12 citations as recorded by crossref.

1. 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
2. Radar-optical fusion of Sentinel-1/2 for high-resolution NDVI reconstruction and landscape-driven carbon flux assessment in Kuala Selangor, Malaysia (2020–2024) P. Jia et al. https://doi.org/10.1016/j.jag.2025.104966
3. Central Asian vegetation is more sensitive to soil moisture drought than to heat and meteorological drought L. Liu et al. https://doi.org/10.1016/j.jenvman.2026.130220
4. Global Multi-Faceted Application and Evaluation of Three Commonly Used NDVI Products for Terrestrial Ecosystem Monitoring Q. Liu et al. https://doi.org/10.3390/su17219790
5. Advancing our understanding of photosynthesis: discoveries and insights from aquatic plants A. Kazmi et al. https://doi.org/10.1016/j.synbio.2026.04.036
6. Grassland degradation and its drivers in the Horn of Africa: insights from multi-index vegetation fusion and grassland cover dynamics D. Chaka & Y. Hu https://doi.org/10.1007/s10113-026-02687-8
7. Human-induced westerly jet shifts coordinate terrestrial productivity at the hemispheric scale X. Yang et al. https://doi.org/10.1038/s41467-026-74039-3
8. 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
9. Response of gross primary productivity to compound dry and hot events in Inner Mongolia under large-scale circulation patterns Y. Kang et al. https://doi.org/10.1016/j.gloplacha.2026.105392
10. 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
11. Are Mongolian rangelands overgrazed? Estimating grassland utilisation and sustainable stocking rates with a vegetation model J. Van Laere et al. https://doi.org/10.1016/j.ecolind.2026.115233
12. 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