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

ACROPOLIS: Munich Urban CO2 Sensor Network
Patrick Aigner, Jia Chen, Felix Böhm, Mali Chariot, Lukas Emmenegger, Lars Frölich, Stuart Grange, Daniel Kühbacher, Klaus Kürzinger, Olivier Laurent, Moritz Makowski, Pascal Rubli, Adrian Schmitt, and Adrian Wenzel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4157,https://doi.org/10.5194/egusphere-2025-4157, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
State-wide California 2020 carbon dioxide budget estimated with OCO-2 and OCO-3 satellite data
Matthew S. Johnson, Sofia D. Hamilton, Seongeun Jeong, Yu Yan Cui, Dien Wu, Alex Turner, and Marc Fischer
Atmos. Chem. Phys., 25, 8475–8492, https://doi.org/10.5194/acp-25-8475-2025,https://doi.org/10.5194/acp-25-8475-2025, 2025
Short summary
Simulating out-of-sample atmospheric transport to enable flux inversions
Nikhil Dadheech and Alexander J. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3441,https://doi.org/10.5194/egusphere-2025-3441, 2025
Short summary
Emulating chemistry-climate dynamics with a linear inverse model
Eric John Mei, Gregory J. Hakim, Max Taniguchi-King, Dominik Stiller, and Alexander J. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3258,https://doi.org/10.5194/egusphere-2025-3258, 2025
Short summary
High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025,https://doi.org/10.5194/acp-25-5159-2025, 2025
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.
Share
Altmetrics
Final-revised paper
Preprint