Articles | Volume 21, issue 10
https://doi.org/10.5194/bg-21-2447-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-2447-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 May 2024
Research article |
| 24 May 2024
| 24 May 2024
Using automated machine learning for the upscaling of gross primary productivity
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, Denmark
Yanghui Kang
CORRESPONDING AUTHOR
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Guy Schurgers
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, Denmark
Trevor Keenan
CORRESPONDING AUTHOR
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-337, https://doi.org/10.5194/essd-2023-337, 2023
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CEDAR-GPP provides spatiotemporally upscaled estimates of Gross Primary Productivity, uniquely incorporating the direct effect of elevated atmospheric CO2 on global photosynthesis. This dataset was produced by upscaling global eddy covariance measurements with multi-source satellite observations and climate data using machine learning. Available at monthly and 0.05° resolution from 1982 to 2020, CEDAR-GPP offers critical insights into ecosystem-climate interaction.
Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, and Trevor Keenan
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CEDAR-GPP provides spatiotemporally upscaled estimates of Gross Primary Productivity, uniquely incorporating the direct effect of elevated atmospheric CO2 on global photosynthesis. This dataset was produced by upscaling global eddy covariance measurements with multi-source satellite observations and climate data using machine learning. Available at monthly and 0.05° resolution from 1982 to 2020, CEDAR-GPP offers critical insights into ecosystem-climate interaction.
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Understanding terrestrial sources of nitrogen is vital to examine lake eutrophication changes. Combining process-based ecosystem modeling and satellite observations, we found that land-leached nitrogen in the Yangtze Plain significantly increased from 1979 to 2018, and terrestrial nutrient sources were positively correlated with eutrophication trends observed in most lakes, demonstrating the necessity of sustainable nitrogen management to control eutrophication.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
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Wim Verbruggen, Guy Schurgers, Stéphanie Horion, Jonas Ardö, Paulo N. Bernardino, Bernard Cappelaere, Jérôme Demarty, Rasmus Fensholt, Laurent Kergoat, Thomas Sibret, Torbern Tagesson, and Hans Verbeeck
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Atmos. Chem. Phys., 18, 13321–13328, https://doi.org/10.5194/acp-18-13321-2018, https://doi.org/10.5194/acp-18-13321-2018, 2018
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The development of eddy-covariance measurements of ecosystem CO2 fluxes began a new era in the field studies of photosynthesis. The interpretation of the very variable CO2 fluxes in evergreen forests has been problematic especially in seasonal transition times. We apply two theoretical needle-level equations and show they can predict photosynthetic CO2 flux between the atmosphere and Scots pine forests. This has strong implications for the interpretation of the global change and boreal forests.
Ylva van Meeningen, Guy Schurgers, Riikka Rinnan, and Thomas Holst
Biogeosciences, 14, 4045–4060, https://doi.org/10.5194/bg-14-4045-2017, https://doi.org/10.5194/bg-14-4045-2017, 2017
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Leaf scale measurements have been performed on English oak, European beech and Norway spruce at a field site in Denmark to study the release of volatile compounds in response to a change in light. Whilst some compounds, like isoprene and sabinene, increased with increasing light, other compounds, like camphene, showed no light response for most of the trees. This can help to increase our knowledge of how species and compounds respond to light and to possibly improve how they can be modeled.
Emilie Öström, Zhou Putian, Guy Schurgers, Mikhail Mishurov, Niku Kivekäs, Heikki Lihavainen, Mikael Ehn, Matti P. Rissanen, Theo Kurtén, Michael Boy, Erik Swietlicki, and Pontus Roldin
Atmos. Chem. Phys., 17, 8887–8901, https://doi.org/10.5194/acp-17-8887-2017, https://doi.org/10.5194/acp-17-8887-2017, 2017
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We used a model to study how biogenic volatile organic compounds (BVOCs) emitted from the boreal forest contribute to the formation and growth of particles in the atmosphere. Some of these particles are important climate forcers, acting as seeds for cloud droplet fomation. We implemented a new gas chemistry mechanism that describes how the BVOCs are oxidized and form low-volatility highly oxidized organic molecules. With the new mechanism we are able to accurately predict the particle growth.
Jing Tang, Guy Schurgers, Hanna Valolahti, Patrick Faubert, Päivi Tiiva, Anders Michelsen, and Riikka Rinnan
Biogeosciences, 13, 6651–6667, https://doi.org/10.5194/bg-13-6651-2016, https://doi.org/10.5194/bg-13-6651-2016, 2016
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Arctic is warming at twice the global average speed and the warming-induced increases in biogenic volatile organic compound (BVOC) emissions from Arctic plants are expected to be drastic. This modelling study aims to investigate BVOC emission responses to warming. The results show that 2 °C summer warming can increase annual emissions by 56 % and the short-term warming responses are strongly impacted by leaf temperature, while the long-time responses are interacted with vegetation changes.
Ylva van Meeningen, Guy Schurgers, Riikka Rinnan, and Thomas Holst
Biogeosciences, 13, 6067–6080, https://doi.org/10.5194/bg-13-6067-2016, https://doi.org/10.5194/bg-13-6067-2016, 2016
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English oak and European beech are common European trees known to release volatile compounds such as isoprene and monoterpenes. By doing leaf chamber measurements at three sites in Europe, the aim was to study how the emission differed for cloned trees growing at different sites. The measured emission rates from clones varied between sites, but the relative compound contribution was stable both within and between sites. This can help to increase our knowledge of emission pattern variability.
Minchao Wu, Guy Schurgers, Markku Rummukainen, Benjamin Smith, Patrick Samuelsson, Christer Jansson, Joe Siltberg, and Wilhelm May
Earth Syst. Dynam., 7, 627–647, https://doi.org/10.5194/esd-7-627-2016, https://doi.org/10.5194/esd-7-627-2016, 2016
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Atmos. Chem. Phys., 16, 5243–5262, https://doi.org/10.5194/acp-16-5243-2016, https://doi.org/10.5194/acp-16-5243-2016, 2016
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We study the potentially contrasting effects of enhanced ecosystem CO2 release in response to warmer temperatures vs. emissions of biogenic volatile organic compounds and their formation of secondary organic aerosol through a combination of measurements and modelling at a remote location in Eastern Siberia. The study aims to highlight the number of potentially opposing processes and complex interactions between vegetation physiology, soil processes and trace-gas exchanges in the climate system.
S. Olin, M. Lindeskog, T. A. M. Pugh, G. Schurgers, D. Wårlind, M. Mishurov, S. Zaehle, B. D. Stocker, B. Smith, and A. Arneth
Earth Syst. Dynam., 6, 745–768, https://doi.org/10.5194/esd-6-745-2015, https://doi.org/10.5194/esd-6-745-2015, 2015
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Croplands are vital ecosystems for human well-being. Properly managed they can supply food, store carbon and even sequester carbon from the atmosphere. Conversely, if poorly managed, croplands can be a source of nitrogen to inland and coastal waters, causing algal blooms, and a source of carbon dioxide to the atmosphere, accentuating climate change. Here we studied cropland management types for their potential to store carbon and minimize nitrogen losses while maintaining crop yields.
