied Mathematics Letters 120:107262. https://doi.org/10.1016/j.aml.2021.107262.
   Web of Science ®Google Scholar
• Monteith, J. L. 1972. “Solar Radiation and Productivity in Tropical Ecosystems.” The Journal of Applied Ecology 9 (3): 747–766. https://doi.org/10.2307/2401901.
   Web of Science ®Google Scholar
• Papale, D., T. A. Black, N. Carvalhais, A. Cescatti, J. Chen, M. Jung, G. Kiely, et al. 2015. “Effect of Spatial Sampling from European Flux Towers for Estimating Carbon and Water Fluxes with Artificial Neural Networks.” Journal of Geophysical Research: Biogeosciences 120 (10): 1941–1957. https://doi.org/10.1002/2015JG002997.
   Web of Science ®Google Scholar
• Pastorello, G., C. Trotta, E. Canfora, H. Chu, D. Christianson, Y.-W. Cheah, C. Poindexter, et al. 2020. “The FLUXNET2015 Dataset and the ONEFlux Processing Pipeline for Eddy Covariance Data.” Scientific Data 7 (1): 1–27. https://doi.org/10.1038/s41597-020-0534-3.
   PubMed Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. 2011. “Scikit-Learn: Machine Learning in Python.” The Journal of Machine Learning Research 12: 2825–2830. https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf.
   Web of Science ®Google Scholar
• Penman, H. L. 1948. “Natural Evaporation from Open Water, Bare Soil and Grass.” Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 193:120–145.
   Google Scholar
• Reichstein, M., M. Bahn, M. D. Mahecha, J. Kattge, and D. D. Baldocchi, 2014. “Linking Plant and Ecosystem Functional Biogeography.” Proceedings of the National Academy of Sciences 111:13697–13702
   Google Scholar
• Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and F. Prabhat. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204. https://doi.org/10.1038/s41586-019-0912-1.
   PubMed Web of Science ®Google Scholar
• Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323 (6088): 533–536. https://doi.org/10.1038/323533a0.
   Web of Science ®Google Scholar
• Saha, S., S. Moorthi, X. Wu, J. Wang, S. Nadiga, P. Tripp, D. Behringer, et al. 2014. “The NCEP Climate Forecast System Version 2.” Journal of Climate 27 (6): 2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1.
   Web of Science ®Google Scholar
• Schaaf, C., and Z. Wang. 2015. “MCD43A3: MODIS. Terra and Aqua BRDF/Albedo.” Daily L3 Global 500.
   Google Scholar
• Schimel, D., R. Pavlick, J. B. Fisher, G. P. Asner, S. Saatchi, P. Townsend, C. Miller, et al. 2015. “Observing Terrestrial Ecosystems and the Carbon Cycle from Space.” Global Change Biology 21 (5): 1762–1776. https://doi.org/10.1111/gcb.12822.
   PubMed Web of Science ®Google Scholar
• Slevin, D. 2016. “Investigating Sources of Uncertainty Associated with the JULES Land Surface Model.” The University of Edinburgh [Ph.D. thesis]. https://era.ed.ac.uk/handle/1842/20953?show=full.
   Google Scholar
• Smith, P., L. Beaumont, C. J. Bernacchi, M. Byrne, W. Cheung, R. T. Conant, F. Cotrufo, et al. 2021. “Essential Outcomes for COP26.” Global Change Biology 28 (1): 1–3. https://doi.org/10.1111/gcb.15926.
   PubMed Web of Science ®Google Scholar
• Strubell, E., A. Ganesh, and A. McCallum. 2020. “Energy and Policy Considerations for Modern Deep Learning Research.” Proceedings of the AAAI Conference on Artificial Intelligence 34 (09): 13693–13696. https://doi.org/10.1609/aaai.v34i09.7123.
   Google Scholar
• Sulkava, M., S. Luyssaert, S. Zaehle, and D. Papale. 2011. “Assessing and Improving the Representativeness of Monitoring Networks: The European Flux Tower Network Example.” Journal of Geophysical Research: Biogeosciences 116. https://doi.org/10.1029/2010JG001562.
   PubMed Web of Science ®Google Scholar
• Sun, Y., C. Frankenberg, M. Jung, J. Joiner, L. Guanter, P. Köhler, T. Magney, et al. 2018. “Overview of Solar-Induced Chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, Cross-Mission Comparison, and Global Monitoring for GPP.” Remote Sensing of Environment 209:808–823. https://doi.org/10.1016/j.rse.2018.02.016.
   Web of Science ®Google Scholar
• Tramontana, G., K. Ichii, G. Camps-Valls, E. Tomelleri, and D. Papale. 2015. “Uncertainty Analysis of Gross Primary Production Upscaling Using Random Forests, Remote Sensing and Eddy Covariance Data.” Remote Sensing of Environment 168:360–373. https://doi.org/10.1016/j.rse.2015.07.015.
