Articles | Volume 15, issue 7
https://doi.org/10.5194/amt-15-2125-2022
© 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/amt-15-2125-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Retrieval of solar-induced chlorophyll fluorescence (SIF) from satellite measurements: comparison of SIF between TanSat and OCO-2
Lu Yao
Carbon Neutral Research Center & Key Laboratory of Middle
Atmosphere and Global Environment Observation, Institute of Atmospheric
Physics, Chinese Academy of Sciences, No. 40, Huayan Li, Chaoyang District,
Beijing 100029, China
Carbon Neutral Research Center & Key Laboratory of Middle
Atmosphere and Global Environment Observation, Institute of Atmospheric
Physics, Chinese Academy of Sciences, No. 40, Huayan Li, Chaoyang District,
Beijing 100029, China
Dongxu Yang
CORRESPONDING AUTHOR
Carbon Neutral Research Center & Key Laboratory of Middle
Atmosphere and Global Environment Observation, Institute of Atmospheric
Physics, Chinese Academy of Sciences, No. 40, Huayan Li, Chaoyang District,
Beijing 100029, China
Zhaonan Cai
Carbon Neutral Research Center & Key Laboratory of Middle
Atmosphere and Global Environment Observation, Institute of Atmospheric
Physics, Chinese Academy of Sciences, No. 40, Huayan Li, Chaoyang District,
Beijing 100029, China
Jing Wang
Carbon Neutral Research Center & Key Laboratory of Middle
Atmosphere and Global Environment Observation, Institute of Atmospheric
Physics, Chinese Academy of Sciences, No. 40, Huayan Li, Chaoyang District,
Beijing 100029, China
Chao Lin
Fine Mechanics and Physics, Changchun Institute of Optics, Changchun 130033, China
Naimeng Lu
National Satellite Meteorological Center, China Meteorological
Administration, Beijing 100081, China
Daren Lyu
Carbon Neutral Research Center & Key Laboratory of Middle
Atmosphere and Global Environment Observation, Institute of Atmospheric
Physics, Chinese Academy of Sciences, No. 40, Huayan Li, Chaoyang District,
Beijing 100029, China
Longfei Tian
Shanghai Engineering Center for Microsatellites, Shanghai 201203,
China
Maohua Wang
Shanghai Advanced Research Institute, Chinese Academy of Sciences,
Shanghai 201210, China
Zengshan Yin
Shanghai Engineering Center for Microsatellites, Shanghai 201203,
China
Yuquan Zheng
Fine Mechanics and Physics, Changchun Institute of Optics, Changchun 130033, China
Sisi Wang
National Remote Sensing Center of China, Beijing 100036, China
Related authors
Xiaoyu Ren, Dongxu Yang, Yi Liu, Yong Wang, Ting Wang, Zhaonan Cai, Lu Yao, Tonghui Zhao, Jing Wang, and Zhe Jiang
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-49, https://doi.org/10.5194/amt-2024-49, 2024
Publication in AMT not foreseen
Short summary
Short summary
We aim to verify the performance of the low-cost CO2 sensors (LUCCN). The measurements show that accuracies of LUCCNs are higher than the medium accuracy standard. And LUCCNs are also sensitive to the changes of CO2 concentrations. These results prove that the LUCCN can measure CO2 concentrations effectively, which means that LUCCN is a powerful tool to achieve the CO2 monitoring network.
Sihong Zhu, Mengchu Tao, Zhaonan Cai, Yi Liu, Liang Feng, Pubu Sangmu, Zhongshui Yu, and Junji Cao
Atmos. Chem. Phys., 25, 9843–9857, https://doi.org/10.5194/acp-25-9843-2025, https://doi.org/10.5194/acp-25-9843-2025, 2025
Short summary
Short summary
Methane (CH4) emissions can be transported into the upper troposphere (UT) via the Asian monsoon anticyclone (AMA), driving CH4 enhancements. Whether emissions or upward transport is the dominant factor remains debated. We analyzed UT CH4 variability with AMA dynamics, finding strong ties between CH4 distribution and the AMA's east–west oscillation. When centered near 80° E, vertical transport largely enhances CH4 anomalies, with circulation effects 1–2 times greater than those of emissions.