N. Sudarchikova, U. Mikolajewicz, C. Timmreck, D. O'Donnell, G. Schurgers, D. Sein, and K. Zhang
Clim. Past, 11, 765–779, https://doi.org/10.5194/cp-11-765-2015, https://doi.org/10.5194/cp-11-765-2015, 2015
S. Olin, G. Schurgers, M. Lindeskog, D. Wårlind, B. Smith, P. Bodin, J. Holmér, and A. Arneth
Biogeosciences, 12, 2489–2515, https://doi.org/10.5194/bg-12-2489-2015, https://doi.org/10.5194/bg-12-2489-2015, 2015
G. Schurgers, F. Lagergren, M. Mölder, and A. Lindroth
Biogeosciences, 12, 237–256, https://doi.org/10.5194/bg-12-237-2015, https://doi.org/10.5194/bg-12-237-2015, 2015
A. Arneth, S. Olin, R. Makkonen, P. Paasonen, T. Holst, M. Kajos, M. Kulmala, T. Maximov, P. A. Miller, and G. Schurgers
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-19149-2014, https://doi.org/10.5194/acpd-14-19149-2014, 2014
Revised manuscript not accepted
N. Unger, K. Harper, Y. Zheng, N. Y. Kiang, I. Aleinov, A. Arneth, G. Schurgers, C. Amelynck, A. Goldstein, A. Guenther, B. Heinesch, C. N. Hewitt, T. Karl, Q. Laffineur, B. Langford, K. A. McKinney, P. Misztal, M. Potosnak, J. Rinne, S. Pressley, N. Schoon, and D. Serça
Atmos. Chem. Phys., 13, 10243–10269, https://doi.org/10.5194/acp-13-10243-2013, https://doi.org/10.5194/acp-13-10243-2013, 2013
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Joyson Ahongshangbam, Liisa Kulmala, Jesse Soininen, Yasmin Frühauf, Esko Karvinen, Yann Salmon, Anna Lintunen, Anni Karvonen, and Leena Järvi
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Ryan Vella, Andrea Pozzer, Matthew Forrest, Jos Lelieveld, Thomas Hickler, and Holger Tost
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The mechanisms of soil CO2 flux in dry desert soils are not fully understood. Yet studies conducted in desert ecosystems rarely discuss potential errors related to using the commonly used flux chambers in dry and bare soils. In our study, the conventional deployment practice of the chambers underestimated the instantaneous CO2 flux by up to 50 % and the total daily CO2 uptake by 35 %. This suggests that desert soils are a larger carbon sink than previously reported.
Rosemary J. Eufemio, Ingrid de Almeida Ribeiro, Todd L. Sformo, Gary A. Laursen, Valeria Molinero, Janine Fröhlich-Nowoisky, Mischa Bonn, and Konrad Meister
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Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen
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Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
Norbert Pirk, Kristoffer Aalstad, Yeliz A. Yilmaz, Astrid Vatne, Andrea L. Popp, Peter Horvath, Anders Bryn, Ane Victoria Vollsnes, Sebastian Westermann, Terje Koren Berntsen, Frode Stordal, and Lena Merete Tallaksen
Biogeosciences, 20, 2031–2047, https://doi.org/10.5194/bg-20-2031-2023, https://doi.org/10.5194/bg-20-2031-2023, 2023
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We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years. The extremely snow-rich conditions in 2020 reduced the total annual evapotranspiration to 50 % and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink to an even stronger source. Our analysis suggests that snow cover anomalies are driving the most consequential short-term responses in this ecosystem’s functioning.
Hamadou Balde, Gabriel Hmimina, Yves Goulas, Gwendal Latouche, and Kamel Soudani
Biogeosciences, 20, 1473–1490, https://doi.org/10.5194/bg-20-1473-2023, https://doi.org/10.5194/bg-20-1473-2023, 2023
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This study focuses on the relationship between sun-induced chlorophyll fluorescence (SIF) and ecosystem gross primary productivity (GPP) across the ICOS European flux tower network. It shows that SIF, coupled with reflectance observations, explains over 80 % of the GPP variability across diverse ecosystems but fails to bring new information compared to reflectance alone at coarse spatial scales (~5 km). These findings have applications in agriculture and ecophysiological studies.
John T. Walker, Xi Chen, Zhiyong Wu, Donna Schwede, Ryan Daly, Aleksandra Djurkovic, A. Christopher Oishi, Eric Edgerton, Jesse Bash, Jennifer Knoepp, Melissa Puchalski, John Iiames, and Chelcy F. Miniat
Biogeosciences, 20, 971–995, https://doi.org/10.5194/bg-20-971-2023, https://doi.org/10.5194/bg-20-971-2023, 2023
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Better estimates of atmospheric nitrogen (N) deposition are needed to accurately assess ecosystem risk and impacts from deposition of nutrients and acidity. Using measurements and modeling, we estimate total N deposition of 6.7 kg N ha−1 yr−1 at a forest site in the southern Appalachian Mountains, a region sensitive to atmospheric deposition. Reductions in deposition of reduced forms of N (ammonia and ammonium) will be needed to meet the lowest estimates of N critical loads for the region.
Yi-Ying Chen and Sebastiaan Luyssaert
Biogeosciences, 20, 349–363, https://doi.org/10.5194/bg-20-349-2023, https://doi.org/10.5194/bg-20-349-2023, 2023
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Tropical cyclones are typically assumed to be associated with ecosystem damage. This study challenges this assumption and suggests that instead of reducing leaf area, cyclones in East Asia may increase leaf area by alleviating water stress.
Deborah F. McGlynn, Graham Frazier, Laura E. R. Barry, Manuel T. Lerdau, Sally E. Pusede, and Gabriel Isaacman-VanWertz
Biogeosciences, 20, 45–55, https://doi.org/10.5194/bg-20-45-2023, https://doi.org/10.5194/bg-20-45-2023, 2023
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Using a custom-made gas chromatography flame ionization detector, 2 years of speciated hourly biogenic volatile organic compound data were collected in a forest in central Virginia. We identify diurnal and seasonal variability in the data, which is shown to impact atmospheric oxidant budgets. A comparison with emission models identified discrepancies with implications for model outcomes. We suggest increased monitoring of speciated biogenic volatile organic compounds to improve modeled results.
Luke D. Schiferl, Jennifer D. Watts, Erik J. L. Larson, Kyle A. Arndt, Sébastien C. Biraud, Eugénie S. Euskirchen, Jordan P. Goodrich, John M. Henderson, Aram Kalhori, Kathryn McKain, Marikate E. Mountain, J. William Munger, Walter C. Oechel, Colm Sweeney, Yonghong Yi, Donatella Zona, and Róisín Commane
Biogeosciences, 19, 5953–5972, https://doi.org/10.5194/bg-19-5953-2022, https://doi.org/10.5194/bg-19-5953-2022, 2022
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As the Arctic rapidly warms, vast stores of thawing permafrost could release carbon dioxide (CO2) into the atmosphere. We combined observations of atmospheric CO2 concentrations from aircraft and a tower with observed CO2 fluxes from tundra ecosystems and found that the Alaskan North Slope in not a consistent source nor sink of CO2. Our study shows the importance of using both site-level and atmospheric measurements to constrain regional net CO2 fluxes and improve biogenic processes in models.
