Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4077-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-4077-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global datasets of leaf photosynthetic capacity for ecological and earth system research
Jing M. Chen
CORRESPONDING AUTHOR
School of Geographical Sciences, Fujian Normal University, Fuzhou, China
Department of Geography and Planning, University of Toronto, Toronto, Canada
Rong Wang
School of Geographical Sciences, Fujian Normal University, Fuzhou, China
Yihong Liu
Department of Geography and Planning, University of Toronto, Toronto, Canada
Liming He
Canada Centre for Remote Sensing, Natural Resources Canada, Toronto, Canada
Holly Croft
School of Biosciences, University of Sheffield, Sheffield, UK
Xiangzhong Luo
Department of Geography, National University of Singapore, Singapore, Singapore
Han Wang
Department of Earth System Science, Tsinghua University, 100084 Beijing, China
Nicholas G. Smith
Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA
Trevor F. Keenan
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Department of Environmental Science, Policy and Management, UC
Berkeley, Berkeley, CA, USA
I. Colin Prentice
Department of Life Sciences, Imperial College London, Silwood Park
Campus, Buckhurst Road, SL57PY Ascot, UK
Department of Earth System Science, Tsinghua University, 100084 Beijing, China
Department of Biological Sciences, Macquarie University, 2109 North Ryde, NSW, Australia
Yongguang Zhang
International Institute of Earth System Science, Nanjing University, Nanjing, China
Weimin Ju
International Institute of Earth System Science, Nanjing University, Nanjing, China
Ning Dong
Department of Life Sciences, Imperial College London, Silwood Park
Campus, Buckhurst Road, SL57PY Ascot, UK
Department of Biological Sciences, Macquarie University, 2109 North Ryde, NSW, Australia
Related authors
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
Short summary
Short summary
Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Peng Li, Rong Shang, Jing M. Chen, Huiguang Zhang, Xiaoping Zhang, Guoshuai Zhao, Hong Yan, Jun Xiao, Xudong Lin, Lingyun Fan, Rong Wang, Jianjie Cao, and Hongda Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2025-1062, https://doi.org/10.5194/egusphere-2025-1062, 2025
Short summary
Short summary
This study explored species-specific relationships between net primary productivity and forest age for seven forest species in subtropical China based on field data using the Semi-Empirical Model. Compared to nationwide relationships, these species-specific relationships improved simulations of aboveground biomass when using the process-based model. Our findings suggest that these species-specific relationships are crucial for accurate forest carbon modeling and management in subtropical China.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan
Earth Syst. Sci. Data, 16, 1283–1300, https://doi.org/10.5194/essd-16-1283-2024, https://doi.org/10.5194/essd-16-1283-2024, 2024
Short summary
Short summary
We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
Peng Li, Rong Shang, Jing M. Chen, Mingzhu Xu, Xudong Lin, Guirui Yu, Nianpeng He, and Li Xu
Biogeosciences, 21, 625–639, https://doi.org/10.5194/bg-21-625-2024, https://doi.org/10.5194/bg-21-625-2024, 2024
Short summary
Short summary
The amount of carbon that forests gain from the atmosphere, called net primary productivity (NPP), changes a lot with age. These forest NPP–age relationships could be modeled from field survey data, but we are not sure which model works best. Here we tested five different models using 3121 field survey samples in China, and the semi-empirical mathematical (SEM) function was determined as the optimal. The relationships built by SEM can improve China's forest carbon modeling and prediction.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
Short summary
Short summary
A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
Short summary
Short summary
We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Joseph Ovwemuvwose, Ian Colin Prentice, and Heather Graven
EGUsphere, https://doi.org/10.5194/egusphere-2025-3785, https://doi.org/10.5194/egusphere-2025-3785, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
This work examines the role of cropland representation and the treatment of photosynthetic pathways in the uncertainties in the carbon flux simulations in Earth System Models (ESMs). Our results show that reducing these uncertainties will require improvement of the representation of C3 and C4 crops and natural vegetation area coverage as well as the theories underpinning the simulation of their carbon uptake and storage processes.
Tuuli Miinalainen, Amanda Ojasalo, Holly Croft, Mika Aurela, Mikko Peltoniemi, Silvia Caldararu, Sönke Zaehle, and Tea Thum
EGUsphere, https://doi.org/10.5194/egusphere-2025-2987, https://doi.org/10.5194/egusphere-2025-2987, 2025
Short summary
Short summary
Estimating the future carbon budget requires an accurate understanding of the interlinkages between the land carbon and nitrogen cycles. We use a remote sensing leaf chlorophyll product to evaluate a terrestrial biosphere model, QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system). Our study showcases how the latest advancements in remote sensing-based vegetation monitoring can be harnessed for improving and evaluating process-based vegetation models.
Shuzhuang Feng, Fei Jiang, Yongguang Zhang, Huilin Chen, Honglin Zhuang, Shumin Wang, Shengxi Bai, Hengmao Wang, and Weimin Ju
EGUsphere, https://doi.org/10.5194/egusphere-2025-2669, https://doi.org/10.5194/egusphere-2025-2669, 2025
Short summary
Short summary
Using satellite data and advanced modeling, this study inverted daily high-resolution anthropogenic CH4 emissions across China and Shanxi Province. We found that China's 2022 CH4 emissions were 45.1 TgCH4·yr⁻¹, significantly lower than previous estimates, especially in coal mining and waste sectors. The inversion substantially reduced emission uncertainties and improved CH4 concentration simulations. These results suggest China’s climate mitigation burden may have been overestimated.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
Short summary
Short summary
Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, and Trevor F. Keenan
Earth Syst. Sci. Data, 17, 3009–3046, https://doi.org/10.5194/essd-17-3009-2025, https://doi.org/10.5194/essd-17-3009-2025, 2025
Short summary
Short summary
CEDAR-GPP provides spatiotemporally upscaled estimates of gross primary productivity (GPP) globally, uniquely incorporating the direct effect of elevated atmospheric CO2 on photosynthesis. This dataset was produced by upscaling eddy covariance data with machine learning and a broad range of satellite and climate variables. Available at monthly and 0.05° resolution from 1982 to 2020, CEDAR-GPP offers critical insights into ecosystem–climate interactions and the global carbon cycle.