Yiguo Pang, Denghui Hu, Longfei Tian, Shuang Gao, and Guohua Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-3631, https://doi.org/10.5194/egusphere-2025-3631, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Satellites can reveal greenhouse gas point sources, but current point source extraction methods rely on manual inspection. We developed a point-object-detection-based deep learning method for fast, automated detection and quantification of these sources. The model was trained on a large synthetic dataset and tested for generalization using two independent datasets, including simulations and satellite observations.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
Atmos. Meas. Tech., 18, 3647–3667, https://doi.org/10.5194/amt-18-3647-2025, https://doi.org/10.5194/amt-18-3647-2025, 2025
Short summary
Short summary
TanSat-2 is designed for global carbon monitoring, offering high-resolution dual-band observations of solar-induced chlorophyll fluorescence – a key indicator of photosynthesis. Simulations show its data processing can retrieve fluorescence with high accuracy. These results suggest TanSat-2 will enhance global tracking of the carbon cycle and vegetation health, providing valuable insights for climate change research.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Yiguo Pang, Longfei Tian, Denghui Hu, Shuang Gao, and Guohua Liu
Atmos. Meas. Tech., 18, 455–470, https://doi.org/10.5194/amt-18-455-2025, https://doi.org/10.5194/amt-18-455-2025, 2025
Short summary
Short summary
The spatial adjacency of methane point sources can result in plume overlapping, presenting challenges for quantification from space. A separation and quantification method combining the Gaussian plume model and the integrated mass enhancement method is proposed. A modern parameter estimation technique is introduced to separate the overlapping plumes from satellite observations. The proposed method is evaluated with synthesized observations and real satellite observations.
Jingxuan Luo, Yubing Pan, Debin Su, Jinhua Zhong, Lingxiao Wu, Wei Zhao, Xiaoru Hu, Zhengchao Qi, Daren Lu, and Yinan Wang
Atmos. Meas. Tech., 17, 3765–3781, https://doi.org/10.5194/amt-17-3765-2024, https://doi.org/10.5194/amt-17-3765-2024, 2024
Short summary
Short summary
Accurate cloud quantification is critical for climate research. We developed a novel computer vision framework using deep neural networks and clustering algorithms for cloud classification and segmentation from ground-based all-sky images. After a full year of observational training, our model achieves over 95 % accuracy on four cloud types. The framework enhances quantitative analysis to support climate research by providing reliable cloud data.
Xiaoyu Ren, Dongxu Yang, Yi Liu, Yong Wang, Ting Wang, Zhaonan Cai, Lu Yao, Tonghui Zhao, Jing Wang, and Zhe Jiang
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-49, https://doi.org/10.5194/amt-2024-49, 2024
Publication in AMT not foreseen
Short summary
Short summary
We aim to verify the performance of the low-cost CO2 sensors (LUCCN). The measurements show that accuracies of LUCCNs are higher than the medium accuracy standard. And LUCCNs are also sensitive to the changes of CO2 concentrations. These results prove that the LUCCN can measure CO2 concentrations effectively, which means that LUCCN is a powerful tool to achieve the CO2 monitoring network.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Ze Chen, Yufang Tian, Yinan Wang, Yongheng Bi, Xue Wu, Juan Huo, Linjun Pan, Yong Wang, and Daren Lü
Atmos. Meas. Tech., 15, 4785–4800, https://doi.org/10.5194/amt-15-4785-2022, https://doi.org/10.5194/amt-15-4785-2022, 2022
Short summary
Short summary
Small-scale turbulence plays a vital role in the vertical exchange of heat, momentum and mass in the atmosphere. There are currently three models that can use spectrum width data of MST radar to calculate turbulence parameters. However, few studies have explored the applicability of the three calculation models. We compared and analysed the turbulence parameters calculated by three models. These results can provide a reference for the selection of models for calculating turbulence parameters.
Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
Short summary
Short summary
Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
Juan Huo, Yufang Tian, Xue Wu, Congzheng Han, Bo Liu, Yongheng Bi, Shu Duan, and Daren Lyu
Atmos. Chem. Phys., 20, 14377–14392, https://doi.org/10.5194/acp-20-14377-2020, https://doi.org/10.5194/acp-20-14377-2020, 2020
Short summary
Short summary
A detailed analysis of ice cloud physical properties is presented based on 4 years of surface Ka-band radar measurements in Beijing, where the summer oceanic monsoon from the ocean and winter continental monsoon prevail alternately. More than 6000 ice cloud clusters were studied to investigate their physical properties, such as height, horizontal extent, temperature dependence and origination type, which can serve as a reference for parameterization and characterization in global climate models.
Yuli Zhang, Mengchu Tao, Jinqiang Zhang, Yi Liu, Hongbin Chen, Zhaonan Cai, and Paul Konopka
Atmos. Chem. Phys., 20, 13343–13354, https://doi.org/10.5194/acp-20-13343-2020, https://doi.org/10.5194/acp-20-13343-2020, 2020
Cited articles
Bösch, H., Toon, G. C., Sen, B., Washenfelder, R. A., Wennberg, P. O.,
Buchwitz, M., de Beek, R., Burrows, J. P., Crisp, D., Christi, M., Connor,
B. J., Natraj, V., and Yung, Y. L.: Space-based near-infrared CO2
measurements: Testing the Orbiting Carbon Observatory retrieval algorithm
and validation concept using SCIAMACHY observations over Park Falls,
Wisconsin, J. Geophys. Res.-Atmos., 111, 1–17,
https://doi.org/10.1029/2006JD007080, 2006.
Cai, Z. N., Liu, Y., and Yang, D. X.: Analysis of XCO2 retrieval sensitivity
using simulated Chinese Carbon Satellite (TanSat) measurements, Sci. China
Earth Sci., 57, 1919–1928, https://doi.org/10.1007/s11430-013-4707-1, 2014.
Chen, A., Mao, J., Ricciuto, D., Xiao, J., Frankenberg, C., Li, X.,
Thornton, P. E., Gu, L., and Knapp, A. K.: Moisture availability mediates
the relationship between terrestrial gross primary production and
solar-induced chlorophyll fluorescence: Insights from global-scale
variations, Glob. Chang. Biol., 27, 1144–1156, https://doi.org/10.1111/gcb.15373,
2021.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S.: The ERA-interim reanalysis: Configuration and performance of
the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
Doughty, R., Köhler, P., Frankenberg, C., Magney, T. S., Xiao, X., Qin,
Y., Wu, X., and Moore, B.: TROPOMI reveals dry-season increase of
solar-induced chlorophyll fluorescence in the Amazon forest, P. Natl.
Acad. Sci. USA, 116, 22393–22398, https://doi.org/10.1073/pnas.1908157116, 2019.
Drusch, M., Moreno, J., del Bello, U., Franco, R., Goulas, Y., Huth, A.,
Kraft, S., Middleton, E. M., Miglietta, F., and Mohammed, G.: The FLuorescence EXplorer Mission Concept – ESA's Earth Explorer 8, IEEE T. Geosci. Remote, 55, 1273–1284, https://doi.org/10.1109/TGRS.2016.2621820, 2017.
Du, S., Liu, L., Liu, X., Zhang, X., Zhang, X., Bi, Y., and Zhang, L.:
Retrieval of global terrestrial solar-induced chlorophyll fluorescence from
TanSat satellite, Sci. Bull., 63, 1502–1512,
https://doi.org/10.1016/j.scib.2018.10.003, 2018.
Frankenberg, C.: OCO-2 Algorithm Theoretical Basis Document: IMAP-DOAS
pre-processor, 2014.
Frankenberg, C., Butz, A., and Toon, G. C.: Disentangling chlorophyll
fluorescence from atmospheric scattering effects in O2 A-band spectra of
reflected sunlight, Geophys. Res. Lett., 38, 1–5,
https://doi.org/10.1029/2010GL045896, 2011a.
Frankenberg, C., Fisher, J. B., Worden, J., Badgley, G., Saatchi, S. S.,
Lee, J. E., Toon, G. C., Butz, A., Jung, M., Kuze, A., and Yokota, T.: New
global observations of the terrestrial carbon cycle from GOSAT: Patterns of
plant fluorescence with gross primary productivity, Geophys. Res. Lett., 38,
1–6, https://doi.org/10.1029/2011GL048738, 2011b.