Pascal Wintjen, Frederik Schrader, Martijn Schaap, Burkhard Beudert, Richard Kranenburg, and Christian Brümmer
Biogeosciences, 19, 5287–5311, https://doi.org/10.5194/bg-19-5287-2022, https://doi.org/10.5194/bg-19-5287-2022, 2022
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For the first time, we compared four methods for estimating the annual dry deposition of total reactive nitrogen into a low-polluted forest ecosystem. In our analysis, we used 2.5 years of flux measurements, an in situ modeling approach, a large-scale chemical transport model (CTM), and canopy budget models. Annual nitrogen dry deposition budgets ranged between 4.3 and 6.7 kg N ha−1 a−1, depending on the applied method.
Michael Staudt, Juliane Daussy, Joseph Ingabire, and Nafissa Dehimeche
Biogeosciences, 19, 4945–4963, https://doi.org/10.5194/bg-19-4945-2022, https://doi.org/10.5194/bg-19-4945-2022, 2022
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We studied the short- and long-term effects of CO2 as a function of temperature on monoterpene emissions from holm oak. Similarly to isoprene, emissions decreased non-linearly with increasing CO2, with no differences among compounds and chemotypes. The CO2 response was modulated by actual leaf and growth temperature but not by growth CO2. Estimates of annual monoterpene release under double CO2 suggest that CO2 inhibition does not offset the increase in emissions due to expected warming.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
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Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Valery A. Isidorov and Andrej A. Zaitsev
Biogeosciences, 19, 4715–4746, https://doi.org/10.5194/bg-19-4715-2022, https://doi.org/10.5194/bg-19-4715-2022, 2022
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Biogenic volatile organic compounds (VOCs) play a critical role in earth-system processes: they are
main playersin the formation of tropospheric O3 and secondary aerosols, which have a significant impact on climate, human health and crops. A complex mixture of VOCs, formed as a result of physicochemical and biological processes, is released into the atmosphere from the forest floor. This review presents data on the composition of VOCs and contribution of various processes to their emissions.
David S. McLagan, Harald Biester, Tomas Navrátil, Stephan M. Kraemer, and Lorenz Schwab
Biogeosciences, 19, 4415–4429, https://doi.org/10.5194/bg-19-4415-2022, https://doi.org/10.5194/bg-19-4415-2022, 2022
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Spruce and larch trees are effective archiving species for historical atmospheric mercury using growth rings of bole wood. Mercury stable isotope analysis proved an effective tool to characterise industrial mercury signals and assess mercury uptake pathways (leaf uptake for both wood and bark) and mercury cycling within the trees. These data detail important information for understanding the mercury biogeochemical cycle particularly in forest systems.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
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Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
Anders Lindroth, Norbert Pirk, Ingibjörg S. Jónsdóttir, Christian Stiegler, Leif Klemedtsson, and Mats B. Nilsson
Biogeosciences, 19, 3921–3934, https://doi.org/10.5194/bg-19-3921-2022, https://doi.org/10.5194/bg-19-3921-2022, 2022
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We measured the fluxes of carbon dioxide and methane between a moist moss tundra and the atmosphere on Svalbard in order to better understand how such ecosystems are affecting the climate and vice versa. We found that the system was a small sink of carbon dioxide and a small source of methane. These fluxes are small in comparison with other tundra ecosystems in the high Arctic. Analysis of temperature sensitivity showed that respiration was more sensitive than photosynthesis above about 6 ℃.
Phillip Papastefanou, Christian S. Zang, Zlatan Angelov, Aline Anderson de Castro, Juan Carlos Jimenez, Luiz Felipe Campos De Rezende, Romina C. Ruscica, Boris Sakschewski, Anna A. Sörensson, Kirsten Thonicke, Carolina Vera, Nicolas Viovy, Celso Von Randow, and Anja Rammig
Biogeosciences, 19, 3843–3861, https://doi.org/10.5194/bg-19-3843-2022, https://doi.org/10.5194/bg-19-3843-2022, 2022
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The Amazon rainforest has been hit by multiple severe drought events. In this study, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon. Using nine different precipitation datasets and three drought indicators we find large differences in drought stress across the Amazon region. We conclude that future studies should use multiple rainfall datasets and drought indicators when estimating the impact of drought stress in the Amazon region.
Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences, 19, 3739–3756, https://doi.org/10.5194/bg-19-3739-2022, https://doi.org/10.5194/bg-19-3739-2022, 2022
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A number of studies have been conducted by using machine learning approaches to simulate carbon fluxes. We performed a meta-analysis of these net ecosystem exchange (NEE) simulations. Random forests and support vector machines performed better than other algorithms. Models with larger timescales had a lower accuracy. For different plant functional types (PFTs), there were significant differences in the predictors used and their effects on model accuracy.
Siqi Li, Wei Zhang, Xunhua Zheng, Yong Li, Shenghui Han, Rui Wang, Kai Wang, Zhisheng Yao, Chunyan Liu, and Chong Zhang
Biogeosciences, 19, 3001–3019, https://doi.org/10.5194/bg-19-3001-2022, https://doi.org/10.5194/bg-19-3001-2022, 2022
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The CNMM–DNDC model was modified to simulate ammonia volatilization (AV) from croplands. AV from cultivated uplands followed the first-order kinetics, which was jointly regulated by the factors of soil properties and meteorological conditions. AV simulation from rice paddy fields was improved by incorporating Jayaweera–Mikkelsen mechanisms. The modified model performed well in simulating the observed cumulative AV measured from 63 fertilization events in China.
Matthias Volk, Matthias Suter, Anne-Lena Wahl, and Seraina Bassin
Biogeosciences, 19, 2921–2937, https://doi.org/10.5194/bg-19-2921-2022, https://doi.org/10.5194/bg-19-2921-2022, 2022
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Because soils are an important sink for greenhouse gasses, we subjected sub-alpine grassland to a six-level climate change treatment.
Two independent methods showed that at warming > 1.5 °C the grassland ecosystem lost ca. 14 % or ca. 1 kg C m−2 in 5 years.
This shrinking of the terrestrial C sink implies a substantial positive feedback to the atmospheric greenhouse effect.
It is likely that this dramatic C loss is a transient effect before a new, climate-adjusted steady state is reached.
Johan A. Eckdahl, Jeppe A. Kristensen, and Daniel B. Metcalfe
Biogeosciences, 19, 2487–2506, https://doi.org/10.5194/bg-19-2487-2022, https://doi.org/10.5194/bg-19-2487-2022, 2022
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This study found climate to be a driving force for increasing per area emissions of greenhouse gases and removal of important nutrients from high-latitude forests due to wildfire. It used detailed direct measurements over a large area to uncover patterns and mechanisms of restructuring of forest carbon and nitrogen pools that are extrapolatable to larger regions. It also takes a step forward in filling gaps in global knowledge of northern forest response to climate-change-strengthened wildfires.
Sparkle L. Malone, Youmi Oh, Kyle A. Arndt, George Burba, Roisin Commane, Alexandra R. Contosta, Jordan P. Goodrich, Henry W. Loescher, Gregory Starr, and Ruth K. Varner
Biogeosciences, 19, 2507–2522, https://doi.org/10.5194/bg-19-2507-2022, https://doi.org/10.5194/bg-19-2507-2022, 2022
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To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United States of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover.
