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

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

doi:https://doi.org/10.5194/bg-19-1777-2022

Articles | Volume 19, issue 6

https://doi.org/10.5194/bg-19-1777-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-19-1777-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 6

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

Supplement

https://doi.org/10.5194/bg-19-1777-2022-supplement

Data sets

TROPOMI SIF high resolution data at 0.005° for CONUS as estimated by the convolutional neural network SIFnet Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen https://doi.org/10.5281/zenodo.6321987

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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.