Articles | Volume 21, issue 10
https://doi.org/10.5194/bg-21-2447-2024
© Author(s) 2024. 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-21-2447-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 May 2024
Research article |
| 24 May 2024
| 24 May 2024
Using automated machine learning for the upscaling of gross primary productivity
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, Denmark
Yanghui Kang
CORRESPONDING AUTHOR
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Guy Schurgers
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, Denmark
Trevor Keenan
CORRESPONDING AUTHOR
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Cited
18 citations as recorded by crossref.
- Estimation of Gross Primary Productivity Using Performance-Optimized Machine Learning Methods for the Forest Ecosystems in China Q. Na et al. https://doi.org/10.3390/f16030518
- Disentangling gross primary productivity drivers of forested areas in China and its climate zones from 1990 to 2018 C. Zhu et al. https://doi.org/10.1016/j.jclepro.2025.145616
- CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization Y. Kang et al. https://doi.org/10.5194/essd-17-3009-2025
- Automated machine learning integrating multi-source satellite observations to predict gross and net CO2 fluxes of coastal wetlands in China N. Ngoc Tu et al. https://doi.org/10.1088/1748-9326/ade731
- Rice yield prediction using UAV-based multispectral imagery and AutoGluon across regions and field scales J. Huo et al. https://doi.org/10.3389/fpls.2026.1866530
- Machine learning and analytical hybridization models for evaluating climate change impacts on solar agrivoltaic systems and photosynthetically active radiation in Nigeria S. Nwokolo et al. https://doi.org/10.1016/j.egyr.2025.11.026
- Unrecognised water limitation is a main source of uncertainty for models of terrestrial photosynthesis S. Biegel et al. https://doi.org/10.5194/bg-22-7455-2025
- Divergent GPP dynamics in alpine and temperate grasslands: Hierarchical climatic controls across the Qinghai-Tibetan and Mongolian Plateaus Y. Zhang et al. https://doi.org/10.1016/j.srs.2025.100360
- Fusing Enhanced Flux Measurements and Multi-Source Satellite Observations to Improve GPP Estimation for the Qinghai–Tibet Plateau Based on AutoML Techniques M. Zhao et al. https://doi.org/10.3390/rs18010130
- Tower-to-global upscaling of terrestrial carbon fluxes driven by MODIS-LAI, Sentinel-3-LAI and ERA5-Land data P. Reyes-Muñoz et al. https://doi.org/10.1016/j.ecolind.2025.113597
- Upscaling Tower-Based Net Ecosystem Productivity to 250 m Resolution with Flux Site Distribution Considerations Q. Han et al. https://doi.org/10.3390/rs17030426
- Flux Footprints: A Critical Link to Bridge Eddy‐Covariance Measurements With Models, Remote Sensing, and Other Observations H. Chu et al. https://doi.org/10.1111/gcb.70887
- Remote sensing proxies underestimate fire-induced gross primary productivity loss and overestimate recovery in forests X. Fan et al. https://doi.org/10.1016/j.agrformet.2025.110963
- Warming Diminishes the Day–Night Discrepancy in the Apparent Temperature Sensitivity of Ecosystem Respiration N. Li et al. https://doi.org/10.3390/plants13233321
- Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data A. Kombate et al. https://doi.org/10.3390/rs17010085
- Assessing the insensitivity of machine learning-based GPP estimation to data expansion and the effectiveness of vegetation partitioning strategies G. Zhe et al. https://doi.org/10.1088/2515-7620/ae3a48
- Comparison of Sentinel-2 and MODIS for estimating GPP along an ecosystem gradient in eastern Germany M. Sayeed et al. https://doi.org/10.1080/22797254.2026.2650340
- A novel memory-based deep learning framework for reliable joint estimation of daily vegetation carbon fluxes X. Chen et al. https://doi.org/10.1016/j.agrformet.2026.111198
18 citations as recorded by crossref.
- Estimation of Gross Primary Productivity Using Performance-Optimized Machine Learning Methods for the Forest Ecosystems in China Q. Na et al. https://doi.org/10.3390/f16030518
- Disentangling gross primary productivity drivers of forested areas in China and its climate zones from 1990 to 2018 C. Zhu et al. https://doi.org/10.1016/j.jclepro.2025.145616
- CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization Y. Kang et al. https://doi.org/10.5194/essd-17-3009-2025
- Automated machine learning integrating multi-source satellite observations to predict gross and net CO2 fluxes of coastal wetlands in China N. Ngoc Tu et al. https://doi.org/10.1088/1748-9326/ade731
- Rice yield prediction using UAV-based multispectral imagery and AutoGluon across regions and field scales J. Huo et al. https://doi.org/10.3389/fpls.2026.1866530
- Machine learning and analytical hybridization models for evaluating climate change impacts on solar agrivoltaic systems and photosynthetically active radiation in Nigeria S. Nwokolo et al. https://doi.org/10.1016/j.egyr.2025.11.026
- Unrecognised water limitation is a main source of uncertainty for models of terrestrial photosynthesis S. Biegel et al. https://doi.org/10.5194/bg-22-7455-2025
- Divergent GPP dynamics in alpine and temperate grasslands: Hierarchical climatic controls across the Qinghai-Tibetan and Mongolian Plateaus Y. Zhang et al. https://doi.org/10.1016/j.srs.2025.100360
- Fusing Enhanced Flux Measurements and Multi-Source Satellite Observations to Improve GPP Estimation for the Qinghai–Tibet Plateau Based on AutoML Techniques M. Zhao et al. https://doi.org/10.3390/rs18010130
- Tower-to-global upscaling of terrestrial carbon fluxes driven by MODIS-LAI, Sentinel-3-LAI and ERA5-Land data P. Reyes-Muñoz et al. https://doi.org/10.1016/j.ecolind.2025.113597
- Upscaling Tower-Based Net Ecosystem Productivity to 250 m Resolution with Flux Site Distribution Considerations Q. Han et al. https://doi.org/10.3390/rs17030426
- Flux Footprints: A Critical Link to Bridge Eddy‐Covariance Measurements With Models, Remote Sensing, and Other Observations H. Chu et al. https://doi.org/10.1111/gcb.70887
- Remote sensing proxies underestimate fire-induced gross primary productivity loss and overestimate recovery in forests X. Fan et al. https://doi.org/10.1016/j.agrformet.2025.110963
- Warming Diminishes the Day–Night Discrepancy in the Apparent Temperature Sensitivity of Ecosystem Respiration N. Li et al. https://doi.org/10.3390/plants13233321
- Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data A. Kombate et al. https://doi.org/10.3390/rs17010085
- Assessing the insensitivity of machine learning-based GPP estimation to data expansion and the effectiveness of vegetation partitioning strategies G. Zhe et al. https://doi.org/10.1088/2515-7620/ae3a48
- Comparison of Sentinel-2 and MODIS for estimating GPP along an ecosystem gradient in eastern Germany M. Sayeed et al. https://doi.org/10.1080/22797254.2026.2650340
- A novel memory-based deep learning framework for reliable joint estimation of daily vegetation carbon fluxes X. Chen et al. https://doi.org/10.1016/j.agrformet.2026.111198
Saved (final revised paper)
Latest update: 21 Jul 2026
Short summary
Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a vital role in the carbon cycle. We can measure GPP locally, but producing larger and continuous estimates is challenging. Here, we present an approach to extrapolate GPP to a global scale using satellite imagery and automated machine learning. We benchmark different models and predictor variables and achieve an estimate that can capture 75 % of the variation in GPP.
Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a...
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