Julien Lamour, Shawn P. Serbin, Alistair Rogers, Kelvin T. Acebron, Elizabeth Ainsworth, Loren P. Albert, Michael Alonzo, Jeremiah Anderson, Owen K. Atkin, Nicolas Barbier, Mallory L. Barnes, Carl J. Bernacchi, Ninon Besson, Angela C. Burnett, Joshua S. Caplan, Jérôme Chave, Alexander W. Cheesman, Ilona Clocher, Onoriode Coast, Sabrina Coste, Holly Croft, Boya Cui, Clément Dauvissat, Kenneth J. Davidson, Christopher Doughty, Kim S. Ely, Jean-Baptiste Féret, Iolanda Filella, Claire Fortunel, Peng Fu, Maquelle Garcia, Bruno O. Gimenez, Kaiyu Guan, Zhengfei Guo, David Heckmann, Patrick Heuret, Marney Isaac, Shan Kothari, Etsushi Kumagai, Thu Ya Kyaw, Liangyun Liu, Lingli Liu, Shuwen Liu, Joan Llusià, Troy Magney, Isabelle Maréchaux, Adam R. Martin, Katherine Meacham-Hensold, Christopher M. Montes, Romà Ogaya, Joy Ojo, Regison Oliveira, Alain Paquette, Josep Peñuelas, Antonia Debora Placido, Juan M. Posada, Xiaojin Qian, Heidi J. Renninger, Milagros Rodriguez-Caton, Andrés Rojas-González, Urte Schlüter, Giacomo Sellan, Courtney M. Siegert, Guangqin Song, Charles D. Southwick, Daisy C. Souza, Clément Stahl, Yanjun Su, Leeladarshini Sujeeun, To-Chia Ting, Vicente Vasquez, Amrutha Vijayakumar, Marcelo Vilas-Boas, Diane R. Wang, Sheng Wang, Han Wang, Jing Wang, Xin Wang, Andreas P. M. Weber, Christopher Y. S. Wong, Jin Wu, Fengqi Wu, Shengbiao Wu, Zhengbing Yan, Dedi Yang, and Yingyi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-213, https://doi.org/10.5194/essd-2025-213, 2025
Preprint under review for ESSD
Short summary
Short summary
We present the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. This repository provides a unique source of information for creating hyperspectral models for predicting photosynthetic traits and associated leaf traits in terrestrial plants.
Peng Li, Rong Shang, Jing M. Chen, Huiguang Zhang, Xiaoping Zhang, Guoshuai Zhao, Hong Yan, Jun Xiao, Xudong Lin, Lingyun Fan, Rong Wang, Jianjie Cao, and Hongda Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2025-1062, https://doi.org/10.5194/egusphere-2025-1062, 2025
Short summary
Short summary
This study explored species-specific relationships between net primary productivity and forest age for seven forest species in subtropical China based on field data using the Semi-Empirical Model. Compared to nationwide relationships, these species-specific relationships improved simulations of aboveground biomass when using the process-based model. Our findings suggest that these species-specific relationships are crucial for accurate forest carbon modeling and management in subtropical China.
Tea Thum, Tuuli Miinalainen, Outi Seppälä, Holly Croft, Cheryl Rogers, Ralf Staebler, Silvia Caldararu, and Sönke Zaehle
Biogeosciences, 22, 1781–1807, https://doi.org/10.5194/bg-22-1781-2025, https://doi.org/10.5194/bg-22-1781-2025, 2025
Short summary
Short summary
Climate change has the potential to influence the carbon sequestration potential of terrestrial ecosystems, and here the nitrogen cycle is also important. We used the terrestrial biosphere model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) in a mixed deciduous forest in Canada. We investigated the usefulness of using the leaf area index and leaf chlorophyll content to improve the parameterization of the model. This work paves the way for using spaceborne observations in model parameterizations, also including information on the nitrogen cycle.
Amin Hassan, Iain Colin Prentice, and Xu Liang
EGUsphere, https://doi.org/10.5194/egusphere-2025-622, https://doi.org/10.5194/egusphere-2025-622, 2025
Short summary
Short summary
Evapotranspiration (ET) is the evaporation occurring from plants, soil, and water bodies. Separating these components is challenging due to the lack of measurements and uncertainty of existing ET partitioning methods. We propose a method that utilizes hydrological measurements such as streamflow to determine the ratio of transpiration (evaporation from plants) to evapotranspiration. The results provide a better understanding of plant-water interactions and new perspective on a challenging topic.
Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3672, https://doi.org/10.5194/egusphere-2024-3672, 2025
Short summary
Short summary
The Russia-Ukraine war in 2022 severely disrupted Ukraine’s economy, with significant reductions in industrial, transportation, and residential activities. Our research used satellite data to track changes in nitrogen oxide emissions, a key indicator of human activity, during the war. We found a 28 % decline in emissions, which was twice of the decrease caused by the COVID-19 pandemic. This study highlights how modern warfare can deeply impact both the environment and economic stability.