Bruna R. F. Oliveira, Jan J. Keizer, and Thomas Foken
Biogeosciences, 19, 2235–2243, https://doi.org/10.5194/bg-19-2235-2022, https://doi.org/10.5194/bg-19-2235-2022, 2022
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This study analyzes the impacts of this windthrow on the aerodynamic characteristics of zero-plane displacement and roughness length and, ultimately, their implications for the turbulent fluxes. The turbulent fluxes were only affected to a minor degree by the windthrow, but the footprint area of the flux tower changed markedly so that the target area of the measurements had to be redetermined.
Minttu Havu, Liisa Kulmala, Pasi Kolari, Timo Vesala, Anu Riikonen, and Leena Järvi
Biogeosciences, 19, 2121–2143, https://doi.org/10.5194/bg-19-2121-2022, https://doi.org/10.5194/bg-19-2121-2022, 2022
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The carbon sequestration potential of two street tree species and the soil beneath them was quantified with the urban land surface model SUEWS and the soil carbon model Yasso. The street tree plantings turned into a modest sink of carbon from the atmosphere after 14 years. Overall, the results indicate the importance of soil in urban carbon sequestration estimations, as soil respiration exceeded the carbon uptake in the early phase, due to the high initial carbon loss from the soil.
Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, and Kai Puolamäki
Biogeosciences, 19, 2095–2099, https://doi.org/10.5194/bg-19-2095-2022, https://doi.org/10.5194/bg-19-2095-2022, 2022
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Causal structure discovery algorithms have been making headway into Earth system sciences, and they can be used to increase our understanding on biosphere–atmosphere interactions. In this paper we present a procedure on how to utilize prior knowledge of the domain experts together with these algorithms in order to find more robust causal structure models. We also demonstrate how to avoid pitfalls such as over-fitting and concept drift during this process.
Makoto Saito, Tomohiro Shiraishi, Ryuichi Hirata, Yosuke Niwa, Kazuyuki Saito, Martin Steinbacher, Doug Worthy, and Tsuneo Matsunaga
Biogeosciences, 19, 2059–2078, https://doi.org/10.5194/bg-19-2059-2022, https://doi.org/10.5194/bg-19-2059-2022, 2022
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This study tested combinations of two sources of AGB data and two sources of LCC data and used the same burned area satellite data to estimate BB CO emissions. Our analysis showed large discrepancies in annual mean CO emissions and explicit differences in the simulated CO concentrations among the BB emissions estimates. This study has confirmed that BB emissions estimates are sensitive to the land surface information on which they are based.
Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen
Biogeosciences, 19, 1777–1793, https://doi.org/10.5194/bg-19-1777-2022, https://doi.org/10.5194/bg-19-1777-2022, 2022
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We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
Shihan Sun, Amos P. K. Tai, David H. Y. Yung, Anthony Y. H. Wong, Jason A. Ducker, and Christopher D. Holmes
Biogeosciences, 19, 1753–1776, https://doi.org/10.5194/bg-19-1753-2022, https://doi.org/10.5194/bg-19-1753-2022, 2022
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We developed and used a terrestrial biosphere model to compare and evaluate widely used empirical dry deposition schemes with different stomatal approaches and found that using photosynthesis-based stomatal approaches can reduce biases in modeled dry deposition velocities in current chemical transport models. Our study shows systematic errors in current dry deposition schemes and the importance of representing plant ecophysiological processes in models under a changing climate.
Ka Ming Fung, Maria Val Martin, and Amos P. K. Tai
Biogeosciences, 19, 1635–1655, https://doi.org/10.5194/bg-19-1635-2022, https://doi.org/10.5194/bg-19-1635-2022, 2022
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Fertilizer-induced ammonia detrimentally affects the environment by not only directly damaging ecosystems but also indirectly altering climate and soil fertility. To quantify these secondary impacts, we enabled CESM to simulate ammonia emission, chemical evolution, and deposition as a continuous cycle. If synthetic fertilizer use is to soar by 30 % from today's level, we showed that the counteracting impacts will increase the global ammonia emission by 3.3 Tg N per year.
Lena Wohlgemuth, Pasi Rautio, Bernd Ahrends, Alexander Russ, Lars Vesterdal, Peter Waldner, Volkmar Timmermann, Nadine Eickenscheidt, Alfred Fürst, Martin Greve, Peter Roskams, Anne Thimonier, Manuel Nicolas, Anna Kowalska, Morten Ingerslev, Päivi Merilä, Sue Benham, Carmen Iacoban, Günter Hoch, Christine Alewell, and Martin Jiskra
Biogeosciences, 19, 1335–1353, https://doi.org/10.5194/bg-19-1335-2022, https://doi.org/10.5194/bg-19-1335-2022, 2022
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Gaseous mercury is present in the atmosphere all over the globe. During the growing season, plants take up mercury from the air in a similar way as CO2. We investigated which factors impact this vegetational mercury uptake by analyzing a large dataset of leaf mercury uptake rates of trees in Europe. As a result, we conclude that mercury uptake is foremost controlled by tree-intrinsic traits like physiological activity but also by climatic factors like dry conditions in the air and in soils.
Junqi Wei, Xiaoyan Li, Lei Liu, Torben Røjle Christensen, Zhiyun Jiang, Yujun Ma, Xiuchen Wu, Hongyun Yao, and Efrén López-Blanco
Biogeosciences, 19, 861–875, https://doi.org/10.5194/bg-19-861-2022, https://doi.org/10.5194/bg-19-861-2022, 2022
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Although water availability has been linked to the response of ecosystem carbon (C) sink–source to climate warming, the mechanisms by which C uptake responds to soil moisture remain unclear. We explored how soil water and other environmental drivers modulate net C uptake in an alpine swamp meadow. Results reveal that nearly saturated soil conditions during warm seasons can help to maintain lower ecosystem respiration and therefore enhance the C sequestration capacity in this alpine swamp meadow.
Martijn M. T. A. Pallandt, Jitendra Kumar, Marguerite Mauritz, Edward A. G. Schuur, Anna-Maria Virkkala, Gerardo Celis, Forrest M. Hoffman, and Mathias Göckede
Biogeosciences, 19, 559–583, https://doi.org/10.5194/bg-19-559-2022, https://doi.org/10.5194/bg-19-559-2022, 2022
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Thawing of Arctic permafrost soils could trigger the release of vast amounts of carbon to the atmosphere, thus enhancing climate change. Our study investigated how well the current network of eddy covariance sites to monitor greenhouse gas exchange at local scales captures pan-Arctic flux patterns. We identified large coverage gaps, e.g., in Siberia, but also demonstrated that a targeted addition of relatively few sites can significantly improve network performance.
Pascal Wintjen, Frederik Schrader, Martijn Schaap, Burkhard Beudert, and Christian Brümmer
Biogeosciences, 19, 389–413, https://doi.org/10.5194/bg-19-389-2022, https://doi.org/10.5194/bg-19-389-2022, 2022
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Fluxes of total reactive nitrogen (∑Nr) over a low polluted forest were analyzed with regard to their temporal dynamics. Mostly deposition was observed with median fluxes ranging from −15 to −5 ng N m−2 s−1, corresponding to a range of deposition velocities from 0.2 to 0.5 cm s−1. While seasonally changing contributions of NH3 and NOx to the ∑Nr signal were found, we estimate an annual total N deposition (dry+wet) of 12.2 and 10.9 kg N ha−1 a−1 in the 2 years of observation.