Frankenberg, C., O'Dell, C., Guanter, L., and McDuffie, J.: Remote sensing of near-infrared chlorophyll fluorescence from space in scattering atmospheres: implications for its retrieval and interferences with atmospheric CO2 retrievals, Atmos. Meas. Tech., 5, 2081–2094, https://doi.org/10.5194/amt-5-2081-2012, 2012.
Frankenberg, C., O'Dell, C., Berry, J., Guanter, L., Joiner, J., Köhler,
P., Pollock, R., and Taylor, T. E.: Prospects for chlorophyll fluorescence
remote sensing from the Orbiting Carbon Observatory-2, Remote Sens.
Environ., 147, 1–12, https://doi.org/10.1016/j.rse.2014.02.007, 2014.
Guanter, L., Alonso, L., Gómez-Chova, L., Amorós-López, J.,
Vila, J., and Moreno, J.: Estimation of solar-induced vegetation
fluorescence from space measurements, Geophys. Res. Lett., 34, 1–5,
https://doi.org/10.1029/2007GL029289, 2007.
Guanter, L., Frankenberg, C., Dudhia, A., Lewis, P. E., Gómez-Dans, J.,
Kuze, A., Suto, H., and Grainger, R. G.: Retrieval and global assessment of
terrestrial chlorophyll fluorescence from GOSAT space measurements, Remote
Sens. Environ., 121, 236–251, https://doi.org/10.1016/j.rse.2012.02.006,
2012.
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, P. Natl. Acad. Sci. USA, 111, E1327–E1333,
https://doi.org/10.1073/pnas.1320008111, 2014.
Guanter, L., Aben, I., Tol, P., Krijger, J. M., Hollstein, A., Köhler, P., Damm, A., Joiner, J., Frankenberg, C., and Landgraf, J.: Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence, Atmos. Meas. Tech., 8, 1337–1352, https://doi.org/10.5194/amt-8-1337-2015, 2015.
Joiner, J., Yoshida, Y., Vasilkov, A. P., Yoshida, Y., Corp, L. A., and Middleton, E. M.: First observations of global and seasonal terrestrial chlorophyll fluorescence from space, Biogeosciences, 8, 637–651, https://doi.org/10.5194/bg-8-637-2011, 2011.
Joiner, J., Yoshida, Y., Vasilkov, A. P., Middleton, E. M., Campbell, P. K. E., Yoshida, Y., Kuze, A., and Corp, L. A.: Filling-in of near-infrared solar lines by terrestrial fluorescence and other geophysical effects: simulations and space-based observations from SCIAMACHY and GOSAT, Atmos. Meas. Tech., 5, 809–829, https://doi.org/10.5194/amt-5-809-2012, 2012.
Joiner, J., Guanter, L., Lindstrot, R., Voigt, M., Vasilkov, A. P., Middleton, E. M., Huemmrich, K. F., Yoshida, Y., and Frankenberg, C.: Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2, Atmos. Meas. Tech., 6, 2803–2823, https://doi.org/10.5194/amt-6-2803-2013, 2013.
Joiner, J., Yoshida, Y., Guanter, L., and Middleton, E. M.: New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY, Atmos. Meas. Tech., 9, 3939–3967, https://doi.org/10.5194/amt-9-3939-2016, 2016.
Joiner, J., Yoshida, Y., Zhang, Y., Duveiller, G., Jung, M., Lyapustin, A.,
Wang, Y., and Tucker, C. J.: Estimation of terrestrial global gross primary
production (GPP) with satellite data-driven models and eddy covariance flux
data, Remote Sens., 10, 1–38, https://doi.org/10.3390/rs10091346, 2018.
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.
Köhler, P., Guanter, L., and Joiner, J.: A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data, Atmos. Meas. Tech., 8, 2589–2608, https://doi.org/10.5194/amt-8-2589-2015, 2015.
Köhler, P., Guanter, L., Kobayashi, H., Walther, S., and Yang, W.:
Assessing the potential of sun-induced fluorescence and the canopy
scattering coefficient to track large-scale vegetation dynamics in Amazon
forests, Remote Sens. Environ., 204, 769–785,
https://doi.org/10.1016/j.rse.2017.09.025, 2018a.