Xingyu Wang, Fei Jiang, Hengmao Wang, Zhengqi Zhang, Mousong Wu, Jun Wang, Wei He, Weimin Ju, and Jing M. Chen
Atmos. Chem. Phys., 25, 867–880, https://doi.org/10.5194/acp-25-867-2025, https://doi.org/10.5194/acp-25-867-2025, 2025
Short summary
Short summary
The role of OCO-3 XCO2 retrievals in estimating global terrestrial carbon fluxes is unclear. We investigate this by assimilating OCO-3 XCO2 retrievals alone and in combination with OCO-2 XCO2. The assimilation of OCO-3 XCO2 alone underestimates global land sinks, mainly at high latitudes, due to the lack of observations beyond 52° S and 52° N, large variations in the number of data, and varying observation times, while the joint assimilation of OCO-2 and OCO-3 XCO2 has the best performance.
Jierong Zhao, Boya Zhou, Sandy P. Harrison, and I. Colin Prentice
EGUsphere, https://doi.org/10.5194/egusphere-2024-3897, https://doi.org/10.5194/egusphere-2024-3897, 2025
Short summary
Short summary
We used eco-evolutionary optimality modelling to examine how climate and CO2 impacted vegetation at the Last Glacial Maximum (LGM, 21,000 years ago) and the mid-Holocene (MH, 6,000 years ago). Low CO2 at the LGM was as important as climate in reducing tree cover and productivity, and increasing C4 plant abundance. Climate had positive effects on MH vegetation, but the low CO2 was a constraint on plant growth. These results show it is important to consider changing CO2 to model ecosystem changes.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
Short summary
Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Ruiying Zhao, Xiangzhong Luo, Yuheng Yang, Luri Nurlaila Syahid, Chi Chen, and Janice Ser Huay Lee
Biogeosciences, 21, 5393–5406, https://doi.org/10.5194/bg-21-5393-2024, https://doi.org/10.5194/bg-21-5393-2024, 2024
Short summary
Short summary
Southeast Asia has been a global hot spot of land-use change over the past 50 years. Meanwhile, it also hosts some of the most carbon-dense and diverse ecosystems in the world. Here, we explore the impact of land-use change, along with other environmental factors, on the ecosystem in Southeast Asia. We find that elevated CO2 imposed a positive impact on vegetation greenness, but the positive impact was largely offset by intensive land-use changes in the region, particularly cropland expansion.
Ran Yan, Jun Wang, Weimin Ju, Xiuli Xing, Miao Yu, Meirong Wang, Jingye Tan, Xunmei Wang, Hengmao Wang, and Fei Jiang
Biogeosciences, 21, 5027–5043, https://doi.org/10.5194/bg-21-5027-2024, https://doi.org/10.5194/bg-21-5027-2024, 2024
Short summary
Short summary
Our study reveals that the effects of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on China's gross primary production (GPP) are basically opposite, with obvious seasonal changes. Soil moisture primarily influences GPP during ENSO events (except spring) and temperature during IOD events (except fall). Quantitatively, China's annual GPP displays modest positive anomalies during La Niña and negative anomalies in El Niño years, driven by significant seasonal variations.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Huajie Zhu, Xiuli Xing, Mousong Wu, Weimin Ju, and Fei Jiang
Biogeosciences, 21, 3735–3760, https://doi.org/10.5194/bg-21-3735-2024, https://doi.org/10.5194/bg-21-3735-2024, 2024
Short summary
Short summary
Ecosystem carbonyl sulfide (COS) fluxes were employed to optimize GPP estimation across ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was developed for simulating the canopy COS uptake under its state-of-the-art two-leaf modeling framework. Our results showcased the efficacy of COS in improving model prediction and reducing prediction uncertainty of GPP and enhanced insights into the sensitivity, identifiability, and interactions of parameters related to COS.
Shuzhuang Feng, Fei Jiang, Tianlu Qian, Nan Wang, Mengwei Jia, Songci Zheng, Jiansong Chen, Fang Ying, and Weimin Ju
Atmos. Chem. Phys., 24, 7481–7498, https://doi.org/10.5194/acp-24-7481-2024, https://doi.org/10.5194/acp-24-7481-2024, 2024
Short summary
Short summary
We developed a multi-air-pollutant inversion system to estimate non-methane volatile organic compound (NMVOC) emissions using TROPOMI formaldehyde retrievals. We found that the inversion significantly improved formaldehyde simulations and reduced NMVOC emission uncertainties. The optimized NMVOC emissions effectively corrected the overestimation of O3 levels, mainly by decreasing the rate of the RO2 + NO reaction and increasing the rate of the NO2 + OH reaction.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Max Gaber, Yanghui Kang, Guy Schurgers, and Trevor Keenan
Biogeosciences, 21, 2447–2472, https://doi.org/10.5194/bg-21-2447-2024, https://doi.org/10.5194/bg-21-2447-2024, 2024
Short summary
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.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan
Earth Syst. Sci. Data, 16, 1283–1300, https://doi.org/10.5194/essd-16-1283-2024, https://doi.org/10.5194/essd-16-1283-2024, 2024
Short summary
Short summary
We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
Mengmeng Liu, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past Discuss., https://doi.org/10.5194/cp-2024-12, https://doi.org/10.5194/cp-2024-12, 2024
Preprint under review for CP
Short summary
Short summary
Dansgaard-Oeschger events were large and rapid warming events that occurred multiple times during the last ice age. We show that changes in the northern extratropics and the southern extratropics were anti-phased, with warming over most of the north and cooling in the south. The reconstructions do not provide evidence for a change in seasonality in temperature. However, they do indicate that warming was generally accompanied by wetter conditions and cooling by drier conditions.