Thomas M. Blattmann
Biogeosciences, 19, 359–373, https://doi.org/10.5194/bg-19-359-2022, https://doi.org/10.5194/bg-19-359-2022, 2022
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This work enunciates the possibility of kerogen oxidation contributing to atmospheric CO2 increase in the wake of glacial episodes. This hypothesis is substantiated by several lines of independent evidence synthesized in this contribution. The author hypothesizes that the deglaciation of kerogen-rich lithologies in western Canada contributed to the characteristic deglacial increase in atmospheric CO2.
Juliana Gil-Loaiza, Joseph R. Roscioli, Joanne H. Shorter, Till H. M. Volkmann, Wei-Ren Ng, Jordan E. Krechmer, and Laura K. Meredith
Biogeosciences, 19, 165–185, https://doi.org/10.5194/bg-19-165-2022, https://doi.org/10.5194/bg-19-165-2022, 2022
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We evaluated a new diffusive soil probe integrated with high-resolution gas analyzers to measure soil gases in real time at a centimeter scale. Using columns with simple silica and soil, we captured changes in carbon dioxide (CO2), volatile organic compounds (VOCs), and nitrous oxide (N2O) with its isotopes to distinguish potential nutrient sources and microbial metabolism. This approach will advance the use of soil gases as important signals to understand and monitor soil fertility and health.
Yujie Wang and Christian Frankenberg
Biogeosciences, 19, 29–45, https://doi.org/10.5194/bg-19-29-2022, https://doi.org/10.5194/bg-19-29-2022, 2022
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Modeling vegetation canopy is important in predicting whether the land remains a carbon sink to mitigate climate change in the near future. Vegetation canopy model complexity, however, impacts the model-predicted carbon and water fluxes as well as canopy fluorescence, even if the same suite of model inputs is used. Given the biases caused by canopy model complexity, we recommend not misusing parameters inverted using different models or assumptions.
Håkan Pleijel, Jenny Klingberg, Michelle Nerentorp, Malin C. Broberg, Brigitte Nyirambangutse, John Munthe, and Göran Wallin
Biogeosciences, 18, 6313–6328, https://doi.org/10.5194/bg-18-6313-2021, https://doi.org/10.5194/bg-18-6313-2021, 2021
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Mercury is a problematic metal in the environment. It is crucial to understand the Hg circulation in ecosystems. We explored the mercury concentration in foliage from a diverse set of plants, locations and sampling periods to study the accumulation of Hg in leaves–needles over time. Mercury was always higher in older tissue: in broadleaved trees, conifers and wheat. Specific leaf area, the leaf area per unit leaf mass, turned out to be critical for Hg accumulation in leaves–needles.
Cited articles
Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., Wiltshire, A., Zaehle, S., and Zeng, N.: The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink, Science, 348, 895–899, https://doi.org/10.1126/science.aaa1668, 2015.
AmeriFlux Management Project: AmeriFlux, AmeriFlux [data set], https://ameriflux.lbl.gov/data/flux-data-products, last access: 13 October 2022.
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C., Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S., Sitch, S., Viovy, N., Wiltshire, A., and Zhao, M.: Spatiotemporal patterns of terrestrial gross primary production: A review: GPP Spatiotemporal Patterns, Rev. Geophys., 53, 785–818, https://doi.org/10.1002/2015RG000483, 2015.
Babaeian, E., Paheding, S., Siddique, N., Devabhaktuni, V. K., and Tuller, M.: Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning, Remote Sens. Environ., 260, 112434, https://doi.org/10.1016/j.rse.2021.112434, 2021.
Balaji, A. and Allen, A.: Benchmarking Automatic Machine Learning Frameworks, ArXiv [preprint], https://doi.org/10.48550/arXiv.1808.06492, 2018.
Barnes, M. L., Farella, M. M., Scott, R. L., Moore, D. J. P., Ponce-Campos, G. E., Biederman, J. A., MacBean, N., Litvak, M. E., and Breshears, D. D.: Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling, Commun. Earth Environ., 2, 1–9, https://doi.org/10.1038/s43247-021-00308-2, 2021.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F. I., and Papale, D.: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate, Science, 329, 834–838, https://doi.org/10.1126/science.1184984, 2010.
Bloomfield, K. J., Stocker, B. D., Keenan, T. F., and Prentice, I. C.: Environmental controls on the light use efficiency of terrestrial gross primary production, Glob. Change Biol., 29, 1037–1053, https://doi.org/10.1111/gcb.16511, 2023.
Bodesheim, P., Jung, M., Gans, F., Mahecha, M. D., and Reichstein, M.: Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product, Earth Syst. Sci. Data, 10, 1327–1365, https://doi.org/10.5194/essd-10-1327-2018, 2018.
Boenisch, G.: Max Planck Institute for Biogeochemistry, Data Portal, https://www.bgc-jena.mpg.de/geodb/projects/Home.php (last access: 11 May 2023), 2020.
Canadell, J. G., Scheel Monteiro, P., Costa, M. H., Cotrim da Cunha, L., Cox, P. M., Eliseev, A. V., Henson, S., Ishii, M., Jaccard, S., Koven, C., Lohila, A., Patra, P. K., Piao, S., Rogelj, J., Syampungani, S., Zaehle, S., and Zickfeld, K.: Global carbon and other biogeochemical cycles and feedbacks, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, Ö., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 673–816, https://doi.org/10.1017/9781009157896.001, 2021.
Caruana, R., Niculescu-Mizil, A., Crew, G., and Ksikes, A.: Ensemble selection from libraries of models, in: Twenty-first international conference on Machine learning – ICML '04, Twenty-first international conference, 4–8 July 2004, Banff, Alberta, Canada, 18, https://doi.org/10.1145/1015330.1015432, 2004.
Carvalho, S., Oliveira, A., Pedersen, J. S., Manhice, H., Lisboa, F., Norguet, J., de Wit, F., and Santos, F. D.: A changing Amazon rainforest: Historical trends and future projections under post-Paris climate scenarios, Global Planet. Change, 195, 103328, https://doi.org/10.1016/j.gloplacha.2020.103328, 2020.
Cawley, G. C. and Talbot, N. L. C.: On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation, J. Mach. Learn. Res., 11, 2079–2107, 2010.
Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturvedi, R. K., Fuchs, R., Brovkin, V., Ciais, P., Fensholt, R., Tømmervik, H., Bala, G., Zhu, Z., Nemani, R. R., and Myneni, R. B.: China and India lead in greening of the world through land-use management, Nat. Sustain., 2, 122–129, https://doi.org/10.1038/s41893-019-0220-7, 2019.
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., and Zona, D.: Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites, Agr. Forest Meteorol., 301–302, 108350, https://doi.org/10.1016/j.agrformet.2021.108350, 2021.
Dannenberg, M. P., Barnes, M. L., Smith, W. K., Johnston, M. R., Meerdink, S. K., Wang, X., Scott, R. L., and Biederman, J. A.: Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing, Biogeosciences, 20, 383–404, https://doi.org/10.5194/bg-20-383-2023, 2023.
Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets, J. Mach. Learn. Res., 7, 1–30, 2006.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: Data from the ESA CCI Soil Moisture project, CEDA Archive [data set], https://data.ceda.ac.uk/neodc/esacci/soil_moisture, last access: 13 October 2022.
