Articles | Volume 19, issue 6
https://doi.org/10.5194/bg-19-1777-2022
https://doi.org/10.5194/bg-19-1777-2022
Research article
 | 
31 Mar 2022
Research article |  | 31 Mar 2022

A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)

Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen

Related authors

The impacts of elevated CO2 on forest growth, mortality, and recovery in the Amazon rainforest
Yitong Yao, Philippe Ciais, Emilie Joetzjer, Wei Li, Lei Zhu, Yujie Wang, Christian Frankenberg, and Nicolas Viovy
Earth Syst. Dynam., 15, 763–778, https://doi.org/10.5194/esd-15-763-2024,https://doi.org/10.5194/esd-15-763-2024, 2024
Short summary
Using a portable FTIR spectrometer to evaluate the consistency of Total Carbon Column Observing Network (TCCON) measurements on a global scale: the Collaborative Carbon Column Observing Network (COCCON) travel standard
Benedikt Herkommer, Carlos Alberti, Paolo Castracane, Jia Chen, Angelika Dehn, Florian Dietrich, Nicholas M. Deutscher, Matthias Max Frey, Jochen Groß, Lawson Gillespie, Frank Hase, Isamu Morino, Nasrin Mostafavi Pak, Brittany Walker, and Debra Wunch
Atmos. Meas. Tech., 17, 3467–3494, https://doi.org/10.5194/amt-17-3467-2024,https://doi.org/10.5194/amt-17-3467-2024, 2024
Short summary
Monitoring the impact of forest changes on carbon uptake with solar-induced fluorescence measurements from GOME-2A and TROPOMI for an Australian and Chinese case study
Juliëtte C. S. Anema, Klaas Folkert Boersma, Piet Stammes, Gerbrand Koren, William Woodgate, Philipp Köhler, Christian Frankenberg, and Jacqui Stol
Biogeosciences, 21, 2297–2311, https://doi.org/10.5194/bg-21-2297-2024,https://doi.org/10.5194/bg-21-2297-2024, 2024
Short summary
A novel data-driven global model of photosynthesis using solar-induced chlorophyll fluorescence
Russell Doughty, Yujie Wang, Jennifer Johnson, Nicholas Parazoo, Troy Magney, Zoe Pierrat, Xiangming Xiao, Luis Guanter, Philipp Köhler, Christian Frankenberg, Peter Somkuti, Shuang Ma, Yuanwei Qin, Sean Crowell, and Berrien Moore III
EGUsphere, https://doi.org/10.22541/essoar.168167172.20799710/v1,https://doi.org/10.22541/essoar.168167172.20799710/v1, 2024
Short summary
Non-steady-state stomatal conductance modeling and its implications: from leaf to ecosystem
Ke Liu, Yujie Wang, Troy S. Magney, and Christian Frankenberg
Biogeosciences, 21, 1501–1516, https://doi.org/10.5194/bg-21-1501-2024,https://doi.org/10.5194/bg-21-1501-2024, 2024
Short summary

Related subject area

Biogeochemistry: Air - Land Exchange
Using automated machine learning for the upscaling of gross primary productivity
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
Interpretability of negative latent heat fluxes from eddy covariance measurements in dry conditions
Sinikka J. Paulus, Rene Orth, Sung-Ching Lee, Anke Hildebrandt, Martin Jung, Jacob A. Nelson, Tarek Sebastian El-Madany, Arnaud Carrara, Gerardo Moreno, Matthias Mauder, Jannis Groh, Alexander Graf, Markus Reichstein, and Mirco Migliavacca
Biogeosciences, 21, 2051–2085, https://doi.org/10.5194/bg-21-2051-2024,https://doi.org/10.5194/bg-21-2051-2024, 2024
Short summary
Forest-floor respiration, N2O fluxes, and CH4 fluxes in a subalpine spruce forest: drivers and annual budgets
Luana Krebs, Susanne Burri, Iris Feigenwinter, Mana Gharun, Philip Meier, and Nina Buchmann
Biogeosciences, 21, 2005–2028, https://doi.org/10.5194/bg-21-2005-2024,https://doi.org/10.5194/bg-21-2005-2024, 2024
Short summary
Compound soil and atmospheric drought events and CO2 fluxes of a mixed deciduous forest: Occurrence, impact, and temporal contribution of main drivers
Liliana Scapucci, Ankit Shekhar, Sergio Aranda-Barranco, Anastasiia Bolshakova, Lukas Hörtnagl, Mana Gharun, and Nina Buchmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-459,https://doi.org/10.5194/egusphere-2024-459, 2024
Short summary
Enhanced net CO2 exchange of a semideciduous forest in the southern Amazon due to diffuse radiation from biomass burning
Simone Rodrigues, Glauber Cirino, Demerval Moreira, Andrea Pozzer, Rafael Palácios, Sung-Ching Lee, Breno Imbiriba, José Nogueira, Maria Isabel Vitorino, and George Vourlitis
Biogeosciences, 21, 843–868, https://doi.org/10.5194/bg-21-843-2024,https://doi.org/10.5194/bg-21-843-2024, 2024
Short summary

Cited articles

Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A next-generation hyperparameter optimization framework, in: KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 4–8 August 2019, Anchorage, AK, USA, 2623–2631, https://doi.org/10.1145/3292500.3330701, 2019. a
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance and terrestrial photosynthesis, Science Advances, 3, e1602244, https://doi.org/10.1126/sciadv.1602244, 2017. a
Benesty, J., Chen, J., Huang, Y., and Cohen, I.: Pearson correlation coefficient, in: Noise reduction in speech processing, 37–40, Springer, ISBN 978-3-642-00295-3, 2009. a, b
Bishop, C. M.: Pattern Recognition and Machine Learning, Springer, 1st edn., New York, NY, ISBN 978-0-387-31073-2, 2007. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Download
Short summary
We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
Altmetrics
Final-revised paper
Preprint