Peng Li, Rong Shang, Jing M. Chen, Mingzhu Xu, Xudong Lin, Guirui Yu, Nianpeng He, and Li Xu
Biogeosciences, 21, 625–639, https://doi.org/10.5194/bg-21-625-2024, https://doi.org/10.5194/bg-21-625-2024, 2024
Short summary
Short summary
The amount of carbon that forests gain from the atmosphere, called net primary productivity (NPP), changes a lot with age. These forest NPP–age relationships could be modeled from field survey data, but we are not sure which model works best. Here we tested five different models using 3121 field survey samples in China, and the semi-empirical mathematical (SEM) function was determined as the optimal. The relationships built by SEM can improve China's forest carbon modeling and prediction.
Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst
Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
Short summary
Short summary
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
Huiying Xu, Han Wang, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 4511–4525, https://doi.org/10.5194/bg-20-4511-2023, https://doi.org/10.5194/bg-20-4511-2023, 2023
Short summary
Short summary
Leaf carbon (C) and nitrogen (N) are crucial elements in leaf construction and physiological processes. This study reconciled the roles of phylogeny, species identity, and climate in stoichiometric traits at individual and community levels. The variations in community-level leaf N and C : N ratio were captured by optimality-based models using climate data. Our results provide an approach to improve the representation of leaf stoichiometry in vegetation models to better couple N with C cycling.
Esmeralda Cruz-Silva, Sandy P. Harrison, I. Colin Prentice, Elena Marinova, Patrick J. Bartlein, Hans Renssen, and Yurui Zhang
Clim. Past, 19, 2093–2108, https://doi.org/10.5194/cp-19-2093-2023, https://doi.org/10.5194/cp-19-2093-2023, 2023
Short summary
Short summary
We examined 71 pollen records (12.3 ka to present) in the eastern Mediterranean, reconstructing climate changes. Over 9000 years, winters gradually warmed due to orbital factors. Summer temperatures peaked at 4.5–5 ka, likely declining because of ice sheets. Moisture increased post-11 kyr, remaining high from 10–6 kyr before a slow decrease. Climate models face challenges in replicating moisture transport.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
Short summary
Short summary
Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
Short summary
Short summary
We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Olivia Haas, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 3981–3995, https://doi.org/10.5194/bg-20-3981-2023, https://doi.org/10.5194/bg-20-3981-2023, 2023
Short summary
Short summary
We quantify the impact of CO2 and climate on global patterns of burnt area, fire size, and intensity under Last Glacial Maximum (LGM) conditions using three climate scenarios. Climate change alone did not produce the observed LGM reduction in burnt area, but low CO2 did through reducing vegetation productivity. Fire intensity was sensitive to CO2 but strongly affected by changes in atmospheric dryness. Low CO2 caused smaller fires; climate had the opposite effect except in the driest scenario.
Giulia Mengoli, Sandy P. Harrison, and I. Colin Prentice
EGUsphere, https://doi.org/10.5194/egusphere-2023-1261, https://doi.org/10.5194/egusphere-2023-1261, 2023
Preprint archived
Short summary
Short summary
Soil water availability affects plant carbon uptake by reducing leaf area and/or by closing stomata, which reduces its efficiency. We present a new formulation of how climatic dryness reduces both maximum carbon uptake and the soil-moisture threshold below which it declines further. This formulation illustrates how plants adapt their water conservation strategy to thrive in dry climates, and is step towards a better representation of soil-moisture effects in climate models.
Mengmeng Liu, Yicheng Shen, Penelope González-Sampériz, Graciela Gil-Romera, Cajo J. F. ter Braak, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past, 19, 803–834, https://doi.org/10.5194/cp-19-803-2023, https://doi.org/10.5194/cp-19-803-2023, 2023
Short summary
Short summary
We reconstructed the Holocene climates in the Iberian Peninsula using a large pollen data set and found that the west–east moisture gradient was much flatter than today. We also found that the winter was much colder, which can be expected from the low winter insolation during the Holocene. However, summer temperature did not follow the trend of summer insolation, instead, it was strongly correlated with moisture.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
Short summary
Short summary
A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Yicheng Shen, Luke Sweeney, Mengmeng Liu, Jose Antonio Lopez Saez, Sebastián Pérez-Díaz, Reyes Luelmo-Lautenschlaeger, Graciela Gil-Romera, Dana Hoefer, Gonzalo Jiménez-Moreno, Heike Schneider, I. Colin Prentice, and Sandy P. Harrison
Clim. Past, 18, 1189–1201, https://doi.org/10.5194/cp-18-1189-2022, https://doi.org/10.5194/cp-18-1189-2022, 2022
Short summary
Short summary
We present a method to reconstruct burnt area using a relationship between pollen and charcoal abundances and the calibration of charcoal abundance using modern observations of burnt area. We use this method to reconstruct changes in burnt area over the past 12 000 years from sites in Iberia. We show that regional changes in burnt area reflect known changes in climate, with a high burnt area during warming intervals and low burnt area when the climate was cooler and/or wetter than today.
