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

DRIVE v1.0: A data-driven framework to estimate road transport emissions and temporal profiles
Daniel Kühbacher, Jia Chen, Patrick Aigner, Mario Ilic, Ingrid Super, and Hugo Denier van der Gon
EGUsphere, https://doi.org/10.5194/egusphere-2025-753,https://doi.org/10.5194/egusphere-2025-753, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Building-resolving simulations of anthropogenic and biospheric CO2 in the city of Zurich with GRAMM/GRAL
Dominik Brunner, Ivo Suter, Leonie Bernet, Lionel Constantin, Stuart K. Grange, Pascal Rubli, Junwei Li, Jia Chen, Alessandro Bigi, and Lukas Emmenegger
EGUsphere, https://doi.org/10.5194/egusphere-2025-640,https://doi.org/10.5194/egusphere-2025-640, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025,https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Greenhouse gas column observations from a portable spectrometer in Uganda
Neil Humpage, Hartmut Boesch, William Okello, Jia Chen, Florian Dietrich, Mark F. Lunt, Liang Feng, Paul I. Palmer, and Frank Hase
Atmos. Meas. Tech., 17, 5679–5707, https://doi.org/10.5194/amt-17-5679-2024,https://doi.org/10.5194/amt-17-5679-2024, 2024
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2918,https://doi.org/10.5194/egusphere-2024-2918, 2024
Short summary

Related subject area

Biogeochemistry: Air - Land Exchange
Constraining 2010–2020 Amazonian carbon flux estimates with satellite solar-induced fluorescence (SIF)
Archana Dayalu, Marikate Mountain, Bharat Rastogi, John B. Miller, and Luciana Gatti
Biogeosciences, 22, 1509–1528, https://doi.org/10.5194/bg-22-1509-2025,https://doi.org/10.5194/bg-22-1509-2025, 2025
Short summary
An elucidatory model of oxygen's partial pressure inside substomatal cavities
Andrew S. Kowalski
Biogeosciences, 22, 785–789, https://doi.org/10.5194/bg-22-785-2025,https://doi.org/10.5194/bg-22-785-2025, 2025
Short summary
Aggregation of ice-nucleating macromolecules from Betula pendula pollen determines ice nucleation efficiency
Florian Wieland, Nadine Bothen, Ralph Schwidetzky, Teresa M. Seifried, Paul Bieber, Ulrich Pöschl, Konrad Meister, Mischa Bonn, Janine Fröhlich-Nowoisky, and Hinrich Grothe
Biogeosciences, 22, 103–115, https://doi.org/10.5194/bg-22-103-2025,https://doi.org/10.5194/bg-22-103-2025, 2025
Short summary
Evaluating adsorption isotherm models for determining the partitioning of ammonium between soil and soil pore water in environmental soil samples
Matthew G. Davis, Kevin Yan, and Jennifer G. Murphy
Biogeosciences, 21, 5381–5392, https://doi.org/10.5194/bg-21-5381-2024,https://doi.org/10.5194/bg-21-5381-2024, 2024
Short summary
Similar freezing spectra of particles in plant canopies and in the air at a high-altitude site
Annika Einbock and Franz Conen
Biogeosciences, 21, 5219–5231, https://doi.org/10.5194/bg-21-5219-2024,https://doi.org/10.5194/bg-21-5219-2024, 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.
Share
Altmetrics
Final-revised paper
Preprint