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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2022
Research article |
| 31 Mar 2022
| 31 Mar 2022
A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)
Johannes Gensheimer
CORRESPONDING AUTHOR
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Philipp Köhler
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
Christian Frankenberg
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
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Cited
22 citations as recorded by crossref.
- A simple approach to enhance the TROPOMI solar-induced chlorophyll fluorescence product by combining with canopy reflected radiation at near-infrared band X. Liu et al. 10.1016/j.rse.2022.113341
- Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data J. Jacobson et al. 10.3390/rs15164038
- A Spatially Downscaled TROPOMI SIF Product at 0.005° Resolution With Bias Correction J. Hu et al. 10.1109/JSTARS.2024.3433371
- Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism D. Zhang et al. 10.3390/rs16081394
- Research on downscaling method of the enhanced TROPOMI solar-induced chlorophyll fluorescence data X. Lu et al. 10.1080/10106049.2024.2354417
- Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF) X. Kang et al. 10.1016/j.rse.2023.113861
- Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands? G. Wu et al. 10.1016/j.agrformet.2023.109532
- A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018 J. Xu et al. 10.1016/j.rse.2023.113550
- Hybrid Machine Learning and Geostatistical Methods for Gap Filling and Predicting Solar-Induced Fluorescence Values J. Tadić et al. 10.3390/rs16101707
- Investigating the spatio-temporal pattern evolution characteristics of vegetation change in Shendong coal mining area based on kNDVI and intensity analysis Z. Chen et al. 10.3389/fevo.2023.1344664
- Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application Z. Zhang et al. 10.1016/j.rse.2023.113699
- Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology P. Zhang et al. 10.3389/ffgc.2023.1257287
- Solar-Induced Chlorophyll Fluorescence (SIF): Towards a Better Understanding of Vegetation Dynamics and Carbon Uptake in Arctic-Boreal Ecosystems R. Cheng 10.1007/s40641-024-00194-8
- Spatio-Temporal Variation and Climatic Driving Factors of Vegetation Coverage in the Yellow River Basin from 2001 to 2020 Based on kNDVI X. Feng et al. 10.3390/f14030620
- Downscaling Solar-Induced Chlorophyll Fluorescence to a 0.05° Monthly Product Using AVHRR Data in East Asia (1995–2003) Y. Jin et al. 10.1109/JSTARS.2024.3369332
- Sun-induced fluorescence as a proxy for primary productivity across vegetation types and climates M. Pickering et al. 10.5194/bg-19-4833-2022
- Matching Satellite Sun-Induced Chlorophyll Fluorescence to Flux Footprints Improves Its Relationship with Gross Primary Productivity L. Zhao et al. 10.3390/rs16132388
- Downscaled Satellite Solar-Induced Chlorophyll Fluorescence Detects the Early Response of Sugarcane to Drought Stress in a Major Sugarcane-Planting Region of China N. Yang et al. 10.3390/rs15163937
- Global patterns and drivers of post-fire vegetation productivity recovery H. Xu et al. 10.1038/s41561-024-01520-3
- Machine learning and global vegetation: random forests for downscaling and gap filling B. van Jaarsveld et al. 10.5194/hess-28-2357-2024
- Local interpretation of machine learning models in remote sensing with SHAP: the case of global climate constraints on photosynthesis phenology A. Descals et al. 10.1080/01431161.2023.2217982
- Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm V. Balamurugan et al. 10.1038/s41598-022-09619-6
21 citations as recorded by crossref.
- A simple approach to enhance the TROPOMI solar-induced chlorophyll fluorescence product by combining with canopy reflected radiation at near-infrared band X. Liu et al. 10.1016/j.rse.2022.113341
- Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data J. Jacobson et al. 10.3390/rs15164038
- A Spatially Downscaled TROPOMI SIF Product at 0.005° Resolution With Bias Correction J. Hu et al. 10.1109/JSTARS.2024.3433371
- Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism D. Zhang et al. 10.3390/rs16081394
- Research on downscaling method of the enhanced TROPOMI solar-induced chlorophyll fluorescence data X. Lu et al. 10.1080/10106049.2024.2354417
- Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF) X. Kang et al. 10.1016/j.rse.2023.113861
- Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands? G. Wu et al. 10.1016/j.agrformet.2023.109532
- A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018 J. Xu et al. 10.1016/j.rse.2023.113550
- Hybrid Machine Learning and Geostatistical Methods for Gap Filling and Predicting Solar-Induced Fluorescence Values J. Tadić et al. 10.3390/rs16101707
- Investigating the spatio-temporal pattern evolution characteristics of vegetation change in Shendong coal mining area based on kNDVI and intensity analysis Z. Chen et al. 10.3389/fevo.2023.1344664
- Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application Z. Zhang et al. 10.1016/j.rse.2023.113699
- Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology P. Zhang et al. 10.3389/ffgc.2023.1257287
- Solar-Induced Chlorophyll Fluorescence (SIF): Towards a Better Understanding of Vegetation Dynamics and Carbon Uptake in Arctic-Boreal Ecosystems R. Cheng 10.1007/s40641-024-00194-8
- Spatio-Temporal Variation and Climatic Driving Factors of Vegetation Coverage in the Yellow River Basin from 2001 to 2020 Based on kNDVI X. Feng et al. 10.3390/f14030620
- Downscaling Solar-Induced Chlorophyll Fluorescence to a 0.05° Monthly Product Using AVHRR Data in East Asia (1995–2003) Y. Jin et al. 10.1109/JSTARS.2024.3369332
- Sun-induced fluorescence as a proxy for primary productivity across vegetation types and climates M. Pickering et al. 10.5194/bg-19-4833-2022
- Matching Satellite Sun-Induced Chlorophyll Fluorescence to Flux Footprints Improves Its Relationship with Gross Primary Productivity L. Zhao et al. 10.3390/rs16132388
- Downscaled Satellite Solar-Induced Chlorophyll Fluorescence Detects the Early Response of Sugarcane to Drought Stress in a Major Sugarcane-Planting Region of China N. Yang et al. 10.3390/rs15163937
- Global patterns and drivers of post-fire vegetation productivity recovery H. Xu et al. 10.1038/s41561-024-01520-3
- Machine learning and global vegetation: random forests for downscaling and gap filling B. van Jaarsveld et al. 10.5194/hess-28-2357-2024
- Local interpretation of machine learning models in remote sensing with SHAP: the case of global climate constraints on photosynthesis phenology A. Descals et al. 10.1080/01431161.2023.2217982
Latest update: 20 Nov 2024
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.
We develop a convolutional neural network, named SIFnet, that increases the spatial resolution...
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