Rahayu Adzhar, Douglas I. Kelley, Ning Dong, Charles George, Mireia Torello Raventos, Elmar Veenendaal, Ted R. Feldpausch, Oliver L. Phillips, Simon L. Lewis, Bonaventure Sonké, Herman Taedoumg, Beatriz Schwantes Marimon, Tomas Domingues, Luzmila Arroyo, Gloria Djagbletey, Gustavo Saiz, and France Gerard
Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, https://doi.org/10.5194/bg-19-1377-2022, 2022
Short summary
Short summary
The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
Stephanie G. Stettz, Nicholas C. Parazoo, A. Anthony Bloom, Peter D. Blanken, David R. Bowling, Sean P. Burns, Cédric Bacour, Fabienne Maignan, Brett Raczka, Alexander J. Norton, Ian Baker, Mathew Williams, Mingjie Shi, Yongguang Zhang, and Bo Qiu
Biogeosciences, 19, 541–558, https://doi.org/10.5194/bg-19-541-2022, https://doi.org/10.5194/bg-19-541-2022, 2022
Short summary
Short summary
Uncertainty in the response of photosynthesis to temperature poses a major challenge to predicting the response of forests to climate change. In this paper, we study how photosynthesis in a mountainous evergreen forest is limited by temperature. This study highlights that cold temperature is a key factor that controls spring photosynthesis. Including the cold-temperature limitation in an ecosystem model improved its ability to simulate spring photosynthesis.
Luis Guanter, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Köhler, Christian Frankenberg, Joanna Joiner, and Yongguang Zhang
Earth Syst. Sci. Data, 13, 5423–5440, https://doi.org/10.5194/essd-13-5423-2021, https://doi.org/10.5194/essd-13-5423-2021, 2021
Short summary
Short summary
Sun-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by plants in the red and far-red parts of the spectrum. It has a functional link to photosynthesis and can be measured by satellite instruments, which makes it an important variable for the remote monitoring of the photosynthetic activity of vegetation ecosystems around the world. In this contribution we present a SIF dataset derived from the new Sentinel-5P TROPOMI missions.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
Short summary
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
Short summary
Short summary
We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Douglas I. Kelley, Chantelle Burton, Chris Huntingford, Megan A. J. Brown, Rhys Whitley, and Ning Dong
Biogeosciences, 18, 787–804, https://doi.org/10.5194/bg-18-787-2021, https://doi.org/10.5194/bg-18-787-2021, 2021
Short summary
Short summary
Initial evidence suggests human ignitions or landscape changes caused most Amazon fires during August 2019. However, confirmation is needed that meteorological conditions did not have a substantial role. Assessing the influence of historical weather on burning in an uncertainty framework, we find that 2019 meteorological conditions alone should have resulted in much less fire than observed. We conclude socio-economic factors likely had a strong role in the high recorded 2019 fire activity.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Cited articles
Ali, A. A., Xu, C., Rogers, A., McDowell, N. G., Medlyn, B. E., Fisher, R. A., Wullschleger, S. D., Reich, P. B., Vrugt, J. A., Bauerle, W. L., Santiago, L. S., and Wilson, C. J.: Global-scale environmental control of
plant photosynthetic capacity, Ecol. Appl., 25, 2349–2365, 2015.
Ali, A. A., Xu, C., Rogers, A., Fisher, R. A., Wullschleger, S. D., Massoud, E. C., Vrugt, J. A., Muss, J. D., McDowell, N. G., Fisher, J. B., Reich, P. B., and Wilson, C. J.: A global scale mechanistic model of photosynthetic capacity (LUNA V1.0), Geosci. Model Dev., 9, 587–606, https://doi.org/10.5194/gmd-9-587-2016, 2016.
Bonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., Lawrence, D. M., and Swenson, S. C.: Improving canopy processes
in the Community Land Model version 4 (CLM4) using global flux fields
empirically inferred from FLUXNET data, J. Geophys. Res.-Biogeosci., 116, G02014, https://doi.org/10.1029/2010JG001593, 2011.
Chen, B., Chen, J. M., Baldocchi, D. D., Liu, Y., Zheng, T., Black, T. A.
and Croft, H.: A new way to include soil water stress in terrestrial
ecosystem models, Agr. Forest Meteorol., 276, 107649,
https://doi.org/10.1016/j.agrformet.2019.107649, 2019.
Chen, J. M. and Leblanc, S. G.: A 4-scale bidirectional reflection
model based on canopy architecture, IEEE T. Geosci. Remote, 35, 1316–1337, 1997.
Chen, J. M. and Leblanc, S. G.: Multiple-scattering scheme useful for
hyperspectral geometrical optical modelling, IEEE T. Geosci. Remote, 39, 1061–1071, 2001.
Chen, J. M., Mo, G., Pisek, J., Deng, F., Ishozawa, M., and Chan, D.: Effects
of foliage clumping on global terrestrial gross primary productivity,
Global Biogeochem. Cy., 26, GB1019, https://doi.org/10.1029/2010GB003996, 2012.
Chen, J. M., Liu, J., Cihlar, J., and Goulden, M. L.: Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model., 124, 99–119, 1999.
Chen, J. M., Ju, W., Ciais, P., Viovy, N., Liu, R., and Liu, Y.: Vegetation
structural change since 1981 significantly enhanced the terrestrial carbon
sink, Nat. Commun., 10, 4259, https://doi.org/10.1038/s41467-019-12257-8, 2019.
Chen, J. M., Wang, R., Liu, Y., He, L., Croft, H., Luo, X., Wang, H., Smith, N. G., Keenan, T. F., Prentice, I. C., Zhang, Y., Ju, W., and Dong, N.: Three
global products of leaf photosynthetic capacity derived from satellite
observations, Zenodo [data set], https://doi.org/10.5281/zenodo.6466968, 2022.