   Web of Science ®Google Scholar
• Tramontana, G., M. Jung, C. R. Schwalm, K. Ichii, G. Camps-Valls, B. Ráduly, M. Reichstein, et al. 2016. “Predicting Carbon Dioxide and Energy Fluxes Across Global FLUXNET Sites with Regression Algorithms.” Biogeosciences 13 (14): 4291–4313. https://doi.org/10.5194/bg-13-4291-2016.
   Web of Science ®Google Scholar
• Ueyama, M., K. Ichii, H. Iwata, E. S. Euskirchen, D. Zona, A. V. Rocha, Y. Harazono, et al. 2013. “Upscaling Terrestrial Carbon Dioxide Fluxes in Alaska with Satellite Remote Sensing and Support Vector Regression.” Journal of Geophysical Research: Biogeosciences 118 (3): 1266–1281. https://doi.org/10.1002/jgrg.20095.
   Web of Science ®Google Scholar
• Verrelst, J., J. P. Rivera, C. van der Tol, F. Magnani, G. Mohammed, and J. Moreno. 2015. “Global Sensitivity Analysis of the SCOPE Model: What Drives Simulated Canopy-Leaving Sun-Induced Fluorescence?” Remote Sensing of Environment 166:8–21. https://doi.org/10.1016/j.rse.2015.06.002.
   Web of Science ®Google Scholar
• Wang, H., I. C. Prentice, T. F. Keenan, T. W. Davis, I. J. Wright, W. K. Cornwell, B. J. Evans, et al. 2017. “Towards a Universal Model for Carbon Dioxide Uptake by Plants.” Nature Plants 3 (9): 734–741. https://doi.org/10.1038/s41477-017-0006-8.
   PubMed Web of Science ®Google Scholar
• Wang, L., H. Zhu, A. Lin, L. Zou, W. Qin, and Q. Du. 2017. “Evaluation of the Latest MODIS GPP Products Across Multiple Biomes Using Global Eddy Covariance Flux Data.” Remote Sensing 9 (5): 418. https://doi.org/10.3390/rs9050418.
   Web of Science ®Google Scholar
• Wolpert, D. H. 1992. “Stacked Generalization.” Neural Networks 5 (2): 241–259. https://doi.org/10.1016/S0893-6080(05)80023-1.
   Web of Science ®Google Scholar
• Yang, F., K. Ichii, M. A. White, H. Hashimoto, A. R. Michaelis, P. Votava, A.-X. Zhu, et al. 2007. “Developing a Continental-Scale Measure of Gross Primary Production by Combining MODIS and AmeriFlux Data Through Support Vector Machine Approach.” Remote Sensing of Environment 110 (1): 109–122. https://doi.org/10.1016/j.rse.2007.02.016.
   Web of Science ®Google Scholar
• Zeng, J., T. Matsunaga, Z.-H. Tan, N. Saigusa, T. Shirai, Y. Tang, S. Peng, et al. 2020. “Global Terrestrial Carbon Fluxes of 1999–2019 Estimated by Upscaling Eddy Covariance Data with a Random Forest.” Scientific Data 7 (1): 1–11. https://doi.org/10.1038/s41597-020-00653-5.
   PubMed Web of Science ®Google Scholar
• Zhang, Y., X. Xiao, C. Jin, J. Dong, S. Zhou, P. Wagle, J. Joiner, et al. 2016. “Consistency Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production of Vegetation in North America.” Remote Sensing of Environment 183:154–169. https://doi.org/10.1016/j.rse.2016.05.015.
   Web of Science ®Google Scholar
• Zhu, S., R. Clement, J. McCalmont, C. A. Davies, and T. Hill. 2022. “Stable Gap-Filling for Longer Eddy Covariance Data Gaps: A Globally Validated Machine-Learning Approach for Carbon Dioxide, Water, and Energy Fluxes.” Agricultural and Forest Meteorology 314:108777. https://doi.org/10.1016/j.agrformet.2021.108777.
   Web of Science ®Google Scholar
• Zhu, S., J. McCalmont, L. M. Cardenas, A. M. Cunliffe, L. Olde, C. Signori-Müller, M. E. Litvak, et al. 2023. “Gap-Filling Carbon Dioxide, Water, Energy, and Methane Fluxes in Challenging Ecosystems: Comparing Between Methods, Drivers, and Gap-Lengths.” Agricultural and Forest Meteorology 332:109365. https://doi.org/10.1016/j.agrformet.2023.109365.
   Web of Science ®Google Scholar
• Zhu, S., L. Olde, K. Lewis, T. Quaife, L. Cardenas, N. Loick, J. Xu, et al. 2023. “Eddy Covariance Fluxes Over Managed Ecosystems Extrapolated to Field Scales at Fine Spatial Resolutions.” Agricultural and Forest Meteorology 342:109675. https://doi.org/10.1016/j.agrformet.2023.109675.
   Web of Science ®Google Scholar