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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Latest update: 15 Jun 2024
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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.
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