Duan, S. and Zhang, X.: AutoML-Based Drought Forecast with Meteorological Variables, arXiv [preprint], https://doi.org/10.48550/arXiv.2207.07012, 23 August 2022.
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A.: AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data, arXiv [preprint], https://doi.org/10.48550/arXiv.2003.06505, 13 March 2020.
Ferreira, L., Pilastri, A., Martins, C. M., Pires, P. M., and Cortez, P.: A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost, in: 2021 International Joint Conference on Neural Networks (IJCNN), 2021 International Joint Conference on Neural Networks (IJCNN), 18–22 July 2021, Shenzhen, China, 1–8, https://doi.org/10.1109/IJCNN52387.2021.9534091, 2021.
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F.: Efficient and Robust Automated Machine Learning, in: Advances in Neural Information Processing Systems, Conference on Neural Information Processing Systems, Montréal, Canada, 7–12 December 2015, 2015a.
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F., Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., and Garnett, R.: auto-sklearn, GitHub [code], https://github.com/automl/auto-sklearn (last access: 13 August 2022), 2015b.
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F.: Practical Automated Machine Learning for the AutoML Challenge 2018, The Thirty-fifth International Conference on Machine Learning, Stockholm, Sweden, 10–15 July 2018, 2018.
Frank, D., Reichstein, M., Bahn, M., Thonicke, K., Frank, D., Mahecha, M. D., Smith, P., van der Velde, M., Vicca, S., Babst, F., Beer, C., Buchmann, N., Canadell, J. G., Ciais, P., Cramer, W., Ibrom, A., Miglietta, F., Poulter, B., Rammig, A., Seneviratne, S. I., Walz, A., Wattenbach, M., Zavala, M. A., and Zscheischler, J.: Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts, Glob. Change Biol., 21, 2861–2880, https://doi.org/10.1111/gcb.12916, 2015.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Gaber, M.: mxgbr/gpp_upscaling: AutoML for GPP upscaling v1.0 (publication), Zenodo [code], https://doi.org/10.5281/zenodo.8262618, 2023.
Gong, L. J., Liu, S. M., Shuang, X., Cai, X. H., and Xu, Z. W.: Investigation of spatial representativeness for surface flux measurements with eddy covariance system and large aperture scintillometer, Plateau Meteorology, 28, 246–257, 2009.
Gray, J. M., Frolking, S., Kort, E. A., Ray, D. K., Kucharik, C. J., Ramankutty, N., and Friedl, M. A.: Direct human influence on atmospheric CO2 seasonality from increased cropland productivity, Nature, 515, 398–401, https://doi.org/10.1038/nature13957, 2014.
Green, J. K., Seneviratne, S. I., Berg, A. M., Findell, K. L., Hagemann, S., Lawrence, D. M., and Gentine, P.: Large influence of soil moisture on long-term terrestrial carbon uptake, Nature, 565, 476–479, https://doi.org/10.1038/s41586-018-0848-x, 2019.
Green, J. K., Ballantyne, A., Abramoff, R., Gentine, P., Makowski, D., and Ciais, P.: Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas, Glob. Change Biol., 28, 2940–2955, https://doi.org/10.1111/gcb.16139, 2022.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
Guevara-Escobar, A., González-Sosa, E., Cervantes-Jiménez, M., Suzán-Azpiri, H., Queijeiro-Bolaños, M. E., Carrillo-Ángeles, I., and Cambrón-Sandoval, V. H.: Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland, Biogeosciences, 18, 367–392, https://doi.org/10.5194/bg-18-367-2021, 2021.
Guyon, I., Sun-Hosoya, L., Boullé, M., Escalante, H. J., Escalera, S., Liu, Z., Jajetic, D., Ray, B., Saeed, M., Sebag, M., Statnikov, A., Tu, W.-W., and Viegas, E.: Analysis of the AutoML Challenge Series 2015–2018, in: Automated Machine Learning: Methods, Systems, Challenges, edited by: Hutter, F., Kotthoff, L., and Vanschoren, J., Springer International Publishing, Cham, 177–219, https://doi.org/10.1007/978-3-030-05318-5_10, 2019.
Hain, C. R. and Anderson, M. C.: Estimating morning change in land surface temperature from MODIS day/night observations: Applications for surface energy balance modeling, Geophys. Res. Lett., 44, 9723–9733, https://doi.org/10.1002/2017GL074952, 2017.
Hanussek, M., Blohm, M., and Kintz, M.: Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark, in: 2020 2nd International Conference on Artificial Intelligence, Robotics and Control, 12–14 December 2020, New York, NY, USA, 29–32, https://doi.org/10.1145/3448326.3448353, 2020.
Haughton, N., Abramowitz, G., De Kauwe, M. G., and Pitman, A. J.: Does predictability of fluxes vary between FLUXNET sites?, Biogeosciences, 15, 4495–4513, https://doi.org/10.5194/bg-15-4495-2018, 2018.
Hutter, F., Kotthoff, L., and Vanschoren, J. (Eds.): Automated Machine Learning: Methods, Systems, Challenges, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-030-05318-5, 2019.
International Geosphere–Biosphere Programme: IGBP, http://www.igbp.net, last access: 8 January 2024.
Joiner, J. and Yoshida, Y.: Satellite-based reflectances capture large fraction of variability in global gross primary production (GPP) at weekly time scales, Agr. Forest Meteorol., 291, 108092, https://doi.org/10.1016/j.agrformet.2020.108092, 2020.
Joiner, J. and Yoshida, Y.: Global MODIS and FLUXNET-derived Daily Gross Primary Production, V2 (2), ORNL Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/1835, 2021.
Joiner, J., Yoshida, Y., Zhang, Y., Duveiller, G., Jung, M., Lyapustin, A., Wang, Y., and Tucker, C.: Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data, Remote Sens.-Basel, 10, 1346, https://doi.org/10.3390/rs10091346, 2018.
Jones, P. W.: First- and Second-Order Conservative Remapping Schemes for Grids in Spherical Coordinates, Mon. Weather Rev., 127, 2204–2210, https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2, 1999.
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D., Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D., Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A., Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M., Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations, J. Geophys. Res., 116, G00J07, https://doi.org/10.1029/2010JG001566, 2011.
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020.
Kalfas, J. L., Xiao, X., Vanegas, D. X., Verma, S. B., and Suyker, A. E.: Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data, Agr. Forest Meteorol., 151, 1514–1528, https://doi.org/10.1016/j.agrformet.2011.06.007, 2011.
Kannenberg, S. A., Anderegg, W. R. L., Barnes, M. L., Dannenberg, M. P., and Knapp, A. K.: Dominant role of soil moisture in mediating carbon and water fluxes in dryland ecosystems, Nat. Geosci., 17, 38–43, https://doi.org/10.1038/s41561-023-01351-8, 2024.
Karmaker, S. K., Hassan, M. M., Smith, M. J., Xu, L., Zhai, C., and Veeramachaneni, K.: AutoML to Date and Beyond: Challenges and Opportunities, ACM Comput. Surv., 54, 175:1–175:36, https://doi.org/10.1145/3470918, 2021.
Keenan, T. F., Prentice, I. C., Canadell, J. G., Williams, C. A., Wang, H., Raupach, M., and Collatz, G. J.: Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake, Nat. Commun., 7, 13428, https://doi.org/10.1038/ncomms13428, 2016.
