12 citations as recorded by crossref.

1. 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
2. Tree species richness relates to long-term forest photosynthesis increase R. Cao et al. https://doi.org/10.1038/s41558-026-02698-7
3. Soil water depletion intensifies ecosystem vulnerability in China’s drylands H. Wang et al. https://doi.org/10.1016/j.agrformet.2026.111412
4. MCI GPP: ensembling a global model- and climate-independent gross primary productivity for 2001–2023 J. Pu et al. https://doi.org/10.1038/s41597-025-06218-8
5. Uncertainty Analysis of Gross Primary Production (GPP) Remote-Sensing Products and Its Influencing Factors in Southwest China Z. Ge et al. https://doi.org/10.3390/rs18050764
6. Constrained Carbon Partitioning: A Self‐Trained Physics‐Informed Machine Learning Model Refines GPP Estimates From Eddy Covariance Measurements S. Ranjbar et al. https://doi.org/10.1111/gcb.70886
7. Exploring the main driving factors of gross primary production in different climate zones of China using the XGBoost–SHAP model S. Na et al. https://doi.org/10.1016/j.jaridl.2026.06.001
8. EGO: a global 0.05° hourly GPP dataset for monitoring diurnal photosynthesis dynamics X. Liu et al. https://doi.org/10.5194/essd-18-6613-2026
9. Large discrepancies in magnitude and trends among state-of-the-art remote sensing based GPP datasets J. Lai et al. https://doi.org/10.1016/j.agrformet.2026.111481
10. Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications D. Szwarcman et al. https://doi.org/10.1109/TGRS.2025.3642610
11. 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
12. 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