Köhler, P., Frankenberg, C., Magney, T. S., Guanter, L., Joiner, J., and
Landgraf, J.: Global Retrievals of Solar-Induced Chlorophyll Fluorescence
With TROPOMI: First Results and Intersensor Comparison to OCO-2, Geophys.
Res. Lett., 45, 10456–10463, https://doi.org/10.1029/2018GL079031, 2018b.
Lee, J. E., Frankenberg, C., Van Der Tol, C., Berry, J. A., Guanter, L.,
Boyce, C. K., Fisher, J. B., Morrow, E., Worden, J. R., Asefi, S., Badgley,
G., and Saatchi, S.: Forest productivity and water stress in Amazonia:
Observations from GOSAT chlorophyll fluorescence, Tohoku J. Exp. Med., 280, 20130171, https://doi.org/10.1098/rspb.2013.0171, 2013.
Li, X., Xiao, J., and He, B.: Chlorophyll fluorescence observed by OCO-2 is
strongly related to gross primary productivity estimated from flux towers in
temperate forests, Remote Sens. Environ., 204, 659–671,
https://doi.org/10.1016/j.rse.2017.09.034, 2018.
Li, X., Xiao, J., Kimball, J. S., Reichle, R. H., Scott, R. L., Litvak, M.
E., Bohrer, G., and Frankenberg, C.: Synergistic use of SMAP and OCO-2 data
in assessing the responses of ecosystem productivity to the 2018 U.S.
drought, Remote Sens. Environ., 251, 112062,
https://doi.org/10.1016/j.rse.2020.112062, 2020.
Liu, X., Guanter, L., Liu, L., Damm, A., Malenovský, Z., Rascher, U.,
Peng, D., Du, S., and Gastellu-Etchegorry, J. P.: Downscaling of
solar-induced chlorophyll fluorescence from canopy level to photosystem
level using a random forest model, Remote Sens. Environ., 231, 110772,
https://doi.org/10.1016/j.rse.2018.05.035, 2019.
Liu, Y., Wang, J., Yao, L., Chen, X., Cai, Z., Yang, D., Yin, Z., Gu, S.,
Tian, L., Lu, N., and Lyu, D.: The TanSat mission: preliminary global
observations, Sci. Bull., 63, 1200–1207,
https://doi.org/10.1016/j.scib.2018.08.004, 2018.
MacBean, N., Maignan, F., Bacour, C., Lewis, P., Peylin, P., Guanter, L.,
Köhler, P., Gómez-Dans, J., and Disney, M.: Strong constraint on
modelled global carbon uptake using solar-induced chlorophyll fluorescence
data, Sci. Rep., 8, 1–12, https://doi.org/10.1038/s41598-018-20024-w, 2018.
NASA JPL: NASA Shuttle Radar Topography Mission Global 30 arc second, NASA EOSDIS Land Processes DAAC [data set],
https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL30.002, 2013.
O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C., Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J., Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval algorithm – Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5, 99–121, https://doi.org/10.5194/amt-5-99-2012, 2012.
Qiu, R., Han, G., Ma, X., Xu, H., Shi, T., and Zhang, M.: A comparison of
OCO-2 SIF, MODIS GPP, and GOSIF data from gross primary production (GPP)
estimation and seasonal cycles in North America, Remote Sens., 12, 258,
https://doi.org/10.3390/rs12020258, 2020.
Reuter, M., Buchwitz, M., Schneising, O., Heymann, J., Bovensmann, H., and Burrows, J. P.: A method for improved SCIAMACHY CO2 retrieval in the presence of optically thin clouds, Atmos. Meas. Tech., 3, 209–232, https://doi.org/10.5194/amt-3-209-2010, 2010.
Sun, K., Liu, X., Nowlan, C. R., Cai, Z., Chance, K., Frankenberg, C., Lee, R. A. M., Pollock, R., Rosenberg, R., and Crisp, D.: Characterization of the OCO-2 instrument line shape functions using on-orbit solar measurements, Atmos. Meas. Tech., 10, 939–953, https://doi.org/10.5194/amt-10-939-2017, 2017.