Keenan, T. F., Migliavacca, M., Papale, D., Baldocchi, D., Reichstein, M., Torn, M., and Wutzler, T.: Widespread inhibition of daytime ecosystem respiration, Nat. Ecol. Evol., 3, 407–415, https://doi.org/10.1038/s41559-019-0809-2, 2019.
Kim, G. E., Steller, M., and Olson, S.: Modeling watershed nutrient concentrations with AutoML, in: Proceedings of the 10th International Conference on Climate Informatics, New York, NY, USA, 86–90, https://doi.org/10.1145/3429309.3429322, 2020.
Kotthoff, L., Thornton, C., Hoos, H. H., Hutter, F., and Leyton-Brown, K.: Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA, in: Automated Machine Learning: Methods, Systems, Challenges, edited by: Hutter, F., Kotthoff, L., and Vanschoren, J., Springer International Publishing, Cham, 81–95, https://doi.org/10.1007/978-3-030-05318-5_4, 2019.
LeDell, E. and Poirier, S.: H2O AutoML: Scalable Automatic Machine Learning, The Thirty-seventh International Conference on Machine Learning, Online, 12–18 July 2020, 2020.
Lee, S., Kim, J., Bae, J. H., Lee, G., Yang, D., Hong, J., and Lim, K. J.: Development of Multi-Inflow Prediction Ensemble Model Based on Auto-Sklearn Using Combined Approach: Case Study of Soyang River Dam, Hydrology, 10, 90, https://doi.org/10.3390/hydrology10040090, 2023.
Madni, H. A., Umer, M., Ishaq, A., Abuzinadah, N., Saidani, O., Alsubai, S., Hamdi, M., and Ashraf, I.: Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques, Water, 15, 475, https://doi.org/10.3390/w15030475, 2023.
Muñoz Sabater, J.: ERA5-Land monthly averaged data from 1950 to present, Copernicus Climate Change Service [data set], https://doi.org/10.24381/CDS.68D2BB30, 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Myneni, R., Knyazikhin, Y., and Park, T.: MCD15A2H MODIS/Terra+Aqua Leaf Area Index/FPAR 8-day L4 Global 500m SIN Grid V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD15A2H.006, 2015.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Orth, R., Destouni, G., Jung, M., and Reichstein, M.: Large-scale biospheric drought response intensifies linearly with drought duration in arid regions, Biogeosciences, 17, 2647–2656, https://doi.org/10.5194/bg-17-2647-2020, 2020.
Papale, D., Black, T. A., Carvalhais, N., Cescatti, A., Chen, J., Jung, M., Kiely, G., Lasslop, G., Mahecha, M. D., Margolis, H., Merbold, L., Montagnani, L., Moors, E., Olesen, J. E., Reichstein, M., Tramontana, G., Gorsel, E., Wohlfahrt, G., and Ráduly, B.: Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks, J. Geophys. Res.-Biogeo., 120, 1941–1957, https://doi.org/10.1002/2015JG002997, 2015.
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D., Bonnefond, J.-M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D'Andrea, E., da Rocha, H., Dai, X., Davis, K. J., Cinti, B. D., Grandcourt, A. de, Ligne, A. D., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., Tommasi, P. di, Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., et al.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N., Rossi, V., Dormann, C., Cornu, G., Viennois, G., Bayol, N., Lyapustin, A., Gourlet-Fleury, S., and Pélissier, R.: Spatial validation reveals poor predictive performance of large-scale ecological mapping models, Nat. Commun., 11, 4540, https://doi.org/10.1038/s41467-020-18321-y, 2020.
Qi, W., Xu, C., and Xu, X.: AutoGluon: A revolutionary framework for landslide hazard analysis, Natural Hazards Research, 1, 103–108, https://doi.org/10.1016/j.nhres.2021.07.002, 2021.
Raschka, S.: Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning, arXiv [preprint], https://doi.org/10.48550/arXiv.1811.12808, 10 November 2020.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Glob. Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., and Dormann, C. F.: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure, Ecography, 40, 913–929, https://doi.org/10.1111/ecog.02881, 2017.
Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., and Hashimoto, H.: A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production, BioScience, 54, 547, https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2, 2004.
Ryu, Y., Jiang, C., Kobayashi, H., and Detto, M.: MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5km resolution from 2000, Remote Sens. Environ., 204, 812–825, https://doi.org/10.1016/j.rse.2017.09.021, 2018.
Ryu, Y., Jiang, C., Kobayashi, H., and Detto, M.: Bess_Rad, Bess_Rad Website [data set], https://www.environment.snu.ac.kr/bess-rad, last access: 13 October 2022.
Schaaf, C. and Wang, Z.: MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global – 500m V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD43A4.006, 2015.
Schucknecht, A., Erasmi, S., Niemeyer, I., and Matschullat, J.: Assessing vegetation variability and trends in north-eastern Brazil using AVHRR and MODIS NDVI time series, Eur. J. Remote Sens., 46, 40–59, https://doi.org/10.5721/EuJRS20134603, 2013.
Smith, W. K., Biederman, J. A., Scott, R. L., Moore, D. J. P., He, M., Kimball, J. S., Yan, D., Hudson, A., Barnes, M. L., MacBean, N., Fox, A. M., and Litvak, M. E.: Chlorophyll Fluorescence Better Captures Seasonal and Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of Southwestern North America, Geophys. Res. Lett., 45, 748–757, https://doi.org/10.1002/2017GL075922, 2018.
Smith, W. K., Dannenberg, M. P., Yan, D., Herrmann, S., Barnes, M. L., Barron-Gafford, G. A., Biederman, J. A., Ferrenberg, S., Fox, A. M., Hudson, A., Knowles, J. F., MacBean, N., Moore, D. J. P., Nagler, P. L., Reed, S. C., Rutherford, W. A., Scott, R. L., Wang, X., and Yang, J.: Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities, Remote Sens. Environ., 233, 111401, https://doi.org/10.1016/j.rse.2019.111401, 2019.
Stocker, B. D., Zscheischler, J., Keenan, T. F., Prentice, I. C., Peñuelas, J., and Seneviratne, S. I.: Quantifying soil moisture impacts on light use efficiency across biomes, New Phytol., 218, 1430–1449, https://doi.org/10.1111/nph.15123, 2018.
Stocker, B. D., Zscheischler, J., Keenan, T. F., Prentice, I. C., Seneviratne, S. I., and Peñuelas, J.: Drought impacts on terrestrial primary production underestimated by satellite monitoring, Nat. Geosci., 12, 264–270, https://doi.org/10.1038/s41561-019-0318-6, 2019.
Sulkava, M., Luyssaert, S., Zaehle, S., and Papale, D.: Assessing and improving the representativeness of monitoring networks: The European flux tower network example, J. Geophys. Res.-Biogeo., 116, G00J04, https://doi.org/10.1029/2010JG001562, 2011.
Sun, W., Fang, Y., Luo, X., Shiga, Y. P., Zhang, Y., Andrews, A. E., Thoning, K. W., Fisher, J. B., Keenan, T. F., and Michalak, A. M.: Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes, AGU Adv., 2, e2020AV000310, https://doi.org/10.1029/2020AV000310, 2021.
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms, in: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 11–14 August 2013, Chicago, USA, 847–855, https://doi.org/10.1145/2487575.2487629, 2013.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016.