Colombo, R., Meroni, M., and Rossini, M.: Development of fluorescence
indices to minimize the effects of canopy structural parameters, Annali Di Botonica, 6, 93–99, 2016.
Croft, H., Chen, J. M., Luo, X. Z., Bartlett, P., Chen, B., and Staebler,
R. M.: Leaf Chlorophyll Content as a Proxy for Leaf Photosynthetic Capacity,
Glob. Change Biol., 23, 3513–3524, https://doi.org/10.1111/gcb.13599, 2017.
Croft, H., Chen, J. M., Wang, R., Mo, G., Luo, S., Luo, X., He, L., Gonsamo, A., Arabian, J., Zhang, Y., Simic-Milas, A., Noland, T. L., He, Y., Homolová, L., Malenovský, Z., Yi, Q., Beringer, J., Amiri, R., Hutley, L., Arellano, P., Stahl, C., and Bonal, D.: Global distribution of leaf chlorophyll
content, Remote Sens. Environ., 236, 111479, https://doi.org/10.1016/j.rse.2019.111479, 2020.
Dechant, B., Ryu, Y., Badgley, G., Zeng, Y., Berry, J. A., Zhang, Y., Goulas, T., Li,
Z., Zhang, Q., Kang, M., Li, J., and Moya, I.: Canopy structure
explains the relationship between photosynthesis and sun-induced chlorophyll
fluorescence in crops, Remote Sens. Environ., 241, 111733, https://doi.org/10.1016/j.rse.2020.111733, 2020.
De Kauwe, M. G., Lin, Y.-S., Wright, I. J., Medlyn, B. E., Crous, K. Y., Ellsworth, D. S., Maire, V., Prentice, I. C., Atkin, O. K., Rogers, A., Niinemets, Ü., Serbin, S. P., Meir, P., Uddling, J., Togashi, H. F., Tarvainen, L., Weerasinghe, L. K., Evans, B. J., Ishida, F. Y., and Domingues, T. F.: A test of the “one-point method” for estimating maximum
carboxylation capacity from field-measured, light saturated photosynthesis,
New Phytol., 210, 1130–1144, 2016.
Dong, N., Prentice, I. C., Wright, I. J., Evans, B. J., Togashi, H. F., Caddy-Retalic, S., McInerney, F. A., Sparrow, B., Leitch, E., and Lowe, A. J.: Components of leaf-trait variation
along environmental gradients, New Phytol., 228, 82–94,
https://doi.org/10.1111/nph.16558, 2020.
Farquhar, G. D., von Caemmerer,
S., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation
in leaves of C3 species, Planta, 149, 78–90,1980.
Feret, J.-B., François, C., Asner, G. P., Gitelson, A. A., Martin, R. E., Bidel, L. P. R., Ustin, S. L., le Maire, G., and Jacquemoud, S.:
PROSPECT-4 and 5: Advances in the leaf optical properties model separating
photosynthetic pigments, Remote Sens. Environ., 112, 3030–3043, 2008.
Fisher, J. B., Badgley, G., and Blyth, E.: Global nutrient limitation in
terrestrial vegetation, Global Biogeochem. Cy., 26, GB3007, https://doi.org//10.1029/2011GB004252, 2012.
Frankenberg, C., Fisher, J. B., Worden, J., Badgley, G., Saatchi, S. S., Lee,
J. E., and Kuze, A.: New global observations of the terrestrial carbon cycle
from GOSAT: Patterns of plant fluorescence with gross primary productivity,
Geophys. Res. Lett., 38, L17706, https://doi.org/10.1029/2011GL048738, 2011.
Gentili, R., Ambroshin, R., Montagnani, C., Caronni, S., and Citterio, S.:
Effect of Soil pH on the Growth, Reproductive Investment and Pollen Allergenicity of Ambrosia artemisiifolia L., Frontiers of Plant Science, 9, 1335, https://doi.org/10.3389/fpls.2018.01335, 2018.
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A., Frankenberg, C., Huete, A. R., Zarco-Tejada, P., Lee, J.-E., Moran, M. S., Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D., Klumpp, K., Cescatti, A., Baker, J. M., and Griffis, T. J.: Global and time-resolved monitoring of crop photosynthesis with
chlorophyll fluorescence, Proc. Natl. Acad. Sci., 111, E1327–E1333, 2014.
Hall, J.M., Paterson E. and Killham, K.: The effect of elevated CO2
concentration and soil pH on the relationship between plant growth and
rhizosphere denitrification potential, Glob. Change Biol., 4, 209–216,
1998.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated high
resolution grids of monthly climatic observations – the CRU TS3.10 data set,
Int. J. Climatol., 34, 623–642, 2014.
He, L., Chen, J. M., Liu, J., Zheng, T., Wang, R., Joiner, J., Chou, S.,
Chen, B., Liu, Y., and Liu, R.: Diverse photosynthetic capacity of global
ecosystems mapped by satellite chlorophyll fluorescence measurements, Remote Sens. Environ., 232, 111344, https://doi.org/10.1016/j.rse.2019.111344, 2019.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global
gridded soil information based on machine learning, PLoS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Houborg, R., Cescatti, A., Migliavacca, M., and Kustas, W. P.: Satellite
retrievals of leaf chlorophyll and photosynthetic capacity for improved
modeling of GPP, Agr. Forest Meteorol., 177, 10–23, 2017.
Islam, A. K. M. S., Edwards D. G., and Asher, C. J.: pH optima for crop growth:
results of a flowing solution culture experiment with six species, Plant Soil, 54, 339–357, 1980.