Sun, Y., Fu, R., Dickinson, R., Joiner, J., Frankenberg, C., Gu, L., Xia,
Y., and Fernando, N.: Drought onset mechanisms revealed by satellite
solar-induced chlorophyll fluorescence: Insights from two contrasting
extreme events, J. Geophys. Res.-Biogeo., 120, 2427–2440,
https://doi.org/10.1002/2015JG003150, 2015.
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.: OCO-2 advances
photosynthesis observation from space via solar-induced chlorophyll
fluorescence, Science, 358, 6360, https://doi.org/10.1126/science.aam5747, 2017.
Sun, Y., Frankenberg, C., Jung, M., Joiner, J., Guanter, L., Köhler, P.,
and Magney, T.: Overview of Solar-Induced chlorophyll Fluorescence (SIF)
from the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison,
and global monitoring for GPP, Remote Sens. Environ., 209, 808–823,
https://doi.org/10.1016/j.rse.2018.02.016, 2018.
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.
van der Tol, C., Rossini, M., Cogliati, S., Verhoef, W., Colombo, R.,
Rascher, U., and Mohammed, G.: A model and measurement comparison of diurnal
cycles of sun-induced chlorophyll fluorescence of crops, Remote Sens.
Environ., 186, 663–677, https://doi.org/10.1016/j.rse.2016.09.021, 2016.
Yang, D., Liu, Y., Cai, Z., Deng, J., Wang, J., and Chen, X.: An advanced
carbon dioxide retrieval algorithm for satellite measurements and its
application to GOSAT observations, Sci. Bull., 60, 2063–2066,
https://doi.org/10.1007/s11434-015-0953-2, 2015.
Yang, D., Liu, Y., Cai, Z., Chen, X., Yao, L., and Lu, D.: First Global
Carbon Dioxide Maps Produced from TanSat Measurements, Adv. Atmos. Sci., 35,
621–623, https://doi.org/10.1007/s00376-018-7312-6, 2018.
Yang, D., Boesch, H., Liu, Y., Somkuti, P., Cai, Z., Chen, X., Di Noia, A.,
Lin, C., Lu, N., Lyu, D., Parker, R. J., Tian, L., Wang, M., Webb, A., Yao,
L., Yin, Z., Zheng, Y., Deutscher, N. M., Griffith, D. W. T., Hase, F.,
Kivi, R., Morino, I., Notholt, J., Ohyama, H., Pollard, D. F., Shiomi, K.,
Sussmann, R., Té, Y., Velazco, V. A., Warneke, T., and Wunch, D.: Toward
High Precision XCO2 Retrievals From TanSat Observations: Retrieval
Improvement and Validation Against TCCON Measurements, J. Geophys. Res.-Atmos., 125, 1–26, https://doi.org/10.1029/2020JD032794, 2020.
Yang, D., Liu, Y., Boesch, H., Yao, L., Di Noia, A., Cai, Z., Lu, N., Lyu,
D., Wang, M., Wang, J., Yin, Z., and Zheng, Y.: A New TanSat XCO2 Global
Product towards Climate Studies, Adv. Atmos. Sci., 38, 8–11,
https://doi.org/10.1007/s00376-020-0297-y, 2021.
Yang, X., Tang, J., Mustard, J. F., Lee, J. E., Rossini, M., Joiner, J.,
Munger, J. W., Kornfeld, A., and Richardson, A. D.: Solar-induced
chlorophyll fluorescence that correlates with canopy photosynthesis on
diurnal and seasonal scales in a temperate deciduous forest, Geophys. Res.
Lett., 42, 2977–2987, https://doi.org/10.1002/2015GL063201, 2015.
Yao, L., Yang, D., Liu, Y., Wang, J., Liu, L., Du, S., Cai, Z., Lu, N., Lyu,
D., Wang, M., Yin, Z., and Zheng, Y.: A New Global Solar-induced Chlorophyll
Fluorescence (SIF) Data Product from TanSat Measurements, Adv. Atmos. Sci.,
38, 341–345, https://doi.org/10.1007/s00376-020-0204-6, 2021.
