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CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO 2 fertilization
Open accessPublished version of a preprint
Abstract
Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing carbohydrates needed for ecosystem metabolism. Despite the importance of GPP, however, existing estimates present significant uncertainties and discrepancies. A key issue is the underrepresentation of the CO 2 fertilization effect, a major factor contributing to the increased terrestrial carbon sink over recent decades. This omission could potentially bias our understanding of ecosystem responses to climate change. Here, we introduce CEDAR-GPP, the first global machine-learning-upscaled GPP product that incorporates the direct CO 2 fertilization effect on photosynthesis. Our product is comprised of monthly GPP estimates and their uncertainty at 0.05° resolution from 1982 to 2020, generated using a comprehensive set of eddy covariance measurements, multi-source satellite observations, climate variables, and machine learning models. Importantly, we used both theoretical and data-driven approaches to incorporate the direct CO 2 effects. Our machine learning models effectively predict monthly GPP ( R 2 ∼ 0.72), the mean seasonal cycles ( R 2 ∼ 0.77), and spatial variabilities ( R 2 ∼ 0.63) based on cross-validation at flux sites. After incorporating the direct CO 2 effects, the predicted long-term GPP trend across global flux towers substantially increases from 3.1 to 4.5–5.4 gC m −2 yr −1 , which aligns more closely with the 7.7 gC m −2 yr −1 trend detected from eddy covariance data. While the global patterns of annual mean GPP, seasonality, and interannual variability generally align with existing satellite-based products, CEDAR-GPP demonstrates higher long-term trends globally after incorporating CO 2 fertilization and reflected a strong temperature control on direct CO 2 effects. The estimated global GPP trend is 0.57–0.76 PgC yr −1 from 2001 to 2018 and 0.32–0.34 PgC yr −1 from 1982 to 2018. Estimating and validating GPP trends in data-scarce regions, such as the tropics, remains challenging, underscoring the importance of ongoing ground-based monitoring and advancements in modeling techniques. CEDAR-GPP offers a comprehensive representation of GPP temporal and spatial dynamics, providing valuable insights into ecosystem–climate interactions. The CEDAR-GPP product is available at https://doi.org/10.5281/zenodo.8212706 (Kang et al., 2024).
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“As many sites are situated in heterogeneous landscapes, featuring multiple land cover types and diverse vegetation conditions, the mismatch between the fixed satellite pixel grid and the measurement footprint introduce substantial biases to the upscaling estimation [43], [50].”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [43]
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“Datadriven approaches using machine learning to integrate satellite data with site-level information (e.g., from eddy covariance measurement networks [42]) provide diagnostic constraints on global GPP, complementing process-based models [43].”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [43]
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Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications
Szwarcman, D., Roy, S., Fraccaro, P., Gislason, T. E., Blumenstiel, B., Ghosal, R., de Oliveira, P. H., de Sousa Almeida, J. L., Sedona, R., Kang, Y., et al. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3642610 · Cited by 100
“However, these approaches face challenges due to scale mismatches between in-situ measurements and satellite data, as well as generalization issues, which could potentially be mitigated by using foundation models like Prithvi that embed generalized representations of high-resolution data [39].”
Self-citationLi, C., Zhang, D., Zhang, S., Wen, Y., Wang, W., Chen, Y., & Peng, J. X. (2025) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2024jg008605 · Cited by 16
Bilir, T. E., Bloom, A. A., Konings, A. G., Liu, J., Parazoo, N. C., Quetin, G. R., Norton, A., Worden, M. A., Levine, P. A., Ma, S., et al. (2025) · AGU Advances · doi:10.1029/2025av001689 · Cited by 8
Self-citationChu, H., Metzger, S., Ouyang, Z., Griebel, A., Yi, K., Durden, D., Wolf, S., Kasak, K., Rey‐Sánchez, C., Rodriguez, L. C. H., et al. (2026) · Global Change Biology · doi:10.1111/gcb.70887 · Cited by 7
Generalizing Tree–Level Sap Flow Across the European Continent
Loritz, R., Wu, C. H., Klotz, D., Gauch, M., Kratzert, F., & Bassiouni, M. (2024) · Geophysical Research Letters · doi:10.1029/2023gl107350 · Cited by 7
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Metadata
- Authors
- Kang, Y., Bassiouni, M., Gaber, M., Lu, X., & Keenan, T. F.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- University of Copenhagen (DK)
- Venue
- Earth System Science Data
- Publisher
- Copernicus GmbH
- Published
- 2025-06-30
- Volume
- 17
- Issue
- 6
- Pages
- 3009-3046
- ISSN
- 1866-3508 (ISSN-L)
- 1866-3516
- OpenAlex
- W4411792583
- Cited by
- 22
- References
- 292
- Citations by year
- 2026: 11 · 2025: 8
- FWCI
- 7.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Earth system science data (publishedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- EarthArXiv (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Energy (DE-SC0021023)
- National Aeronautics and Space Administration (80NSSC21K1705, 80NSSC24K1562, 80NSSC20K1801, 80NSSC25K7327)
- U.S. Department of Agriculture (2023-67012-40086)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric and Environmental Gas Dynamics
- Keywords
- gross primary productivity, CO2 fertilization, terrestrial carbon sink, eddy covariance flux towers, seasonal cycle, interannual variability, temperature effects on photosynthesis, satellite observations, data-scarce regions, climate-ecosystem interactions, uncertainty estimation
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