Kattge, J., Knorr, W., Raddatz, T., and Wirth, C.: Quantifying Photosynthetic
Capacity and Its Relationship to Leaf Nitrogen Content for Global-Scale
Terrestrial Biosphere Models, Glob. Change Biol., 15, 976–991, 2009.
Kattge, J., Bönisch, G., Díaz, S., et al.: TRY plant trait database – enhanced coverage
and open access, Glob. Change Biol. 26, 119–188, 2020.
Li, X., Xiao, J., He, B., Altaf Arain, M., Beringer, J., Desai, A. R., Emmel, C., Hollinger, D. Y., Krasnova, A., Mammarella, I., Noe, S. M., Ortiz, P. S., Rey-Sanchez, A. C., Rocha, A. V., and Varlagin, A.:
Solar-induced chlorophyll fluorescence is strongly correlated with
terrestrial photosynthesis for a wide variety of biomes: first global
analysis based on OCO2 and flux tower observations, Glob. Change Biol., 24, 3990–4008, 2018.
Liu, Y., Chen, J. M., He, L., Zhang, Z., Wang, R., Rogers, C., Fan, F., de
Oliveira, G., and Xie, X.: Non-linearity between gross primary production
and far-red solar-induced chlorophyll fluorescence emitted from major
biomes, Remote Sens. Environ. 271, 112896, https://doi.org/10.1016/j.rse.2022.112896, 2022.
Lu, X., Ju, W., Li, J., Croft, H., Chen, J. M., and Luo, Y.: Maximum
carboxylation rate estimation with chlorophyll content as a proxy of
RuBisCo, J. Geophys. Res.-Biogeosci. 125, e2020JG005748, https://doi.org/10.1029/2020JG005748, 2020.
Luo, X., Croft, H., Chen, J. M., Bartlett, P., Staebler, R., and Froelich, N.:
Incorporating leaf chlorophyll content into a terrestrial biosphere model
for estimating carbon and water fluxes at a forest site, Agr. Forest Meteorol., 248, 156–168, 2017.
Luo, X., Croft, H., Chen, J. M., He, L., and Keenan, T. F.: Improved estimation
of global photosynthesis using information on leaf chlorophyll content,
Glob. Change Biol., 25, GCB14624, https://doi.org/10.1111/gcb.14624, 2019.
Maire, V., Wright, I. J., Prentice, I. C., Batjes, N. H., Bhaskar, R., van Bodegom, P. M., Cornwell, W. K., Ellsworth, D., Niinemets, Ü., Ordonez, A., Reich, P. B., and Santiago, L. S.: Global effects of soil and climate on leaf
photosynthetic traits and rates, Global Ecology and Biogeography, 24, GEB12296, https://doi.org/10.1111/geb.12296, 2015.
Medlyn, B. E., Badeck, F.-W., De Pury, D. G. G., Barton, C. V. M., Broadmeadow, M., Ceulemans, R., De Angelis, P., Forstreuter, M., Jach, M. E., Kellomäki, S., Laitat, E., Marek, M., Philippot, S., Rey, A., Strassemeyer, J., Laitinen, K., Liozon, R., Portier, B., Roberntz, P., Wang, K., and Jstbid, P. G.: Effects of elevated
[CO2] on photosynthesis in European forest species: a meta analysis of model
parameters, Plant Cell Environ., 22, 1475–1495, 1999.
Mohammed, G. H., Colombo, R., Middleton, E. M., Rascher, U., van der Tol, C., Nedbal, L., Goulas, Y., Pérez-Priego, O., Damm, A., Meroni, M., Joiner, J., Cogliati, S., Verhoef, W., Malenovský, Z., Gastellu-Etchegorry, J.-P., Miller, J. R., Guanter, L., Moreno, J., Moya, I., Berry, J. A., Frankenberg, C., and Zarco-Tejada, P. J.: Remote sensing of
solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of
progress, Remote Sens. Environ., 231, 111177, https://doi.org/10.1016/j.rse.2019.04.030, 2019.
Osnas, J. L. D., Lichstein, J. W., Reich, P. B., and Pacala, S. W.: Global
leaf trait relationships: mass, area, and the leaf economics spectrum,
Science, 340, 741–744, 2013.
Paillassa J., Wright, I. J., Prentice, I. C., Pepin, S., and Smith, N. G.: When
and where soil is important to modify the carbon and water economy of
leaves, New Phytol., 15, NPH16702, 2020.
Pinto, F., Damm, A., Schickling, A., Panigada, C., Cogliati, S., Müller-Linow, M., Ballvora, A., and Rascher, U.: Sun-induced chlorophyll
fluorescence from high-resolution imaging spectroscopy data to quantify
spatio-temporal patterns of photosynthetic function in crop canopies, Plant Cell Environ., 39, 1500–1512, 2016.
Porcar-Castell, A., Tyystjärvi, E., Atherton, J., van der Tol, C., Flexas, J., Pfündel, E. E., Moreno, J., Frankenberg, C., and Berry, J. A.: Linking chlorophyll a fluorescence to
photosynthesis for remote sensing applications: Mechanisms and challenges,
J. Exp. Bot., 65, 4065–4095, 2014.
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V., and Wright, I. J.:
Balancing the costs of carbon gain and water transport: testing a new
theoretical framework for plant functional ecology, Ecol. Lett., 17, 82–91,
2014.
Reed, C. C. and Loik, M. E.: Water relations and photosynthesis along an
elevation gradient for Artemisia tridentata during and historic drought,
Oecologia, 181, 65–76, https://doi.org/10.1007/s00442-015-3528-7, 2016.