Tramontana, G., Migliavacca, M., Jung, M., Reichstein, M., Keenan, T. F., Camps-Valls, G., Ogee, J., Verrelst, J., and Papale, D.: Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks, Glob. Change Biol., 26, 5235–5253, https://doi.org/10.1111/gcb.15203, 2020.
Traoré, K. R., Camero, A., and Zhu, X. X.: Compact Neural Architecture Search for Local Climate Zones Classification, in: 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, The 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), online, 6–8 October 2021, 393–398, https://doi.org/10.14428/esann/2021.ES2021-55, 2021.
Truong, A., Walters, A., Goodsitt, J., Hines, K., Bruss, C. B., and Farivar, R.: Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools, in: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 1471–1479, https://doi.org/10.1109/ICTAI.2019.00209, 2019.
Turner, D. P., Ritts, W. D., Cohen, W. B., Gower, S. T., Running, S. W., Zhao, M., Costa, M. H., Kirschbaum, A. A., Ham, J. M., Saleska, S. R., and Ahl, D. E.: Evaluation of MODIS NPP and GPP products across multiple biomes, Remote Sens. Environ., 102, 282–292, https://doi.org/10.1016/j.rse.2006.02.017, 2006.
van der Laan, M. J., Polley, E. C., and Hubbard, A. E.: Super Learner, Stat. Appl. Genet. Mo. B., 6, 25, https://doi.org/10.2202/1544-6115.1309, 2007.
von Buttlar, J., Zscheischler, J., Rammig, A., Sippel, S., Reichstein, M., Knohl, A., Jung, M., Menzer, O., Arain, M. A., Buchmann, N., Cescatti, A., Gianelle, D., Kiely, G., Law, B. E., Magliulo, V., Margolis, H., McCaughey, H., Merbold, L., Migliavacca, M., Montagnani, L., Oechel, W., Pavelka, M., Peichl, M., Rambal, S., Raschi, A., Scott, R. L., Vaccari, F. P., van Gorsel, E., Varlagin, A., Wohlfahrt, G., and Mahecha, M. D.: Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones, Biogeosciences, 15, 1293–1318, https://doi.org/10.5194/bg-15-1293-2018, 2018.
Walther, S., Besnard, S., Nelson, J. A., El-Madany, T. S., Migliavacca, M., Weber, U., Carvalhais, N., Ermida, S. L., Brümmer, C., Schrader, F., Prokushkin, A. S., Panov, A. V., and Jung, M.: Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set, Biogeosciences, 19, 2805–2840, https://doi.org/10.5194/bg-19-2805-2022, 2022.
Wan, Z., Hook, S., and Hulley, G.: MOD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD11A1.006, 2015.
Wang, J., Feng, L., Palmer, P. I., Liu, Y., Fang, S., Bösch, H., O'Dell, C. W., Tang, X., Yang, D., Liu, L., and Xia, C.: Large Chinese land carbon sink estimated from atmospheric carbon dioxide data, Nature, 586, 720–723, https://doi.org/10.1038/s41586-020-2849-9, 2020.
Warm Winter 2020 Team and ICOS Ecosystem Thematic Centre: Warm Winter 2020 ecosystem eddy covariance flux product for 73 stations in FLUXNET-Archive format-release 2022-1 (1.0), ICOS Carbon Portal [data set], https://doi.org/10.18160/2G60-ZHAK, 2022.
Wei, S., Yi, C., Fang, W., and Hendrey, G.: A global study of GPP focusing on light-use efficiency in a random forest regression model, Ecosphere, 8, e01724, https://doi.org/10.1002/ecs2.1724, 2017.
Wohlfahrt, G. and Galvagno, M.: Revisiting the choice of the driving temperature for eddy covariance CO2 flux partitioning, Agr. Forest Meteorol., 237–238, 135–142, https://doi.org/10.1016/j.agrformet.2017.02.012, 2017.
Xiao, J., Zhuang, Q., Baldocchi, D. D., Law, B. E., Richardson, A. D., Chen, J., Oren, R., Starr, G., Noormets, A., Ma, S., Verma, S. B., Wharton, S., Wofsy, S. C., Bolstad, P. V., Burns, S. P., Cook, D. R., Curtis, P. S., Drake, B. G., Falk, M., Fischer, M. L., Foster, D. R., Gu, L., Hadley, J. L., Hollinger, D. Y., Katul, G. G., Litvak, M., Martin, T. A., Matamala, R., McNulty, S., Meyers, T. P., Monson, R. K., Munger, J. W., Oechel, W. C., Paw U, K. T., Schmid, H. P., Scott, R. L., Sun, G., Suyker, A. E., and Torn, M. S.: Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data, Agr. Forest Meteorol., 148, 1827–1847, https://doi.org/10.1016/j.agrformet.2008.06.015, 2008.
Xu, H., Xiao, J., and Zhang, Z.: Heatwave effects on gross primary production of northern mid-latitude ecosystems, Environ. Res. Lett., 15, 074027, https://doi.org/10.1088/1748-9326/ab8760, 2020.
Yan, D., Scott, R. L., Moore, D. J. P., Biederman, J. A., and Smith, W. K.: Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data, Remote Sens. Environ., 223, 50–62, https://doi.org/10.1016/j.rse.2018.12.029, 2019.
Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.-F., Tu, W.-W., Yang, Q., and Yu, Y.: Taking Human out of Learning Applications: A Survey on Automated Machine Learning, arXiv [preprint], https://doi.org/10.48550/arXiv.1810.13306, 16 December 2019.
Yu, T., Sun, R., Xiao, Z., Zhang, Q., Liu, G., Cui, T., and Wang, J.: Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data, Remote Sens.-Basel, 10, 327, https://doi.org/10.3390/rs10020327, 2018.
Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law, B., Arain, A., Beringer, J., Bernhofer, C., Black, A., Blanken, P. D., Cescatti, A., Chen, Y., Francois, L., Gianelle, D., Janssens, I. A., Jung, M., Kato, T., Kiely, G., Liu, D., Marcolla, B., Montagnani, L., Raschi, A., Roupsard, O., Varlagin, A., and Wohlfahrt, G.: Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database, Agr. Forest Meteorol., 192–193, 108–120, https://doi.org/10.1016/j.agrformet.2014.03.007, 2014.
Zhang, Y.: A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2000–2022), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Ecolo.tpdc.271751, 2021.
Zhang, Y., Xiao, X., Zhou, S., Ciais, P., McCarthy, H., and Luo, Y.: Canopy and physiological controls of GPP during drought and heat wave, Geophys. Res. Lett., 43, 3325–3333, https://doi.org/10.1002/2016GL068501, 2016.
Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S., and Gentine, P.: A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks, Biogeosciences, 15, 5779–5800, https://doi.org/10.5194/bg-15-5779-2018, 2018.
Zöller, M.-A. and Huber, M. F.: Benchmark and Survey of Automated Machine Learning Frameworks, arXiv [preprint], https://doi.org/10.48550/arXiv.1904.12054, 26 January 2021.
Short summary
Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a vital role in the carbon cycle. We can measure GPP locally, but producing larger and continuous estimates is challenging. Here, we present an approach to extrapolate GPP to a global scale using satellite imagery and automated machine learning. We benchmark different models and predictor variables and achieve an estimate that can capture 75 % of the variation in GPP.
Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a...
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