Yao, L., Liu, Y., and Yang, D.: TanSat and OCO-2 SIF dataset by IAPCAS/SIF algorithm from March 2017 to February 2018, China GEOSS Data Sharing Network, http://www.chinageoss.cn/tansat/index.html, last access: 2 April 2022.
Yin, Y., Byrne, B., Liu, J., Wennberg, P. O., Davis, K. J., Magney, T.,
Köhler, P., He, L., Jeyaram, R., Humphrey, V., Gerken, T., Feng, S.,
Digangi, J. P., and Frankenberg, C.: Cropland Carbon Uptake Delayed and
Reduced by 2019 Midwest Floods, AGU Adv., 1, 1–15,
https://doi.org/10.1029/2019av000140, 2020.
Yoshida, Y., Ota, Y., Eguchi, N., Kikuchi, N., Nobuta, K., Tran, H., Morino, I., and Yokota, T.: Retrieval algorithm for CO2 and CH4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite, Atmos. Meas. Tech., 4, 717–734, https://doi.org/10.5194/amt-4-717-2011, 2011.
Yoshida, Y., Kikuchi, N., Morino, I., Uchino, O., Oshchepkov, S., Bril, A., Saeki, T., Schutgens, N., Toon, G. C., Wunch, D., Roehl, C. M., Wennberg, P. O., Griffith, D. W. T., Deutscher, N. M., Warneke, T., Notholt, J., Robinson, J., Sherlock, V., Connor, B., Rettinger, M., Sussmann, R., Ahonen, P., Heikkinen, P., Kyrö, E., Mendonca, J., Strong, K., Hase, F., Dohe, S., and Yokota, T.: Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data, Atmos. Meas. Tech., 6, 1533–1547, https://doi.org/10.5194/amt-6-1533-2013, 2013.
Yoshida, Y., Joiner, J., Tucker, C., Berry, J., Lee, J. E., Walker, G.,
Reichle, R., Koster, R., Lyapustin, A., and Wang, Y.: The 2010 Russian
drought impact on satellite measurements of solar-induced chlorophyll
fluorescence: Insights from modeling and comparisons with parameters derived
from satellite reflectances, Remote Sens. Environ., 166, 163–177,
https://doi.org/10.1016/j.rse.2015.06.008, 2015.
Yu, L., Wen, J., Chang, C. Y., Frankenberg, C., and Sun, Y.: High-Resolution
Global Contiguous SIF of OCO-2, Geophys. Res. Lett., 46, 1449–1458,
https://doi.org/10.1029/2018GL081109, 2019.
Zhang, Y., Guanter, L., Berry, J. A., Joiner, J., van der Tol, C., Huete,
A., Gitelson, A., Voigt, M., and Köhler, P.: Estimation of vegetation
photosynthetic capacity from space-based measurements of chlorophyll
fluorescence for terrestrial biosphere models, Glob. Chang. Biol., 20,
3727–3742, https://doi.org/10.1111/gcb.12664, 2014.
Zhang, Y., Xiao, X., Zhang, Y., Wolf, S., Zhou, S., Joiner, J., Guanter, L.,
Verma, M., Sun, Y., Yang, X., Paul-Limoges, E., Gough, C. M., Wohlfahrt, G.,
Gioli, B., van der Tol, C., Yann, N., Lund, M., and de Grandcourt, A.: On
the relationship between sub-daily instantaneous and daily total gross
primary production: Implications for interpreting satellite-based SIF
retrievals, Remote Sens. Environ., 205, 276–289,
https://doi.org/10.1016/j.rse.2017.12.009, 2018.
Short summary
A physics-based SIF retrieval algorithm, IAPCAS/SIF, is introduced and applied to OCO-2 and TanSat measurements. The strong linear relationship between OCO-2 SIF retrieved by IAPCAS/SIF and the official product indicates the algorithm's reliability. The good consistency in the spatiotemporal patterns and magnitude of the OCO-2 and TanSat SIF products suggests that the combinative usage of multi-satellite products has potential and that such work would contribute to further research.
A physics-based SIF retrieval algorithm, IAPCAS/SIF, is introduced and applied to OCO-2 and...