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

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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
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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.
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