Reich, P. B.: The world-wide “fast-slow” plant economics spectrum: a
traits manifesto, J. Ecol., 102, 275–301, 2014.
Reich, P. B., Wright, I. J., and Lusk, C. H.: Predicting leaf physiology from
simple plant and climate attributes: a global GLOPNET analysis, Ecol. Appl., 17, 1982–1988, 2007.
Rogers, A.: The use and misuse of Vc, max in Earth System Models,
Photosynth. Res., 119, 15–29, 2014.
Rogers, A., Medlyn, B. E., Dukes, J. S., Bonan, G., von Caemmerer, S., Dietze, M. C., Kattge, J., Leakey, A. D. B., Mercado, L. M., Niinemets, Ü., Prentice, I. C., Serbin, S. P., Sitch, S., Way, D. A., and Zaehle, S.: A roadmap for improving the
representation of photosynthesis in Earth system models, New Phytol., 213, 22–42, 2017.
Ryan, M. G.: Foliar maintenance respiration of subalpine and boreal trees and
shrubs in relation to nitrogen concentration, Plant Cell Environ., 18,
765–772, 1995.
Sack, L., Scoffoni, C., John, G. P., Poorter, H., Mason, C. M., Mendez-Alonzo, R., and Donovan, L. A.: How do leaf veins influence the worldwide
leaf economic spectrum? Review and synthesis, J. Exp. Bot., 64, 4053–4080, 2013.
Sela, G.: Fertilization and irrigation: theory and best practices,
Independently Published, 261 pp., ISBN 9798793313865, 2021.
Smith, N. G. and Dukes, J. S.: Drivers of leaf carbon exchange capacity
across biomes at the continental scale, Ecology, 99, 1610–1620, 2018.
Smith, N. G., Keenan, T. F., Colin Prentice, I., Wang, H., Wright, I. J., Niinemets, Ü., Crous, K. Y., Domingues, T. F., Guerrieri, R., Yoko Ishida, F., Kattge, J., Kruger, E. L., Maire, V., Rogers, A., Serbin, S. P., Tarvainen, L., Togashi, H. F., Townsend, P. A., Wang, M., Weerasinghe, L. K., and Zhou, S.-X.: Global
photosynthetic capacity is optimized to the environment, Ecol. Lett., 22, 506–517,
https://doi.org/10.1111/ele.13210, 2019.
Smith, N., McNellis, R., and Keenan, T.: SmithEcophysLab/optimal_vcmax_R: Optimal Vcmax version 3.0 (v3.0), Zenodo [code], https://doi.org/10.5281/zenodo.5899564, 2022.
Song, X., Zhou, G., He, Q., and Zhou, H.: Quantitative response of maize
Vcmax25 to persistent drought stress at different growth stages, Water, 13, 1971, https://doi.org/10.3390/w13141971, 2021.
Sun, Y., Frankenberg, C., Wood, J. D., Schimel, D. S., Jung, M., Guanter, L., Drewry, D. T., Verma, M., Porcar-Castell, A., Griffis, T. J., Gu, L., Magney, T. S., Köhler, P., Evans, B., and Yuen, K.: OCO2 advances photosynthesis observation from space via
solar-induced chlorophyll fluorescence, Science, 358, eaam5747, https://doi.org/10.1126/science.aam5747, 2017.
Verhoef, W.: Light scattering by leaf layers with application to canopy
reflectance modeling: The SAIL model, Remote Sens. Environ., 16, 125–141, 1984.
Wang, H., Prentice, I. C., Keenan, T. F., Davis, T. W., Wright, I. J., Cornwell, W. K., Evans, B. J., and Peng, C.: Towards a universal model for carbon dioxide uptake by plants,
Nat. Plants, 3, 734–741, 2017.
Wang, X., Chen, J. M., and Ju, W.: Photochemical Reflectance Index (PRI) can
be used to improve the relationship between gross primary productivity (GPP)
and sun-induced chlorophyll fluorescence (SIF), Remote Sens. Environ., 246, 111888, https://doi.org/10.1016/j.rse.2020.111888, 2020.
Walker, A. P., Quaife, T., van Bodegom, P. M., De Kauwe, M. G., Keenan, T. F., Joiner, J., Lomas, M. R., MacBean, N., Xu, C., Yang, X., and Woodward, F. I.: The impact of alternative
trait-scaling hypotheses for the maximum photosynthetic carboxylation rate
(Vcmax) on global gross primary production, New Phytol., 215, 1370–1386, 2017.
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer, M., Flexas, J., Garnier, E., Groom, P. K., Gulias, J., Hikosaka, K., Lamont, B. B., Lee, T., Lee, W., Lusk, C., Midgley, J. J., Navas, M.-L., Niinemets, Ü., Oleksyn, J., Osada, N., Poorter, H., Poot, P., Prior, L., Pyankov, V. I., Roumet, C., Thomas, S. C., Tjoelker, M. G., Veneklaas, E. J., and Villar, R.: The worldwide leaf economics spectrum,
Nature, 428, 821–827, 2004.
Xu, C., Fisher, R., Wullschleger, S. D., Wilson, C. J., Cai, M., and
McDowell, N. G.: Toward a mechanistic modeling of nitrogen limitation on
vegetation dynamics, PLoS ONE, 7, 1–11, 2012.
Zhang, Y., Chen, J. M., Miller, J. R., and Noland, T. L.: Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery, Remote Sensing of Environment, 112, 3234–3247, https://doi.org/10.1016/j.rse.2008.04.005, 2008.
Short summary
Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit...
Altmetrics
Final-revised paper
Preprint